Videos

Strategic Budget Optimization through Marketing Mix Modeling (MMM)

Stop guessing which channels drive growth. Join Isabella Velásquez and Daniel Chen for this end-to-end workflow demo on modeling marketing spend, building 'what-if' scenario planners in Shiny, and deploying automated Quarto reports that give non-technical stakeholders a clear, data-backed view of ROI. We will explore: 1. The Workflow Architecture: A look at the “plumbing” required to move from data to insights. 2. The Interface: Wrapping an existing model in a Shiny dashboard for real-time “what-if” scenario planning. 3. The Delivery: Automating communication via Quarto reports to keep stakeholders aligned. 4. The Deployment: Using Posit Connect to turn your code into an accessible business asset for leaders. This workflow demo grew out of the incredible energy at our recent Data Science Hangout with Ryan Timpe at LEGO. We were so inspired by the community’s interest in MMM that we decided to build this session to address your specific questions. Thank you for the great discussion. We’re excited to show you what we’ve put together! Demo References MMM Demo GitHub Repo (https://github.com/chendaniely/dashboard-mmm) MMM Demo Dashboard (https://danielchen-dashboard-mmm.share.connect.posit.cloud/) Ryan Timpe’s posit::conf(2023) talk, tidymodels: Adventures in Rewriting a Modeling Pipeline - posit::conf(2023) (https://www.youtube.com/watch?v=R7XNqcCZnLg) MMM Resources Bayesian Media Mix Modeling for Marketing Optimization, PyMC Labs (https://www.pymc-labs.com/blog-posts/bayesian-media-mix-modeling-for-marketing-optimization) Modernizing MMM: Best Practices for Marketers, IAB (https://www.iab.com/wp-content/uploads/2025/12/IAB_Modernizing_MMM_Best_Practices_for_Marketers_December_2025.pdf) The Future of Media Mix Modeling, Measured (https://info.measured.com/hubfs/Guides/Measured_Guide%20The%20Future%20of%20Media%20Mix%20Modeling.pdf?hsCtaAttrib=184767817336) Media Mix Modeling: How to Measure the Effectiveness of Advertising, Hajime Takeda, PyData (https://www.youtube.com/watch?v=u4U_PUTasPQ) An Analyst's Guide to MMM, Meta (https://facebookexperimental.github.io/Robyn/docs/analysts-guide-to-MMM/) Full Python Tutorial: Bayesian Marketing Mix Modeling (MMM) SPECIAL GUEST: PyMC Labs, Matt Dancho (https://www.youtube.com/watch?v=lJ_qq_IVUgg) Reading MMM Outputs: Dashboards and Decisions for Small Teams, SmartSMSSolutions (https://smartsmssolutions.com/resources/blog/business/reading-mmm-dashboards-article)

Driving Shiny adoption by removing friction | Jonathan Lin | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Jonathan Lin, Adviser of Data Science and AI at Ovintiv, to chat about scaling data science infrastructure with Kubernetes, the "show, don't tell" method for leadership buy-in, creating internal handbooks to support users, and even a little chat around navigating a sparse job market. In this Hangout, we explore Jon's approach to driving adoption and scaling impact without becoming a bottleneck. He discusses moving from simple virtual machines to using Kubernetes to manage memory for over 900 users and 60 publishers (wow!!). John also details how he built a comprehensive internal "handbook" to answer repeated questions, allowing him to support a growing community of R and Python users while encouraging self-service learning. Resources mentioned in the video and zoom chat: Posit Conf 2026 Call for Talks → https://posit.co/blog/posit-conf-2026-call-for-talks/ Data Science Lab → https://pos.it/dslab R Screencasts (David Robinson) → https://www.rscreencasts.com/ Python for Data Analysis (Wes McKinney) → https://wesmckinney.com/book/ Cool 3D printing ggplots and maps using Rayshader! (shared in chat) → https://www.tylermw.com/posts/data_visualization/3d-printing-rayshader.html If you didn’t join live, one great discussion you missed from the zoom chat was about Dragon Boating! Jon coaches a team, and the chat lit up with curiosity, explanations, and excitement about the sport (and its potential crossover with 3D printing paddles? We are the best community). Have you ever watched a dragon boat event?! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 10:38 "What are tips for navigating a sparse job market?" 14:33 "How do you support all of your users with different skills?" 17:49 "What types of fraud were you working with?" 21:12 "How do you get people to actually read it?" 24:55 "How did you learn R?" 29:18 "What are some techniques you use to get people to trust the tech?" 33:35 "Do you block time each week for documentation?" 36:50 "How would you recommend getting a company to buy in?" 42:15 "How do you pick the topics to learn more about?" 45:34 "Advice on how to overcome fear and feel confident enough to ask questions?" 49:09 "Do you program much in your current position?" 51:34 "Is it truly necessary to have deployment experience to get a data science job?"

The one skill AI can't replace | Alexander Schacht | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Alexander Schacht, Senior Director at Cytel and Host of The Effective Statistician, to chat about the tensions between technical and executive work, how to increase visibility in your organization, and ethical considerations in data science (specifically regarding patient data). In this Hangout, we explore the "engine vs. wheels" analogy for data professionals. Alexander explains that while technical skills (the engine) are vital, they are useless without communication and leadership skills (the wheels) to put that power on the road. He shares advice on how to stop being the "p-value police" and instead build trust by understanding stakeholders' fears, hopes, and business goals. Resources mentioned in the video and zoom chat: The Effective Statistician Podcast → https://theeffectivestatistician.com/podcast/ Alexander’s Book: How to be an Effective Statistician → https://theeffectivestatistician.com/book-launch/ Nonviolent Communication (Book Recommendation from Libby) → https://www.amazon.com/Nonviolent-Communication-Language-Life-Changing-Relationships/dp/189200528X If you didn’t join live, one great discussion you missed from the zoom chat was about the pressure to "look busy" versus actually delivering results, sparked by Alexander's advice to decline more meetings. The chat debated the difficulty of doing this in a "grind culture" and how to prioritize signal over noise. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 05:28 "What are some of the tensions between technical work and executive type work?" 08:56 "Do you have any advice for increasing visibility where bias or prejudice exists?" 14:26 "Have you been able to influence the top down people?" 18:31 "How do you reconcile with some of the ethical things about work?" 23:00 "How can I constructively deal with concerns managed upwards towards me as a manager?" 30:15 "What are some strategies that you use to manage expectations?" 34:23 "Most valuable skill set to invest in this age where Gen AI can do so much?" 40:14 "Where would people acquire the niche technical skills without getting experience?" 41:57 "What is your advice to look for in a prospective employer or manager?" 45:34 "How do you juggle both [being an employee and a business owner] at the same time?" 48:56 "Have you experienced stakeholders trusting AI more than they trust experts?" 52:19 "How do you get a feeling for discussions you need to go to versus things that will work themselves out?"

Using R package structure for data science projects | Kylie Ainslie | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We'd love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Kylie Ainslie who walks through how structuring data science projects as R packages provides a consistent framework that integrates documentation for you and facilitates collaboration with others by organizing things really well. Kylie says, "I stumbled on using an R package structure to organize my projects a number of years ago and it has changed how I work in such a positive way that I want to share it with others! In a world where our attention is constantly being pulled in many directions, efficiency is crucial. Structuring projects as R packages is how I work more efficiently." Hosting crew from Posit: Libby Heeren, Isabella Velasquez Kylie's Bluesky: @kylieainslie.bsky.social Kylie's LinkedIn: https://www.linkedin.com/in/kylieainslie/ Kylie's Website: https://kylieainslie.github.io/ Kylie's GitHub: https://github.com/kylieainslie Resources from the hosts and chat: posit::conf(2026) call for talks: https://posit.co/blog/posit-conf-2026-call-for-talks/ Kylie's posit::conf(2025) talk: https://www.youtube.com/watch?v=YzIiWg4rySA {usethis} package: https://usethis.r-lib.org/ R Packages (2e) book: https://r-pkgs.org/ Paquetes de R (R Packages in Spanish): https://davidrsch.github.io/rpkgs-es/ {box} package: https://github.com/klmr/box extdata docs in Writing R Extensions: https://cran.r-project.org/doc/manuals/R-exts.html#Data-in-packages-1 Tan Ho's talk on NFL data: https://tanho.ca/talks/rsconf2022-github/ {rv} package: https://a2-ai.github.io/rv-docs/ Whether to Import or Depend: https://r-pkgs.org/dependencies-mindset-background.html#sec-dependencies-imports-vs-depends {pkgdown} package: https://pkgdown.r-lib.org/ Edgar Ruiz's {pkgsite} package: https://github.com/edgararuiz/pkgsite Attendees shared examples of data packages in the chat! Here they are: https://kjhealy.github.io/nycdogs/ https://kjhealy.github.io/gssr/ https://github.com/deepshamenghani/richmondway https://github.com/kyleGrealis/nascaR.data https://github.com/ivelasq/leaidr ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps: 00:00 Introduction 06:17 Reviewing the disorganized project example 10:01 Creating the package structure using create_package 17:50 Organizing external data and scripts in the inst folder 22:55 Adding a README and License 29:06 "What are the advantages to packaging a project?" 33:35 Writing Roxygen2 documentation 36:06 "Do you type return at the end of your functions?" 41:55 Handling dependencies with use_package 43:53 "Can you just use require(dplyr) at the top?" 47:45 Setting up a pkgdown site 50:11 Creating vignettes 52:22 "What is the role of the usethis package?" 54:18 Loading the package with devtools::load_all

Edgar Ruiz

Positron workflows that make life easier | Andrew Heiss | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We'd love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Andrew Heiss as he demonstrates some tips and tricks about his personal workflow and tools that he actually uses to make life easier in Positron. This is the ultimate list of data life hacks to make your workflow soooo much nicer. Check out Andrew's blog post here to follow along with the tools he mentions: https://andhs.co/dsl Hosting crew from Posit: Libby Heeren, Isabella Velasquez Andrew's Website: https://www.andrewheiss.com/ Andrew's Bluesky: https://bsky.app/profile/andrew.heiss.phd Andrew's LinkedIn: https://www.linkedin.com/in/andrewheiss/ Resources from the hosts and chat: Andrew's blog post containing links to all of the tools he mentions: https://www.andrewheiss.com/blog/2026/01/13/dsl-positron-workflow/ Open VSX Registry: https://open-vsx.org/ DataPasta: https://milesmcbain.github.io/datapasta/ Pastum (like datapasta for Positron): https://open-vsx.org/extension/atsyplenkov/pastum Positron Project docs: https://positron.posit.co/migrate-rstudio-rproj.html Garrick's data science extension bundle package: https://github.com/gadenbuie/positron-plus-1-e Emil's keyboard shortcut blog post: https://emilhvitfeldt.com/post/positron-key-bindings/ Native Tabs for Mac: https://lucasprag.com/posts/underrated-vscode-feature-native-tabs/ Andrew's posit::conf(2025) Talk: https://youtu.be/UCloM4GcfVY Arc browser that Andrew is using: https://arc.net/ Andrew's YAML headers he sets up using espanso: https://github.com/andrewheiss/espanso/blob/52da6c43c6d1ebaf3231770b1b66971d1dfb374a/match/markdown-pandoc-quarto.yml#L118 ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps: 00:00 Introduction 01:50 Switching to Positron full-time 03:19 Extensions in Positron 04:44 How to evaluate if an extension is safe 07:05 Air extension (auto-formatting) 08:21 Better Comments extension 10:15 Moving the Activity Bar 12:20 Pastum extension (DataPasta equivalent) 14:26 Rainbow CSV extension 15:34 Spell Right extension 17:34 Managing projects in Positron vs RStudio 20:18 "Do you know if there are extensions... that will conditionally format cells?" 20:40 "Do you explicitly add a dot here file?" 21:44 Project Manager extension 25:34 "How did you discover all of these?" 26:38 "How is GitHub integrated into Positron?" 29:10 Peacock extension 31:16 The Connections pane 36:38 "When I change the Peacock color, it's changing colors for everything." 37:59 "Does he use any DuckDB extensions?" 39:05 Raycast 43:35 Raycast scripts 44:30 NotebookLM 45:31 "Is there a hack to manage a repo that is both a project and an R package?" 48:00 Espanso 53:15 "Is Raycast a replacement for Spotlight and Bartender?" 54:00 "Is there an easy way to see all of the shortcuts?"

Hackathons for education, not competition | Allissa Dillman | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Allissa Dillman, Founder and CEO at Biodata Sage LLC, to chat about her transition from NIH to entrepreneurship, the importance of social learning and community in coding (Libby's favorite topic!!), organizing hackathons for education rather than just competition, and strategies for networking regardless of whether you are an introvert or an extrovert. In this Hangout, we explore the power of social learning and community. Allissa discusses why she believes we learn and grow more confidently when working with others rather than in isolation, noting how shared struggles can actually help overcome imposter syndrome. She also shares her philosophy on running hackathons that prioritize networking and experiential learning over high-pressure competition, and talks about how she navigated the leap from a stable job to starting your own business. Resources mentioned in the video and zoom chat: Allissa’s posit::conf(2025) Talk: From Solo to Social → https://youtu.be/HP3VfGkHQ1c How to network in a non-slimy way (Article by Ted Laderas shared in the chat) → https://laderast.github.io/articles/2021-09-13-how-to-network-in-a-non-slimy-way/how-to-network-in-a-non-slimy-way.html If you didn’t join live, one great discussion you missed from the zoom chat was about the dynamics of introverts versus extroverts in networking. Attendees discussed the benefits of finding a friendly neighborhood extrovert to help navigate conferences and even proposed the idea of "willing to adopt an introvert" stickers for future events. Would you be open to buddying up with an introvert at conf? Would you like an extrovert friend to adopt you? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 04:11 "Recap what you talked about at posit::conf" 07:58 "What would you say are the largest challenges of setting out on your own as an entrepreneur?" 11:47 "How did you make that transition from NIH to entrepreneurship smoothly?" 16:01 "What were some of your wins at NIH?" 19:15 "Could you expand a little bit on your work at NIH?" 22:45 "Any advice for introverts on networking?" 26:45 "Any tips on standing up those little community based groups within organizations?" 32:17 "What are some of the success stories that you've had with helping communities learn to code?" 35:23 "What conditions were met for you to feel comfortable bringing someone on as an employee?" 40:12 "How do you go from having connections to clients without experiencing much friction?" 45:18 "Do you market yourself on purpose?" 49:05 Rapid Fire: "Have you used tidy models in your work?" 49:18 Rapid Fire: "Was there a specific moment where you decided to turn your boss/colleagues into your clients?" 49:54 Rapid Fire: "Are there things you miss about the NIH?" 51:05 "When you organize hackathons, how do you ensure the well-being of participants and make it a fun experience?" 52:45 "Do you have a piece of career advice for us?"

The dessert-first approach to teaching data science | Mine Cetinkaya-Rundel | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Mine Çetinkaya-Rundel, Professor of the Practice at Duke University and Senior Developer Advocate at Posit PBC, to chat about her cake-first teaching philosophy, strategies for communicating technical results to non-technical stakeholders, and career advice on learning new tools later in life, among other things. In this Hangout, we explore Mine's unique approach to explaining difficult topics to students. She describes her method as "let them eat cake first," where she shows learners the final, compelling result (like a visualization) before teaching them the "ingredients" or technical steps required to get there. By establishing the motivation and the destination first, she finds students are more willing to invest the effort into learning the necessary coding and data cleaning logic. Resources mentioned in the video and zoom chat: Mine's Coursera 4-course series: Data Science with R Specialization → https://www.coursera.org/specializations/data-science-r TidyTuesday (Weekly Data Project) → https://github.com/rfordatascience/tidytuesday, The Test Set Podcast → https://podcasts.apple.com/us/podcast/the-test-set-by-posit/id1823736938 If you didn’t join live, one great discussion you missed from the chat was about the transition from Excel to coding languages like R and Python. Attendees agreed that Excel often acts as a "gateway drug" to data science, shared war stories about managing massive VLOOKUPs (laugh/cry emojis for everyone), and debated the undeniable and lingering utility of spreadsheets for communicating with business stakeholders. Spreadsheets will never go away and many of us are totally ok with that because we still use them every day! Let us know below if you’d like to hear more about this topic ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 04:42 "Why R initially and why still R?" 10:30 "How do you balance it all?" 15:28 "How do you stay up to date on newer techniques?" 20:13 "What is your approach for explaining difficult topics to students?" 25:58 "How do you identify appropriate datasets for beginners?" 32:21 "How frustrating is it when you see statistics being used in a misleading way?" 38:26 "How to communicate with people from different fields?" 44:23 Career advice 48:28 "Have you ever convinced an organization to abandon Excel?" 49:25 "How is statistics viewed these days in the context of things like AI and ML?" 52:38 "Was there a tipping point where you felt experienced enough to give keynotes?"

Mine Çetinkaya-Rundel

People Analytics at different stages of company growth | Adrian Perez | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Adrian Perez, Head of People Analytics at GitLab, to chat about the role of people analytics in business, how he's navigated the changing HR-data-sphere, advice for moving from an individual contributor to a manager, and the modern data stack used at GitLab. In this Hangout, we explore one of our favorite topics here: People Analytics! Adrian discusses the scale at which companies should establish a dedicated function and shares a specific case study from his time at H-E-B where survival analysis was used to improve recruiting processes. He also provides insights on how to manage relationships with stakeholders who may be skeptical of data results, emphasizing the importance of partnership over purely proving someone wrong. (Libby heavily agrees - it's about human relationships and trust!) Resources mentioned in the video and zoom chat: GitLab Company Handbook (AMAZINGLY open resource) → https://handbook.gitlab.com/ GitLab People Analytics Team Page → https://handbook.gitlab.com/handbook/people-group/people-ops-tech-analytics/people-analytics/ Positron IDE → https://positron.posit.co/ If you didn’t join live, one great discussion you missed from the zoom chat was about advice for first-time managers, including a recommendation for the book "The Making of a Manager" by Julie Zhuo. Let us know below if you’d like to hear more about the IC-to-manager move! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 03:58 "Do you have a enormous team? Are you guys small and mighty?" 06:26 "Is there a minimum company size before people analytics becomes worthwhile?" 09:25 "Can you share some specific types of analysis or modeling tasks that are done in people analytics?" 15:30 "How do you manage HR professionals when they don't like the results that the data showed them?" 19:28 "Does that include implementing third party applicant tracking systems, keyword analysis?" 24:26 "Any advice you're willing to share for a first time manager?" 30:35 "What tools do you use at GitLab?" 33:59 "Have you tried Positron?" 35:00 "How do you think that this would work in the sports industry?" 39:24 "How do you manage to respect privacy of the data you work with?" 44:18 "Have you done any work on skills analysis and or leveraging graph databases?" 46:42 "Is there a qualitative element to your analysis as well?" 48:19 "Is people analytics considered a subset of human factors?" 50:35 "Is it possible to break into people analytics with data experience but no HR experience?" 51:12 "Could you leave us with a piece of career advice?"

What even is dbt? An Analytics engineer explains | Laurie Merrell & Michael Chow | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We'd love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Jarvis Innovations Lead Analytics Engineer Laurie Merrell and Posit Principal Software Engineer Michael Chow as they walk us through a beginner dbt project and let us ask as many questions as we like (and we do, we ask all the questions, including, WHAT EVEN IS dbt??). This is a super friendly, MESSY, collaborative, and curious peek at dbt. It's is a tool that's often mysterious to data scientists and it's a big enough framework that it can feel tough to get started with. Walking through the basics makes it way easier to get into! Hosting crew from Posit: Libby Heeren, Isabella Velasquez Laurie's LinkedIn: https://www.linkedin.com/in/laurie-merrell/ Michael's socials and urls: LinkedIn: https://www.linkedin.com/in/michael-a-chow/ Bluesky: https://bsky.app/profile/mchow.com GitHub: https://github.com/machow Resources from the hosts and chat: Michael Chow's talk about dbt at the Coalesce Conference in 2022: https://www.youtube.com/watch?v=EYdb1x1cO9U Beginner dbt project Michael is using: https://github.com/dbt-labs/jaffle_shop_duckdb Laurie's Coalesce talk with Ian and Jenna: https://www.youtube.com/watch?v=6aX7tAfMmIM Link to installation page for the DuckDB CLI: https://duckdb.org/install/?platform=macos&environment=cli "Why is dbt so important" shared by Jenna in the chat: https://highgrowthengineering.substack.com/p/why-is-dbt-so-important- dbtplyr: https://hub.getdbt.com/emilyriederer/dbtplyr/latest/ Parquet: https://parquet.apache.org/ From stored procedures to dbt: A modern migration playbook: https://www.getdbt.com/blog/stored-procedures-dbt-migration-playbook How to structure our dbt projects: https://docs.getdbt.com/best-practices/how-we-structure/1-guide-overview Jenna Jordan's blog on dbt mesh: https://jennajordan.me/blog/data-mesh-dbt ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps 00:00 Introduction 01:09 Guest introductions: Michael Chow and Laurie Merrell 04:15 Overview of today's session 05:51 Setting up the GitHub Codespace 07:00 The data science workflow vs. organizational needs 10:06 Why dbt is hard to learn in the abstract 13:34 "Could we back up and explain what dbt is again?" 19:12 Running 'dbt build' 20:00 Inspecting the database with DuckDB CLI 26:21 "Does dbt have concurrency or dependency capabilities?" 27:37 Understanding the 'ref' macro 29:52 "Is dbt an orchestrator?" 31:14 "Starting a project from scratch with just SQL?" 32:04 "How is this better than writing Python scripts?" 35:46 "Is data source detection dynamic with dbt?" 38:36 Generating and serving dbt docs 46:51 "Is dbt an IDE like RStudio, but for SQL?" 52:32 Branching and development environments 53:57 "Where would you begin on a brand new project?" 56:38 "How would you validate dependencies and downstream impacts?" 57:48 Defining a view versus a table

Michael Chow

Inspecting websites to find JSON data APIs | Marcos Huerta | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We'd love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Marcos Huerta, a Data Science Manager at Carmax, as he walks us through the guts of websites looking for data we can play with. He shows us how to find hidden REST/JSON APIs by using the web inspector in Safari/Firefox and then how to get what's necessary to pull the same data programmatically in python or R. Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Daniel Chen Marcos's urls: Website: https://marcoshuerta.com GitHub: https://github.com/astrowonk/ Resources from the hosts and from participants in the Discord chat: Postman: https://www.postman.com/ Insomnia (open source alternative to Postman): https://insomnia.rest/ Baseball Savant website Marcos is using: https://baseballsavant.mlb.com/gamefeed/?gamePk=777076 Isabella Velasquez's blog on using {polite} R package to help scrape Wikipedia: https://ivelasq.rbind.io/blog/politely-scraping/ Festivas Mac app Marcos used to add the lights to his desktop: https://festivitas.app/ Ted Laderas blog post on parsing JSON in R: https://laderast.github.io/intro_apis_json_cascadia/#/how-does-r-translate-json New rvest read_html_live() function: https://rvest.tidyverse.org/reference/read_html_live.html yyjsonr R package: https://github.com/coolbutuseless/yyjsonr tuber R package: https://github.com/gojiplus/tuber WikipediaR R package: https://www.quantargo.com/help/r/latest/packages/WikipediaR/1.1/WikipediaR-package rookiepy python package: https://pypi.org/project/rookiepy/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps 00:00 Introduction 03:05 Web scraping vs. API calls 04:12 Server-side rendering vs. client-side JSON 06:12 Warning: Rate limits and business ethics (ahem) 08:39 Demo: Baseball Savant website 08:57 Using browser Developer Tools and the Network tab 12:15 "What is curl?" 13:30 Importing curl into Postman 16:03 Generating Python code from Postman 16:50 "Are there open source alternatives to Postman?" 17:50 Using the generated code in Python/Jupyter 22:28 R packages for JSON (jsonlite, yyjsonr) 25:09 Demo: Massachusetts Lottery website 28:17 Example: scripts Marcos automated with Cron jobs 30:17 Handling logins and cookies with RookiePie 32:19 Demo: CNN Election Data 34:26 Inspecting ESPN's website 36:58 "Can you scrape YouTube?" 38:19 Finding hidden JSON in CardsMania history 45:00 Benefits of API inspection over Beautiful Soup 46:59 New rvest function: read_html_live 50:40 Inspecting LinkedIn and finding GraphQL 53:58 Encouragement on handling API pagination

querychat in R: Query Your Data with Natural Language | Shiny + LLMs | Veerle van Leemput

Learn how to use querychat, an R package that lets users query data using natural language instead of predefined filters. Perfect for data scientists who want to build flexible, interactive Shiny dashboards without anticipating every possible user question. In this tutorial, you'll discover: - How querychat translates natural language to SQL using LLMs - Setting up querychat in seconds with your data - Building custom Shiny interfaces with reactive data frames - Why it's reliable, transparent, and safe for production use - Enterprise deployment with Azure, AWS Bedrock, or Google Vertex AI Key Features: Drop-in Shiny component Works with any DBI-compatible database Read-only by design for data safety Full SQL transparency for reproducibility Supports Claude, GPT, Gemini via ellmer Resources: - querychat Documentation: [https://posit-dev.github.io/querychat/py/index.html] - Blog Post: "Where Questions Become Queries" [https://shiny.posit.co/blog/posts/querychat-python-r/ - Github repo: [https://github.com/posit-dev/querychat/tree/main/pkg-r] Perfect for R users working with Shiny, data exploration, and interactive analytics. Timestamps 00:00 - Introduction: The Dashboard Problem 00:50 - Why Dashboards Can't Answer Every Question 01:47 - What is querychat? 02:46 - Getting Started (Installation & Setup) 03:08 - API Key Setup (Claude, GPT, Gemini) 03:20 - First querychat App in 5 Lines of Code 04:04 - Live Demo: Querying Data with Natural Language 05:01 - How querychat Works Under the Hood 06:44 - Four Key Benefits (Reliability, Transparency, Reproducibility, Safety) 07:37 - Tool Calling & DuckDB Integration 08:46 - Building Custom Shiny Interfaces 09:01 - Customizing querychat Behavior (Greetings, Data Descriptions, Instructions) 11:00 - Creating Custom Dashboards with Reactive Data Frames 12:00 - Complete App Structure Walkthrough 13:10 - Safety & Security Deep Dive 14:00 - Enterprise Deployment (Azure, AWS, Google Vertex AI) 15:12 - Resources & Documentation

querychat in Python: Query Your Data with Natural Language | Shiny + LLMs | Veerle van Leemput

Learn how to use QueryChat to add natural language querying to your Python data applications. This tutorial shows you how to let users ask questions about data without building complex filters or dashboards. In this video, data scientist Veerle Eeftink demonstrates how to integrate QueryChat with Shiny for Python to create interactive data exploration tools powered by LLMs (Claude, GPT-4, Gemini). What You'll Learn: - Set up QueryChat in minutes with any data source - Understand how QueryChat translates natural language to SQL - Build custom dashboards with reactive data frames - Configure LLM behavior with greetings, data descriptions, and instructions - Ensure safety and read-only access for production environments Key Features: Works with Claude, GPT-4, Gemini, and other LLMs Transparent SQL generation (no black box) Integrates with Shiny, Streamlit, and other frameworks Uses DuckDB or bring your own SQL database Read-only operations for data safety Enterprise-ready with private LLM support Resources: - QueryChat Documentation: [https://posit-dev.github.io/querychat/py/index.html] - Blog Post: "Where Questions Become Queries" [https://shiny.posit.co/blog/posts/querychat-python-r/] - SheScores Demo App [https://github.com/hypebright/shescores-dashboard/tree/main/Python] #Python #DataScience #LLM #AI #Shiny #QueryChat #NaturalLanguage #SQL #DataVisualization Timestamps 00:00 - Introduction: The dashboard problem 01:28 - What is QueryChat? 02:36 - Demo: Three things you'll learn today 03:00 - Setup: LLM API keys and installation 04:04 - Quick start: First QueryChat app in 5 lines 04:27 - Live demo: Natural language queries 06:10 - How QueryChat works under the hood 07:19 - Four benefits: Reliability, transparency, reproducibility, safety 08:17 - Tool calling explained 08:54 - Schema information and DuckDB integration 09:29 - Building custom interfaces 09:45 - Customizing behavior: Greetings and data descriptions 11:16 - Adding custom instructions to the LLM 11:35 - Building a Shiny dashboard with QueryChat 13:09 - Code walkthrough: Sidebar chat and reactive data frames 14:14 - Complete dashboard demo 14:52 - Safety and security deep dive 16:22 - Resources and links

Teaching out loud and becoming StatQuest | Josh Starmer | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Josh Starmer, Founder and Creator of the massively popular YouTube channel StatQuest with Josh Starmer, to chat about his process for simplifying complex technical topics, overcoming imposter syndrome and feeling cringe, the enduring value of foundational statistics over fleeting AI trends, and how to use AI tools responsibly without sacrificing true understanding. In this Hangout, we explore Josh's unique approach to teaching and learning difficult concepts. He reveals that he specifically avoids watching other videos on a subject to ensure his explanations remain original. Instead, he focuses on finding the "simplest possible example" that still highlights the method's utility—such as creating a neural network with only three data points to visualize the math. Josh emphasizes that the hardest part of his work is constantly asking, "Can I make it simpler?" and extracting the essence of an equation so that learners can "feel" the statistics rather than just memorizing them. Resources mentioned in the video and zoom chat: StatQuest with Josh Starmer (YouTube) → https://www.youtube.com/c/joshstarmer The StatQuest Illustrated Guide to Neural Networks and AI → https://www.amazon.com/dp/B0DRS71QVQ The StatQuest Illustrated Guide to Machine Learning → https://www.amazon.com/dp/B0BLM4TLPY The StatQuest Store → https://statquest.org/statquest-store/ Introduction to Statistical Learning (ISLR) → https://www.statlearning.com/ Grokking Machine Learning by Luis Serrano → https://www.manning.com/books/grokking-machine-learning Hands-On Large Language Models by Jay Alammar & Maarten Grootendorst → https://www.maartengrootendorst.com/book/ Statistical Methods for Research Workers by R. A. Fisher → https://link.springer.com/chapter/10.1007/978-1-4612-4380-9_6 If you didn’t join live, one great discussion you missed from the zoom chat was the "Smug Bayesian" vs. Frequentist debate, which sparked jokes about p-hacking and a community demand for "Smug Bayesian" t-shirts. Will Mike K. Smith answer this call?? Would you wear a Smug Bayesian shirt? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 01:47 "Who are you and how did you get to the YouTube space?" 05:38 "What was it that you really wanted to do as a career?" 07:52 "Have you got any top tips on how to make video editing easier?" 10:30 "What is your process for breaking down a difficult concept?" 15:56 "What's the most common statistical or machine learning mistake you see in real projects?" 19:40 "How do you keep up with the hands-on stuff?" 21:43 "What is the one book that every data science professional should read?" 26:13 "What statistical concept do people think they understand but almost always misuse?" 30:42 "What would be your advice for a new grad to break out as a data science professional?" 35:55 "What are your tips for generating confidence to teach out loud?" 40:18 "Do you ever feel cringe? How do you deal with that?" 42:25 "Do you have any career advice?" 45:28 "How have your musical talents helped with your work in data?" 47:25 "How would you integrate AI as a responsible assistant and where would you forbid it?" 47:43 "What data tools and IDEs do you use?" 52:25 "What is a memorable concept that you remember having a personal moment through teaching?" 53:10 "What are you most excited for in 2026?"

Open source development practices | Isabel Zimmerman & Davis Vaughan | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Isabel Zimmerman and Davis Vaughan, Software Engineers at Posit, to chat about the life of an open source developer, strategies for navigating complex codebases, and how to leverage AI in data science workflows. Plus, NERDY BOOKS! In this Hangout, we explore the differences between maintaining established ecosystems like the Tidyverse as well as building new tools like the Positron IDE. Davis and Isabel (and sometimes Libby ) share practical advice for developers, such as the utility of AI for writing tests and "rubber ducking", and their various approaches to writing accessible documentation that bridges the expert-novice gap. Resources mentioned in the video and zoom chat: Positron IDE → https://posit.co/positron/ Air (R formatter) → https://posit-dev.github.io/air/ Python Packages Book (free) → https://py-pkgs.org/ R Packages Book (free) → https://r-pkgs.org/ DeepWiki (AI tool mentioned for docs) → https://deepwiki.com/tidyverse/vroom If you didn’t join live, one great discussion you missed from the zoom chat was about Brandon Sanderson's Cosmere books and the debate between starting with Mistborn vs. The Stormlight Archive. Are you a Cosmere fan?! Which book did you start with? (Libby started with Elantris years before picking up Mistborn Era 1 book 1, but she'd now recommend maybe starting with Warbreaker!) ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 04:41 "What does a day in the life of an open source dev look like?" 09:43 "What got you into building your own R packages?" 13:00 "Personal tips for working with code bases you're not familiar with?" 16:35 "How much of what you build is in R/Python vs. lower-level languages?" 19:57 "Does Air work inside code chunks in Positron?" 20:12 "Changing the Python Quarto formatter in Positron without an extension" 22:56 "What do your side projects look like?" 26:40 "How do you approach writing documentation?" 30:55 "What interesting trends in data science are you noticing?" 33:38 "How do you leverage AI in your work?" 37:30 "What are the hexes on Davis's back wall?" 38:50 "What career advice would you give to someone in a similar position?" 43:45 "How can I be more resilient when things go wrong?" 47:59 "Do you have keyboard preferences?" 49:25 "What is the best way to report bugs in packages?" 50:56 "Open source dev work vs. in-house dev work" 51:50 "Tips for getting started with Positron"

Davis Vaughan, Isabel Zimmerman

Integrating Shiny with Epic EHR | Matt Maloney | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Matt Maloney, Director of Applied AI and Data Science at City of Hope, to chat about applying data science to cancer care operations, integrating open source data science tools like Shiny with Electronic Health Records (like Epic), and the evolving governance of generative AI in healthcare. In this Hangout, we explore the technical and operational strategies behind integrating custom data science applications directly into clinical workflows. Matt discusses how his team moves beyond standalone tools by embedding Shiny apps and other solutions into Epic, allowing medical coders and providers to access predictions and summaries without leaving their primary software environment-of-choice. He also mentions the "build vs. buy" decision-making process as vendors release their own AI solutions, emphasizing the importance of validating external models against their specific patient population. Resources mentioned in the video and zoom chat: City of Hope → https://www.cityofhope.org Unity Health Toronto Customer Story → https://posit.co/about/customer-stories/unity-health-toronto/ pointblank (Data Validation package) → https://rstudio.github.io/pointblank/ If you didn’t join live, one great discussion you missed from the zoom chat was about *where* data science teams sit within community members' organizations and whether they like it or not, specifically the pros and cons of being housed within IT versus embedded inside business units. Participants debated access to infrastructure versus proximity to business stakeholders, with several sharing their own experiences of shifting between these departments (or between companies with different structures). Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 03:37 "What does the data science function at City of Hope help with?" 08:52 "Tell us a little bit more about how you're integrating Posit with Epic" 16:08 "How do you handle the needs of privacy with the push to adopt AI?" 18:40 "How do you manage to stay abreast of technical advancements?" 22:45 "At what point do you hand off your data work to the software engineering team?" 27:23 "How much has development that involves LLMs and generative AI taken hold?" 30:38 "Does your team evaluate a lot of the things that Epic might be throwing your way?" 34:41 "How does Epic pass an encounter number or a patient ID to Posit Connect?" 35:57 "How does your team handle these nuanced pieces of clinical information?" 40:29 "Do the administrators appreciate the time that it takes to do things?" 44:22 "What happens in the academic division?" 46:10 "Do you have a piece of career advice for us?"

How to deploy Shiny apps in 2026 | Alex Chisholm | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We'd love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Posit product manager Alex Chisholm as he walks through the evolution of shiny app deployment over the years, how to deploy shiny apps in the modern era, and peeks into Posit's roadmap for future development. Do you call it "deployment" or "publishing" when it comes to Shiny apps? 🤔 This is a super friendly and conversational space, and being there live in the Discord chat can't be beat!! We hope you get to join us sometime soon. Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Daniel Chen, Alex Chisholm Alex Chisholm's LinkedIn: http://www.linkedin.com/in/chisholm1 Resources from the hosts and chat: Posit Connect Cloud for deploying Shiny apps in the modern era: https://connect.posit.cloud/ Install Positron: https://positron.posit.co/ Simon Couch's blog post on local LLMs not being good enough yet: https://www.simonpcouch.com/blog/2025-12-04-local-agents/ Blue-Green Shiny App Deployments using Posit Connect posit::conf(2025) talk by Ryszard Szymański: https://youtu.be/QEEGLWj0nas Digital Ocean: https://www.digitalocean.com/ Ollama local LLM: https://ollama.com/ py-sidebot app template: https://shiny.posit.co/py/templates/sidebot/ querychat app template: https://shiny.posit.co/py/templates/querychat/ Dan Chen mentioned Render in the chat as an alternative to Digital Ocean: https://render.com/ Alex Chisholm's AB testing GitHub repo example: https://github.com/alex-chisholm/shiny-r-abtesting Edward in the chat shared a GitHub repo for using GitHub actions to execute remote SSH commands: https://github.com/appleboy/ssh-action Abu in the chat shared blue-green vs. canary deployments: https://octopus.com/devops/software-deployments/blue-green-vs-canary-deployments/ Frank in the chat mentioned Simon's blog on using local LLMs with the chores package: https://www.simonpcouch.com/blog/2025-12-10-chores-0-3-0/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps 00:00 Introduction 03:03 Meaningful applications and value creation 05:31 The evolution of Shinyapps.io and Posit Connect 08:12 DigitalOcean and Droplets 09:36 DigitalOcean vs. commercial cloud providers, 11:48 Comparisons: DigitalOcean, Azure, and AWS 14:47 Replicating local environments with Docker 16:51 The open-source Shiny Server 18:20 Use case: University of Illinois CITL 20:02 Key considerations for deployment decisions 21:53 GitHub Actions and version control 23:31 Addressing single points of failure and maintainability 24:38 Posit Connect Cloud features and portfolio 26:01 Beyond Shiny: Quarto, Streamlit, and Dash 27:07 Handling secrets and database credentials 28:56 Custom vanity links vs. UUIDs 30:04 Blue-Green deployment strategies 31:55 "Is it easy to set up a developer workflow?" 34:46 Guardrails for AI powered apps and token usage 37:32 Small language models and Ollama 38:29 Sidebot AI demo and LLM integration 39:41 Understanding manifest.json and dependencies 45:00 Automatic publish on GitHub push 46:51 The future of Shinyapps.io and migration 48:33 "Did you just build a custom agent for that specific dashboard?" 51:43 Publishing from RStudio IDE to Connect Cloud 54:16 Preview: Inspecting website APIs for data harvesting

Simon Couch

Advent of Code for R users | Emil Hvitfeldt | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We'd love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Posit engineer Emil Hvitfeldt as he walks through Day 1 of Advent of Code 2026 using R. This is a super friendly, collaborative, and cheery intro to AoC! Don't forget, you can do Advent of Code at any ole time of year Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Daniel Chen, Emil Hvitfeldt Emil's socials and urls: website: https://emilhvitfeldt.com/ GitHub: https://github.com/emilhvitfeldt Bluesky: https://bsky.app/profile/emilhvitfeldt.bsky.social LinkedIn: https://www.linkedin.com/in/emilhvitfeldt/ Resources from the hosts and chat: Advent of Code: https://adventofcode.com/ Install Positron: https://positron.posit.co/ Eric Wastl, Advent of Code: Behind the Scenes: https://www.youtube.com/watch?v=_oNOTknRTSU AoC Subreddit: https://www.reddit.com/r/adventofcode/ Kieran Healy shared a reddit post with an Advent of Code answer done in Minecraft: https://www.reddit.com/r/adventofcode/comments/1pbeyxx/2025_day_01_part_2_advent_of_code_in_minecraft/ Emil’s Solutions: https://github.com/EmilHvitfeldt/rstats-adventofcode Emil’s helper package: https://github.com/EmilHvitfeldt/aocfuns purrr::accumulate() function: https://purrr.tidyverse.org/reference/accumulate.html And, for anyone hangin' in there at the end, Emil updated us on Discord that he figured out why his cumsum() didn't work: he forgot to start the dial at 50! Once you fix that, it works to solve part 1 :) ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps 00:00 Introduction 01:01 Tour of the Advent of Code website 02:30 Dashboard overview and puzzle schedule 03:23 How to view and access previous years' events 03:37 Structure of puzzles: Two parts and stars 04:40 Understanding the global leaderboard 05:08 "Does that ASCII art build itself? 06:16 Setting up private leaderboards for friend 07:54 Starting Day 1: Story prompt and mechanics 09:30 Understanding unique puzzle inputs 10:51 Submission feedback and delay penalties 11:44 Safe dial logic: Left, Right, and circularity 12:50 Starting position and Part 1 success criteria 14:09 Setting up the project in Positron 16:26 Strategy for speed: Reading from the bottom up 18:49 Problem-solving strategies: Pen, paper, and visualization 19:22 Walking through the logic with a sample case 20:52 Coding Part 1: Data parsing and vectorization 23:17 Positron keyboard shortcuts for duplicating lines 24:40 Debugging the logic and handling negative numbers 26:03 Explaining the Modulo operator (%%) 28:15 Managing large inputs of over 4,000 instructions 29:21 Submitting Part 1 and transitioning to Part 2 32:03 Part 2 challenge: Counting zero "clicks" 34:02 Brainstorming Part 2 code modifications 36:19 Checking important warnings for edge cases 37:00 Coding Part 2: Nested loops and incrementing counters 38:23 Hint: Modulo vs. integer division 40:40 Success with the Part 2 test case 42:30 Alternative method: Vectorized cumulative sums 45:29 "What's the difference between % and %%?" (percent vs modulo) 46:50 Mathematical optimization to avoid inner loops

Emil Hvitfeldt

How Open Source, Python and AI Are Shaping the Data Future with Wes McKinney

The future of analytics isn’t just about bigger models — it’s about building smarter, more interoperable data systems. Wes McKinney, Principal Architect of Posit PBC, Chief Scientist of Voltron Data and a General Partner at Composed Ventures, joins us to explore how the modern data stack is evolving and what it means for the future of analytics. Wes reflects on his journey building pandas and Apache Arrow, sharing how open-source ecosystems grow, transform and shape the way organizations work with data today. Wes also highlights the rising importance of semantic layers, agentic workflows and defensive coding practices as teams embrace AI-driven development. Key Takeaways: 00:00 Introduction. 02:32 Wes didn’t expect pandas to drive AI but he recognized Python’s unrealized potential. 05:09 A lucky convergence helped Python’s tools snowball into the AI standard. 10:40 Early big data focused on essentials, not the interoperable stacks we rely on today. 15:44 The composable data stack grew through bottom-up, grassroots open-source momentum. 21:56 Many “data science” roles ultimately became business intelligence and dashboard work. 25:24 Complex statistical work still depends on human judgment, not fully autonomous agents. 30:27 Frontier models retrieve table data reliably, while smaller models fail dramatically. 35:16 Better models and coding agents shifted Wes from an AI skeptic to an adopter. 40:07 AI-driven code demands stronger testing and review to avoid costly failures. 45:14 An AI-built finance project ballooned, revealing how agents inflate codebases. Resources Mentioned: Wes McKinney https://www.linkedin.com/in/wesmckinn/ Posit PBC | LinkedIn https://www.linkedin.com/company/posit-software/ Posit PBC | Website https://posit.co/ Voltron Data | LinkedIn https://www.linkedin.com/company/voltrondata/ Voltron Data | Website https://voltrondata.com/ Composed Ventures | LinkedIn https://www.linkedin.com/company/composedvc/ Composed Ventures | Website https://composed.vc/ pandas https://pandas.pydata.org/ Apache Arrow https://arrow.apache.org/ DuckDB https://duckdb.org/ DataFusion https://datafusion.apache.org/ Jupyter Notebook https://jupyter.org/ Parquet https://parquet.apache.org/ Iceberg https://iceberg.apache.org/ Delta Lake https://delta.io/ Thanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data. #DataStrategy #DataManagement #DataMastersPodcast

Exploring Positron settings | Isabel Zimmerman & Davis Vaughan | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We'd love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Posit engineers Isabel Zimmerman and Davis Vaughan as they share some of their favorite settings in Positron, a super customizable data science IDE. Come laugh with us as we can't seem to figure out that VSCode calls rainbow parentheses "bracket pair colorization" Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Daniel Chen, Isabel Zimmerman, Davis Vaughan Resources from the hosts and chat: Install Positron: https://positron.posit.co/ Positron docs on keyboard shortcuts: https://positron.posit.co/keyboard-shortcuts.html Nathan Jeffery's "click to open a .RDS file" keybinding: https://nathan-jeffery.netlify.app/blog/2025-08-26-read-rds-positron/ Positron R pipe setting (paste in browser and it'll open in Positron): positron://settings/positron.r.pipe One of Dan Chen's faves, the native tab feature in VSCode + Positron: https://lucasprag.com/posts/underrated-vscode-feature-native-tabs/ The list of RStudio keybindings that you get when you turn on RStudio keybindings in Positron: https://positron.posit.co/migrate-rstudio-keybindings.html Indent rainbow extension: https://open-vsx.org/extension/oderwat/indent-rainbow Rainbow brackets setting (paste in browser and it'll open in Positron): positron://settings/editor.bracketPairColorization.enabled Setting hierarchy (User vs Workspace settings) in Positron: https://code.visualstudio.com/docs/configure/settings#_settings-precedence Rainbow CSV extension (not by Posit): https://marketplace.visualstudio.com/items?itemName=mechatroner.rainbow-csv Positron +1ePositron, an extension pack for dev and data science, by Garrick Aden-Buie: https://open-vsx.org/extension/grrrck/positron-plus-1-e Publishing from VS Code or Positron: https://docs.posit.co/connect/user/publishing-positron-vscode/ Posit Connect Cloud plans: https://connect.posit.cloud/plans Enter Folder extension that Libby mentions: https://open-vsx.org/extension/xiangda/enter-folder Catppuccin themes (shared by Rory Lawless, and now some of Libby's favorites!): https://open-vsx.org/extension/Catppuccin/catppuccin-vsc ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps 00:00 Introduction 00:42 Guest Introductions: Isabel and Davis 02:41 Positron Settings overview 04:11 How to enable "Format on Save" 04:34 "How do I open settings in JSON or UI?" 05:10 Auto Save on focus change 08:26 Enabling RStudio key bindings 09:28 "Why doesn't the cursor move with code edits?" 12:18 User vs. Workspace settings 14:34 Creating and using Profiles 16:13 "Can I use the magrittr pipe with Control+Shift+M?" 17:23 Searching and managing keyboard shortcuts 19:42 Creating custom code snippets 21:31 The Indent Rainbow extension 24:04 Enabling rainbow parenthesis/brackets 25:08 Managing Python and R interpreters 26:32 Rearranging and hiding UI panes 28:04 Rainbow CSV and favorite extensions 29:26 Using the Enter Folder extension 31:05 Understanding the setting hierarchy 32:48 Adding symbols to Quick Open search 36:00 "Is there a way to shift focus using keyboard shortcuts?" 38:04 Modifying keybindings JSON for specific languages 39:20 "How do you find trustworthy extensions?" 43:11 "How can I publish to shinyapps.io from Positron?" 44:03 Deploying with Posit Publisher and Connect Cloud 48:32 Customizing themes with RainGlow extension 50:36 "Is there an Import Data Set wizard in Positron?" 53:01 Conclusion and community resources

Davis Vaughan, Garrick Aden-Buie, Isabel Zimmerman

Rodrigo Silva Ferreira-When Rivers Speak Analyzing Massive Water Quality Datasets-PyData Boston 2025

Rivers have long been storytellers of human history. From the Nile to the Yangtze, they have shaped trade, migration, settlement, and the rise of civilizations. They reveal the traces of human ambition... and the costs of it. Today, from the Charles to the Golden Gate, US rivers continue to tell stories, especially through data. Over the past decades, extensive water quality monitoring efforts have generated vast public datasets: millions of measurements of pH, dissolved oxygen, temperature, and conductivity collected across the country. These records are more than environmental snapshots; they are archives of political priorities, regulatory choices, and ecological disruptions. Ultimately, they are evidence of how societies interact with their environments, often unevenly. In this talk, I’ll explore how Python and modern data workflows can help us "listen" to these stories at scale. Using the United States Geological Survey (USGS) Water Data APIs and Remote SSH in Positron, I’ll process terabytes of sensor data spanning several years and regions. I’ll demonstrate that, while Parquet and DuckDB enable scalable exploration of historical records, using Remote SSH is paramount in order to enable large-scale data analysis. By doing so, I hope to answer some analytical questions that can surface patterns linked to industrial growth, regulatory shifts, and climate change. By treating rivers as both ecological systems and social mirrors, we can begin to see how environmental data encodes histories of inequality, resilience, and transformation. Whether your interest lies in data engineering, environmental analytics, or the human dimensions of climate and infrastructure, this talk will explore topics at the intersection of environmental science, will offer both technical methods and sociological lenses to understand the stories rivers continue to tell

Bayesian MMM and silly side projects | Ryan Timpe | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Ryan Timpe, a Lead Data Scientist, to chat about Marketing Mix Modeling (MMM), silly data science side projects like brickr (see the link below to his 2020 posit::conf() talk), the benefits of working out loud, and using LLMs (Large Language Models) for data science coding (something he gave a whole talk about at posit::conf(2025)). In this Hangout, we explore a bit of what Ryan does, including Marketing Mix Modeling (MMM) and helping business partners better use their data. He shared that his work utilizes Bayesian methods to manage the complexity and high seasonality of his data, especially the Q4 holiday-buying spikes seen in most retail sales data. Using Bayesian priors helps keep the models within constraints and prevents overfitting. The foundation of the internal MMM package that Ryan developed uses tidy models, and you can check out the link to his 2023 posit::conf() talk to hear more about his model pipeline! The theme here is that Ryan gives lots of great talks - go check them out! Resources mentioned in the video and zoom chat: Ryan Timpe's brickr GitHub Repository → https://github.com/ryantimpe/brickr Ryan Timpe's 2020 talk on learning R with silly projects → https://youtu.be/oOG-aXP_ICI Ryan Timpe's 2023 talk on model pipelines → https://youtu.be/R7XNqcCZnLg Ryan Timpe's 2025 talk on learning from LLM pitfalls → https://youtu.be/vJrIahZWCw4 If you didn’t join live, one great discussion you missed from the zoom chat was about the importance of working out loud and publishing (even imperfect) data science projects. Attendees emphasized that overcoming their inner critic and sharing work helps with communication and professional development (but can be super challenging still). One participant shared that they got their current role by creating a fake MMM project with fake data, but that it showed their interviewer they could take initiative! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction of Ryan 04:41 "What kind of MMM do you guys do?" 07:01 "What was your favorite silly project that you did?" 08:06 "What is brickr?" 11:11 "Do you think brickr helped you get the job you have now?" 14:23 "How do you scope your side projects?" 17:42 "What do you recommend for setting up a Python environment?" 21:16 "When do you recommend not using LLMs, and why?" 24:19 "How do you shut up your own inner critic?" 28:00 "What kind of data do you use for your LLMs and models?" 30:32 "Do you use open source MMM package like Meridian or Robyn?" 35:13 "How do you communicate complex modeling nuances to non-technical stakeholders?" 38:06 "Does your love for LEGO help you build code better?" 41:00 "What drives priority for what your team works on next?" 43:21 "What resources do you recommend for learning marketing analytics?" 44:46 "What tools do you use for Bayesian analysis?" 45:50 "Was your career move to the LEGO Group intentional or accidental?" 47:49 "What is your end product delivery?" 49:21 "What tool do you use to implement a Bayesian MCMC model?" 49:52 "How do you make model-based reporting faster and more automated?" 51:31 "Do you have a piece of career advice that's meaningful to you?"

The skydiver to data scientist pipeline | Kevin Dalton | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Kevin Dalton, Senior Data Scientist at Great American Insurance Group, to chat about data science in the insurance industry, MLOps and productionalization strategies, Bayesian modeling, and career development. In this Hangout, we explore MLOps and productionalization strategies, especially the distinction between "little P" and "big P" production. Kevin explained that "small p" production involves internal dashboards, interactive applications, or analytics reports used to support the research process and assess the business, generally having a lower risk profile. In contrast, "big P" production refers to ML models deployed for inference in core business systems, which demand strict Service Level Agreements (SLAs). Kevin noted that the industry is moving towards automated, cloud-centric, real-time inference engines, although production systems can still be as simple as running a notebook over new data. Resources mentioned in the video and zoom chat: The (new!) Data Science Lab on Tuesdays → https://pos.it/dslab Bayes Rules! Book → https://www.bayesrulesbook.com/ Positron link → https://positron.posit.co/ If you didn’t join live, one great discussion you missed from the zoom chat was about the humorous observations regarding Kevin's unique professional history, with some referring to it as the "skydiving to actuary pipeline" There was some speculation about whether professional skydivers have "No fear to deploy models" and then there was a subsequent realization about all the insurance needed for a skydiving business. That tracks! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 05:06 "What does it actually mean to put models into production at an insurance company?" 07:27 "How do you actually explain that to people at the company that there's this need for two different types of production?" 09:21 "Have you guys, in a large company, found any application for smaller language models in, like, production pipelines?" 10:17 "Are there any lessons from the skydiving experience that you apply to your data science work?" 12:45 "What recent innovative ML models in the insurance sectors have you brought live if you're allowed to share?" 17:33 "I'm just curious to hear from you or others in chat who spend a lot of time in R if there are strategies you found effective in bridging the gap to using Python." 20:54 "What was the main thing that got you from "who is this for?" when you first opened Positron to now using it?" 22:46 "Could you share a little bit about what your tool stack looks like at Great American Insurance Group?" 24:27 "So you actually run Posit within Snowflake. Is that right?" 26:32 "What sort of guardrails are you using that are specific to insurance to guard against drift?" 29:26 "Do you think that'll be the future when it comes to test driven development?" 30:55 "What are some of your favorite public data sources for trying to quantify and model things like geopolitical risk?" 32:07 "If you wanna learn more about using tools you mentioned for building synthetic data, where would you recommend that people start?" 34:27 "So with the AI evolving, what kind of techniques are being recently used?" 35:28 "Broadly, how is AI affecting your day to day work in the insurance industry?" 38:21 "Is there a piece of career advice that's been especially meaningful to you, whether it's something that you've heard from a mentor or you've given this advice to other people?" 40:04 "How would you advise people to get domain expertise before they've gotten a chance to do a job in that industry?" 46:19 "Which skills would you say are evergreen irrespective of the tools that you're using?" 47:51 "What are you learning about change management?" 49:31 "What does seeking mentorship mean for you?" 53:21 "Do you have any strategies or advice for keeping that statistical intuition sharp?" 54:16 "How do you partner with actuaries at work? Are there things that you need from the actuaries that make your job easier?"

Integrating video & data in sports analytics | Arielle Dror | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Arielle Dror, Director of Data and Analytics at Bay FC, a team in the US National Women's Soccer League (NWSL), to chat about working out loud, integrating video with data analysis, technology they use in sports analytics, and quantifying intangible player traits. Oh, and ICE CREAM! In this Hangout, Arielle discusses how she works to integrate data into Bay FC’s decision-making processes, including recruitment, tactical analysis, and game preparation. She uses tools like Quarto and Posit Connect to automate weekly match reports. To enhance understanding among non-technical staff, Ariel’s team also uses proprietary sports software (Sportscode) to build dashboards on top of timestamped game footage with specific events tagged in it. This allows end-users to click on specific data points, such as those related to chance creation, and immediately view the corresponding video play that demonstrates the data's meaning. This visual context is essential for translating data results to coaches. Resources mentioned in the video and zoom chat: R and AI Conference → https://rconsortium.github.io/RplusAI_website/ Bay FC Data Video on LinkedIn → https://www.linkedin.com/posts/wearebayfc_bayfc-activity-7390100562731712512-xKIY?utm_source=share&utm_medium=member_desktop&rcm=ACoAACSkp_4BRz9mhkQZvnAk0Wdehn749sDDYJY If you didn’t join live, one great discussion you missed from the zoom chat was about the "passion penalty". Attendees discussed whether working in a field you love, such as sports, typically comes with lower pay than other industries, especially given the high supply of passionate people who want to work in the space. Do you think the passion penalty exists? 🤔 ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 03:19 "What traits in players do you wish you were able to quantify?" 06:48 "How is the data science work implemented on the field?" 10:50 "How do you translate results to coaches?" 12:44 "What tools do you wish you had access to?" 18:27 "Tell us about how you began working out loud and what that led to." 23:26 "Why is it so hard to recruit for sports?" 24:31 "Do you feel like there's a passion penalty for working in the sports world?" 28:31 "Are you working with PyTorch, scikit-learn, or OpenCV computer vision?" 30:28 "Do you ever correct anyone and tell them it's football, not soccer? Is the same set of data available for everyone?" 34:08 "Were there any sporting myths you've been able to analyze, like home ground advantage?" 37:28 "What's the best way to break into sports data roles?" 40:11 "Are athletes willing to share personal health data from wearables?" 42:06 "Is there commercially viable technology for 3D modeling movements?" 43:43 "How do you handle scenarios where play on the field contradicts data predictions?" 45:33 "How did you pick the analytics stack you use?" 47:41 "Do you have any advice for new people learning data science skills?" 48:44 "Is there a danger players will optimize individual statistics over team performance?" 50:22 "Is there a 'Believe' sign in the locker room?" 50:59 "What's the most important thing you learned about communicating analytics to stakeholders?"

Investing in community to drive organizational change | Dooti Roy | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Dooti Roy, Head of Clinical Data Science Capability Management at Boehringer Ingelheim Pharmaceuticals, to chat about organizational change in data science, investing in internal open-source community, transitioning from SAS to R, and motivating long-term learning and adoption of new tools. In this Hangout, we explore the instrumental role of internal community in driving organizational change. Dooti shared that during the preparation for the transformation process from closed source (like SAS) to R and open source, she was advised by Rachael Dempsey (creator of the DS hangout!) to invest in community and "don't sleep on that." Dooti reported that following this advice proved successful, as the internal community grew from the early adopters and became a safe space where colleagues help each other understand new concepts and overcome implementation challenges. This thriving community, which includes team members from the US, Europe, China, and Japan, serves as a springboard for testing new ideas, tools, and processes, generating global feedback that helps pull the institutional change forward. Maybe our favorite part, Dootie shared that strategies like naming internal meetings "beginR" (for beginner office hours) help set the right expectation, ensuring people feel comfortable asking questions without feeling afraid of appearing naive. Resources mentioned in the video and zoom chat: Positron Data Science Lab Session Registration → pos.it/dslab R/Pharma Conference Registration (Free Event) → https://rinpharma.com/ Community Building Tips & Tricks Site → https://pos.it/community-building Boehringer Ingelheim's Open Source Davinci Modular Framework → https://github.com/Boehringer-Ingelheim/davinci If you didn’t join live, one great discussion you missed from the zoom chat was about the struggle of finding time to learn new skills. Many attendees agreed that they are sooooo tired after work and that the learning often needs to be a work assignment to make time for it. This resonated with Dooti Roy's observation that "I don't have time" is one of the biggest pain points for colleagues attempting to learn new tools. Do you have the energy after work to do ambitious learning projects? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 02:52 "What did organizational change look like and how did community help?" 06:19 "Do leaders understand the commitment to open source?" 15:25 "Was this your first time promoting open source?" 18:04 "What does data science look like at Boehringer Ingelheim?" 22:55 "How do you motivate long-term learning and development?" 31:05 "What is your costume for Halloween?" 31:55 "What is your team's workflow and technology stack?" 44:50 "How do you motivate data professionals to document their code?" 48:40 "How do you encourage people to keep up with open source developments?"

Scaling a shiny app to 100K monthly users | Tan Ho | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Tan Ho, Engineering manager at Teamworks Intelligence Soccer, to chat about scaling Shiny applications, working out loud and building a personal brand, defining "code in production," and best practices for R package development. In this Hangout, we explore how Tan built a successful Shiny application that scaled to serve thousands of users, challenging the misconception that R does not scale. Tan shared his firsthand experience running the Dynasty Process Shiny app which peaked at 200,000 monthly users. He explained that the app was able to handle this load because user sessions were kept very short (around 30 seconds to a minute). Key advice for performance optimization includes focusing on small round-trip times for users, moving as much logic out of the app as possible (such as pre-computing or caching common requests), reducing the interface, and optimizing the actual queries (for instance, by rewriting them in data.table). Resources mentioned in the video and zoom chat: DynastyProcess GitHub → https://github.com/DynastyProcess Tan Ho's Github → https://github.com/tanho63 Tan Ho talk: A minimum viable Shiny infrastructure for serving 95,000 monthly users → https://www.youtube.com/watch?v=zb3umo3Ka2g&t=3s Tan Ho talk: Shiny in Production 2023: Tan Ho - Effective logging for Shiny → https://www.youtube.com/watch?v=wbUXrK_YWfI Show Your Work by Austin Kleon → https://austinkleon.com/show-your-work/ If you didn’t join live, one great discussion you missed from the zoom chat was about display setups, with participants debating whether one screen is better for focus or if multiple screens (up to four!) are essential for data science work. How many monitors do you roll with? Libby can't live without at least two external monitors, but admits that sometimes just one laptop monitor is better for focusing. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 04:15 "Tell us just, like, a snippet of your journey into R/Shiny" 07:36 "How many users did you have on your shiny app?" 09:44 "How did the techniques and skills you learned in NFLverse help translate to your professional role?" 13:03 "Do you feel like working out loud played a big part in building a personal brand?" 15:29 "What is your top advice for thinking about code in production?" 21:30 "Do you have resources for people starting their Shiny journey?" 25:01 "What's the trigger point when you decide to bundle products into one package?" 27:25 "What were the biggest hurdles faced in scaling the Shiny app with high user load?" 31:23 "How can I do a better job of evaluating fantasy football players than ESPN projections?" 32:59 "What packages are most helpful for benchmarking and writing fast code?" 35:29 "Do you start with use case first or generic code and then refine?" 37:48 "What soft skills or personality traits help manage bugs efficiently?" 41:49 "Did you have instances when you felt you were going overboard with curiosity?" 45:25 "What are the best practices for reproducible production workflows for non-technical teams?" 49:00 "What piece of career advice would you give?"

People over computers: engineering leadership | Elliot Murphy | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Elliot Murphy, a VP of Engineering at Posit, to chat about engineering leadership philosophy, balancing maintenance versus innovation and risk, non-traditional career paths and the value of lifelong learning, and data science tool breakdowns including Posit Cloud, Posit Connect Cloud, and shinyapps.io. In this Hangout, we explore some philosophical approaches to engineering and management, where Elliot stresses that focusing on people is more important than computers, and human relationships tend to outlast relationships with a given company or employer. Elliot discusses his role as primarily listening and synthesizing the brilliance of others, creating a supportive environment, and making space for people to take on risk and propose new ideas. He also highlighted the value of clear communication when addressing high-pressure environments (like when you're sunsetting a software product). Resources mentioned in the video and zoom chat: Cognition in the Wild (Book discussed regarding joint cognition) → https://mitpress.mit.edu/9780262581462/cognition-in-the-wild/ datapasta (R package) → https://github.com/MilesMcBain/datapasta AI Capabilities in Positron Workflow Demo → https://pos.it/workflow-demos If you didn’t join live, one great discussion you missed from the zoom chat was about the surprising value and broad resources offered by local libraries, including free access to services like LinkedIn Learning, paid periodicals like the NY Times, and streaming platforms like Kanopy. Attendees praised libraries as the "best resource we hardly hear about" and key providers of freedom of information. Do you love your local library?! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 02:04 "Can you tell us a little bit about your journey to where you are?" 04:10 "What does a VP of engineering do? And what types of things are important to you from a philosophical standpoint for engineering and management?" 06:21 "What about your job lets you focus on the people?" 06:33 "Is there also a pressure to innovate?" 09:28 "How do you argue to leadership at a strategic level that that maintenance work is important to take up people's time?" 11:48 "How do you balance engineers and data scientists wanting to incorporate best practices with fast prototyping?" 16:46 "What distinguishes science from engineering in data and also more broadly?" 18:28 "Any resources or any books that helped you bridge the gap between a non traditional background and your current position?" 23:00 "What is the secret to creating a good environment for the team?" 25:23 "Are there basics that you feel analysts or data scientists should be aware of in terms of engineering that would help their work?" 28:47 "How do you make decisions about when to either sunset a project or change directions?" 31:20 "What can those doing maintenance work do to add value to the organization to be safe from being displaced by automation?" 35:02 "What do you think many data scientists or analysts are missing about engineering infrastructure today that could simplify their day to day workflows?" 35:52 "How can a professional transition from a business intelligence role to a data scientist position?" 43:47 "What is your mantra or best practice to crack these professional certification exams?" 45:22 "Tell us the difference between Posit Cloud, Posit Connect Cloud, Posit Connect, and shinyapps.io." 49:03 "How can a team avoid the trap of over-engineering?"

AI-Powered Data Science in Positron

We're so excited to introduce Positron, a free, next-generation data science IDE that makes it easy to work in both R and Python. Positron builds on our years of experience developing RStudio and is a fork of VS Code, designed specifically for data work. This means you get a modern coding interface with features tailored for data science, like a built-in data explorer, AI assistance, interpreter management, and more. This is our 2nd event in our Positron series and focused on AI-Powered Data Science. Here at Posit, we strive to create products where AI works with you, not against you. In efforts to continue this mission, we are excited to introduce agentic AI capabilities in Positron, our new, free code editor for R and Python, that are designed from the ground up to follow these principles. Positron’s AI capabilities automate the tedious parts of the data science workflow, but always keep you, the expert, in control. 00:00 Introduction 00:32 What is Positron? (The Next-Gen Data Science IDE) 03:43 Introducing Positron Assistant 05:10 Bring your own LLM 05:47 Your environment as context 07:19 Inline code suggestions 07:58 Introducing Databot 08:22 The WEAR loop 10:15 Demo time 10:56 Opening Positron Pro in Posit Team 14:29 Opening a new folder in Positron 15:22 Cloning a repo from GitHub 17:24 Positron icons 19:15 Positron search bar and command palette 20:18 Changing interpreters and opening a Quarto document 21:30 Running code and populating the Variables Pane 22:41 Using the Data Explorer 25:28 Creating a plot and debugging with Positron Assistant 30:46 Editing code using inline code suggestions 34:42 Sharing a Quarto document 36:12 Opening Databot 37:02 Exploring data with Databot 43:37 Creating a report using Databot findings 45:12 Wrap up Additional resources: GitHub Repo for this Example: https://github.com/posit-dev/positron-ai-workshop Getting Started with Positron: Quick Tour: https://posit.co/resources/videos/get-started-with-positron-a-quick-tour-and-community-qa/ Introducing Databot (Blog Post): https://posit.co/blog/introducing-databot/ Posit AI Newsletter: https://posit.co/blog/2025-09-26-ai-newsletter/ Positron Assistant: https://positron.posit.co/assistant.html

Lessons from a Broad & Varied Data Science Career | Arcenis Rojas | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Arcenis Rojas, Data Scientist at Indeed, to chat about econometrics, public vs private sector data science, navigating a varied career trajectory, AI integration in the hiring sphere, and making friends at conferences. In this Hangout, Arcenis talked about how his career journey has been wide as opposed to vertically narrow. He shared that this breadth of experience has given him confidence that he can quickly figure out any dataset. He feels it also taught him how to communicate effectively about data to people at different levels and across various domains. He also shared his tech stack at Indeed, including RStudio, Positron, AWS, Snowflake, Quarto for reporting, Shiny for apps, and Posit Connect for deploying them. An attendee asked about the impacts of AI on the job search space, and Arcenis shared the AI at Work Report (linked below) from the Indeed Hiring Lab. He says, based on research, generative AI is expected to assist many people but only replace small segments of the workforce in the coming 5-10 years, and that entry-level knowledge work is predicted to be the most highly impacted area. Resources mentioned in the video and zoom chat: Indeed Hiring Lab: AI at Work Report 2025 → https://www.hiringlab.org/2025/09/23/ai-at-work-report-2025-how-genai-is-rewiring-the-dna-of-jobs/ To Explain or to Predict? (Galit Shmueli, 2010) → https://arxiv.org/abs/1101.0891 Announcing the 2025 table and plotnine contests → https://posit.co/blog/announcing-the-2025-table-and-plotnine-contests/ If you didn’t join live, one great discussion you missed from the zoom chat was about the wide variety of data types data scientists work with. Attendees shared that their data included genomics, finance/trading, environmental/natural resources, e-commerce products, and medical/clinical data. What kind of data types do you work with? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 06:16 "What do you like to do for fun?" 08:51 "What are the unique aspects of financial and economic data science?" 15:07 "What are econometrics?" 16:02 "Is the difference that hard sciences stats is trying to explain what happened where econometrics might be what might happen in the future?" 19:39 "Suggestions for making data friends and going to a conference alone." 23:26 "Do you see any misconceptions about the job market online, specifically the ATS thing?" 29:52 "How has your varied career trajectory been an advantage or a challenge in data science?" 34:08 "How is the recent hype wave of AI integration manifesting in the hiring sphere?" 40:08 "What are the tools that you use in your job for reporting?" 41:42 "How do you know when it is time to pivot and leave your role because your skills are stagnating?" 45:56 "How would you persuade leadership to use R or Python?" 49:32 "Did you find yourself always trying to use more complex models when simpler ones would serve the audience better?"

Pair Programming for Sustainable Deployment | Kris Fabick | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Kris Fabick, data scientist at Nissan North America, to chat about pair programming as a mechanism for sustainable deployment, data science in supply chain operations, the challenges of integrating pair programming into a team, and even a little about Kris's hobby, reffing soccer! In this Hangout, we explore how pair programming can serve as a mechanism for sustainable deployment. Kris explained that using pair programming is the team's solution to successfully deploying projects in a sustainable way, especially since they have a small data science team without extensive enterprise-level tools or IT support. Pair programming creates human redundancy in a project, meaning two (or more) people are familiar with it, allowing the team to react quickly to changes in development and deployment, and ensuring that someone can cover maintenance even if the original programmer is on vacation. When pitching the investment of time to leadership, a crucial metric was cited: pair programming requires only 15% more time to implement but results in 15% fewer defects. The value proposition is that saving defects saves far more time and downstream costs than the initial investment, offering stability that was a great comfort to leadership. Resources mentioned in the video and zoom chat: Shiny State Blog Post (by Eric Nantz) → https://shinydevseries.com/blog/2025-09-24-shinystate-cran/ Guest's posit::conf() GitHub Repo → https://github.com/kfabick03/positconf2025 We don't have the link just yet, but Kris's conf talk will be on YouTube soon! You can search for the title, "Self-sufficient deployment- using pair programming to have fun and sell your solutions" and the speakers are Kris Fabick & Kristin Carr If you didn’t join live, one great discussion you missed from the zoom chat was about the difference between using AI for coding versus using human pair programming. The group discussed how "Pair programming == everyone learns MORE about the codebase" while "AI coding == everyone understands LESS about the codebase". Chris noted that when he uses AI to help with code, he actually knows less about it and would not be able to explain the functionality to stakeholders if changes were requested. Attendees also noted that chatbots don't show their logic or mistakes and how they back out of them, which is a valuable source of learning. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Time Stamps 00:00 Introduction 01:47 "What is the best and worst thing about being a referee?" 04:58 "Kris, could you give us a little recap of your posit::conf(2025) talk about pair programming?" 08:52 "What was the most persuasive thing or metric that you feel like you were able to use to persuade leadership to let you adopt pair programming?" 13:52 "Are there any challenges or additional benefits of pair programming to the individual, that you've noticed?" 17:58 "Do you have any tips for being self-conscious while coding in front of someone?" 19:53 "Could you give a high level overview of supply chain?" 21:56 "Is it better to have both devs be at the same level of experience or expertise when pair coding?" 27:32 "I was just wondering if you could tell us a little bit about the container fill ratio problem you mentioned in your talk." 31:41 "How as a pair of programmer do you set realistic expectation from the stakeholder? How do you manage the communication gap?" 36:50 "Do you think that AI and specifically things like Copilot might be able to replace some aspects of pair programming?" 40:59 "Do you have a piece of career advice that has really stuck with you or that you like to give people that you find impactful?" 43:36 "Have you ever done any fun data stuff related to your refereeing?" 48:36 "How do you balance the time that gets added on with pair programming with tasks that need to be done quickly?" Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Migrating to Open Source & the Future of Biostatistics | Beth Atkinson | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Beth Atkinson, principal biostatistician at Mayo Clinic, to chat about the challenges of migrating from SAS to R, working with diverse and noisy data types (including wearable data and omics projects), foundational tooling like RMarkdown and Quarto, and maintaining statistical fundamentals amidst the hype cycle of new tools like AI. In this Hangout, we explore the challenges of working with complex, high-volume data, like the data derived from wearable devices and medical charts. A challenge with wearable device data is that it can be super noisy, with issues like computers not syncing up, people forgetting to wear the device, or someone else wearing it. Medical chart data can be inconsistent; some things are recorded, and some are not. She also talks about the R/Medicine conference, the future of modern biostatistics, and the journey of compassionately helping an organization move from proprietary tools like SAS to open source tools like R. Beth also works on omics projects, including genomics (looking at DNA), metabolomics, exposomics (chemical exposures), and multiomics, which involves looking at all of this information together in a holistic way. We hope you'll come along with us if you're interested in learning about the biomedical world of data! Resources mentioned in the video and zoom chat: R/Medicine Conference website → https://rconsortium.github.io/RMedicine_website/ arsenal R package (MayoVerse) → https://mayoverse.github.io/arsenal/ (The arsenal package was created to help encourage transition from SAS to R by providing equivalent functionality for summary reporting macros that people relied on.) 2025 Posit Table and Plotnine Contests → https://posit.co/blog/announcing-the-2025-table-and-plotnine-contests/ If you didn’t join live, one great discussion you missed from the zoom chat was about the general dislike of regular expressions (regex) and the tendency to rely on tools like ChatGPT to write complex regex syntax. Many in the chat agreed that regex is difficult to commit to memory but acknowledged the power of the tool. So... do you use an LLM to help with regex? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 03:25 "You do a lot of things that end in -omics. What are those things?" 05:02 "What are the types of data that you work with and some of the challenges that you face with those data?" 09:37 "What was your favorite new feature that made your work easier?" 11:42 "What is your favorite data science tool or R package that you find helpful in health research as a biostatistician?" 14:04 "I wanted to see if that's consistent with your experience [that 80% of workflow is data prep]" 17:07 "Does it scare you to hand off data to be cleaned by someone else?" 18:11 "What have you noticed that we still need to adhere to [regarding statistics fundamentals]?" 22:48 "Do you also produce reporting products as part of your role, and is your audience primarily internal and narrow, or do you communicate with a broader external audience as well?" 26:35 "Can you talk about a little bit of your personal SAS experience as well as the bigger organizational change maybe that Mayo is is doing?" 30:05 "What are some of the roadblocks that are faced in a SAS-to-R journey and and how can we find compassion for the people that we are helping to transition?" 33:55 "What is the community aspect internally at Mayo Clinic around R?" 35:43 "How do you store and manage all of that [data]?" 40:41 "What tools and skill sets should we focus on if we want to get into biostats today? Do you think it's important for people to still learn SAS if they're coming in fresh? And how about the future of biostatistics as a role separate from data science?" 45:48 "Is it possible for someone with a nontraditional background to make these transitions [into computational epidemiology]?" 48:10 "What's the source of most of these innovations?" 50:05 "Could you talk a little bit about R/Medicine conference?"

Supporting 100 Data Scientists with a Small Team | Mike Thomson | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Mike Thomson, Data Science Manager at Flatiron Health, to chat about managing open source tools and maintaining R packages, creating reproducible reports for Word and Excel using Quarto, the “hub and spoke” support model for data scientists, and applying R and Posit tools in Real World Evidence (RWE) oncology space. In this Hangout, we explore creating reproducible outputs using Quarto for formats like Word and Excel. Flatiron Health uses Quarto because it allows the reproducible publication of analyses to multiple formats simultaneously (like HTML and a downloadable Word document) from the same source code. A specific challenge discussed was outputting formatted analytic tables to Excel, as this is not natively supported by Quarto. Erica Yim, from Mike's team, detailed how they built an internal R function that uses the flexlsx package along with flextable to easily output pre-existing formatted tables from a Quarto document into an Excel template. Resources mentioned in the video and zoom chat: flexlsx R package GitHub repository → https://github.com/pteridin/flexlsx DBPlier PR for Snowflake Translations (contributed to by Flat Iron Health) → https://github.com/tidyverse/dbplyr/pull/860 If you didn’t join live, one great discussion you missed from the zoom chat was about the pain points of exporting data from Quarto to Word or Excel, particularly concerning table formatting and styles. Attendees in the chat strongly highlighted the difficulty of managing table formatting, including issues with table cross-references, headers, and footers. They noted that dealing with styles often requires workarounds, such as creating flextables that match desired Word styles instead of relying on default table styles. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 02:23 "Can you talk about what Flatiron does and what your teams do?" 03:29 "Could you give us a few examples of the data types or collections that you might be working with?" 05:00 "Do you have longitudinal data?" 07:46 "Are you aware of any computer vision applications in the health care industry from your perspective?" 09:38 "Do you use mixed models or Bayesian MCMC?" 10:56 "How does your team use Quarto?" 16:59 "How do you convince stakeholders of the value of going open source (and handle security concerns)?" 22:56 "Do you allow people to have a certain amount of time to contribute back to open source?" 26:03 "I just want to understand a little bit about your support model for that group." 29:57 "Do you have any tips for asynchronous working?" 31:02 "Are you like a Jira team or an Asana team for assigning tasks or tickets?" 32:10 "How many people on your platform team support Posit teams?" 34:24 "What does your team use for unstructured document analysis?" 36:24 "How important is domain knowledge in your recruitment?" 40:02 "Where do you store all of this stuff (data storage and databases)?" 42:04 "What is the approximate timeline from the time you do analysis to final deployment of results in the real world?" 44:31 "Is there a process for people getting things approved to use in your environment?" 47:39 "How do you handle the challenge of going back from Word to Quarto source code (after changes are tracked)?" 50:22 "What does a typical Workday look like for you?" 51:47 "Is there a piece of career advice that has either really helped you, that you've really liked, that you try to give to other people?"

R & Python Interoperability in Data Science Teams | Dave Gruenewald | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Dave Gruenewald, Senior Director of Data Science at Centene, to chat about polyglot teams, data science best practices, right-sizing development efforts, and process automation. In this Hangout, we explore working in a polyglot team and fostering interoperability (a word that Libby loves, but struggles to pronounce out loud). Dave Gruenewald emphasizes that teams should use the tools they are comfortable with, whether that's R or Python. Some strategies for collaboration across languages that Dave suggests include tools like Quarto to seamlessly run R and Python code in the same report. Teams utilize data science checkpoints, saving outputs as platform-agnostic file types like Parquet so that they can be accessed by any language. The use of REST APIs allows R processes to be accessed programmatically by Python (and vice versa), which can be a real game-changer. The newly released nanonext package was also highlighted as a promising development for improved interoperability. Resources mentioned in the video and zoom chat: Posit Conf 2025 Table and Plotnine Contests → https://posit.co/blog/announcing-the-2025-table-and-plotnine-contests/ nanonext 1.7.0 Tidyverse Blog Post → https://www.tidyverse.org/blog/2025/09/nanonext-1-7-0/ If you didn’t join live, one great discussion you missed from the zoom chat was about pivoting away from academia, including leaving PhD programs. Many attendees shared their personal experiences of making the difficult decision to drop out of a PhD program. The community suggested alternative terms like "pivot," "reallocating your resources," or being a "refugee fleeing academia" instead of "drop out." Dave Gruenewald shared that he himself left a PhD program but has "no regrets about that." Did you leave a PhD program? You're not alone! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 02:21 "What types of data do your teams use?" 06:53 "Which of the three pillars you mentioned is your personal favorite to work on?" 09:26 "How do you avoid or divert scope creep?" 11:41 "How much of the project should be "planning" before any code happens?" 13:53 "Do you feel like people are just hopping in and going, hey, LLM, make me a POC?" 14:28 "Do you give them what they say they want, or do you give them what they need?" 16:40 "I'm wondering what public data do you wish existed?" 18:48 "Why not Positron yet?" 20:43 "How do you unify as a team and make it so that I can always read everybody else's code?" 23:10 "Could you talk a little bit about how R and Python work together?" 27:28 "How to start package development with a team who are very new to package development." 33:01 "What's your greatest regret career wise?" 35:53 "What about your biggest wins, specifically in your early career?" 39:40 "How would you recommend building a data science culture and community from scratch?" 41:49 "Would you set a specific timeline for EDA, exploratory analysis, to scope the project better?" 45:15 "How do you define fun projects, and how much time do you allocate for exploration in those?" 48:21 "Does your team use DVC or something similar for data version control?" 50:00 "Can you talk a bit more about your pivot from academia into data science?" 51:31 "Any advice on where to look for opportunities in data science after getting a masters degree?"

Laboratory science to data science & the art of the growth gig | Lisa Elkin | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Lisa Elkin, Senior Principal Computational Toxicologist at Pfizer, to chat about her career transition from a lab scientist to a data scientist, building Shiny apps for scientific audiences, and the value of internal programs for up-skilling and data literacy. In this Hangout, we explore how to foster data science skills and build communities within a large organization. Lisa shares her experience with programs like an "Analytics Exchange," where employees could dedicate 20% of their time to working on data science projects from across the company, allowing them to learn by doing. She provides tips on how to propose similar "growth gig" opportunities to leadership, highlighting the value of up-skilling the entire department to increase efficiency and innovation without necessarily hiring more specialists. Lisa also gives advice for starting a new internal community from the ground up, emphasizing the need for a few passionate individuals and the importance of repeating introductory sessions to support newcomers. Lisa runs a Data Science Hangout internally at Pfizer!! Resources mentioned in the video and zoom chat: Mastering Shiny Book → https://mastering-shiny.org/ Jenny Bryan - Code Smells and Feels Talk → https://github.com/jennybc/code-smells-and-feels Posit Academy → https://posit.co/products/enterprise/academy/ If you didn’t join live, one great discussion you missed from the zoom chat was about what a "mainframe" computer is. The conversation sparked memories of punch cards, floppy disks, and how the cloud is in some ways a return to the mainframe paradigm. Let us know below if you’d like to hear more about it! Better yet, join us live on Thursdays :) pos.it/dsh ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 04:50 "What is toxicology?" 06:19 "What are the data types that your tool users are interfacing with?" 07:19 "What is an assay?" 08:15 "How does your science background benefit or disadvantage you as a data scientist?" 14:55 "What kind of audience do you build Shiny apps for and what is your workflow?" 18:05 "What made you decide to switch to industry and what were the bottlenecks between academia and industry?" 24:39 "Do you have any tips on proposing an 'analytics exchange' program to leadership?" 28:38 "What tools or systems do you use for sharing apps and what tools do you use daily?" 32:33 "How much oversight do you have on your analysis and do you ever get stuck in skeptic paralysis?" 35:18 "Do you have any podcast recommendations?" 38:01 "How can organizations resolve the tension between allowing employees space to learn and being productive?" 41:12 "What advice would you give to somebody starting a brand new community within their company?" 46:05 "Any resources for learning modular Shiny apps or tool building in general?" 47:40 "What is the career progression like for a data scientist and is it possible to grow as an individual contributor?"

Jenny Bryan

SAS to R, data harmonization, & a career in pharma | Dony Unardi | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Dony Unardi, Principal Data Scientist at Genentech, to chat about the Teal framework for Shiny, data cleaning and harmonization in the pharmaceutical industry, and career transitions from SAS (and web development!) to R. In this Hangout, we explore Teal, an open-source framework built around Shiny to accelerate the creation of clinical trial analysis apps. Donnie explains that Teal provides out-of-the-box features like advanced filtering and, most importantly, code reproducibility. This allows users to generate the exact R code needed to reproduce a visualization or analysis from the app in their local environment, a critical feature for validation and submission in the pharma industry. Resources mentioned in the video and zoom chat: Teal Gallery → https://insightsengineering.github.io/teal.gallery/ Pharmaverse → https://pharmaverse.org/ SAS to R: A Data Snack from the Fred Hutch Data Science Lab → https://hutchdatascience.org/data_snacks/r_snacks/sas2r.html A SAS to R Success Story (Posit Video) → https://posit.co/resources/videos/a-sas-to-r-success-story/ R Consortium Submissions Working Group → https://rconsortium.github.io/submissions-wg/ If you didn’t join live, one great discussion you missed from the zoom chat was about making the transition from SAS to R. Many community members shared personal experiences, valuable resources, and advice, emphasizing the importance of learning R's idioms rather than attempting a direct, one-to-one translation of SAS code. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 06:34 "What is the teal shiny app framework?" 12:10 "Does being in the Bay Area make a difference for data careers?" 16:44 "What kind of sass are you talking about? (SAS!)" 18:07 "How do you approach technical interviews?" 20:22 "Do you have any tips for a SAS programmer being told to use R?" 26:10 "What are some of the data quality challenges that you find in pharma data?" 29:43 "How is reproducibility defined in the pharma industry?" 32:37 "What does Teal offer for reproducibility that a Git repo does not?" 36:48 "What are the pharma job titles for roles focused on data cleaning and QAQC?" 39:11 "What do you mean by harmonizing data?" 42:09 "How do you prioritize projects and collaborate with your teammates?" 44:36 "Should personal data science projects be for learning or for a resume?" 47:58 "How did you get leadership support to contribute to the open-source community?"

Data viz, Shiny app design, & technical career paths | Kiegan Rice | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Keegan Rice, Senior Statistician at NORC at the University of Chicago, to chat about data visualization design, bridging the gap between technical and non-technical teams, and building large-scale, public-facing Shiny apps. In this Hangout, we explore how to effectively communicate statistical results and design intuitive data visualizations for non-technical audiences. Keegan shares her top tips, emphasizing the importance of knowing your audience and not assuming they have the same level of data literacy as a practitioner. She suggests testing your charts on friends or colleagues outside the data space to ensure they are understandable. The conversation also covers the challenges of explaining concepts like statistical significance and the value of forcing stakeholders to articulate their core business questions rather than just asking for a specific table or chart. Resources mentioned in the video and zoom chat: Kiegan's posit::conf talk, "Wait, that's shiny" → https://www.youtube.com/watch?v=SAbV6d7Pn0U Kiegan's reproducible GitHub repo from the talk → https://github.com/kiegan/wait-thats-shiny Deepsha Menghani's talk on crosstalk → https://youtu.be/AbuK2F57NEs The NCVS dashboard, an example of a tool built for a federal client → https://ncvs.bjs.ojp.gov/Home The Medicare Current Beneficiary Survey Chartbook → https://chartbook.mcbs.org/ If you didn’t join live, one great discussion you missed from the zoom chat was about favorite board games and sci-fi/fantasy books. Recommendations ranged from board games like Ark Nova, Dominion, and Terraforming Mars to book series like The Expanse, The Stormlight Archive, and Gideon the Ninth. Come hang out with us live to chat about this stuff!! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 07:40 "What are your top tips for data visualization design?" 11:15 "What are the challenges you’ve faced when communicating statistical results to non-statisticians?" 18:30 "Do you experience any imposter syndrome because you’re not really doing stats anymore?" 22:04 "How do you see data visualization evolving and what are your book recommendations?" 26:56 "How do you get federal clients to modernize beyond using tables for everything?" 29:37 "How do you decide what aspects to drop when a data visualization is too busy?" 35:57 "Is there a middle ground for Shiny between Quarto and JavaScript?" 39:51 "Which sci-fi or fantasy books are you loving?" 42:19 "What advice do you have for someone just getting started with interactive data visualization?" 47:40 "What advice do you have for attending or giving a talk at posit::conf for the first time?" 50:35 "How important is it to show the lineage of data so people can trust your visualizations?"

Sports analytics, baseball metrics, and Shiny apps | Brian Chase | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Brian Chase, Vice President of Baseball Systems at the Miami Marlins, to chat about the role of baseball systems and data analytics in professional sports, leveraging technology and hardware for player performance and team strategy, the evolution of sports analytics since the "Moneyball" era, and career insights for breaking into niche industries (like baseball!). In this Hangout, we explore how the Miami Marlins leverage advanced technology and data analytics to enhance player performance and team strategy. Brian details the use of innovative hardware like the Trajekt robotic pitching machine that allows batters to practice against simulated versions of opposing pitchers with exact spin rates and velocities. He also discusses the Hawkeye cameras and other systems, which capture real-time player and ball tracking data, including bio-mechanical information, using 12 cameras at LoanDepot Park. This extensive data is then platformed in tools like Snowflake and Google Cloud for analysis. The team also utilizes wearables to track bat speed and other metrics. Insights are delivered to coaches and players through pre-game and post-game reports and dashboards, often built using Posit Connect and Shiny apps, which helps translate complex data into easily digestible stories. Resources mentioned in the video and zoom chat: Miami Marlins Job Opportunities → https://www.teamworkonline.com/baseball-jobs/miamibaseball/miami-marlins Technology & AI Magazine Feature on Miami Marlins → https://technologymagazine.com/brochure/miami-marlins-using-ai-to-accelerate-competitive-sport Trajekt Sports (Trajekt pitching machine) → https://www.trajektsports.com/ MIT Sloan Sports Analytics Conference → https://www.sloansportsconference.com/ Astroball by Ben Reiter → https://www.porchlightbooks.com/products/astroball-ben-reiter-9780525576648 The MVP Machine by Ben Lindbergh and Travis Sawchik → https://www.sandmanbooks.com/book/9781541698925?srsltid=AfmBOorVPNYGMdhNFsiaUfJ2N88em1qiGfEf6Kok5Z3LmaREs-Iv25ac If you didn’t join live, one great discussion you missed from the zoom chat was about mascots! Libby shared her love for mascots, especially the San Antonio Missions "puffy taco". Attendees chimed in, sharing their favorite team mascot pics - you really have to be there live to appreciate all the fun in the chat!! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 00:55 "Tell us a little bit about what you do and how you got here?" 09:15 "How did you get your foot in the door for an MLB job?" 14:02 "What coding languages, tools, and systems do your teams work with the most?" 19:35 "What does data analytics have to do with baseball hardware?" 23:51 "What do pre-game and post-game reports look like, and how do you use Shiny?" 26:39 "How do you measure ROI for technologies, and how do you get buy-in?" 30:41 "How has stats analytics evolved since the Moneyball era?" 34:25 "Is qualitative analysis still used alongside quantitative data from scouts?" 35:27 "Who will play you in the future of Moneyball 2?" 36:27 "Will AI benefits plateau if all teams have similar access to tools and data?" 40:22 "Are you measuring off-field factors like sleep, nutrition, and injuries in your models?" 42:57 "What's the thought process behind developing new features and actionable metrics in sports analytics?" 47:53 "What might someone be surprised by when turning a hobby into a job?" 50:40 "Did you ever feel discouraged during your 4-year job search?" 50:50 "Was getting your MBA worth it for your career in sports?"

New data science tools & old laptops on fire | Jenny Bryan | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were joined by Jenny Bryan, Senior Software Engineer at Posit, to chat about (setting laptops on fire,) adapting careers to embrace change and new technologies, behind-the-scenes technical advancements powering the R ecosystem with tools like Positron, demystifying project-based workflows, plus LLM integration and best practices in programming. Listen to this episode to hear us chat about topics like this: - the benefits and limitations of using Large Language Models (LLMs) in programming. Jenny shared her initial skepticism towards LLMs for coding in R, but her attitude changed significantly when applying LLMs to problems involving languages she was less familiar with, like Rust or TypeScript. - adapting in your career to embrace change and new technologies. Jenny, who describes herself as being on a "third career", transitioned from management consulting to a statistics professor, and then to a senior software engineer at Posit. She talks a bit about her career journey and how she's embracing new stuff (ahem, Typescript) so that she gets to keep doing cool stuff! - Positron IDE for R package development. She specifically praises Positron's unique test explorer and reliable console, and its integrated Data Explorer. For many, Positron offers out-of-the-box data science functionality, unlike other IDEs that require extensive customization. - what new technologies like Ark, Air, and Positron mean for the longterm health of R. Jenny's been working on lots of nerdy things behind the scenes at Posit and she talks all about how they're great for developers, package builders, data scientists, and engineers alike. Another tidbit from this hangout: Jenny gave some advice for those looking to branch into software engineering without formal training: try reading code from admired developers, inviting code reviews, and undertaking small, recreational package development projects to gain practical experience and confidence. She also advocates for adopting a project-oriented workflow (associated with her famous "laptop on fire" remark, of course) using tools like the here package for managing project paths. Resources mentioned in the video and zoom chat: Positron IDE → https://positron.posit.co/ Happy Git with R → https://happygitwithr.com/ Jenny Bryan's "Project-oriented workflow" blog post → https://www.tidyverse.org/blog/2017/12/workflow-vs-script/ Air R code formatter → https://posit-dev.github.io/air/ The here() package → https://here.r-lib.org/ Posit Conf → https://posit.co/conference/ Tidy Dev Day 2025 → https://www.tidyverse.org/blog/2025/07/tdd-2025/ R Packages book → https://r-pkgs.org/ If you didn’t join live, you missed a ROARINGLY active chat. Let's just say, if you've ever broken down in tears over a programming project, you're not alone! Come join us live each week if you'd like to hang out in the chat with us! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 03:39 "Is that a Wooble on your desk?" (Spoiler, it's a gnome!!) 06:23 "As a builder of data science tools, what are the tool features data scientists want most?" 08:43 "Have you experienced needing to adapt to change recently and how have you embraced it?" 13:46 "What is 'setting laptops on fire' about?" 13:50 "How did you decide to change your career a few times?" 21:23 "What are your thoughts on the ease of putting models into production in Python versus R and does it make sense to shift everybody to one language or the other?" 27:30 "How do you navigate the 'I have a hammer so everything looks like a nail' feeling when working with emerging tools like LLMs?" 33:24 "Do you have any general advice for those data scientists who find themselves wanting to branch out more into software engineering but don't have formal training?" 39:39 "Why should I use Positron instead of Versus Code?" 47:57 "Can you speak to the value of developing an R package and how to clear the mental hurdle of it being a huge challenge?" 52:34 "What does your career trajectory look like and what is your advice for other people who are looking to grow their career but don't know if they want to be an IC or a manager? Does being a manager mean you don't get to write code anymore?"

Jenny Bryan

Data Science in the Energy Industry | Frank Hull | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Frank Hull, Director of Data Science and Analytics at ACES, to chat about forecasting energy demand and prices, managing over a thousand data models, full-stack data science, and advanced machine learning techniques for time series analysis. In this Hangout, we explore the necessity of managing a vast number of data models in the energy industry. Frank's team at ACES oversees over a thousand models (nearly 2,000, actually!), a staggering number explained by the complexity and fragmentation of the wholesale energy market. The United States is divided into various Independent System Operators (ISOs), each possessing unique regulations and diverse resource mixes. Each of ACES's 40+ portfolios can operate in different geographical areas within an ISO, presenting distinct challenges that necessitate individual modeling. These models are used to simulate a wide range of time horizons, from the next hour or day-ahead market to long-term financial planning and infrastructure decisions spanning 25 years. This intricate modeling helps in understanding hourly price shapes, demand patterns, supply mixes, and evaluating the effectiveness of new energy generators or hedging strategies, all with the goal of lowering variable costs for cooperatives and mitigating critical risks like blackouts during peak demand. Resources mentioned in the video and zoom chat: Tidymodels → https://www.tidymodels.org/ Orbital Project → https://orbital.tidymodels.org/ U.S. Energy Information Administration (EIA) Open Data → https://www.eia.gov/opendata/ Kuzco R Package → https://posit.co/blog/kuzco-computer-vision-with-llms-in-r/ If you didn’t join live, one great discussion you missed from the zoom chat was about handling imbalanced binary classification models. Participants discussed why techniques like SMOTE might not perform well in production with real-world data, shared experiences with alternative methods such as standard up/downsampling, and highlighted challenges in maintaining prediction accuracy in deployment despite strong training results. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 04:05 "What's ISO?" 08:20 "What are your go to models for analysis in the energy field?" 10:48 "Do you tend to use traditional stochastic models for time series analysis or more of the recent ML methods?" 13:30 "What is a full stack data scientist? What's the overlap between a full stack data scientist and something like an ML engineer or a data engineer?" 18:38 "Is there a specific data science skill set that's needed to get into energy analysis?" 19:59 "What is the portfolio model?" 23:36 "How have you found convincing regulators and other stats oriented stakeholders to trust and believe your AI fancy machine learning models that they can't really dive in and and prove to themselves that that's being statistically valid? Or have you found some good ways to demonstrate that?" 26:50 "Are there any good examples of open data in energy?" 27:54 "How are you keeping on top of the documentation for all of these models? Over a thousand models is a lot. Is there any learning you could share from that experience to help other people keep on top of their documentation?" 30:33 "How would you suggest handling missing data in time series forecasting?" 33:10 "Do you see long term electricity prices decreasing in the next twenty five years due to the abundance of renewables like wind and solar in lower population areas?" 35:14 "Do you have any career advice?" 36:50 "How do you see data science evolving within the energy industry?" 38:39 "How do you keep up to date on new packages?"

Deploying Scikit-learn models for in-database scoring with Snowflake and Posit Team

Modern data science workflows often face a critical challenge: how to efficiently deploy machine learning models where the data lives. Traditional approaches require moving large datasets out of the database for scoring, creating bottlenecks, security concerns, and unnecessary data movement costs. In this demo, Nick Pelikan at Posit highlights how orbital bridges the gap between Python’s rich ML ecosystem and database-native execution by converting scikit-learn pipelines to optimized SQL. This enables data scientists to: 1. Use familiar Python tools for model development 2. Automatically translate complex feature engineering to SQL 3. Deploy models without rewriting code in different languages 4. Maintain model accuracy through faithful SQL translations While this example focuses on Python, Nick also gave this demo using Orbital for R users: https://youtu.be/pnEjYNgOG9c?feature=shared Helpful resources: 1. Follow-along blog post: https://posit.co/blog/snowflake-orbital-scikit-learn-models/ 2. Supported models for Orbital: https://orbital.tidymodels.org/articles/supported-models.html 3. Orbital (Python) GitHub Repo: https://github.com/posit-dev/orbital 4. Orbital (R) GitHub Repo: https://github.com/tidymodels/orbital 5. Databricks and Orbital for R and Python model deployment: https://posit.co/blog/databricks-orbital-r-python-model-deployment/ 6. Emily Riederer's XGBoost example with Orbital: https://www.emilyriederer.com/post/orbital-xgb/ 7. Sara Altman's blog post on Shiny + Databases: https://posit.co/blog/shiny-with-databases/ 8. Emil's introduction of orbital: https://emilhvitfeldt.com/talk/2024-08-13-orbital-positconf/ 9. Nick's Blog on Running Tidymodel Prediction Workflows with Orbital: https://posit.co/blog/running-tidymodel-prediction-workflows-inside-databases/ If you'd like to learn more about using Posit Team, you can always schedule time to chat with Posit here: https://posit.co/schedule-a-call/

Uniting the pharma industry with data science | Ross Farrugia | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Ross Farrugia, Data & Insights Engineering Product Family Lead at Roche/Genentech, to chat about Pharmaverse, open source in pharma, career progression in the industry, and the impacts of AI to the pharma industry. In this Hangout, Ross tells us all about the Pharmaverse, an open-source initiative he co-founded, which aims to unite companies in the pharmaceutical industry. Instead of independently repeating similar work, companies co-develop and share the effort for building, maintaining, and testing code, especially for clinical trials and submissions to regulatory bodies. This collaborative approach, primarily built around R but open to other languages like Python, helps accelerate the delivery of drugs to patients and improves the review and approval process by health authorities. The Pharmaverse fosters a strong community, allowing for shared intellectual property (IP) and increasing trust and sustainability of packages, as demonstrated by the Admiral package with 10 co-development companies. The community, which includes nearly 2,000 people and 400 code contributors, democratically decides on package inclusion, connecting individuals across the industry to supercharge development efforts. Resources mentioned in the video and zoom chat: Pharmaverse Website → https://pharmaverse.org/ Pharmaverse Slack Community → Access via https://pharmaverse.org/FAQ Posit Podcast: The Test Set → https://posit.co/thetestset/ Monthly Workflow Demos (Orbital package) → https://posit.co/workflow-demo/seamless-r-python-model-deployment-with-snowflake-and-orbital/ Book Recommendation: The Situational Leader → https://store.situational.com/collections/books/products/situational-leader-book?utm_source=google&utm_medium=ppc&utm_campaign=CLS+Store&utm_term=the+situational+leader+book&gad_source=1&gad_campaignid=21067538390&gbraid=0AAAAACkXJNBKGqigIGr_i6NfE2yjK6IZr&gclid=CjwKCAjw1ozEBhAdEiwAn9qbzXXYLJFHz9wJSI6iVuEPZ-zO2NcrqjOTlPSOyTfRqWOvgT_zA99pDRoCsNEQAvD_BwE#gad_source_1 Libby's Podcast Recommendation: Change Technically by Cat Hicks & Ashley Juavinett → https://www.changetechnically.fyi/ Phuse (A Global Healthcare Data Science Community) → https://phuse.global/ Ross's Blog on Contributing to Open Source → https://pharmaverse.github.io/blog/posts/2024-03-11_tips_for__first_.../tips_for__first__time__contributors.html If you didn’t join live, one great discussion you missed from the zoom chat was about top tips for contributing to open source (though we talked about it quite a bit live!). Ross and community members emphasized starting small, learning basic Git, and contributing not just code but also by commenting on issues, raising bugs, and providing feedback. It was highlighted that helping write documentation, such as vignettes, can be a valuable contribution for newcomers, as fresh eyes can identify gaps that developers might miss. Yes, your fresh, newbie eyes are so valuable! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 03:50 "How is Pharmaverse different from a CRAN task view?" 06:20 "How is Pharmaverse curation handled?" 08:29 "How to convince competing companies to collaborate on open source?" 13:07 "What does career progression look like in the pharma industry?" 16:21 "Is it possible to break into pharma without prior experience?" 18:42 "Are there roles in pharma for open source package development?" 21:50 "Thoughts on the merging of R and Python communities in pharma?" 27:30 "Top tips for contributing to open source?" 31:40 "Is the pharma industry slow to adopt new software?" 35:25 "Focus on randomized control trials versus observational data?" 40:40 "Is clinical trials experience a requirement for entering pharma?" 41:27 "What career advice do you have for us?" 44:13 "Book and podcast recommendations" 46:26 "What data science professional bodies would you recommend joining?" 48:51 "How will AI influence the pharma industry and Pharmaverse?" 52:56 Wrap-up

Helping developers thrive | Cat Hicks | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Dr. Cat Hicks, Psychologist for software teams at Catharsis Consulting and upcoming positconf 2025 keynote speaker, to chat about the psychology of software teams, organizational culture and team dynamics, debunking tech industry myths, and strategies for professional visibility. Cat's work is in applied social science, bringing evidence-based answers to real-world workplace questions, especially for software development teams. She's not a "couch psychologist" but rather focuses on "structures of beliefs and behavioral science and team practices". Here are some of the themes Cat touches on in the conversation: - Understanding developer (and Data Scientist!) thriving - Debunking industry myths, especially the "10x Developer" myth - "Designing an organization that wants to understand itself" - Fostering psychological safety in remote, multi-disciplinary teams - Getting recognized for work by being strategically performative Cat has a unique perspective that bridges psychology and the practical realities of tech and data roles. She's got our backs! Resources mentioned in the video and zoom chat: Cat Hicks's newsletter, "Fight for the Human" → https://www.fightforthehuman.com/ Cat Hicks's podcast, "Change, Technically" (specifically the episode "You Deserve Better Brain Research") → https://www.changetechnically.fyi/2396236/episodes/17378968-you-deserve-better-brain-research Cat Hicks's Bluesky profile → https://bsky.app/profile/grimalkina.bsky.social Developer Thriving paper (AI skill threat paper) → https://osf.io/preprints/psyarxiv/2gej5_v2 Code Review Anxiety paper: "Understanding and effectively mitigating code review anxiety" → https://link.springer.com/article/10.1007/s10664-024-10550-9 Code Review Anxiety workbook → https://developer-success-lab.gitbook.io/code-review-anxiety-workbook-1 Ted Laderas's post on how improvisational comedy rules can help collaborations → https://laderast.github.io/posts/2022-02-27-yes-and-is-the-foundation-of-collaboration/ Posit Conference information → https://posit.co/conference/ useR 2025 conference information → https://user2025.r-project.org ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Intro 01:11 Cat Hicks Introduces herself 04:04 What does Catharsis Consulting help with? 07:45 Preview of Cat Hicks's Posit conference keynote 10:14 Tips on how to objectively assess team comfort with change 19:14 How much of developer thriving comes from your immediate team vs. the larger organization? 23:56 Strategies to foster psychological safety and empathy in remote, multi-disciplinary teams 29:04 Have you had instances where you felt the need to correct misconceptions about a paper? (the myth of "10x developers" or "10x engineers") 34:34 Have you looked at interventions that explicitly scaffold information exchange in groups? 40:48 Ways organizations can balance top-down and bottom-up approaches to culture 47:19 What can staff with less formal power do to sow positive seeds in a low-morale culture? 52:00 How to get noticed for long-term, high-impact work without appearing unproductive or overly performative

Next-gen data science: a conversation with the Ravit Show and Eric Pité

Eric Pité, SVP of Product & Strategy, sat down with the Ravit Show at Databricks Data + AI Summit to have a conversation about the evolution of open source data science, the Posit and Databricks integration, and the place of AI in modern data science. Some highlights from the conversation are below: -- The evolution from RStudio to Posit It’s more than just a name change — it’s a signal of how seriously they’re embracing Python while staying true to their R roots and open-source mission. -- Open source meets enterprise Eric shared how Posit is navigating the balance between community contribution and commercial sustainability — and why that mission still matters in an AI-first world. -- Positron + Databricks Their strategic partnership is one to watch. Positron is making it easier to do high-quality data science (in R and Python) inside the Databricks ecosystem, with an emphasis on collaboration, reproducibility, and performance. -- AI’s place in modern data science Eric and Ravit chatted about how Posit sees the role of AI in helping data teams be more productive, without losing rigor or transparency. -- The power of storytelling with code One of our favorite parts is the emphasis on communication. Building models is not enough—data scientists need to share insights clearly, and Posit is building for that. Learn more about the Databricks and Posit partnership by exploring the partnership page and resources: https://posit.co/use-cases/databricks/

Building Tailored Dashboards: Drill Down Visualizations and LLM-Powered Summaries with Shiny

Led by Isabella Velásquez, Sr Product Marketing Manager at Posit PBC Scenario: Imagine the weekly scramble at DemoCo. Isabella, a lonely Marketing analyst, spends hours every Friday manually compiling marketing lead performance data from Salesforce, website logs, and event records into a sprawling Excel report. The tedious, redundant process is both a time sink and a bottleneck. Marketing Leadership, who need to identify top-performing platforms, find the mass amount of information in the report confusing. They constantly return to Isabella for clarification or, even worse, don’t reach back out and make misinformed decisions on critical budget allocation. Determined, Isabella decides it’s finally time to automate and tailor this critical dashboard, and save headaches for both Leadership and herself. In this demo, you will learn how to: 1. Transform redundant, manual, error-prone data compilation into an automated lead performance dashboard with Shiny 2. Integrate diverse marketing data sources for a holistic view 3. Build tailored marketing KPI dashboards that address Leadership’s specific questions 4. Use drill-downs, data visualizations, and LLM-powered summaries to reduce data overload and clarify complex takeaways instantly 5.Provide flexibility in dashboard functionality to meet diverse preferences and needs, from pre-selected insights derived directly from the data to implementing an LLM-powered analytics option Helpful resources: 1. Demo Slides: https://github.com/ivelasq/2025-06-25_marketing-demo/blob/main/README.md 2. Demo Marketing Dashboard: https://pub.current.posit.team/public/marketing-demo/ 3. GitHub Repo (including links to packages used: pointblank, querychat, etc.): https://github.com/ivelasq/2025-06-25_marketing-demo?tab=readme-ov-file If you have specific follow-up questions about using Posit in your organization, we'd love to chat with you: https://posit.co/schedule-a-call/

Why regression still matters | Keith McNulty | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Keith McNulty, Analytics Leader at McKinsey & Company, to chat about his unique career path into data science, the importance of regression and network analysis, strategies for self-learning and balancing work, and navigating the evolving landscape of AI. In this Hangout, Keith talks about navigating the evolving landscape of AI, including trustworthiness, risks, and its impact on the data science profession. Keith highlights that AI is rapidly changing and discusses the balance between acquiring technical AI development skills and understanding the trustworthiness and inherent risks of large language models. He suggests prioritizing knowledge of AI's reliability, as these challenges are likely to endure, and recommends identifying trusted sources to sift through the hype. The discussion also covers AI's capacity to identify complex patterns in large datasets, which can be beyond human capabilities. However, it also acknowledges AI's tendency to be convincingly incorrect. A significant point raised by Keith is his concern that data scientist could be pushed more towards software engineering, and he asked practitioners to consider if they are still engaging in the "science" aspect of data science. Resources mentioned in the video and zoom chat: Keith McNulty on LinkedIn → https://www.linkedin.com/in/keith-mcnulty/ Keith's Regression Book (Handbook of Regression and People Analytics) → https://peopleanalytics-regression-book.org/ Keith's ONA/Graph Book (Handbook of Graphs and Networks in People Analytics) → https://ona-book.org/ PositConf Registration (including virtual option) → https://posit.co/conference/ "Storytelling with Data" book → https://www.storytellingwithdata.com/ If you didn’t join live, one great discussion you missed from the zoom chat was about the evolving role of data scientists and the importance of "putting the science back in data science". Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! 00:00 Introduction 00:46 "Will you let us tell us a little bit about yourself, what you do, what you like to do for fun?" 03:10 "Were you that active (on social media) before you were in data science?" 05:33 "I was just wondering if you could maybe talk a little bit about your most favorite resources that you used while retraining yourself as a data scientist?" 10:12 "You wanna give a quick pitch for both of (your books) so we have some context?" 14:52 "What do you think about the possibility of extracting latent character traits about people from text?" 19:51 "How do you balance self learning with working time?" 25:16 "What kind of questions are you using networks to ask in the people analytics space?" 30:09 "What would you say is the most important AI related skill to learn for the future and to stay ahead in the workplace?" 35:59 "I'm interested in how talent is measured among white collar workers. Could you share some of the metrics you use and how you address the limitations of more straightforward measures like time in the office?" 40:26 "So I was wondering if that (early) role, that engagement role, helped you bridge this switch from academia to industry, or, how did that role help you choose the skill you wanted to build." 43:54 "I just wanted to get your perspective on imposter syndrome, especially for people who are making some sort of transition in their career into data science." 47:00 "Where do you source your daily math problems?" 48:19 "Do you have any thoughts on policy for AI in the current setting and and also, specifically thinking about there's a lot of people in my company that are hesitant because of the environmental impact of AI." 51:32 "What did we not talk about that you wish we had talked about?"

Four steps for managing data teams | Toby Hall | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Toby Hall, Executive Vice President and CIO at Delta Dental of Michigan, to chat about all kinds of things, including: data science team management, AI governance and the ethical use of data in healthcare, insurance data types and use cases, and hiring for traits vs. skills in data science. In this Hangout, among other topics, Toby mentions Delta Dental's "guilty until proven innocent" approach to AI. This involves a two-stage approval process that critically examines use cases for ethical implications, data handling (ensuring data stays within dedicated tenants and doesn't train public models), and tool appropriateness. The discussion also touches on the regulatory skepticism towards AI from departments of insurance and the broader environmental considerations of AI use. Toby also gives loads of career advice, but our favorite was his four-step process for managing a data team: - Put the right people in the right role - Give them the right vision of what you're trying to accomplish, ensuring it's clear and everyone is aligned on "what that is and why we're doing it" - Give them the right culture to get it done - Get out of their way! Resources mentioned in the video and zoom chat: Posit Conference → https://posit.co/conference/ Delta Dental Tooth Fairy Poll → https://www.deltadental.com/us/en/tooth-fairy/the-original-poll.html TidyTuesday Data Repo (get your reps in!) → https://github.com/rfordatascience/tidytuesday Resources for doing TidyTuesday stuff in Python (PydyTuesday?) → https://github.com/posit-dev/python-tidytuesday If you didn’t join live, one great discussion you missed from the zoom chat was about the Delta Dental Tooth Fairy data, including its outrageously expensive rates and the humorous debate around the inflation of self-reported flossing data. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! 00:00 Introduction 02:28 "Tell us a little bit about your day to day, but also the data teams at Delta Dental of Michigan, what is the type of data that they work with, and what is it that they solve with it, or what decisions are made based on it?" 06:04 "What about third party data?" 07:57 "How do you all decide which language to use and for what applications?" 10:37 "How do you make sure your AI pipeline is accurate in the SQL and the regulation because you are working with health health care data?" 23:22 "What are some tips for leaning into those challenge assignments as the IC, the individual contributor?" 23:31 "How do you identify the challenge assignments? Like, how do you know this is the right thing with the stakes that are right to give to somebody?" 25:54 "Do you share your data as an API or for research?" 36:45 "Coming from a core statistical background, how much would you expect from a fresh data science graduate about statistical knowledge if you were an interviewer for a modeling role? Any specific concepts that you might ask them about in the interview?" 38:17 "How do you make sure your team stays current with new data science tools or skills or methods?" 40:18 "Did you have a ranking of necessary roles that you wanted to hire first, like data engineer versus data scientist or analyst or maybe dedicated reporting staff? And what tools or platforms did you consider essential from day one?" 43:53 "How much of your background in teaching high school students has spilled over into managing your team?" 46:21 "Do you have any advice for how someone could maybe emphasize those traits on their resume?" 49:19 "Do you have a piece of career advice that has either really, really helped you or that you try to give to everybody?"

Quarto Dashboards: from zero to publish in one hour

From R/Medicine 2025 You already analyze and summarize your data with R and Quarto. What’s next? You can share your insights or allow others to make their own conclusions in eye-catching dashboards and straight-forward to author, design, and deploy Quarto Dashboards. With Quarto Dashboards, you can create elegant and production-ready dashboards using a variety of components, including static graphics, interactive widgets, tabular data, value boxes, text annotations, and more. Additionally, with intelligent resizing of components, your Quarto Dashboards look great on devices of all sizes. And importantly, you can author Quarto Dashboards without leaving the comfort of your “home” – in plain text markdown with any text editor. In this one-hour demo we will build and publish a Quarto Dashboard – you can code-along or sit back and enjoy the show! Mine Çetinkaya-Rundel, Professor of the Practice at Duke University and Developer Educator at Posit Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. Mine works on the OpenIntro project, whose mission is to make educational products that are free, transparent, and lower barriers to education. As part of this project she co-authored four open-source introductory statistics textbooks – latest is the 2nd edition of Introduction to Modern Statistics. She is also a co-author on R for Data Science, the creator and maintainer of Data Science in a Box, and she teaches popular data analysis and data science with R courses on Coursera. Mine is a Fellow of the ASA and Elected Member of the ISI as well as a Waller and Hogg award winner for teaching excellence. In 2024, she was elected as Vice President of the International Association for Statistical Education (IASE). Resources R/Medicine: https://rconsortium.github.io/RMedicine_website/ R Consortium: https://www.r-consortium.org/

Mine Çetinkaya-Rundel

Posit Conf 2025 Keynote Previews | Kieran Healy & Jonathan McPherson | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! Thursdays at 12PM US Eastern We were recently joined by upcoming Posit Conf 2025 keynote speakers Kieran Healy, Professor of Sociology at Duke University, and Jonathan McPherson, Software Architect at Posit PBC, to chat about how and why open-source IDEs like RStudio and Positron get made, how to do data visualization for discovery and explanation, what their keynotes are going to be about, and what’s next for Posit’s IDE development, including AI integration. In this Hangout, Kieran talked about the trustworthy data visualization. He highlighted that while data visualization is a powerful way to condense and present information, often creating compelling and authoritative artifacts, phrases like "visual storytelling" can be problematic if they encourage presenting a predetermined narrative not fully supported by data. He emphasized that the trustworthiness of visualizations does not come solely from the techniques used or the software, but from a "web of social processes and individual commitments" that cannot be easily automated. Jonathan talked about the future of Positron and its relationship with RStudio, addressing whether Positron is intended to replace RStudio. He clarified that the long-term goal for Positron is to make it the best Integrated Development Environment (IDE) for working with data in any language. He explained that Positron is built with an extensibility layer, allowing anyone to write plugins for new languages or capabilities, making it a robust and evolving data science workbench. It does not have all of RStudio's features and makes different design trade-offs. RStudio, having evolved over decades, is highly optimized for specific R-based workflows and remains the best at what it does for those use cases. Resources mentioned in the video and zoom chat: Posit Conference 2025 Registration → https://posit.co/conference/ Kieran Healy's Website → https://kieranhealy.org Kieran Healy's book "The Ordinal Society" → https://theordinalsociety.com/ Kieran Healy's book "Data Visualization: A Practical Introduction" → https://socviz.co/ Jonathan McPherson's LinkedIn → https://www.linkedin.com/in/jonathanmcpherson Joe Cheng’s AI Talk on Harnessing LLMs for Data Analysis → https://youtu.be/owDd1CJ17uQ?feature=shared TidyTuesday GitHub → https://github.com/rfordatascience/tidytuesday Positron IDE → https://positron.posit.co/ Will R Chase's talk on making clear plots → https://www.youtube.com/watch?v=h5cTacaWE6I If you didn’t join live, one great discussion you missed from the zoom chat was about the ongoing debate and practical tips for moving from presenting tables of numbers to visualizations. Community members shared various strategies, including using color-mapped tables as an intermediate step, providing both tables and visuals, and ensuring accessibility and interpretability for diverse audiences. Are you team tables or team graphs? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Joe Cheng

Data Science Career Growth | Pallas Horwitz | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Pallas Horwitz, Analytics Consultant & Professional Development Coach, to chat about data career growth, effectively communicating business value in the AI era, navigating workplace dynamics, and future-proofing data science careers. In this Hangout, we explore how data science projects, even those that don't yield expected results, contribute to valuable organizational learning. Pallas emphasized that what might seem like a "failure" is actually an opportunity to learn what doesn't work, saving future time and resources. This broader definition of "impact" helps data professionals frame their contributions effectively on resumes and in conversations, by focusing on moving business conversations forward and enabling informed decision-making. She also highlighted the importance of translating technical insights into language that resonates with business stakeholders, often by understanding their underlying goals rather than just their explicit requests. This approach helps turn a "not yet" into a yes by empathizing with the audience and understanding what they care about. Resources mentioned in the audio recording and zoom chat.txt: Pallas Horwitz's Coaching Calendar → https://tidycal.com/pallashorwitz/45-minute-introductory-session Pallas Horwitz LinkedIn → https://www.linkedin.com/in/pallashorwitz/ Posit Conf 2025 Virtual Experience Registration → https://posit.co/blog/posit-conf-2025-virtual-experience-registration/ How to Network in a Non-Slimy Way by Ted Laderas → https://laderast.github.io/articles/2021-09-13-how-to-network-in-a-non-slimy-way/how-to-network-in-a-non-slimy-way.html Libby Heeren's talk, Stop Networking and Start Making Friends→ https://www.youtube.com/watch?v=V3XdLVAwmX0 If you didn’t join live, one great discussion you missed from the zoom chat was about the role of Excel in data analysis, with many participants affirming that there's "no shame in excel!" and even calling it "the gateway drug to data management". The chat also highlighted the importance of networking, with attendees sharing personal success stories of finding jobs through connections made in unexpected places like the gym. You really should join live! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co/ Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Forecasting AI Demand at Microsoft | Sajay Suresh | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Sajay Suresh, Senior Director of Data and Applied Science at Microsoft, to chat about data center supply chain planning, forecasting AI demand, and navigating data science careers. In this Hangout, we explored how the emergence of technologies like LLMs changed projections for data center demand. Sajay discussed how forecasting for something with little historical data, like AI demand, required drawing analogies from the past, such as comparing the training/inferencing model to the iPhone and its App Store. A major complexity in current supply chain planning is the lack of fungibility with modern GPUs requiring specific infrastructure like liquid cooling, meaning data centers designed for GPUs cannot easily be repurposed for traditional compute/storage workloads, increasing investment risk if demand is lower than planned. Resources mentioned in the video and zoom chat: LLM Workflow Demo with Joe Cheng → https://pages.posit.co/05-28WorkflowDemo.html Posit::conf 2025 Virtual Registration → https://posit.co/blog/posit-conf-2025-virtual-experience-registration/ Sajay Suresh on LinkedIn → https://www.linkedin.com/in/sajay-suresh-12687631/ Find mentors on ADPList → https://adplist.org/ Officeverse R packages for Office documents → https://ardata-fr.github.io/officeverse/ Microsoft team meetup video on capacity planning → https://www.youtube.com/live/07j22d4B_hA?feature=shared Seattle Data And AI Security community → https://www.linkedin.com/posts/seattle-data-and-ai-security_microsoft-fabric-tour-seattle-data-ai-security-6891902675280633856-xLw3?utm_source=share&utm_medium=member_desktop Quarto Gallery → https://quarto.org/docs/gallery/ Quarto Guide → https://quarto.org/docs/guide/ If you didn’t join live, one great discussion you missed from the zoom chat was about communities and meetups recommended for networking and learning in data science. Participants shared various groups like R-Ladies, Data Book Club, local tech meetups, and specific conference recommendations like Shiny Conf and DataConf.ai NYC. What's your favorite data community? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Joe Cheng

Being a Data Viz Consultant | Cara Thompson | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Cara Thompson, Data Visualization Consultant at Building Stories with Data, to chat about data visualization, consulting and niche specialization, working out loud, and her uniquely winding path to data visualization as a specialty. In this Hangout, we explore all things data visualization. Cara emphasizes its role in helping data teams effectively communicate research outcomes, particularly assisting teams of specialists in communicating with a non-specialist audience. She sees the value in making viz understandable - not just making it pretty (though she's great at that part, too!). Cara advises sometimes starting the data visualization process with pen and paper before using digital tools. A common pitfall discussed is trying to include too much information in a single visualization, rather than focusing on what the user needs to see first. Resources mentioned in the video and zoom chat: Cara Thompson's LinkedIn → https://www.linkedin.com/in/cararthompson/ Cara Thompson's Blog → https://www.cararthompson.com/posts Tidy Tuesday GitHub Repo → https://github.com/rfordatascience/tidytuesday Data Visualization Catalogue → https://datavizcatalogue.com/ Data to Viz → https://www.data-to-viz.com/ Cecilia Baldoni's Shrew Scrollytelling Website → https://cecibaldoni.github.io/projects.html Kinga Stryszowska-Hill, Ph.D.'s LinkedIn (mentioned for Canva use with plots) → https://www.linkedin.com/in/kingashill/ Libby's Favorite Data Viz Palette Tool (by Susie Lu)→ https://projects.susielu.com/viz-palette/ If you didn’t join live, one great discussion you missed from the zoom chat was about... pie charts 🥧 While Cara mentioned they do have their place despite everyone loving to pick on them, the chat saw lively debate, including comments on their utility in specific niche fields like high-throughput chemistry, humorous links to examples of "bad" 3D pie scatterplots (?!), and also some general ambivalence about their use. Maybe not everyone hates pie charts? What do you think? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: [https://www.posit.co](https://www.posit.co/) Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

People Analytics at Pinterest | Trevor Fry | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Trevor Fry, Lead Data Analyst at Pinterest, to chat about people analytics, bridging the data science practice gap, the future of the field including AI, and measuring the value of data teams. We also celebrated a birthday In this Hangout, we explore bridging the data science practice gap—the challenge of effectively translating research findings and data insights back to business leaders and stakeholders. Trevor discusses how the nature of people data is particularly sensitive, requiring careful handling, and emphasizes the importance of adapting your communication and language to your specific audience. He also shares insights into the future of people analytics, especially the potential of AI and LLMs for analyzing unstructured data like survey comments, while noting the challenges of ensuring reliability for scientific applications. Trevor also touched upon the difficulty of measuring the return on investment (ROI) for people analytics functions and discussed common analytical methods used in the field, such as Structural Equation Modeling (SEM) and relative weight analysis (RWA). Resources mentioned in the video and zoom chat: Storytelling with Data by Cole Nussbaumer Knaflic → https://www.storytellingwithdata.com/ Society of Industrial Organizational Psychology (SIOP) → https://www.siop.org/events/the-annual-conference/ Relative Weight Analysis (RWA) R package → https://martinctc.github.io/rwa/ People Analytics: Regression Modeling by Keith McNulty → https://peopleanalytics-regression-book.org/ Structural Equation Modeling (SEM) info → https://stats.oarc.ucla.edu/r/seminars/rsem/ Ollama (local LLMs) → https://ollama.com/ Posit blog: Secure AI-Assisted data science in R with Posit and Snowflake → https://posit.co/blog/ai-assisted-ai-posit-snowflake-cortex/ TidyTuesday GitHub repo → https://github.com/rfordatascience/tidytuesday TidyTuesday in Python GitHub repo → https://github.com/posit-dev/python-tidytuesday Forecasting: Principles and Practice (Python version) → https://otexts.com/fpp3/ (R version also mentioned) Causal Inference: The Mixtape by Scott Cunningham → https://mixtape.scunning.com/ Text Analysis using Quanteda (R package) YouTube video → https://www.youtube.com/watch?v=tf5FmXiwEQE If you didn’t join live, one great discussion you missed from the zoom chat was about the definition of a "data scientist" and whether individuals feel like they are "really" data scientists, highlighting the varied backgrounds and nonlinear paths in the field. If you have any imposter syndrome around data work, you're not alone! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Open Source in Pharma | Harvey Lieberman | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Harvey Lieberman, Associate Director of Data Science at Novartis, to chat about R/Pharma, automating processes, career advice, and data science in drug discovery vs. development. In this Hangout, Harvey talks about a lot of things, like the power of automating processes. He shares examples of how automating mundane tasks can save significant time and identify errors that humans might miss (we all know human error is a thing!). For instance, he automated the analysis of data from 48 Excel sheets that had previously taken a colleague about three months to process by hand; Harvey completed the automated analysis in one hour over lunch and found copying and pasting errors in the original manual process! Automating processes not only increases efficiency but can also help move people into more data-focused roles. Harvey suggests demonstrating that automation speeds things up and, most importantly, removes errors, which is when people start to pay attention and get interested. Resources mentioned in the video and zoom chat: R/Pharma website → https://rinpharma.com/ Cecilia Baldoni's scrollytelling project (on shrews!) → https://cecibaldoni.github.io/projects.html Advent of Code → https://adventofcode.com/ Pharmaverse.org (pharmaceutical R packages) → https://pharmaverse.org GSK's Journey to R → https://www.youtube.com/watch?v=xDrt6txplek Roche's Journey to R → https://www.youtube.com/watch?v=BlJNILSoZlM R/Pharma March 2025 newsletter (LinkedIn) → https://www.linkedin.com/pulse/rpharma-march-2025-newsletter-open-source-in-pharma-wmf5c/ ggplot2 extenders club → https://ggplot2-extenders.github.io/ggplot-extension-club/ Coursera: Making Data Science Work for Clinical Reporting Course → https://www.coursera.org/learn/making-data-science-work-for-clinical-reporting hiring.cafe (for finding R jobs) → https://hiring.cafe/ Posit's PydyTuesday GitHub → https://github.com/posit-dev/python-tidytuesday Joy's Law (management concept) Wikipedia → https://en.wikipedia.org/wiki/Joy%27s_law_(management) If you didn’t join live, one great discussion you missed from the zoom chat was about the diverse backgrounds of attendees. Many participants shared that they came to data science "sideways," holding degrees in fields such as sociology, psychology, mathematics, atmospheric science, education, history, chemistry, and various engineering disciplines, rather than traditional statistics or computational degrees. so many data scientists have non-traditional paths into the field! But we're all better together. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Inside Sports Analytics | Nick Wan | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Nick Wan, Senior Director of Baseball Analytics at the Cincinnati Reds, to chat about baseball analytics, data sources and tools, applying analytics in practice, and streaming data science. In this Hangout, we explore the types of data used in baseball analytics, ranging from traditional box score data collected since the late 1890s to modern high-tech measurements. This includes physics-based data like pitch speed and spin rate, influenced by environmental factors. Computer vision and radar tracking capture detailed information. Recent advancements allow for skeletal data capture from multiple high-frame rate cameras, quantifying biomechanical stress to help prevent injury. Data is also gathered via computer vision models tagging events and manual official scorers. This diverse data is used to predict events, evaluate players, and inform strategies such as optimizing lineups based on matchups or recommending adjustments to pitch movement. We all learned A LOT about baseball in this hangout! Resources mentioned in the video and zoom chat: Nick Wan's Twitch Stream → https://www.twitch.tv/nickwan_datasci Nick Wan's Free Data Science Bootcamp (Day 1) → https://www.youtube.com/watch?v=pocp0KdrIdk VizBuzz YouTube Playlist → https://www.youtube.com/playlist?list=PL6PX3YIZuHhwZ-C-jZ427D-XkLGNWFPVB baseballr R package (for baseball data) → https://r.lib.hash.kr/baseballr/ Sports-reference website → https://www.sports-reference.com/ Vestaboard (Split-Flap Display) → https://shop.vestaboard.com/products/vestaboard Posit + Databricks Integrations → https://posit.co/blog/databricks-and-posit-announce-new-integrations If you didn’t join live, one great discussion you missed from the zoom chat was about the Vestaboard behind Nick (see linked resources above!). Chat members were curious about what it was and how it worked, with Nick explaining that it's connected to his Twitch stream and can display messages from chat. How cool is that?! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

The epigenomics tech stack | Varun Dwaraka | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Varun Dwaraka, Director of Bioinformatics and Principal Investigator at TruDiagnostic, to chat about leveraging DNA methylation and machine learning for determining biological age, the journey from wet lab science to bioinformatics, the tech stack used in epigenomics research, and challenges in handling and analyzing genomic data. In this Hangout, we explore how TruDiagnostic leverages DNA methylation, a biomarker that changes with lifestyle and environment, to develop machine learning models for predicting biological age and other health metrics like smoking impact. These tests aim to provide proactive health insights, allowing individuals to track trends and modulate their biological age through lifestyle changes. But the real draw of this episode of the Hangout is Varun's willingness to open up and talk about himself and his journey from not-so-great student who wanted to be a musician to finding himself as a PhD student and then joining a start-up (to relax a little, of course!). Resources mentioned in the video and zoom chat: Varun Dwaraka's LinkedIn → https://www.linkedin.com/in/varunbdwaraka/ methylclock Bioconductor vignette → https://www.bioconductor.org/packages/devel/bioc/vignettes/methylclock/inst/doc/methylclock.html Optuna website → https://optuna.org/ NCBI Gene Expression Omnibus (GEO) → https://www.ncbi.nlm.nih.gov/geo/ CRAN Task View: Handling Missing Data → https://cran.r-project.org/web/views/MissingData.html Human Microbiome Project (NIH) → https://commonfund.nih.gov/human-microbiome-project-hmp minfi Bioconductor package → https://bioconductor.org/packages/release/bioc/html/minfi.html If you didn’t join live, one great discussion you missed from the zoom chat was about the complexities of data imputation in bioinformatics, including the challenges of handling missing data and the nuances of choosing appropriate methods. Data imputation is a hot topic! Let us know below if you’d like to hear more about it in the future. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Gaining trust & driving change | Victoria L. Prince | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Dr. Victoria L. Prince, Senior Manager of Statistics at Takeda Pharmaceuticals, to chat about career paths for statisticians, navigating career transitions, building foundational knowledge, and earning respect and influence in the workplace. In this Hangout, we explore Dr. Prince's journey transitioning from academia to the pharmaceutical industry. Dr. Victoria L. Prince is a statistician and data scientist with 15 years of experience spanning academia and industry. Don't miss this hangout if you're interested in gaining trust and driving change (for example, adopting R or Python) in your org. Dr. Prince discussed her deliberate effort to become an unofficial go-to person for questions about R across different parts of her company. She learned through trial and error that gaining trust involves understanding your strengths and adding value to others' lives by being generous with your knowledge. She achieved this by volunteering her expertise, not saying no to help requests unless in a complete time crunch, and openly sharing information. She believes that as you share, people share back, which enriches your own knowledge. She also mentioned making herself more visible by organizing and sending invitations to R workshops. Consequently, colleagues would recognize her name and connect it to "R" when they saw it. Resources mentioned in the video and zoom chat: Victoria Prince on LinkedIn → https://www.linkedin.com/in/viktoriialiublinska/ Takeda Pharmaceuticals Careers → https://jobs.takeda.com/ Statistics courses on edX → https://www.edx.org/learn/statistics ShinyConf website → https://www.shinyconf.com/ If you didn’t join live, one great discussion you missed from the zoom chat was about the varied titles for entry-level data roles across different organizations. Libby asked attendees to share the entry-level data person job title at their organization and attendees shared all kinds of titles, including: "specialist", "strategy and planning analyst", "associate", "data analyst", "Statistical Programmer I", "Biostatistician I", "Data Architect" (in a small organization), "Staff 1", "Intern", "Policy & Planning Analyst I", "Graduate analyst", and "Associate Scientist". This just shows the importance of understanding the actual responsibilities behind a job title when exploring opportunities, as job names can be deceiving. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co/ Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

KEYNOTE: Dr. Jeroen Janssens - Embrace the Unix Command Line and Supercharge Your PyData Workflow

www.pydata.org Discover why the Unix command line remains a powerful and relevant tool for data scientists, even in a Python-dominated landscape. This talk will demonstrate how embracing the command line and leveraging its many tools can significantly enhance your productivity, streamline data workflows, and complement your Python skills. Jeroen Janssens, PhD, is a polyglot data science consultant and certified instructor. His expertise lies in visualizing data, implementing machine learning models, and building solutions using Python, R, JavaScript, and Bash. Jeroen is passionate about open source and sharing knowledge. He is the author of Data Science at the Command Line (O’Reilly, 2021) and is currently writing Python Polars: The Definitive Guide (O’Reilly, 2025). Every now and then he blogs at https://jeroenjanssens.com. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Jeroen Janssens

The actuary to data scientist pipeline | Bill Wilkins | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Bill Wilkins, SVP, Advanced Analytics with Practical Applications at Safety National Casualty Corporation, to chat about the evolving role of actuaries in the age of data science, the impact of technology on risk and pricing strategies in insurance, the use of third-party data and advanced analytics models, and the future challenges and opportunities for the insurance industry. Bill, who has 40 years of experience in the insurance industry and started as an actuary, shared his insights on how data science technology is affecting the actuarial profession. He noted that while the core data hasn't changed significantly, the increased computing power allows for the utilization of more complex math, speeding up processes. However, the actuarial profession is still adapting to these technological advancements, especially considering the strict regulatory environment and the crucial issue of unintentional bias in data. Bill discussed the distinction between data scientists and actuaries, highlighting the specialized knowledge of actuaries. There was also a discussion on whether a data science background could be a pathway to becoming an actuary, with the observation that it's more common to see actuaries moving into data science roles (as opposed to the other way around!). If you didn’t join live, one great moment you missed from the zoom chat was about what "finger sticks" referred to, after Bill mentioned it as a significant issue in hospital systems that his team was working to prevent. There was definitely a humorous reaction in the chat as we all dreaded finding out what that meant! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Should you build or buy AI? | Jay Timmerman | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Jay Timmerman, Head of Data Science & AI Platforms at Biogen, to chat about practical applications of generative AI in the pharmaceutical industry, navigating the challenges of AI like hallucinations and data privacy, the evolving role of data science teams in the age of AI, and the strategic considerations for evaluating AI vendors and building in-house solutions using platforms like Posit Connect. In this Hangout, we explore the strategic considerations for evaluating AI vendors and the concept of "AI grift". Jay emphasized the importance of having in-house expertise to discern genuine value from vendor offerings and suggested that organizations can often build equivalent capabilities themselves by properly framing generative AI problems. This discussion highlighted a pragmatic approach to adopting AI and the need to be discerning when considering vendor solutions versus developing internal tools. Resources mentioned in the video and zoom chat: Abigail Haddad's Blog on Unstructured Text Data with LLMs → https://presentofcoding.substack.com/ Hard Fork Podcast episode on vibe coding → https://podcasts.apple.com/us/podcast/is-google-search-cooked-were-getting-a-u-s-crypto/id1528594034?i=1000698254448 Posit Blog on Deploying a Streamlit Application with Posit Connect → https://posit.co/blog/deploying-a-streamlit-application-with-posit-connect/ Posit Blog on Deploying a Dash Application to Posit Connect → https://posit.co/blog/deploying-a-dash-application-to-posit-connect/ ragnar package for R users (mentioned in chat for RAG) → https://github.com/t-kalinowski/ragnar If you didn’t join live, one great discussion you missed from the zoom chat was about "vibe coding", with some attendees sharing humorous takes and a mention of a Hard Fork podcast episode that featured examples of such projects. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Great Tables 3: Data Color and Polishing

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: 'How could I do that?' Other videos in this series: Great Tables 1: Structure, Format, and Style: https://youtu.be/QM7DbsY-nc4 Great Tables 2: Introducing Units Notation: https://youtu.be/SN0_vIL1Rhk About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He's been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

Great Tables 2: Introducing Units Notation

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: 'How could I do that?' Other videos in this series: Great Tables 1: Structure, Format, and Style: https://youtu.be/QM7DbsY-nc4 Great Tables 3: Data Color and Polishing https://youtu.be/Huteb5OmcrA About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He's been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

Great Tables 1: Structure, Format, and Style

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: 'How could I do that?' Other videos in this series: Great Tables 2: Introducing Units Notation: https://youtu.be/SN0_vIL1Rhk Great Tables 3: Data Color and Polishing https://youtu.be/Huteb5OmcrA About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He's been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

Data Science at the Command Line and Polars | Jeroen Janssens | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Jeroen Janssens, Senior Developer Relations Engineer at Posit, to chat about his career journey from machine learning to developer relations, the advantages of using the command line for data science, his books "Data Science at the Command Line" and "Python Polars", and advice for aspiring DevRel professionals. In this Hangout, we explore the benefits of working on the command line versus not. Jeroen explained that while the initial command line interface might seem stark, it offers a very different and powerful way to interact with your computer. The Unix command line is ubiquitous across various systems, from Raspberry Pis to supercomputers. Its strength lies in the ability to connect tools together through standard output and input, allowing for quick and iterative solutions by combining specialized tools. This fosters an interactive nature with a short feedback loop and provides closer interaction with the file system, making ad hoc data exploration efficient. Resources mentioned in the video and zoom chat: Jeroen's LinkedIn → https://www.linkedin.com/in/jeroenjanssens/ Data Science at the Command Line → https://jeroenjanssens.com/dsatcl/ Python Polars: The Definitive Guide → https://polarsguide.com/ Plotnine → https://plotnine.org/ Winner of the 2024 plotnine Plotting Contest → https://posit.co/blog/winner-of-the-2024-plotnine-plotting-contest/ Talk about plotnine → https://www.youtube.com/watch?v=xdD8r84sqYY R for Data Science → https://r4ds.had.co.nz/ Jeroen's plotnine translation of R for Data Science → https://jeroenjanssens.com/plotnine/ froggeR package → https://azimuth-project.tech/froggeR/ Reticulate → https://rstudio.github.io/reticulate/ Install Windows Subsystem for Linux (WSL) → https://learn.microsoft.com/en-us/windows/wsl/install UTM for macOS (Virtualization) → https://mac.getutm.app fish shell → https://fishshell.com/ Quartodoc → https://github.com/machow/quartodoc Focusmate (Accountability Partner Tool) → https://www.focusmate.com/ Surface Area of Luck → https://modelthinkers.com/mental-model/surface-area-of-luck CRAN R Extensions Manual → https://cran.r-project.org/doc/manuals/r-release/R-exts.html If you didn’t join live, one great thing you missed from the zoom chat was people sharing their varied experiences with the command line, with many admitting they primarily use it for basic navigation or only when necessary, and some sharing helpful tools and tips for those less familiar. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Jeroen Janssens

Data Science at the Census Bureau | Jessica Klein | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Jessica Klein, Data Scientist at the Census Bureau, to chat about open-source adoption at the Census Bureau, the growth of R and Python communities within the agency, training opportunities and internal support systems, and working with complex census datasets. In this Hangout, Jessica talked about the agency's move towards the Cloud and its efforts to reduce reliance on proprietary software like SAS and ArcGIS. She shared insights into the internal support system, mentioning training programs, workshops, and resources for using the Census API. She also discussed the challenges and strategies involved in transitioning from SAS to R & Python, including the importance of double coding for validation with mathematical statisticians and the increasing use of version control with Git GitLab. The development of internal technical support groups for R and Python was a key element in fostering knowledge sharing and breaking down silos within the agency. Resources mentioned in the video and zoom chat: Jessica Klein's LinkedIn → https://www.linkedin.com/in/jessica-klein-8a5a35196/ tidy census package → https://walker-data.com/tidycensus/ tigris package → https://github.com/walkerke/tigris Fast Link package for probabilistic linking → https://github.com/kosukeimai/fastLink StatQuest YouTube Channel → https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw 3Blue1Brown YouTube Channel → https://www.youtube.com/c/3blue1brown {sf} package for R spatial analysis → https://r-spatial.github.io/sf/ Resources on spatial analysis in R → https://rspatial.org/ and https://rspatialdata.github.io/index.html and https://asdar-book.org/ Apache Superset (open-source visualization tool) → https://superset.apache.org/ Coding it Forward Civic Digital Fellow program → https://blog.codingitforward.com/tagged/civic-digital-fellowship Resources shared directly by Jessica in the chat: Analyzing US Census Data: Methods, Maps, and Models in R by Kyle Walker: https://walker-data.com/census-r/index.html Webinar series on using tidycensus for analyzing and mapping Census data: https://ssdan.net/events/the-2025-ssdan-webinar-series-2023-acs-data-with-r-mapping-tools-and-the-2020-census/ A Guide to Working with US Census Data in R: https://rconsortium.github.io/censusguide/ Using the API requires a getting an API Key, which you can get for free here: https://api.census.gov/data/key_signup.html See here for the Census Data API User Guide: https://www.census.gov/content/dam/Census/data/developers/api-user-guide/api-guide.pdf Explore the ABCs of the Census API with Census Bureau staff: https://www.youtube.com/watch?v=JbThy7GUg3k See here for a tutorial on Census API Basics: Simple Steps to Better Data Access: https://www.youtube.com/watch?v=d-FJ2IyVfdk See here for a tutorial on Using the API to Get All Results for an ACS Table: https://www.census.gov/library/video/2020/using-api-all-results-for-acs-table.html See here for a tutorial on Using the API to Get Results for Multiple Estimates: https://www.census.gov/library/video/2020/using-api-results-for-multiple-estimates.html Explore ACS Data Stories–Stats in Action! to see how the public is using ACS data: https://www.census.gov/programs-surveys/acs/about/acs-data-stories.html Visit the Slack show-and-tell channel and show how you use Census data: https://app.slack.com/client/T6AL55003/CRY4R9R7S If you didn’t join live, one great discussion you missed from the zoom chat was about the enthusiastic reception and usefulness of the tidycensus package for accessing and working with census data in R, with many attendees expressing their appreciation and sharing resources related to it. Let us know below if you’d like to hear more about it! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Bringing data science to the construction industry | Blake Abbenante | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Blake Abbenante, Director of Analytics and Data Science at Suffolk Construction, to chat about his career journey in data science, implementing modern data practices in the construction industry, innovative applications of AI and data science in construction, and building a data-driven culture in a traditionally less tech-focused sector. In this Hangout, we explore innovative applications of AI and data science in construction. Blake shared how Suffolk Construction is leveraging cutting-edge technologies like AI to revolutionize traditional processes. One focus is their GenAI scheduling tool, which aims to augment and speed up the design and planning phases of building projects. This tool has the potential to significantly reduce the time planners spend on creating schedules, moving from weeks to potentially minutes or hours for an 80% completion rate. Blake discussed the development and implementation of safety models that forecast risk on projects, enabling proactive measures to ensure safer construction sites by predicting which projects might require additional safety personnel based on historical data. Resources mentioned in the video and zoom chat: The ellmer R package → https://ellmer.tidyverse.org/ The chatlas R package → https://github.com/posit-dev/chatlas Posit Blog Post on ellmer → https://posit.co/blog/announcing-ellmer/ If you didn’t join live, one great discussion you missed from the zoom chat was about the challenges of data collection and analysis when encountering pushback from those whose work is being analyzed, and strategies to build trust and demonstrate value. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Quarto Live: WebAssembly powered data science learning | pyOpenSci

How Quarto Live Brings Code to Life in the Browser! Quarto Live takes interactive coding & publishing to the next level with WebAssembly-powered execution—run Python & R right in the browser, no setup needed! Real-time code execution without servers ️ Dynamic exercises & instant feedback for learners Full integration with Quarto for publishing & sharing Works on any device—even mobile! Watch as George Stagg (Posit) who develops and works on Quarto Live breaks down how it works & why it matters! ----------------------------- CHAPTERS 00:00:00 Intro 00:01:00 What is Quarto? 00:03:15 Add functionality to Quarto with extensions 00:03:30 Quarto and other interactive technologies like pyodide and web assembly 00:06:03 Web assembly powers interactive data science in the browser 00:07:53 How to use Quarto Live to create your own online interactive data science environment with Quarto 00:13:24 Create interactive data science enviornments for mobile 00:14:27 Demo interactive data science activities using quarto and quarto live 00:16:38 Wrap up and summary! -------------------------------- pyOpenSci makes learning open source and open science concepts easier. LEARN PYTHON PACKAGING Build a pure Python package tutorial: https://www.pyopensci.org/python-package-guide/tutorials/intro.html Explore packaging tools & best practices: https://www.pyopensci.org/python-package-guide/ 🤝 SCIENTIFIC PYTHON SOFTWARE REVIEW Learn how peer review works: https://www.pyopensci.org/software-peer-review/ Check out our peer-reviewed packages: https://www.pyopensci.org/python-packages.html We partner with the Journal of OpenSource Software and other communities - learn more: https://www.pyopensci.org/software-peer-review/partners/scientific-communities.html Get Involved Submit your packagei for review: https://github.com/pyopensci/software-submission/ ‍️ pyOpenSci governance: https://www.pyopensci.org/governance/ Connect with pyOpenSci pyopensci.org 🦣 fosstodon.org/@pyOpenSci 🦋 bsky.app/profile/pyopensci.bsky.social www.linkedin.com/company/pyopensci ABOUT pyOpenSci pyOpenSci is an inclusive and vibrant community of practice that breaks down technical and social open source barriers. promotes open and reproducible research through peer-review of scientific Python packages. Through peer review, education, and community support, we help make scientific Python software more accessible, citable, and impactful. Join our community & level up your open source game and support open science! #pyopensci #opensource #openscience

George Stagg

Pure math to data science at YouTube | Mrinal Raghupathi | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Mrinal Raghupathi, Data Scientist at YouTube, to chat about using data science to understand user behavior, causal inference versus correlation, and experimentation in the tech world, among many other things! In this Hangout, we also explored networking strategies. Mrinal shared his approach to networking, including randomly selecting people from the org chart to have coffee chats with and setting personal goals around networking each year, which let him meet people across the org without selection bias. He emphasized the importance of balancing attending talks at conferences with having hallway conversations and making connections. We here at the Hangout LOVE hallway conversations! Resources mentioned in the video and zoom chat: Trustworthy Online Controlled Experiments → https://www.amazon.com/Trustworthy-Online-Controlled-Experiments-Practical/dp/1108724264 Mrinal's textbook, "An Introduction to the Theory of Reproducing Kernel Hilbert Spaces" → https://www.amazon.com/Introduction-Reproducing-Cambridge-Advanced-Mathematics/dp/1107104092 Ted’s Guide to Conferences for Introverts → https://laderast.github.io/posts/2018-05-17-a-introvert-s-survival-guide-to-conferences/ If you didn’t join live, one great discussion you missed from the zoom chat was about how many hours a week people get to program in their language of choice. Sixteen people chimed in to share how much they get to code, and answers varied from 0-5 hours per week to 35-40 hours per week, with most people saying they got to spend upwards of 75% of their time coding How much do you get to code every week?! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Shiny community, hackathons, and his AI mindset | Joe Cheng | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Joe Cheng, CTO at Posit, to chat about the Shiny contest, the use of AI in data science, and designing hackathons for learning new technologies. We were joined by several past and present Shiny contest winners who gave great advice on how to get started if you want to participate (and we really hope you do)! In this Hangout, we explore the evolution of the Shiny contest since its inception, including what made the 2024 submissions unique and the ways the contest encourages community contribution and learning. Joe also shared about his personal journey from feeling skepticism about AI to seeing and embracing its potential. We got some amazing questions from the Hangout attendees! We hope you join us live next time to ask some of your own questions Resources mentioned in the video and zoom chat: 2024 Shiny Contest Winners → https://posit.co/blog/winners-of-the-2024-shiny-contest/ Joe's AI Hackathon Slides → https://jcheng5.github.io/llm-quickstart/quickstart.html Shiny Assistant → https://gallery.shinyapps.io/assistant/ Isabella's blog post on prototyping with Shiny Assistant → https://posit.co/blog/ai-powered-shiny-app-prototyping/ Posit Conf Workshops → https://reg.rainfocus.com/flow/posit/positconf25/attendee-portal/page/sessioncatalog?tab.day=20250916&search.sessiontype=1675316728702001wr6r Shiny Conference 2025 → https://www.shinyconf.com/ Call for Speakers Shiny Conf 2025 → https://sessionize.com/shiny-conf-2025/ Shiny Tableau → https://rstudio.github.io/shinytableau/ Echarts4r → https://echarts4r.john-coene.com Elmer package on Github → https://github.com/tidyverse/ellmer All the Shiny app links mentioned in the video and zoom chat: Eric Nantz 2021 Shiny Contest Submission → https://forum.posit.co/t/the-hotshots-racing-dashboard-shiny-contest-submission/104925 Eric Nantz's R/Pharma conference keynote on AI → https://youtu.be/AfMa1CVUdXU?si=ThLsKFyonntxzBUF Eric Nantz's Haunted Places app → https://youtu.be/vX09QGMuOfo?si=K5_uPfK5bcfZZ92l Umair Durrani's Shiny Storytelling app → https://umair.shinyapps.io/storytimegcp/ Umair's Blue Sky profile → https://bsky.app/profile/transport-talk.bsky.social Umair's Shiny meetings project on Github → https://github.com/shiny-meetings/shiny-meetings Abby Stamm's Shiny Accessibility app → https://github.com/ajstamm/shiny-a11y-app If you didn’t join live, one great discussion you missed from the zoom chat was about everyone's favorite interactive plotting tools. Someone asked whether Plotly was the best option, and lots of people said they loved ggiraph, echarts4r, ObservableJS, and others. What about you?! What's your favorite interactive plotting library? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Joe Cheng

The changing landscape of data science | Kanchana Padmanabhan | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Kanchana Padmanabhan, Director of Data and AI at Homebase, to chat about data science team structures, the role of math in understanding LLMs, building effective hackathons, and communicating model insights to stakeholders. In this Hangout, we explore the importance of understanding the probabilistic nature of LLMs and how that understanding should influence how data scientists approach their work. We also discussed how to structure a hackathon to encourage learning, centering on the customer problem, and collaboration between technical teams and business stakeholders. Resources mentioned in the video and zoom chat: List of R Conferences for 2025 → https://rworks.dev/posts/r-conferences-2025/ Posit Conference Call for Talks → https://posit.co/blog/speak-at-posit-conf-2025/ Julia Silge's workflow demo on model cards → https://www.linkedin.com/posts/posit-software_join-us-for-a-live-workflow-demo-on-creating-activity-7287998741557522432-jseQ?utm_source=share&utm_medium=member_desktop Shiny Assistant Gallery → https://gallery.shinyapps.io/assistant Data Science Hangout Playlist → https://www.youtube.com/playlist?list=PL9HYL-VRX0oTu3bUoyYknD-vpR7Uq6bsR Add Posit Team End-to-End Workflows to calendar → https://evt.to/aoimiohuw Making of a Manager Book → https://www.amazon.com/Making-Manager-What-Everyone-Looks/dp/0735219567 If you didn’t join live, one great discussion you missed from the zoom chat was about how to gain domain knowledge for a new industry, where attendees shared their experiences and advice. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Julia Silge

Communicating insights for impact | Noah Branham | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Noah Branham, senior manager of analytics at DraftKings, to chat about marketing measurement, data science career paths, and communicating analysis to stakeholders. In this Hangout, one topic we explore is Noah's three-trimester framework for career development, focusing on how he progressed from developing basic data analytics skills to mastering predictive modeling and optimization problems, and finally transitioning to a management role. He emphasizes the importance of understanding business problems before applying data techniques and how the skills needed for success as an analyst differ from those needed for a manager. Resources mentioned in the video and zoom chat: Speak at Posit Conf 2025 → https://posit.co/blog/speak-at-posit-conf-2025/ Cascadia R Conf → https://cascadiarconf.com/ Unified Branding with brand.yml → https://posit.co/blog/unified-branding-across-posit-tools-with-brand-yml/ The Motivated Speaker Book → https://www.articulationinc.com/the-motivated-speaker-book/ Model cards for transparent reporting → https://vetiver.posit.co/learn-more/model-card.html Upcoming End-to-End Workflow Demo → https://pos.it/team-demo If you didn’t join live, one great discussion you missed from the zoom chat was all about running, including marathon advice, favorite races, and being a proud member of the Slow AF Run Club It didn't have much to do with data, but it was definitely about being a community. You should really join us live to get in on the fun! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

We want GREAT tables! | Richard Iannone & Michael Chow | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Rich Iannone and Michael Chow, software engineers at Posit, to chat about their experiences building GT and Great Tables, how they drive community engagement around their packages, and their career advice for package developers. GT and Great Tables are R and Python packages for creating static tables in R and Python. They were created to fill a need for a good, maintained solution for generating tables with different output types from data frames. They've gained loads of popularity, and have an active community of users! Resources mentioned in the video and zoom chat: Great Tables Blog → https://posit-dev.github.io/great-tables/blog/ 2024 Table Contest Winners → https://posit.co/blog/2024-table-contest-winners/ Contributing to Public Transit Data Analysis and Tooling → https://posit-dev.github.io/great-tables/blog/open-transit-tools/ Tables as Powerful Representational Tools → https://www.researchgate.net/publication/363345970_Tables_as_Powerful_Representational_Tools The MockUp - 10+ Guidelines for Better Tables in R → https://themockup.blog/posts/2021-01-13-10-guidelines-for-better-tables-in-r/ Show Me the Numbers by Stephen Few → https://analyticspress.com/smtn.php Excel spreadsheets to gt package called {forgts} → https://github.com/luisDVA/forgts What They Forgot to Teach You About R → https://rstats.wtf/ David Robinson Talk called "The unreasonable effectiveness of public work" → https://www.youtube.com/watch?v=th79W4rv67g Publication quality tables in 2024 → https://posit.co/blog/what-we-did-with-publication-quality-tables-in-2024/ Great Tables Design Philosophy → https://posit-dev.github.io/great-tables/blog/design-philosophy/ gtsummary-to-excel → https://www.pipinghotdata.com/posts/2024-07-26-gtsummary-to-excel/ happy git with R → https://happygitwithr.com/ freeCodeCamp - how to contribute to open source → https://github.com/freeCodeCamp/how-to-contribute-to-open-source/ If you didn’t join live, one great discussion you missed from the zoom chat was about how people manage their personal and work GitHub accounts, and whether to have one account for all their work or separate accounts for each employer. Let us know YOUR thoughts on this topic below! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! #pythoncontent

Michael Chow, Rich Iannone

Wes McKinney & Hadley Wickham (on cross-language collaboration, Positron, career beginnings, & more)

We hosted a special event hosted by Posit PBC with Wes McKinney (Pandas & Apache Arrow) and Hadley Wickham (rstats & tidyverse) to ask questions, share your thoughts, and exchange insights about cross-language collaboration with fellow data community members. Here's a preview into what came up in conversation: 1. Cross-language collaboration between R and Python 2. Positron, a new polyglot data science IDE 3. Open source development, how Wes and Hadley got involved in open source and their experiences in building and maintaining open-source projects such as Pandas and the tidyverse. 4. Documentation for R and Python, especially in the context of teams that use both languages (shoutout to Quarto!) 5. The use of LLMs in data science 6. The emergence of libraries like Polars and DuckDB 7. Challenges of switching between the two languages 8. Package development and maintenance for polyglot teams that have internal packages in both languages 9. The future of data science The chat was on fire for this conversation and we've gathered most of the links shared among the community below: Documentation mentioned: Positron, next-generation data science IDE built by Posit: https://positron.posit.co/ Quarto tabset documentation: https://quarto.org/docs/output-formats/html-basics.html#tabset-groups Packages / Extensions mentioned: Pins: https://pins.rstudio.com/ Vetiver: https://vetiver.posit.co Orbital: https://orbital.tidymodels.org Elmer: https://elmer.tidyverse.org Tabby Extension: https://quarto.thecoatlessprofessor.com/tabby/ Blog posts: AI chat apps with Shiny for Python: https://shiny.posit.co/blog/posts/shiny-python-chatstream/ Using an LLM to enhance a data dashboard written in Shiny: R Sidebot & Python Sidebot Marco Gorelli Data Science Hangout (polars): https://youtu.be/lhAc51QtTHk?feature=shared Emily Riederer's blog post on Polars: https://www.emilyriederer.com/post/py-rgo-polars/ Jeffrey Sumner’s tabset example: https://rpy.ai/posts/visualizations%20with%20r%20and%20python/r_python_visualizations Emily Riederer's blog post on Python and R ergonomics: https://www.emilyriederer.com/post/py-rgo/11 Sam Tyner's blog post on Lessons from "Tidy Data": https://medium.com/@sctyner90/10-lessons-from-tidy-data-on-its-10th-anniversary-dbe2195a82b7 Other: Hadley Wickham's cocktails website: https://cocktails.hadley.nz5 Posit subscription management to find out about new tools, events, etc.: https://posit.co/about/subscription-management/ New to Posit? Posit builds enterprise solutions and open source tools for people who do data science with R and Python. (We are also the company formerly called RStudio) We'd love to have you join us for future community events! Every Thursday from 12-1pm ET we host a Data Science Hangout with the community and invite you to join us! You can add that event to your calendar with this link: https://www.addevent.com/event/Qv9211919

Hadley Wickham

DuckDB and the future of databases | Hannes Mühleisen | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Hannes Mühleisen, Co-founder and CEO at DuckDB Labs, to chat about the evolution of database systems, the unique architecture of DuckDB, and how to find opportunities in areas others might consider "boring." He also showed us a picture of the real life duck, Wilbur, that inspired the name DuckDB! 🦆 In this Hangout, we explore how DuckDB is changing the traditional concept of a database by making it accessible to a wide range of users and use cases. We also discuss its origins and how it was inspired by the challenges of data analysis and the importance of focusing on real user problems when building tools. What is a database and where does it live? DuckDB is challenging database assumptions by saying... maybe your expensive laptop can hold a database instead of just pinging one? Maybe it can do more than just run web browsers! Hannes wants you to know that database doesn't have to be a bad word, and that you can actually have a great experience with one. If the data community's affinity and enthusiasm for DuckDB is any indication, we think he's right. We hope you listen in to hear Hannes talk about: a simple explanation of what DuckDB is, DuckDB's in-process architecture and why it's so cool, different DuckDB use cases (should you be using DuckDB?), DuckDB's compatibility with cloud data systems, and more. Resources mentioned in the video and zoom chat: DuckDB Website → https://duckdb.org/ DuckDB Labs Website → https://duckdblabs.com/ Posit Conf talk by Hannes → https://www.youtube.com/watch?v=GELhdezYmP0 Sarah Alman's webinar on using DuckDB and Shiny → https://www.youtube.com/watch?v=6AGroJb4zPM Forbes Article on Imposter Syndrome → https://www.forbes.com/sites/marycrossan/2024/12/18/how-to-combat-imposter-syndrome-by-developing-character/ MotherDuck → https://motherduck.com/ MotherDuck Blog Post "Big Data is Dead" → https://motherduck.com/blog/big-data-is-dead/ Posit Blog post on Shiny with Databases → https://posit.co/blog/shiny-with-databases/ DSH episode with Marco Gorelli → https://www.youtube.com/watch?v=lhAc51QtTHk Emil Hvitfeldt’s talk at posit::conf about orbital → https://youtu.be/Qnm1y0KPxVM Marcos Huerta's Blog post on DuckDB and Bert → https://marcoshuerta.com/posts/duckdb-and-bert/ If you didn’t join live, one great discussion you missed from the zoom chat was about whether industry teams who use and benefit from open source tools could devote 5-10% of their time to giving back to the open source projects with code contributions or other support. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Emil Hvitfeldt

Data science and automation in publishing | Sophia Tee | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Sophia Tee, Senior Director of Data Science at Penguin Random House, to chat about marketing automation, the use of data science in publishing, and the transition from ID to manager in the realm of data science. In this Hangout, we explore lots of topics, like how Sophia's team has automated the campaign creation process, reducing a two-week process to 30 minutes, while also improving campaign performance. That's pretty amazing! Needless to say, we had lots of questions. She also had some great insights to share on the data science skills to strive for in the world of publishing as she experiences it. How organized is your code? It needs to be well-organized so that it "can be shared and understood by a lot of different people." She recognizes that not all data scientists have experience productionizing models, but that it's important in her line of work to be a full stack data scientist. Communication is paramount. Sophia says, "at the end of the day it doesn't matter how great your model is if you can't explain it. It's really hard to sell it. So, I think being able to speak in plain English about what you did, and what the outcome is, and what you expect people to do about it is really important." She stresses the importance of understanding the underlying data and the business context, too. Remember who you are speaking to and how your results relate to their KPIs. You'll have to listen in to hear more! Resources mentioned in the video and zoom chat: Sophia Tee on LinkedIn → https://www.linkedin.com/in/sophia-tee-7069b65/ Today's Top Books at Penguin Random House → https://www.penguinrandomhouse.com/interactive/todays-top-books CloseRead Prize → https://posit.co/blog/closeread-prize-announcement/ Bioinformatics Training & Education Program from the NIH → https://bioinformatics.ccr.cancer.gov/btep/ Prefect AI workflow tool → https://www.prefect.io/controlflow Pixi and uv integration blog → https://prefix.dev/blog/uv_in_pixi We also talked about book recommendations! Of course we did, this was a discussion about Penguin Random House! Here are some of the data-flavored recommendations mentioned by the community live and in the chat: The Bayesian Hitman by Robert Kubinec: https://www.amazon.com/Bayesian-Hitman-Robert-M-Kubinec/dp/B0D6M4WNRZ The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives by Deirdre Nansen McCloskey & Steve Ziliak : https://press.umich.edu/Books/T/The-Cult-of-Statistical-Significance2 The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century by David Salsburg: https://us.macmillan.com/books/9781466801783/theladytastingtea/ If you didn’t join live, one great discussion you missed from the zoom chat was about different perspectives and advice on transitioning from academia to industry (or the reverse!). Let us know below if you’d like to hear more about this topic. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Sharpening your axe and the BAU trap | Steph Locke | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Steph Locke, Digital and App Innovation Leader at Microsoft, to chat about how to persuade your manager to give you more time for professional development, why "business as usual" (BAU) work can choke development, and how killing projects is actually a good thing. In this Hangout, we explore the importance of investing time in skill development and how this can lead to long-term gains in efficiency and quality. Steph shares advice on how to talk to managers about the value of "sharpening your axe," and why it is more efficient to train in order to do things well initially than to spend time on continuous maintenance of subpar work. Is a bunch of "business as usual" work bogging down the potential output of development teams? This is where Steph's concept of investing in high quality work up-front comes in. She talks about the dangers of rushing to release products that aren't built with high quality and low ongoing maintenance in mind: "...when [people] don't necessarily have time to invest in doing things in a way that's going to have high quality and low maintainability requirements and is easy to extend and create new things, when people aren't doing that, anything they ship is going to then cost them more time to do something to to look after whatever they've shipped. That then gives them less time to ship the next thing." If this resonates with you, give Steph a follow on LinkedIn and make sure you read her article. Resources mentioned in the video and zoom chat: Steph's article on how BAU chokes development → https://www.linkedin.com/pulse/developer-velocity-choked-bau-stephanie-locke-fsiqe Posit's documentation on using copilot in the RStudio IDE → https://docs.posit.co/ide/user/ide/guide/tools/copilot.html A YouTube video about using Shiny apps for data entry into a backend database → https://www.youtube.com/watch?v=zDJc8sXh2qw If you didn’t join live, one great discussion you missed from the zoom chat was around the discrepancies in pay and responsibilities between individual contributor (IC) and management tracks in tech companies, and the perception that management roles often have better compensation and advancement opportunities, even when the work may not be more valuable than IC contributions. Let us know below if you’d like to hear more about this topic! Is there a need for more non-people-leader technical leadership roles for highly skilled individual contributors? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Navigating technology for data teams | Stanislav Seltser | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Stanislav Seltser, VP of Enterprise Architecture and AI Infrastructure at State Street Global Advisors, to chat about how to strategically approach the business leadership about investing in new tools, forecasting technology obsolescence for data work, and fostering innovation and creativity in data science teams. In this Hangout, we explore the factors that can help forecast when a technology will become obsolete, including the rate of commits to open-source projects and the frequency of vendor releases. Stanislav also shares his approach to evaluating technology based on usability, cost, and maintainability, as well as how to ensure that chosen technologies provide a return on investment. Stanislav recommends treating the need for a new tool the way you would if you were pitching a start-up, with a business plan and a technical prototype, to really show your understanding of what you're asking for. Stan also shared some solid career advice, so stick around for that. No surprise here, his advice starts with community - being social and connecting with as many people as you can - but he really emphasizes mentoring and helping others succeed. We agree, Stan! Resources mentioned in the video and zoom chat: Libby's blog post on career advice from the Data Science Hangout → https://posit.co/blog/lessons-from-four-senior-leaders-in-the-data-space/ Databricks + Spark documentation for sparklyr → https://spark.posit.co/deployment/databricks-connect.html Rachael's post on communicating the ROI of data science projects → https://posit.co/blog/tips-for-communicating-roi-of-data-science-projects/ Jim Hester's talk on dependencies → https://youtu.be/mum13N7CGUI?feature=shared If you didn’t join live, one great discussion you missed from the zoom chat was a conversation around the struggles of getting out of Jupyter notebooks for developing coding projects in python and R. Do you love notebooks? Are you trying to get away from them, too? Opinions from the Hangout attendees were mixed and the discussion was illuminating! Let us know below if you’d like to hear more about this topic. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

From paramedic to leading analytics in pharma | Dan Haight | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Dan Haight, Director of Continuous Improvement Analytics at Merck, to talk about his unconventional career path, the value of lateral career moves (check out his book recommendation on this!), and the importance of creating "great charters" for successful stakeholder communication in projects. Dan's advice about project management in this episode is so valuable. He recognizes how tempting it is to rush into a project without knowing all the facts beforehand, but then your scope starts to balloon, you see the project getting behind, and there are all these constraints you weren't aware of. So, he says to take time to build a charter, be upfront with stakeholders about realistic timelines, and then use the charter as a contract to negotiate scope changes and resource allocation. He also discusses the challenges of working with incomplete data and the need to triangulate information from multiple sources to ensure accuracy and build confidence in the findings. If you're a job seeker or looking to transition, Dan came with great advice to pursue continuous learning, network, and hone the ability to effectively communicate the value of their work. You'll have to listen to learn more! If you didn't attend live, you missed the party in the zoom chat People were excited about posit::conf 2025 early bird tickets getting released, and there was a SUPER great discussion about how to share your work when it's all under NDA or very private due to security reasons. There was some great suggestions, like: 🥸 Anonymize data if it's possible, or present a similar project abstractly Use public data to demonstrate the methods you used, even if you can't showcase the findings Emphasize lessons learned during a project (ah-hah moments) instead of the business impacts IF the overall project failed or wasn't implemented (as is the fate of many DS projects!) Document projects as you go, in a format that you can safely take with you, so that you can remember all these things even after you leave your role Lean on your friends and colleagues to help you describe the impacts you had on a project, or the importance of what you did (we all need to be hyped up sometimes when it comes to writing resumes!!) Resources mentioned in the video and zoom chat: Up is Not the Only Way Book → https://www.amazon.com/Up-Not-Only-Way-Rethinking/dp/1523083484 Shiny Assistant helps you build shiny apps → https://shiny.posit.co/blog/posts/shiny-assistant/ Brag Documents - Julia Evans → https://jvns.ca/blog/brag-documents/ Lessons From Four Senior Leaders in the Data Space → https://posit.co/blog/lessons-from-four-senior-leaders-in-the-data-space/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Data for good, mentoring, and stellar internships | Sebastien Ouellet | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Sebastian Ouellet, Senior Staff Developer at Kinaxis, to chat about volunteering with data-for-good initiatives, his career path in data science, and developer/intern mentorship. We talked all about the value of mentorship for both the mentor and mentee, and how Kinaxis's internship program allows interns to innovate and grow by owning brand new projects. Sebastien emphasized the positive reciprocal nature of mentorship, with advantages for both sides: mentees and interns gain invaluable knowledge, guidance, and support from experienced professionals, and regular communication enables interns to receive timely feedback, ask clarifying questions, and course-correct their approach when necessary; for mentors, the act of teaching and explaining concepts to someone new really forces them to re-examine their understanding of familiar processes, which can help identify gaps in documentation or reveal areas for improvement in existing systems. Sebastien also covered the strategic benefits of allowing interns to spearhead new and innovative (instead of existing) projects. Basically, allowing interns to own projects, coupled with a supportive mentorship structure, creates a win-win scenario with tangible benefits for both the interns and the company: - Innovation: By entrusting interns with fresh projects, they foster an environment of innovation. Interns bring a new perspective, allowing them to challenge the status quo and come up with novel solutions. - Knowledge Management: Interns build valuable resources, including documentation, code repositories, and presentations, as they progress through their projects. These resources create a knowledge base that can be leveraged by other team members, even after the intern leaves the team or the company. - Project Pipeline: the company gets to use these intern-driven projects as a proving ground for new ideas. Successful projects can evolve into production-ready solutions (and have already)! If you didn't attend live, you missed some GREAT conversations in the zoom chat, including one in which we all admitted to the many times we've reinvented the wheel before talking to people about what we were trying to do and having them tell us there was an easier way. Join our community and make some data friends so you too can prevent yourself from reinventing the wheel Resources mentioned in the video and zoom chat: Closeread Contest, Scrollytelling with Quarto → https://posit.co/blog/closeread-prize-announcement/ DataKind → https://www.datakind.org/join-us/volunteer/ DataKind's Datakit Event → https://www.eventbrite.com/e/datakinds-datakit-event-tickets-1079849321389 rOpenSci Champions Program → https://ropensci.org/champions/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

From Chemical Engineer to Dow Data Science Leader | Michael Hausinger | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Michael Hausinger, Scientist in Research and Development at Dow, to chat about his non-traditional career path to becoming a data science leader. We talked about how learning R through Posit Academy helped him to transition from bench science to building tools that enhance the efficiency of other scientists at Dow. Michael shared valuable insights about the importance of finding the right tools for the job, effectively communicating the value of data science to stakeholders, and building a supportive data science community. In this Hangout, we explore the growing importance of data skills in non-traditional fields like chemical engineering. Michael's story shows how individuals with diverse backgrounds can leverage data science tools like R and python to make a significant impact in their organizations. The conversation highlights the need for companies to invest in up-skilling their workforce in data science, and the value of creating supportive communities of practice where individuals can learn and grow together. Resources mentioned in the video and zoom chat: Dow Customer Spotlight → https://posit.co/about/customer-stories/dow/ Posit Academy → https://posit.co/products/enterprise/academy/ Upskill to R Programming with Posit & Atorus Research → https://posit.co/blog/upskill-to-r-programming-with-posit-and-atorus-research/ Creating Business Value with Community at Dow → https://youtu.be/SNneEKmSyyU?feature=shared If you didn’t join live, one great discussion you missed from the zoom chat was about transitioning into a data-focused role without a formal degree in data science. Michael shared he doesn’t have a data science degree specifically, as did many others! Let us know below if you’d like to hear more about this topic ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Generate 100s of custom reports in minutes with Python & Quarto! (Parameterized report automation)

A practical guide to generating hundreds of customized reports using Quarto and Python. Learn how to leverage Quarto's parameter system to create PDFs and HTML reports at scale. Using a movie dataset example, we'll cover: - How to automate report generation with Python and Quarto - Create dynamic templates with data visualization and formatting - Adapt parameters to work with both Python and R code - Build scalable reporting workflows for any dataset This tutorial demonstrates how to transform what could be hours of manual reporting work into an efficient automated process. Quarto Crash Course Video: https://youtu.be/_VKxTPWDhA4?si=Q09V5xXlyo1YVQqs Generating Analytics Reports (typst) Video: https://youtu.be/Q3phTByW138?si=FIMiAWAHP6HhUnhG Link to Code (starting): https://github.com/KeithGalli/quarto-crash-course Link to Code (finished): https://github.com/KeithGalli/quarto-crash-course/tree/parameterized-reports Information on the dataset can be found here: https://github.com/KeithGalli/quarto-crash-course/tree/master?tab=readme-ov-file#resources Video timeline! 0:00 - Video Overview 0:58 - Walking through some starter code (linked in description) 3:20 - Passing parameters with Python into Quarto Markdown Files 4:27 - Passing parameters using R 5:18 - Using a Python script to render many reports with variable parameters. 6:36 - Dynamically generating markdown sections with Python (output: asis - execution option) 11:56 - Adding images, table of contents, & pagebreaks to our reports 15:00 - Adding graphs to our reports 17:54 - Tying everything together 21:47 - Adding fenced divs & styling/formatting dynamically with code

Quarto Websites 4: Add lists of content with listings | Charlotte Wickham | Posit

Adding a listing page to your website is a great way to showcase your projects, talks, publications or blog posts. In this video you’ll learn how to create a listing page in Quarto and see two ways to populate it with content: Quarto documents, or a yaml file. In this video: 0:50 Use a listing to add a blog 3:36 Listing options 5:47 Why use a listing? 7:22 Use a YAML file to populate a project portfolio 9:50 Customize the display of a listing 12:10 Advanced customization of listings 13:42 Remove pages Links: Listings: https://quarto.org/docs/websites/website-listings.html Andrew Heiss’ teaching listing: https://www.andrewheiss.com/teaching/ Code: Starter source code: https://github.com/cwickham/quarto-website-video/tree/v0.3 Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.4 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: - Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) - Are comfortable editing plain text documents (e.g .qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) - Want to walk away with your own personal website Taught by: Charlotte Wickham (https://www.cwick.co.nz/) Emil Hvitfeldt (https://emilhvitfeldt.com/) Videos in this series: 1. Build your homepage [https://youtu.be/l7r24gTEkEY] 2. Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

Quarto Websites 3: Customize appearance with CSS/SCSS | Emil Hvitfeldt | Posit

You now have a set of content you are happy with on your website, but how do you customize the look and feel of your site beyond options set in YAML? In this video, you’ll start by learning the basics of CSS and SCSS and how to make good design choices. Then, you’ll see how to apply these choices to your Quarto website. In this video: 0:14 What is HTML? 3:23 CSS Selectors 8:05 CSS Attributes 8:25 Layout attributes 10:24 Reducing repetition with SASS/SCSS? 15:26 Consistent design 16:36 Choosing colors 17:50 Choosing fonts 19:28 Maintaining accessibility 22:13 Apply SCSS to your website 24:16 Change the appearance of headings 25:28 Change the appearance of navigation bar 30:30 Use google fonts Links: Color contrast checker: https://colourcontrast.cc/ Google fonts: https://fonts.google.com/ Code: Starter source code: https://github.com/cwickham/quarto-website-video/tree/v0.2 Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.3 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: - Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) - Are comfortable editing plain text documents (e.g .qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) - Want to walk away with your own personal website Taught by: Charlotte Wickham (https://www.cwick.co.nz/) Emil Hvitfeldt (https://emilhvitfeldt.com/) Videos in this series: 1. Build your homepage [https://youtu.be/l7r24gTEkEY] 2. Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

Quarto Websites 2: Add pages and navigation | Charlotte Wickham | Posit

Now you’ve got a homepage, you’ll likely want to add some other pages. In this video, learn how to add pages to your website, and help people find them, by adding them to your website navigation. In this video: 1:00 Add a page to your website 2:54 Your file structure determines your URL structure 5:49 Add a link to your page in navigation 7:50 Customize navigation item text and icon 9:12 Control where items appear in the navigation bar 10:16 Navigation bar options 11:11 Switch to side navigation 12:22 Other types of navigation 16:30 Wrap Up Links: List of icons you can use in navigation items: https://icons.getbootstrap.com/ Top navigation bar options: https://quarto.org/docs/websites/website-navigation.html#top-navigation Quarto website navigation: https://quarto.org/docs/websites/website-navigation.html Code: Starter source code: https://github.com/cwickham/quarto-website-video/tree/v0.1 Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.2 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: - Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) - Are comfortable editing plain text documents (e.g .qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) - Want to walk away with your own personal website Taught by: Charlotte Wickham (https://www.cwick.co.nz/) Emil Hvitfeldt (https://emilhvitfeldt.com/) Videos in this series: 1. Build your homepage [https://youtu.be/l7r24gTEkEY] 2. Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

Quarto Websites 1: Build your homepage | Charlotte Wickham & Emil Hvitfeldt | Posit

In this video, you’ll get a running start by using a template we've designed to be functional and attractive, and that will serve as a foundation for the rest of the video series. You’ll customize the content of your homepage, and how it looks, and along the way learn about the two key files in a Quarto website index.qmd and _quarto.yml. Finally, you’ll learn one way to publish your website so other people can see it. In this video: 0:21 Use a template to get started 2:33 Preview the template homepage, index.qmd 4:12 Customize the content of your homepage 5:45 “About” pages 7:22 Customize the image on your homepage 9:24 Website configuration, _quarto.yml 10:40 Customize colors with YAML 13:45 Customize fonts with YAML 17:00 Publish your site Links: About pages: https://quarto.org/docs/websites/website-about.html Appearance options you can set in YAML: https://quarto.org/docs/output-formats/html-themes.html#basic-options Code: Starter source code: https://github.com/EmilHvitfeldt/website-template Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.1 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: - Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) - Are comfortable editing plain text documents (e.g .qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) - Want to walk away with your own personal website Taught by: Charlotte Wickham (https://www.cwick.co.nz/) Emil Hvitfeldt (https://emilhvitfeldt.com/) Videos in this series: 1. Build your homepage [https://youtu.be/l7r24gTEkEY] 2. Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

Quarto Crash Course | Create Professional Reports, Dashboards & Websites w/ Markdown & Python Code!

Welcome to this comprehensive Quarto crash course using Python! Whether you're a complete beginner or an experienced user, this tutorial covers the topics you need to know about the Quarto publishing system. We'll explore: - Basic setup and installation - Creating HTML reports, PDFs, and interactive dashboards from your Python script or Jupyter notebook. - Building presentations with Revealjs - Customizing outputs with CSS and layouts (fenced divs, classes, and more) - Working with parameters for dynamic reports - Publishing to Posit Connect Cloud - Creating complete websites - Automated report generation Perfect for data scientists, analysts, and developers looking to create beautiful, reproducible reports from their code. We'll use Python throughout the tutorial with real-world examples using movie analytics data. Let's dive in! Video by @KeithGalli Github repo: https://github.com/KeithGalli/quarto-crash-course Deploy with Posit Connect Cloud! https://pos.it/keith_qc #python #quarto #posit — Resources Mentioned — Slideshow example: https://quarto.org/docs/presentations/revealjs/demo/#/title-slide Slideshow example (source code): https://github.com/quarto-dev/quarto-web/blob/main/docs/presentations/revealjs/demo/index.qmd Example HTML report: https://019302a7-e9e3-3454-3575-23148999a7f7.share.connect.posit.cloud/ Quarto Gallery: https://quarto.org/docs/gallery/ Bootstrap Icons: https://icons.getbootstrap.com/ Video timeline! 0:00 - About the Crash Course 0:50 - Quarto Overview 2:12 - Installation & Setup 6:22 - Markdown Basics 8:46 - Quarto Markdown Features 19:37 - Quarto Styling & Formatting (fenced divs, CSS classes, etc.) 34:53 - Parameters & CLI Options 40:46 - HTML & Publishing 49:46 - Static Docs (PDFs, Docx) 54:53 - Dashboards 1:06:50 - Slideshows (Revealjs) 1:16:11 - Websites 1:19:52 - Automated Report Generation (Parameterized Reports)

Polars, pandas, and Narwhals, oh my! | Marco Gorelli | Data Science Hangout

Welcome back to the Data Science Hangout! This week, we're joined by Marco Gorelli, a senior software engineer at Quansight Labs and a core developer of Pandas, Polars, and the author of Narwhals. Marco shares his insights on the evolving landscape of data frame libraries in Python, focusing on Polars, its advantages over Pandas, and the role of Narwhals in creating data frame agnostic tools. Marco discusses his journey into open source, highlighting the importance of finding your niche and contributing to areas that others might find "boring" but are essential for a project's success. He also emphasizes the significance of fostering welcoming and inclusive open source communities. This episode explores several key topics: Polars: We learn about Polars' key features like lazy execution, its performance benefits, and the power of its expressions API. Marco explains how lazy execution can lead to significant performance gains and answers questions about its relevance even when working with smaller datasets. Narwhals: We discover how Narwhals enables developers to write tools that work seamlessly with various data frame libraries, promoting interoperability and simplifying development workflows. Open Source Challenges: Marco addresses the challenges of maintaining work-life balance while being deeply involved in open-source projects. He offers practical advice on prioritization and managing the constant influx of tasks and requests. Resources mentioned in the chat: Pandas: https://pandas.pydata.org/ Polars: https://pola.rs/ Narwhals: https://narwhals-dev.github.io/narwhals/ Awesome Polars: https://github.com/ddotta/awesome-polars Ibis: https://ibis-project.org/ Polars Plugins Tutorial: https://marcogorelli.github.io/polars-plugins-tutorial/ Understanding Polars Expressions: https://www.youtube.com/watch?v=E7cHgN9rd9c Great Tables: https://posit-dev.github.io/great-tables/articles/intro.html Great Tables Blog - Polars Styling: https://posit-dev.github.io/great-tables/blog/polars-styling/ Great Tables Blog - BYODF: https://posit-dev.github.io/great-tables/blog/bring-your-own-df/ Ruff: https://astral.sh/ruff UV: https://docs.astral.sh/uv/ R/Pharma 2024: https://rinpharma.com/ PyladiesCon: https://conference.pyladies.com/ Beeminder: https://www.beeminder.com/ Todoist: https://todoist.com/ Nyctography: https://en.wikipedia.org/wiki/Nyctography This episode offers valuable insights for anyone working with data in Python, particularly those interested in exploring the benefits of Polars and the power of Narwhals for building data-frame-agnostic tools. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software To join future data science hangouts, add to your calendar here: https://pos.it/dsh (All are welcome! We'd love to see you!) Thanks for hanging out with us!

Running unified attribution at scale | Martin Stein @ Conversion Logix | Data Science Hangout

Join us for a conversation with Martin Stein, Chief Analytics Officer at Conversion Logix, to chat about running unified attribution at scale, the tools they leverage, languages Conversion Logix's uses, and the workflow and operations to make it all happen. Featured Leader Bio: Martin Stein is a seasoned data scientist, entrepreneur, and Chief Analytics Officer at Conversion Logix. With over 20 years of experience in AI, machine learning, and data science, he brings a wealth of expertise to the multifamily industry, developing cutting-edge MarTech solutions that empower marketing leaders to make data-driven decisions to maximize their investments. Stein is also the co-founder of Bend.ai, an AI/machine learning company acquired by Conversion Logix. His career includes significant roles at Apple, IDG/IDC, G5, RStudio, and startups like FileThis, Defined.ai, and Union.ai. Throughout his career, he has demonstrated a proven ability to build and scale products to over $200 million in revenue. Martin shares insights on topics like: The problem with "last-touch" attribution in marketing and how his team is building solutions to provide a more comprehensive understanding of marketing channel effectiveness. Common data science struggles, including data access, data inconsistency, production challenges, and team dynamics. The value of community in a data scientist's journey, particularly for those exploring new areas like AI. Tips for effectively communicating data science concepts and building business cases, both internally and with clients. Approaches to measuring channel success in marketing and comparing it to copy success, emphasizing the importance of considering factors beyond individual channel performance. How tools like Pins and Connect facilitate data management, workflow efficiency, and model deployment. Insights into the use of vetiver for model evaluation, highlighting its ability to assess model behavior with known and unknown data and its relevance to MLOps practices. Don't miss this insightful episode packed with practical advice and real-world examples for data scientists at all levels! A few people in the chat said it felt like they were listening to a really great mentor speak, and we agree ️ Resources mentioned in the episode and chat: Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R by Martin Huber https://mitpress.mit.edu/9780262545914/causal-analysis/ Causal Inference: The Mixtape by Scott Cunningham https://mixtape.scunning.com/ Kevin Ushey’s R Studio Conf talk on Renv https://www.youtube.com/watch?v=4wRiPG9LM3o Rami Krispin’s VS Code and R setup https://github.com/RamiKrispin/vscode-r ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: (https://www.linkedin.com/company/posit-software) Twitter: (https://x.com/posit_pbc) To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!) Thanks for hanging out with us!

GSK's R Journey: From Pilot Projects to Enterprise Adoption | Hosted by Posit

Discover how GSK, a leading pharmaceutical company, transforms its biostatistics toolkit by embracing R. In this web event, you'll learn: -GSK's Open-Source Commitment and Journey: Discover how GSK began their journey with open-source software, particularly R, and the pilots they’ve initiated to foster broader acceptance across the organization. -Challenges and Goals in R Adoption: Understand the obstacles GSK faces in shifting to end-to-end use of R due to existing processes and timelines, and learn about their internal goals to achieve parity between R and other proprietary languages in their biostatistics toolkit. -Evolution of Training Programs: See how GSK upskills teams through evolving training programs while meeting key deliverables. -Contributing Back to Open-Source: Explore the importance GSK places on giving back to the open-source community as part of their adoption journey. -Ascending the Open-Source Mountain: Join us to uncover the steps GSK has taken from pilot projects to setting large-scale enterprise adoption goals. Meet the Speakers: Andy Nicholls is the Head of Data Science Innovation and Engineering Team at GSK. Andy brings nearly 20 years of experience working with and promoting the R language. Formerly a professional R trainer and advocate at Mango Solutions, he taught over 100 R training courses and authored the book "R in 24 Hours". Andy co-founded the R Validation Hub and co-authored an influential white paper on risk-based approaches to R package assessments. At GSK, he plays a vital role in defining and delivering the R strategy for Biostatistics. Ben Arancibia is a Director of Data Science on GSK's Data Science Innovation and Engineering Team. Ben is a seasoned data scientist with 13 years of experience utilizing open-source languages. His career includes building analytical products for international finance and cloud computing environments for cybersecurity. At GSK, he leads teams developing tools for clinical trial analysis, focusing on innovative R training and upskilling approaches. Becca Krouse is a Data Scientist on GSK's Data Science Innovation and Engineering Team. With over 14 years in clinical research, Becca is a trained biostatistician specializing in developing R-based tools. Her expertise bridges statistical theory and practical application, ensuring GSK's biostatistics remains innovative. Want to learn more about Posit's work in pharma? Access the full pharma playlist through our YouTube channel!

Data Science in CPG and the Zero-to-One journey | Joel Ash @ Kraft Heinz | Data Science Hangout

Welcome back to the Data Science Hangout! This week, we're joined by Joel Ash, Senior Data Science Manager at Kraft Heinz, for a discussion on the unique challenges and opportunities of applying data science in the consumer packaged goods (CPG) industry. Joel provides a behind-the-scenes look at how Kraft Heinz leverages data to optimize promotional spending, which exceeds a billion dollars annually. He discusses the complexities of navigating relationships with retail partners like Target and Walmart, emphasizing the need to balance cost reduction with maintaining strong customer relationships. With some internet issues, we were only able to learn from Joel the first half of the Hangout. We switched to an *open leader* format with the community for the rest of the conversation. Thank you to those who jumped in as well! Discussion topics with Joel included: Decoding the CPG landscape: Joel explains the nuances of the CPG industry, highlighting the distinction between their customers (retailers) and end consumers. He provides specific examples, such as the market dynamics of cream cheese, to illustrate the challenges of predicting demand and optimizing promotional strategies. Embracing the "zero to one" journey: Joel introduces the concept of "zero to one," referencing the challenges of building data science capabilities from the ground up within established organizations. He discusses the importance of integrating data-driven insights into legacy systems and processes, often requiring adaptability and a focus on interpretable models. The battle against cannibalization in CPG: Joel explains the concept of "cannibalization" in the CPG context, where the introduction of new products or promotions can negatively impact sales of existing products. He discusses the strategies Kraft Heinz employs to minimize cannibalization and maximize the overall return on promotional investments. Resources mentioned: Ego is the Enemy by Ryan Holiday https://egoistheenemy.com/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: (https://www.linkedin.com/company/posit-software) Twitter: (https://x.com/posit_pbc) To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!) Thanks for hanging out with us!

Quarto Dashboards 3: Theming and Styling | Mine Çetinkaya-Rundel | Posit

Theming and styling Quarto dashboards built with R and/or Python. Before watching this video, you might want to watch Parts 1 & 2. This video takes you through 0:00 - Theming (including Bootswatch themes, light/dark mode, customizing themes with SCSS) 3:55 - Styling 4:55 - Live coding demo Slides can be found at https://mine.quarto.pub/quarto-dashboards/3-theming-styling and the starter documents for the accompanying exercises at https://github.com/mine-cetinkaya-rundel/olympicdash. Materials for all parts of the videos can be accessed at https://mine.quarto.pub/quarto-dashboards. You already analyze and summarize your data in computational notebooks with R and/or Python. What’s next? You can share your insights or allow others to make their own conclusions in eye-catching dashboards and straight-forward to author, design, and deploy Quarto Dashboards, regardless of the language of your data processing, visualization, analysis, etc. With Quarto Dashboards, you can create elegant and production-ready dashboards using a variety of components, including static graphics (ggplot2, Matplotlib, Seaborn, etc.), interactive widgets (Plotly, Leaflet, Jupyter Widgets, htmlwidgets, etc.), tabular data, value boxes, text annotations, and more. Additionally, with intelligent resizing of components, your Quarto Dashboards look great on devices of all sizes. And importantly, you can author Quarto Dashboards without leaving the comfort of your “home” – in plain text markdown with any text editor (VS Code, RStudio, Neovim, etc.) or any notebook editor (JupyterLab, etc.). This workshop will walk you through building an increasingly complex dashboard using various layout options and deploy them as static web pages (with no special server required) as well as with a Shiny Server on the backend for enhanced interactivity. This course is for you if you: * do data analysis in computational notebooks * share your results with your audience in static or interactive dashboards * want to improve the design, user interface, and experience of your dashboards

Mine Çetinkaya-Rundel

Quarto Dashboards 1: Hello, Dashboards! | Mine Çetinkaya-Rundel | Posit

You already analyze and summarize your data in computational notebooks with R and/or Python. What’s next? You can share your insights or allow others to make their own conclusions in eye-catching dashboards and straight-forward to author, design, and deploy Quarto Dashboards, regardless of the language of your data processing, visualization, analysis, etc. With Quarto Dashboards, you can create elegant and production-ready dashboards using a variety of components, including static graphics (ggplot2, Matplotlib, Seaborn, etc.), interactive widgets (Plotly, Leaflet, Jupyter Widgets, htmlwidgets, etc.), tabular data, value boxes, text annotations, and more. Additionally, with intelligent resizing of components, your Quarto Dashboards look great on devices of all sizes. And importantly, you can author Quarto Dashboards without leaving the comfort of your “home” – in plain text markdown with any text editor (VS Code, RStudio, Neovim, etc.) or any notebook editor (JupyterLab, etc.). This video takes you through 0:00 - Overview of building dashboards with Quarto 0:15 - Dashboard basics 7:40 - First dashboard in R 10:30 - First dashboard in Python 11:43 - Live coding demo Slides can be found at https://mine.quarto.pub/quarto-dashboards/1-hello-dashboards/#/title-slide and the starter documents for the accompanying exercises at https://github.com/mine-cetinkaya-rundel/olympicdash. Materials for all parts of the videos can be accessed at https://mine.quarto.pub/quarto-dashboards

Mine Çetinkaya-Rundel

Zac Davies - Elevating enterprise data through open source LLMs

In an era where data privacy and security are paramount, many organizations are keen on leveraging Large Language Models (LLMs) in conjunction with their proprietary data without exposing it to third-party services. Recognizing this need, our talk, "Elevating Enterprise Data Through Open Source LLMs," showcases an approach that integrates the capabilities of Databricks and Posit, enabling businesses to maintain ownership and control over their data and LLMs while delivering value to their customers. The core of our discussion revolves around a system architecture that synergizes the strengths of Databricks and Posit technologies, providing a comprehensive solution for enterprise data and open-source LLMs. Databricks is responsible for data management and processing, offering a seamless environment for hosting, serving, and fine-tuning open-source LLMs. Keeping data and models in the secure perimeter of Databricks lowers the risk of data exfiltration tremendously, and also benefits from the scalable data processing and machine learning capabilities - including the recent acquisition MosaicML - that Databricks delivers. Posit steps in to streamline the process through Posit Workbench, the developer platform for data science with custom integrations for working with Databricks. This allows developers to write, test, and refine their code in a familiar and powerful setting while still being able to access the data, compute, and model serving offered by Databricks. In addition, Posit Connect offers an easy-to-use platform for deploying these applications, ensuring that the end-to-end process, from development to deployment, is efficient, secure, and aligned with enterprise standards. Attendees of this talk will gain valuable insights into constructing and deploying LLM-powered applications using their enterprise data. By the end of the session, you will have a clear understanding of how to leverage Databricks for optimal data management and LLM operations, alongside Posit's streamlined development and deployment processes. This knowledge will empower you to deliver secure, effective, and scalable LLM-powered applications, driving innovation and value from your enterprise data while upholding the highest standards of data privacy and security. Talk by Zac Davies Slides: https://github.com/zacdav-db/presentations

Mark Wang - Using GitHub Copilot in R Shiny Development

Generative AI tools, like the GitHub Copilot, are revolutionizing software development, by automating routine tasks and boosting programmers' productivity. R Shiny development is uniquely positioned to benefit from GitHub Copilot. Copilot can accelerate the process by automating front-end tasks, especially those involving HTML and JavaScript, as well as testing and documentation, thus letting R Shiny developers focus more on the value-added aspects of data science and statistics. The basic workflow of using GitHub copilot in R Shiny development has three parts: prompt, context, and iteration. Three types of prompts: command, question, and lead-in, all have strong use cases in R development. Specific prompts create more helpful results as compared to vague ones, and Copilot can help add more specificity to your prompts when you are not familiar with the domain topic. The most important source of context for Copilot is typically your codebase, which should be well-organized and open in your IDE. Additionally, knowledge on the internet, as well as simple and concrete examples, can also be valuable contexts. However, sensitive personal information in your context can stop Copilot from generating appropriate responses. Iteration is modifying and enriching your prompt and context to improve the quality of Copilot's responses. Function and module documentation is a valuable step of iteration. As R Shiny is often used with fast-hanging data, Copilot serves as a powerful tool in the iteration from static to dynamic app testing in the shinytest2 framework. As we iterate over our prompt and context to generate better responses from Copilot, it is worthwhile to build an internal knowledge base in the form of prompt dictionaries. A Copilot knowledge base can include information specific to your organization that can be reused across projects, such as security and compliance conventions, code and documentation standards, and employment process and environment. Talk by Mark Wang Slides: https://1drv.ms/b/c/fd70c8a06be32527/ER8WuxYEw_NKksTI6t5wCDgBrO9slTr0HaumYp1eg2xYYA?e=KwQSfb GitHub Repo: https://github.com/ZIBOWANGKANGYU/posit_2024_prez/tree/master/presentation

The importance of relevant analytics | Nechama Katan @ Pfizer | Data Science Hangout

We were recently joined by Nechama Katan, Director of Innovative Data Analytics at Pfizer, for a conversation about relevant analytics, the importance of understanding business context, and strategies for bridging silos within organizations. "Technology on its own doesn't do you any good." Nechama highlighted the common pitfall of organizations prioritizing technology over business needs. She stressed that technology is only valuable when it directly addresses a business problem. Often, companies invest in complex data infrastructure and tools without a clear understanding of how they will be used to answer critical business questions. This can lead to frustration and a lack of adoption by business users. Nechama encouraged data professionals to prioritize understanding the business context behind the data. By asking questions, collaborating with colleagues in other departments, and actively seeking to understand the challenges and goals of the business, data scientists can ensure their work delivers tangible value. How would you recommend acquiring the business background needed to succeed? In our Hangout chat, Nechama stressed the importance of building relationships and breaking down silos within organizations. She encouraged data professionals to: ● Be curious: Ask questions relentlessly, seeking to understand the perspectives and challenges of colleagues in other departments. ● Offer help: Identify areas where your expertise can be valuable to others and proactively offer assistance. ● Be kind: Approach interactions with empathy and a genuine desire to build connections. In sharing her career advice, Nechama advocated for "managing down," prioritizing collaboration and support for peers. Building strong relationships and fostering a culture of teamwork is essential for solving problems and achieving impactful outcomes Resources from the Chat: [Mosaic Crochet Shiny App](https://apps.machlis.com/shiny/crochetapp/) [Cascades: How to Create a Movement that Drives Transformational Change by Greg Satell](https://www.amazon.com/Cascades-Create-Movement-Drives-Transformational/dp/1260454010) [Launching DyslexicU, the World's First University of Dyslexic Thinking](https://www.virgin.com/branson-family/richard-branson-blog/launching-dyslexicu-the-worlds-first-university-of-dyslexic-thinking) [Move Fast and Break Shit: Burning Man's Lessons for Success in Work and Life by Heather White](https://www.amazon.com/Move-Fast-Break-Shit-Burn-ebook/dp/B08JNWBQ9Y) [One Data Scientist’s Assimilation of the Product Lexicon](https://medium.com/@eugene.geis/one-data-scientists-assimilation-of-the-product-lexicon-3dd0e7b05be1) [Spurious Correlations](https://tylervigen.com/spurious-correlations) [There's Only One R in Pfizer](https://www.youtube.com/watch?v=bnRdI6jvGpM) ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!) Thanks for hanging out with us!

How Can Data Teams Get Out of Their Own Way (Alex Gold) - KNN Ep. 196

Today, I had the pleasure of interviewing Alex Gold. Alex is the Director of Solutions Engineering + Support at posit and author of DevOps for Data Science. In this episode, we talk about Alex's experience working in data for presidential campaigns, his experience in solutions engineering, and the role that DevOps plays in the data domain. Book: https://www.routledge.com/DevOps-for-Data-Science/Gold/p/book/9781003213345 (You can use Code AFLY04 for a 20% discount) Alex's Links: LinkedIn - https://www.linkedin.com/in/alexkgold/ Twitter - https://x.com/alexkgold Podcast Sponsors, Affiliates, and Partners: - Pathrise - http://pathrise.com/KenJee | Career mentorship for job applicants (Free till you land a job) - Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job - 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today - Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee | Interview prep questions Listen to Ken's Nearest Neighbors on all the main podcast platforms! On Apple Podcasts: https://podcasts.apple.com/us/podcast/kens-nearest-neighbors/id1538368692 (Please rate if you enjoy it!) On Spotify: https://open.spotify.com/show/7fJsuxiZl4TS1hqPUmDFbl On Google: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS8xNDMwMDQxLnJzcw?sa=X&ved=0CAMQ4aUDahcKEwjQ2bGBhfbsAhUAAAAAHQAAAAAQAQ MORE DATA SCIENCE CONTENT HERE: My Twitter - https://twitter.com/KenJee_DS LinkedIn - https://www.linkedin.com/in/kenjee/ Kaggle - https://www.kaggle.com/kenjee Medium Articles - https://medium.com/@kenneth.b.jee Github - https://github.com/PlayingNumbers My Sports Blog - https://www.playingnumbers.com ️ 66DaysOfData Discord Server - https://discord.com/invite/4p37sy5muZ

Ralph Asher @ Data Driven Supply Chain | Data Science Hangout

In this Hangout, Ralph talks about his experiences in the supply chain industry - a hidden gem of a data job field - and his educational background in Operations Research. He talks about the types of problems supply chain analysts work to solve, how 2020 reshaped the landscape of supply chain, and how he decided to start his own business. He also gives some great advice about marketing yourself, whether you’re starting your own consulting company or looking for a new role: be specific about what you want and the problems you solve. If you’ve been curious about supply chain jobs, you’ll want to hear Ralph’s perspective! Speaker bio: Ralph Asher is the founder of Data Driven Supply Chain LLC, a Minneapolis-based consultancy working at the intersection of supply chain and decision science. Since 2021, Ralph and his team have used decision science and data science to build advanced supply chain models, helping clients make tough supply chain decisions around network design, inventory optimization, and more. Timestamps: 03:00 About Ralph Asher 05:35 What type of problems do supply chain analysts work on? 07:55 What type of data jobs are there in the Supply Chain Industry? 10:15 How did COVID-19 impact supply chain jobs? 13:10 How do you know you’re ready to start a consulting business? 15:25 With so much data, how do you make sure insights are actionable and not irrelevant? 17:30 What are some examples of data management or governance challenges in supply chain? 18:15 What career advice would you give new grads or people looking to switch careers? 21:10 Why being specific about what you want to do gets you job opportunities 23:55 Book recommendation: The Black Swan and preserving optionality, keeping doors open 25:20 Is a supply chain consultant more valuable and effective than a supply chain department? 26:50 How do you learn all the parts of running a business by yourself? The E-Myth Revisited book 29:20 Do you ever have to convince people to move past Excel? Supply chain runs on Excel. 31:05 Career advice - lean into something you’re good at. The Algebra of Wealth book 33:30 How do you manage data and metadata? How do you manage knowledge for your business? 36:25 How do you determine which clients to accept and which to turn away as a consultant? 37:45 How do you do discovery work with a consulting client to make sure they’re a fit? 39:10 Saying no to the wrong jobs so that you can say yes to the right jobs 40:00 Can client discovery be done as a paid project? Could it be its own product? 44:00 As a consultant, how do you make the unseen hours of labor visible? 46:10 What is a good entry point for a company that doesn’t have a dedicated supply chain team? 47:35 Should you go back to school for another degree to get a job that feels out of reach? 49:10 How does your career growth change between IC and management? 50:55 What are some supply chain communities to join? 52:10 Do supply chain teams work within data teams or separately? 55:50 As a consultant on your own, how do you keep your skills sharp and up to date? 57:20 How do you use Shiny in your supply chain consulting jobs? Resources mentioned in the episode: The Black Swan by Nassim Taleb: https://www.randomhousebooks.com/books/176226/ The E-Myth Revisited by Michael Gerber: https://www.michaelegerbercompanies.com/product/the-e-myth-revisited/ The Irresistible Consultant’s Guide to Winning Clients by David Fields: https://www.davidafields.com/books/#section-1 The Algebra of Wealth by Scott Galloway: https://www.penguinrandomhouse.com/books/730881/the-algebra-of-wealth-by-scott-galloway/ Notion: https://www.notion.so/ HubSpot: https://www.hubspot.com/products/crm CSCMP, Council for Supply Chain Management Professionals: https://cscmp.org/ ASCM, the Association for Supply Chain Management: https://www.ascm.org/ ISM, Institute for Supply Management: https://www.ismworld.org/ INFORMS, Institute for Operations Research and Management Sciences: https://www.informs.org/ ________________________ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software The Hangout is a gathering place for the whole data science community to chat about data science leadership and questions you're all facing that happens every Thursday at 12 ET. To join future data science hangouts, add to your calendar here: https://pos.it/dsh We'd love to have you join us in the conversation live! Thanks for hanging out with us!

What is DevOps? And advice for those just starting! | Alex Gold @ Posit | Data Science Hangout

In this episode, we talked with Alex Gold about what it’s like running the solutions engineering and support team at Posit. He shared his journey into DevOps, how he became a manager, and what it was like writing a technical book in Quarto. Alex shares some amazing advice around what he as a hiring manager looks for in data job applicants, and he talks about the different struggles solo data practitioners face in organizations. He even has a little advice about how to do a handstand, and answers the questions, “Can R be put into production,” and “Can you mix R and Python in the same pipeline?” We know you’ll get a lot out of this conversation! Timestamps: 03:36 About Alex Gold 08:20 What is DevOps? What does a solutions engineer do? 13:25 How do you ask the right questions as a solutions engineer? 19:35 Do you need a PhD to be a data scientist? 24:55 Can R be put into production? Is ‘R can’t be put into production’ a myth? 28:10 Advice for people starting in devops? 31:50 What did you learn from the process of writing a book? 34:20 What tool did you use to write your book? 36:00 Any tips for doing a handstand? 37:15 Is it harder to make devops changes in a smaller or larger company? 40:55 Going from an individual contributor (IC) to managing a team 47:30 Not being able to get promoted without becoming a manager 49:45 What do you look for when hiring for a data role? 53:20 Did you use a template for publishing with CRC press? 55:25 Can you mix R and Python in the same data pipeline? Resources mentioned in this episode: Alex’s book, DevOps for Data Science: https://do4ds.com/ Alex’s blog post about dropping out of his PhD program: https://alexkgold.space/posts/2022-09-16-phd-dropout/index.html Alex’s blog post about the first year as a manager: https://alexkgold.space/mfy.html ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software The Hangout is a gathering place for the whole data science community to chat about data science leadership and questions you're all facing that happens every Thursday at 12 ET. To join future data science hangouts, add to your calendar here: https://pos.it/dsh We'd love to have you join us in the conversation live! Thanks for hanging out with us!

Empowering individuals through AI tools, technologies, and custom apps | Chris Engelhardt @ Gen Re

“Empathy, I think, is a hallmark of a well functioning team.” Chris Engelhardt might lead data teams and AI initiatives, but he’s also got a PhD in Social Psychology and a keen interest in helping people work more happily. This episode of the Hangout went to amazing and unexpected places as we talked about AI use cases, getting buy-in from leadership, and building stakeholder trust alongside building psychological safety in your teams, code review anxiety, and team wellbeing. If you’re curious about enterprise level AI use cases, or how Posit integrates with Databricks, there’s lots to learn from Chris! We hope you’ll join us live soon! Timestamps 02:35 About Chris Engelhardt Sr. Data and AI Operations Manager at Gen Re 05:55 How do you get people engaged in a vision for AI? 08:10 What are some use cases for AI? 11:40 How do you manage international data science teams? 13:15 What one AI skill data analysts should learn? 14:50 What people skills should data analysts and data scientists learn? 16:25 How do you get buy-in from leadership and data scientists for AI initiatives? 18:25 What is AI? Where do I start? What are pitfalls of AI to avoid? 22:40 Integrating Posit with Databricks, Unity Catalog, and pins 25:55 How do you handle it when two different teams manage Posit versus Databricks? 27:30 What if AI tools provide inaccurate results? How do you regain trust with stakeholders? 29:50 How do you protect your own and your data team’s wellbeing at work? 34:20 How has AI changed hiring and job descriptions? Has AI changed data roles? 36:30 Code review anxiety and psychological safety around code critiques and pull requests 40:00 Helping your team feel safe and willing to talk about things and collaborate 41:40 How do you encourage psychological safety in a cross-cultural international team? 47:15 How do you use Posit pins with Databricks? 49:50 Career advice for working in data - you can’t control everything, but you control your effort 51:15 Don’t underestimate the value of connecting with others 53:33 Does using pins with Databricks Unity Catalog present a challenge with data governance? 56:45 How do you encourage learning and upskilling within your team? Resources mentioned in this episode: Code Review Anxiety Toolkit: https://developer-success-lab.gitbook.io/code-review-anxiety-workbook-1/part-three-mini-code-review-anxiety-toolkit/introduction Matthew Montero from Gen Re’s episode: https://posit.co/data-science-hangout/56-matthew-montero/ Schedule time to chat about Databricks and Posit here: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks Posit Team Workflow Demos: https://pos.it/team-demo The Psychological Safety Handbook Daren mentioned: https://handbook.gitlab.com/handbook/leadership/emotional-intelligence/psychological-safety/

Data-level permissions using Posit Connect (with Databricks, Snowflake, OAuth)

Should one viewer of your app be able to see more (or different) data than another? Maybe colleagues in California should only see data relevant to them? Or managers should only have access to their own employee data? The Connect team joined us for a demo on inheriting data-level permissions using Posit Connect and Databricks Unity Catalog. While this workflow uses Databricks to illustrate federated data access controls, this same methodology can also be applied to Snowflake or any external data source that supports OAuth. During this workflow demo, you will learn: 1. How to define row-level access controls in Databricks Unity Catalog 2. How to create a Databricks OAuth integration in Posit Connect 3. How to write interactive applications that utilize the viewer’s Databricks credentials when reading data from Databricks Unity Catalog, providing the viewer with a personalized experience depending on their level of data access 4. How to deploy this application to Posit Connect and share it within your organization If you’d like to talk further with our team 1:1 about doing this, you can find a time to chat here: https://posit.co/schedule-a-call/?booking_calendar__c=WorkflowDemo Ps. To enable OAuth integrations, your team will need to upgrade to Posit Connect 2024.08.0. This feature is available in Enhanced and Advanced product tiers. Helpful resources for this workflow: Full examples to get you started: https://github.com/posit-dev/posit-sdk-py/tree/main/examples/connect Admins will likely be most interested in starting here: https://docs.posit.co/connect/admin/integrations/oauth-integrations/databricks/ End users will be most interested here: https://docs.posit.co/connect/user/oauth-integrations/ Q&A Link: https://youtube.com/live/TZQY6rm6hU4?feature=share Additional resources shared: Release Blurb: https://docs.posit.co/connect/news/#posit-connect-2024.08.0 Security: https://docs.posit.co/connect/admin/integrations/oauth-integrations/security.html Publishing Quarto: https://docs.posit.co/connect/how-to/basic/publish-databricks-quarto-notebook/ sparklyr: https://github.com/sparklyr/sparklyr?tab=readme-ov-file#connecting-through-databricks-connect-v2 odbc: https://github.com/r-dbi/odbc?tab=readme-ov-file#odbc-

So You Think You Can ANALYZE? (Data Content Creator Hackathon)

Watch your favorite data science content creators compete for a piece of history. They will either prove their skills in front of the world or fail in the pursuit of eternal glory. This is so you think you can analyze. Special thanks to Posit for sponsoring this competition and make this all possible! Competitors: Team Jack @averysmith Jack Blandin @KeithGalli Team MMA @AlexTheAnalyst @nerdnourishment @Miki_ML Team Null Nick Singh Mark Freeman @SeattleDataGuy Team Shashank @ShashankData Team Posit-ively Skewed Greg Coquillo @datascienceharp Richad Nieves-Becker Ian Greengross Interviewer: Elijah Butler - https://www.tiktok.com/@imelijahbutler Posit Team - Michael Chow - Joe Cheng - Carolos Scheidegger - Julia Silge Sponsors, Affiliates, and Partners: - Pathrise - http://pathrise.com/KenJee | Career mentorship for job applicants (Free till you land a job) - Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job - 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today - Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee | Interview prep questions MORE DATA SCIENCE CONTENT HERE: My Twitter - https://twitter.com/KenJee_DS LinkedIn - https://www.linkedin.com/in/kenjee/ Kaggle - https://www.kaggle.com/kenjee Medium Articles - https://medium.com/@kenneth.b.jee Github - https://github.com/PlayingNumbers My Sports Blog -https://www.playingnumbers.com Check These Videos Out Next! My Leaderboard Project: https://www.youtube.com/watch?v=myhoWUrSP7o&ab_channel=KenJee 66 Days of Data: https://www.youtube.com/watch?v=qV_AlRwhI3I&ab_channel=KenJee How I Would Learn Data Science in 2021: https://www.youtube.com/watch?v=41Clrh6nv1s&ab_channel=KenJee My Playlists Data Science Beginners: https://www.youtube.com/playlist?list=PL2zq7klxX5ATMsmyRazei7ZXkP1GHt-vs Project From Scratch: https://www.youtube.com/watch?v=MpF9HENQjDo&list=PL2zq7klxX5ASFejJj80ob9ZAnBHdz5O1t&ab_channel=KenJee Kaggle Projects: https://www.youtube.com/playlist?list=PL2zq7klxX5AQXzNSLtc_LEKFPh2mAvHIO

Joe Cheng, Julia Silge, Michael Chow

What did journalism teach you about data storytelling? | Sharon Machlis | Data Science Hangout

In this episode, Sharon Machlis shares her journey from journalist to journalist-who-codes. You’ll hear her talk about the serendipitous way she found herself covering business and technical news, the lessons she learned about storytelling with data over the years, and how she knew it was time to retire. You’ll also learn how to find the data community on social media in a post-twitter world, including a primer on Mastodon and how it works without an algorithm. Timestamps: 02:45 About Sharon Machlis 04:55 How did you decide to start using R? 07:45 What did journalism teach you about data storytelling? 10:15 What’s your favorite headline you ever wrote? 10:45 How do you write high quality articles quickly? 12:30 How do you find people to follow on Mastodon? 15:35 How do you decide what new data topics to learn about or focus on? 20:55 What do you think about specialization in a world where everyone now codes? 23:25 Is domain expertise in data jobs undervalued? 27:00 What resources do you have for learning to write well? 32:00 Have you ever had a career setback that was a blessing in disguise? 34:05 How do you make data presentations more interesting? 37:00 Where is the data community now that Twitter is dead? Bluesky? Threads? 40:45 Has the evolution of graphics like the New York Times uses increased interest in data? 42:15 How did you decide you were ready to retire? 45:30 What data journalists should we be following? 47:55 What factors contribute to someone deciding to retire? 50:35 What projects can you focus on in retirement that you weren’t able to do before? 55:35 What resources do you recommend for learning Python? 57:25 Career advice from a data journalist Links mentioned in this episode: Sharon’s retirement journey: https://nextchapter.machlis.com Links Sharon shared in the chat: https://apps.machlis.com/shiny/positconf/ One way to see public posts on Mastodon with the #rstats hashtag without needing an account: https://fosstodon.org/tags/rstats Guide to R: https://www.computerworld.com/article/1533880/beginners-guide-to-r-introduction.html How to find better content on Mastodon by Sharon: https://www.infoworld.com/article/2338712/6-ways-to-find-better-content-on-mastodon.html Resources Sharon shared after the Hangout Suggested prompts to use LLMs to help you learn a skill, by Lilach Mollick and Ethan Mollick https://www.moreusefulthings.com/instructor-prompts Journalism-related resources for learning Python: 1. First Python Notebook: A step-by-step guide to analyzing data with Python and the Jupyter notebook, by Ben Welsh, currently news applications editor at Retures https://palewi.re/docs/first-python-notebook/ 2. Python's Not (Just) for Unicorns, by J Soma, journalism prof at Columbia University https://littlecolumns.com/learn/python/ Journalism-related resources to learn R - both a bit old but still work: 1. R for Journalists by Andrew Ba Tran, investigative data reporter at the Washington Post https://learn.r-journalism.com/en/ 2. My book Practical R for Mass Communication and Journalism Several chapters free: https://www.machlis.com/R4Journalists/ My Shiny app with a searchable list of Mastodon accounts who post about R with the #RStats hashtag: https://apps.machlis.com/shiny/rstats/ Searchable table of all my Mastodon posts (it's a public Shiny app, you don't need a Mastodon account): https://www.machlis.com/mastodon.php Mosaic crochet Shiny app, the most popular app I ever published https://apps.machlis.com/shiny/crochetapp/ Additional Shiny apps: https://apps.machlis.com/ Thoughts on using generative AI to help with your writing: How to . . . use ChatGPT to boost your writing: https://www.oneusefulthing.org/p/how-to-use-chatgpt-to-boost-your or I, Cyborg: Using Co-Intellgience: https://www.oneusefulthing.org/p/i-cyborg-using-co-intelligence both by Ethan Mollick

6 core ideas for public speaking | Blythe Coons & Acacia Duncan @ Articulation | DS Hangout

A must-see episode of the Hangout, especially if you have ever needed to give a presentation (and who hasn’t??). Acacia and Blythe from Articulation are experts at helping presenters craft their messages, and they’ve worked with many, many Posit Conference speakers over the years. If you’ve ever heard conf speakers gush about getting to work with Articulation to prepare their talks, you’ll understand why by the end of this discussion. There’s a reason more than 200 hangout members attended this one! Timestamps: 07:15 What are threshold concepts? What does Articulation do? 09:25 Threshold concept 1 - Speaking is habitual (not natural) 10:15 Threshold concept 2 - Speaking is embodied 11:30 Threshold concept 3 - Speaking is social 12:50 Threshold concept 4 - Speaking is messy 13:45 Threshold concept 5 - Speaking contains multiple genres 15:25 Threshold concept 6 - Speaking requires feedback 17:15 How do you construct a story when presenting data? What resources are there? 19:40 How do you structure a story? 21:05 Why is the Hero’s Journey the wrong structure for storytelling with data? (Your audience is Luke Skywalker, not you!) 24:25 Where do data people go wrong? How can data communicators improve? 25:40 How much information should you include in a presentation? How much is too much? 29:00 Should you create a document to hand out alongside your slides? 32:10 How do you change a company culture of having wordy PowerPoint slides? 34:35 What happens when PowerPoint slides are too wordy? 36:10 How do you reduce stress responses when giving a presentation? 43:15 How can you slow down if you’re a fast talker or nervously speed up while presenting? 46:20 What are some tools for planning presentations? 48:20 How does presenting on Zoom differ from presenting in person? 52:50 Tips for switching back and forth between in person and Zoom 55:00 How do you coach someone on a technical presentation if you’re not an expert in it? 57:45 How can you be yourself when giving a presentation? Here are a few of the key topics covered in the discussion: The threshold Concepts of speaking. These are the core ideas you must understand in order to really succeed in public speaking. Data Storytelling. Spoiler! There is more than one type of story in your data presentation, and you’re not the hero of any of them. Structuring a story. The basics of structure, but also how to answer the right question (spoiler again, it’s not the question you’re interested in). Common struggles in data communication. Do you bury the lede? Are you a non-linear thinker? Do you get super anxious and lost? Do you talk way too fast??? Coping with Zoom. Why presenting on zoom can be a challenge and what we can we do about it. Links from this episode: Blythe on LinkedIn: https://www.linkedin.com/in/blythe-coons-05196485/ Acacia on LinkedIn: https://www.linkedin.com/in/acacia-duncan-919899b8/ Articulation’s website: https://www.articulationinc.com/ Threshold Concepts: https://www.articulationinc.com/threshold-concepts-reflections-effective-speaker/ Storytelling with Data Podcast: https://www.storytellingwithdata.com/podcast Speeko, an app for analyzing your speaking pace and tone: https://www.speeko.co/ Milanote, a tool for organizing creative projects: https://milanote.com/ Did you miss joining us in person? If so, you missed out on some awesome resources from the chat! We’ve got you covered: Isabella shared some hex wall Zoom backgrounds in the virtual swag bag for positconf2024: https://github.com/posit-marketing/posit-conf-virtual-swag-bag Several people suggested the book Storytelling with Data by Cole Nussbaumer Knaflic: https://www.storytellingwithdata.com/books Watch Laura Gast’s talk from PositConf2023 about why good design is worth the time when it comes to data communication: https://www.youtube.com/watch?v=Kp-LQe3WgDU Tom Belanger shared the Stats + Stories Podcast: https://statsandstories.net/ Jared Cornell suggested the book The Fearless Mind: https://abinoda.com/book/the-fearless-mind Michael Kaminsky shared the book Resonate by Nancy Duarte: https://www.duarte.com/resources/books/resonate/ Laura Gast shared the book “If I understood you would I have this look on my face” by Alan Alda about his days interviewing for PBS Nova: https://www.penguinrandomhouse.com/books/533869/if-i-understood-you-would-i-have-this-look-on-my-face-by-alan-alda/ The Quarto Keeper! Like a Trapper Keeper, but in Quarto, and for making notes at big meetings or conferences: https://github.com/ats/quarto-keeper (by Alan Schussman) Lydia Gibson shared that the last chapter of R4DS has some resources listed in the summary for effective communication in different types of formats (presentations, public speaking, etc.) https://r4ds.hadley.nz/quarto-formats The Center Cam for making eye contact on Zoom: https://thecentercam.com/

Jason Foster @ Marathon Asset Management | Data Science Hangout

We were recently joined by Jason Foster, Director at Marathon Asset Management. In this episode, Jason talks about his journey into risk management and the evolving role of code-first analytics that he's experienced in finance. He covers various aspects of risk management, describes some of his experiences in package development, and shares his approach to cross-functional communications with non-technical colleagues (you’ve got to be able to speak their language). Jason also highlights the impacts that community involvement had on his professional growth. You'll hear him discuss his experiences with the R in Finance conference and how presenting his work and engaging with the community has led to valuable feedback and personal development. For those navigating today’s competitive job market, he offers advice on how to stand out by punctuating your strong quantitative skills with effective communication and really showing your passion for the field and its unique challenges. Timestamps: 03:50 - About Jason and his role 11:05 - What is risk management? 14:30 - How was your transition from R to Python? 19:00 - How do you help your team learn to use code-based tools? 22:10 - Was being a trainer part of your role or did your role grow in that direction organically? 25:30 - How do you share code-based solutions with non-coding colleagues? 32:15 - How do you convince stakeholders to listen to you? 35:20 - How do you get the right data for things that are hard to quantify? 42:30 - How do you work across teams that have poor working relationships? 45:50 - What is the next big thing is coding that excites you? 50:00 - How has your involvement in the community impacted your career? 57:00 - How can someone with less experience stand out in this crazy job market? Links from this episode: Jason Foster on LinkedIn: https://www.linkedin.com/in/jasonjfoster/ R in Finance Conference: https://www.rinfinance.com/ Jason's {roll} package: https://github.com/jasonjfoster/roll (it's also on CRAN!)

Quarto: Elevating R Markdown for Advanced Publishing | Christophe Dervieux

In the dynamic landscape of data analysis and scientific publishing, R Markdown has been pivotal for the R community, allowing users to seamlessly blend code, narrative and results in a cohesive narrative. Now, Quarto emerges as a powerful tool that builds on years of experience but also goes beyond R Markdown, providing more flexibility and power in scientific communication. This talk aims to present Quarto as the new alternative for scientific publishing. We will delve into how Quarto enhances the user experience for R enthusiasts, maintaining the syntax familiarity of R Markdown while introducing innovative and improved functionalities across multiple formats, similar to R Markdown ones. Why switch to Quarto from R Markdown? In which cases? How does Quarto integrate with existing workflows? Hopefully everyone will feel inspired to try out Quarto! https://quarto.org/docs/get-started/ Timestamps: 0:00 Introduction 0:41 Quarto is an open-source, scientific and technical publishing system 1:22 Computational documents and scientific markdown made easy for single source publishing 3:08 How to use Quarto 4:24 Quarto works with VS Code, Positron, Jupyter, & RStudio 5:22 Quarto's multi-language workflow 7:21 Quarto syntax 8:40 Quarto formats (html, pdf, docx, typst, beamer, pptx, revealjs, etc.) 12:19 HTML Theming 14:10 Typst CSS for nice table output in PDF 16:24 Publishing (Quarto Pub, GitHub Pages, Posit Connect, Posit Cloud, Netlify, Confluence, Hugging Face, etc.) 17:36 Shortcodes 19:10 Quarto Extensions 19:49 Quarto Projects 22:53 Project configuration examples for a website and a book 23:42 Resources to get started!

Christophe Dervieux

How to automatically detect data changes for your Shiny Calendar app (ft: Jira, pins, Posit Connect)

Do you manage constantly changing data and need your Shiny app to automatically update? On August 28th at 11 am ET, Isabella Velásquez demonstrated a streamlined workflow for handling frequently updated datasets in Shiny. You'll see how to simplify your process for keeping dynamic data current and how to reflect those changes in your app or dashboard. Github repo to follow along or make it your own! https://github.com/posit-marketing/shiny-calendar Timestamps: 1:03 - Introduction of the project (end goal: calendar that integrates with Jira to track and visualize a schedule for managing deadlines of content) 2:26 - Pulling data from an API in Python or R 2:56 - Introduction to pins (and scheduling automatic refreshes of it in Posit Connect) 4:30 - Introduction to Shiny for both Python and R (its power lies in reactivity) 5:10 - Enter pin_reactive_read() function 6:12 - Introduction to Posit Team 6:37 - Opening a new session within Posit Workbench and overview of code needed to create the calendar [Github repo: https://github.com/posit-marketing/shiny-calendar] 12:07 - toastui package used for Calendar (ex: adding colors to labels) 12:47 - Writing clean data to Posit Connect board 13:16 - Rendered Quarto doc for pulling Jira data from the board 14:00 - Deploying Quarto to Posit Connect (using push button deployment) and scheduling to run 16:54 - Using the data just pinned in the Shiny app 21:17 - Overview of Shiny Content Calendar application 23:04 - Creating an issue in Jira board and adjusting schedule in Posit Connect to show new item in Shiny calendar. 24:00 - pin_reactive_read automatically detects change and shows it in the Shiny app During this workflow demo, you will learn: 1. How {pins} stores and retrieves ever-changing data with ease 2. How to use pin_reactive_read() in Shiny to automatically trigger updates when your data changes 3. How Posit Connect can be set up to rerun your {pin} on a schedule, ensuring your app is updated without disruption 4. How to deploy an always-up-to-date app for seamless sharing with stakeholders Other helpful links: pin_reactive_read: https://pins.rstudio.com/reference/pin_reactive_read.html Basic reactivity in Mastering Shiny: https://mastering-shiny.org/basic-reactivity.html#reactive-programming Understanding reactivity on the Shiny site: https://shiny.posit.co/r/articles/build/understanding-reactivity/ Github repo: https://github.com/posit-marketing/shiny-calendar Shiny Calendar: https://pub.demo.posit.team/public/shiny-calendar/ Q&A Recording If you like these workflow demos, you can join us monthly! They happen the last Wednesday of every month at 11 am ET. Add it to your calendar here: https://pos.it/team-demo

Great Tables: Make beautiful, publication quality tables in Python | Rich Iannone & Michael Chow

Tables are undeniably useful for data work. We have many great DataFrame libraries available in Python, and they give us flexibility in terms of manipulating data at will, but what happens when presenting tables to others? It's nice to display tables. Tables can efficiently carry information, just like plots do, and at times it is the better way of presenting data. Indeed, it is time to bridge the divide between raw DataFrame output and wondrously structured tables suitable for publication. Now, let us turn our attention to the state of 'display tables' in 2024. Let us go over what comprises key components for building effective information displays in tables. It may surprise one how new a well-crafted table can be hewn. We'll take a look at the combinations of Python packages that fit together to make this important task possible, and marvel together at the tabular results they can provide! Learn more at https://posit-dev.github.io/great-tables/ Timestamps: 0:00 Intro: Meet Rich and Michael 0:41 What we mean by "publication ready tables" 1:29 Overview of what we'll talk about in this video 1:44 Table Goals: Ways to make a table beautiful 4:41 Tables made from reproducible code! 5:11 The history of table generation to influence our API 6:15 Our modern take on a table display framework 6:35 The problem with Excel 7:38 Introducing Great Tables! 8:00 Key Ingredients of making a Great Table 8:24 Structure: Title, column spanners and nice column labels 8:52 Format: Compact dollar values and percentages 9:23 Styling: Fill color and bold text 10:09 Imports and Polars Selectors 11:08 Coding the structure 12:27 Coding the format 13:07 Coding the styling 15:03 Putting images and plots in your table cells 15:49 Advanced Design 16:07 .fmt_nanoplot(): Small plots within table cells 19:08 .data_color(): Heat maps in tales 20:51 Powerful and plentiful methods to format cell values 22:48 To sum up: TABLES RULE

Michael Chow, Rich Iannone

Data driven decision making in pharma | Ning Leng @ Roche-Genentech & Jing Huang @ Veracyte

We were recently joined by both Ning Leng, Global Head, Data Sciences Acceleration at Roche-Genentech & Jing Huang, SVP of Bioinformatics and Data Science at Veracyte to chat about embedding modernized data science solutions in pharma to enable data driven decision making. Links mentioned in the chat: R Consortium R Submissions Pilot 4 documentation portal: https://rconsortium.github.io/submissions-pilot4/ Bay Area Biotech-Pharma Statistics Workshop: https://www.bbsw.org/ San Francisco Bay Area Chapter of American Statistical Association: https://sites.google.com/view/sfasa-org/home?authuser=1/ Dahshu Data Science Symposium: https://dahshu.wildapricot.org/ DS Hangout Survey/Feedback: https://docs.google.com/forms/d/e/1FAIpQLSdqXKPCPOwC7tSLygc43gf2ezkNO4bhM4TAgjUQMeoPe_GOmg/viewform Speaker Bios: Ning Leng: Ning Leng is the ad-interim Global Head of the Data Science Acceleration Enabling Platform, under Roche Product Development Data Sciences. Ning joined Roche-Genentech in 2016 as a statistician and worked on both early and late phase oncology development, with a special interest in utilizing diverse data sources and advanced methodologies to generate insights for personalized healthcare. Since then she has been driving a number of internal and cross-industry projects on modernizing Data Science solutions in pharma. Prior to joining Roche-Genentech, Ning obtained her PhD in Statistics from University of Wisconsin-Madison and worked at the Morgridge Institute for Research. Jing Huang: Jing Huang holds a B.A. in Statistics and Probability from Peking University and a Ph.D. in Statistics and M.S. in Epidemiology from Stanford University. With over 20 years in the biomedical field, her research focuses on statistical methodologies in clinical trial design, genomic analysis, and machine learning. As SVP of Bioinformatics & Data Science at Veracyte Inc., she oversees bioinformatics pipelines, algorithm development, and statistical analyses for product development. Jing has co-authored over 30 peer-reviewed articles, with more than ten thousand citations, and is a co-inventor on over 20 patent filings. She actively promotes data science through volunteer work, including as founding president of DahShu, 2024 president of BBSW, and chapter representative of the American Statistical Association San Francisco Bay Area Chapter (SFASA). In 2023, she was elected as a Fellow of the American Statistical Association to recognize her outstanding contributions to the medical research community in the field of statistics; for numerous statistical innovations in genomic tests; and for exemplary leadership and community service to the profession. ________________________ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software The Hangout is a gathering place for the whole data science community to chat about data science leadership and questions you're all facing that happens every Thursday at 12 ET. To join future data science hangouts, add to your calendar here: https://pos.it/dsh We'd love to have you join us in the conversation live! Thanks for hanging out with us!

How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python)

This is part 5 of our multi-part series on creating professional dashboards with Shiny for Python. In this video, we dive into advanced styling techniques to enhance the visual appeal and professionalism of your Shiny dashboards. We'll cover: - Adding a logo and custom title - Making use of custom HTML elements - Using CSS to style Shiny components - Customizing Altair charts for a polished look - Advanced Plotly chart modifications - Applying a consistent color theme and layout By the end of this video, you'll have a styled dashboard, ready for a professional presentation. Access the GitHub repo with all parts of this project: https://github.com/KeithGalli/shiny-python-projects Shiny for Python Homepage: https://shiny.posit.co/py/ Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Video by @KeithGalli **Video Timeline:** 0:00 - Video Overview & Setup 1:50 - Modifying HTML and CSS in Shiny 7:44 - Adding a Logo Image 10:26 - Styling Labels and Containers (Aligning our Image w/ the Title — Custom Divs) 20:20 - Customizing Altair Charts (Gridlines, Font, Axis Labels, Etc.) 27:49 - Customizing Plotly Visualizations 37:16 - Customizing Seaborn & Folium Heatmaps 44:15 - Final Touches, Clean Up, Recap and Next Steps If you enjoyed this series, give it a thumbs up and subscribe to the channel to stay updated! All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

Build Captivating Display Tables in Python With Great Tables | Real Python Podcast #214

Do you need help making data tables in Python look interesting and attractive? How can you create beautiful display-ready tables as easily as charts and graphs in Python? This week on the show, we speak with Richard Iannone and Michael Chow from Posit about the Great Tables Python library. Links from the show: https://realpython.com/podcasts/rpp/214/ Michael and Richard discuss the design philosophy and history behind creating display tables. We dig into the grammar of tables, the background of the project, and an ingenious way to build a collection of examples for a library. We briefly cover how Richard and Michael started contributing to open source. We also discuss practicing data skills with challenges and resources like Tidy Tuesday. This episode is sponsored by Mailtrap. Topics: - 00:00:00 -- Introduction - 00:02:00 -- Michael's background in open source - 00:04:07 -- Rich's background in open source - 00:05:27 -- Advice for someone starting out - 00:08:55 -- What do you mean by the term "display" table - 00:11:32 -- What components were missing from other tables? - 00:13:31 -- Using examples to explain features - 00:16:09 -- Why was there an absence of this functionality in Python? - 00:19:35 -- A progressive approach and the grammar of tables - 00:21:26 -- Sponsor: Mailtrap - 00:22:01 -- The design philosophy of great tables - 00:25:31 -- Nanoplots, spark lines, and column spanners - 00:27:06 -- Building a gallery of examples - 00:28:56 -- Heat mapping cells and automatically adjusting text color - 00:32:54 -- Output formats for the tables - 00:34:46 -- Building in accessibility - 00:36:55 -- Dependencies - 00:37:42 -- What is the common workflow? - 00:41:39 -- Video Course Spotlight - 00:43:15 -- Adding graphics - 00:46:41 -- Using a table contest to get examples - 00:49:47 -- quartodoc and documenting the project - 00:55:00 -- Tidy Tuesday and data science community - 01:00:29 -- What are you excited about in the world of Python? - 01:03:46 -- What do you want to learn next? - 01:08:05 -- How can people follow the work you do online? - 01:09:57 -- Thanks and goodbye Links from the show: https://realpython.com/podcasts/rpp/214/

Michael Chow

How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python)

This is part 4 of our multi-part series on creating professional dashboards with Shiny for Python. In this video, we'll explore how to integrate popular Python visualization libraries like Matplotlib, Plotly, Seaborn, and Altair into your Shiny apps. This allows you to leverage your existing visualization skills and seamlessly include them in interactive dashboards. We'll cover: - Integrating Matplotlib and Seaborn for detailed visualizations - Utilizing Altair and Plotly for dynamic charts - Implementing Folium for interactive maps - Customizing data tables with additional filters and selection modes By the end of this video, you'll have a rich and diverse set of visualizations in your Shiny dashboard, setting the stage for the final styling touches in the next video. Shiny for Python Homepage: https://shiny.posit.co/py/ Access the GitHub repo with all parts of this project: https://github.com/KeithGalli/shiny-python-projects Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Video by @KeithGalli Video Timeline! 0:00 - Video Overview & Recap of Previous Video Dashboards 1:38 - Getting Setup with the Code (cloning branch from GitHub) 3:15 - Adding Matplotlib-based visualizations (render.plot Shiny for Python decorator) 10:15 - Create a Seaborn Heatmap Chart (Sales Volume by Hour of the Day) 14:59 - Creating Interactive Charts with Jupyter Widgets (Plotly, Altair, Bokeh, Pydeck, & More…) | render_widget decorator 20:14 - Implementing Folium for Location-Based Heatmaps (render.ui decorator) 25:32 - Enhancing DataFrames with Filters and Selection Modes (render.data_frame, render.DataGrid, render.DataTable, etc.) 28:49 - Additional Rendering Options, Final Touches and Next Steps Stay tuned for part 5, where we'll focus on styling and finalizing our dashboard. If you enjoyed this video, give it a thumbs up and subscribe to the channel to stay updated! All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

Matt Iorio and Rumil Legaspi @ Neuros Medical | Data Science Hangout

We were recently joined by Matt Iorio and Rumil Legaspi @ Neuros Medical to discuss how a novel clinical trial design in chronic post-amputation pain became the catalyst for the founding and growth of the company's clinical data department. Speaker Bios: Matt Iorio: Matt is the Senior Manager of Clinical Affairs and Data Analytics at Neuros Medical, a clinical stage neuromodulation company where he specializes in overseeing end-to-end clinical data operations within the organization, including design of data pipelines, design and validation of databases and patient-reported outcome systems, as well as analysis and visualizations for regulatory submissions and commercial initiatives. He is also an adjunct Instructor in Biomedical Engineering at Case Western Reserve University, where he teaches Clinical and Regulatory Affairs and their impact on data collection and analysis. Matt holds a BSE and an MS in Biomedical Engineering from Case Western Reserve University and an MBA in Marketing from Cleveland State University. In his free time, he enjoys mentoring/teaching on all things data and Clinical Trials, playing soccer, supporting the Northeast Ohio Entrepreneurial Ecosystem and spending time with friends and family. Rumil Legaspi: Rumil is a Clinical Data Specialist at Neuros Medical, a biotech startup developing medical devices for treating chronic pain in lower limb post-amputee patients using high-frequency neuromodulation. Using his background in public health and a master’s in statistics, Rumil leverages the capabilities of R and other data science tools to analyze, automate, and optimize clinical data management and operations, ensuring patient safety and efficacy. Outside of work, he enjoys powerlifting, traveling to Japan, and exploring coffee and tea shops in Southern California. ________________________ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software The Hangout is a gathering place for the whole data science community to chat about data science leadership and questions you're all facing that happens every Thursday at 12 ET. To join future data science hangouts, add to your calendar here: https://pos.it/dsh We'd love to have you join us in the conversation live! Thanks for hanging out with us!

Predicting Lending Rates with Databricks, tidymodels, and Posit Team

Machine learning algorithms are reshaping financial decision-making, changing how the industry manages financial risk. For our workflow demo today on June 26th at 11 am ET, Garrett Grolemund at Posit will show how to use both Posit and Databricks to apply machine learning methods to the consumer credit market, where accurately predicting lending rates is critical for customer acquisition. *Please note that while the workflow focuses on a financial example, the general workflow will be useful to those using Databricks and R together across any industry. During this workflow demo, you will learn how to: 1. Connect to historical lending rate data stored in Databricks Delta Lake 2. Tune and cross-validate a penalized linear regression (LASSO) that predicts interest rates 3. Select variables with the penalized linear regression model (LASSO) 4. Build an interactive Shiny app to provide a customer-facing user interface for our model 5. Deploy the app to production on Posit Connect, and arrange for the app to access Databricks Resources for the demo: GitHub repo for today's materials: https://github.com/posit-dev/databricks-finance-app Accompanying Guide: https://pub.demo.posit.team/public/predicting-lending-rates/lending-rate-prediction.html Q&A Recording: https://youtube.com/live/wNI3AhHP7uM Additional follow-up links: GitHub Repo: https://github.com/posit-dev/databricks-finance-app Accompanying Guide: https://pub.demo.posit.team/content/fec42b3d-3aa9-43e1-8312-0ff553d09851/lending-rate-prediction.html While this demo uses ODBC package to connect to Databricks, you can also use sparklyr R package. Learn more about both here: https://docs.posit.co/ide/server-pro/user/rstudio-pro/guide/databricks.html Example using sparklyr instead of ODBC: https://posit.co/blog/reporting-on-nyc-taxi-data-with-rstudio-and-databricks/ Posit Workbench provides additional features for managing Databricks Credentials, learn more here: https://docs.posit.co/ide/server-pro/user/posit-workbench/guide/databricks.html#databricks-with-r For more on the Posit x Databricks partnership: https://posit.co/solutions/databricks/ Blog post on Edgar’s workshop on Databricks at conf: https://posit.co/blog/using-databricks-with-r-conf-workshop/ Solutions article on ODBC and Databricks: https://solutions.posit.co/connections/db/databases/databricks/ Want to chat more with Posit? To talk with Posit about integrating Posit & Databricks: https://posit.co/schedule-a-call/?booking_calendar__c=DatabricksJune2024Demo Had fun and want to join again? You can add the monthly recurring event to your calendar with this link: https://pos.it/team-demo

How to make your Python Dashboard look Professional! (Layouts in Shiny)

This is part 3 of our multi-part series on creating professional dashboards with Shiny for Python. In this video, we focus on enhancing the visual appeal and structure of your Shiny dashboards using various layout components. We'll cover: - Implementing layout templates for quick setup - Using cards and sidebars for a clean, organized look - Customizing columns and grids for better data presentation - Adding tab options for multiple views within a dashboard By the end of this video, you'll have a more polished and professional-looking dashboard, setting the stage for advanced visualizations in upcoming videos. Shiny for Python Homepage: https://shiny.posit.co/py/ Access the GitHub repo with all parts of this project: https://github.com/KeithGalli/shiny-python-projects Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Video by @KeithGalli Video Timeline! 0:00 - Video Overview & Progress Thus Far 1:28 - Using Shiny Templates to Get Started Fast 3:21 - Using Layout Components to Customize our Apps (Cards, Sidebars, Tabs, etc.) 7:55 - Adding a Sidebar within a Card 12:38 - Adding a Card with Tabs to Display Various Visualizations 17:35 - Structuring Data in Columns / Grids (layout_columns() & layout_column_wrap()) 26:30 - Final Touches & Tips (Filling in Visualizations into our Tab Views) Stay tuned for part 4, where we'll add more advanced visualizations, and part 5, where we'll focus on customizing styles and adding final touches. If you enjoyed this video, give it a thumbs up and subscribe to the channel to stay updated! All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

How to make Interactive Python Dashboards! (Reactivity in Shiny)

This is a quick-start guide to Shiny for Python, part 2 of a multi-part series. Data scientists need to quickly build web applications to create and share interactive visualizations, giving others a way to interact with data and analytics. Shiny helps you do this. In this video, we'll build off of the last tutorial where we learned the basics of building, sharing, and deploying a Shiny app in Python. This video specifically focuses on reactivity in Shiny. You can watch this video as a standalone, but it may be helpful to watch the previous video (https://youtu.be/I2W7i7QyJPI). We'll cover: ⬡ Creating toggle options for dynamic visualizations ⬡ Understanding Shiny's reactivity model ⬡ Implementing various input selectors ⬡ Building reactive components and visualizations ⬡ Using reactive calculations and effects ⬡ Adding and formatting plots with Plotly ⬡ Documentation walkthrough to learn more about reactivity (reactivity.effect, reactivity.event, reactivity.isolate, reactivity.invalidate_later, etc…) Video Resources: Video #1: https://youtu.be/I2W7i7QyJPI?si=nx1dk5ovPc91pvlB Starter Code (from end of video #1): https://github.com/KeithGalli/shiny-python-projects/tree/video1 Final App: https://keithgalli.shinyapps.io/final-app/ Shiny Resources: Shiny for Python Homepage: https://shiny.posit.co/py/ Component Gallery: https://shiny.posit.co/py/components/ Express Documentation: https://shiny.posit.co/py/api/express/ Gordon Shotwell’s “How does Shiny Render Things?”: https://youtu.be/jvV4y2xogf8?si=8uGP8ZfboUj1QM4p Joe Cheng’s “Shiny Programming Practices”: https://youtu.be/B2JzHv4FOTU?si=t4Atii-RSc5ojgom Stay tuned for part 3, where we'll explore how to make your dashboard look more professional (layouts in Shiny). Video by @KeithGalli --- Video Timeline! 0:00 - Intro & Overview 1:01 - Getting Started with Code 2:08 - Adding Shiny Components (Inputs, Outputs, & Display Messages) 3:21 - Creating an Additional Visualization (Sales Over Time by City) 7:55 - What are Reactive.Calcs and How Do We Use Them Properly? (DataFrame Best Practices) 10:27 - Creating an Additional Visualization (Sales Over Time by City) — Continued 14:30 - Filtering City Data with Select Inputs (UI.Input_Selectize) 21:15 - Rendering Shiny Inputs Within Text 22:15 - Quick Formatting Adjustments 22:54 - Understanding the Shiny Reactivity Model (How Does Shiny Render Things?) 24:23 - Adding a Checkbox Input to Change Out Bar Chart Marker Colors 28:00 - Deploying Our Updated App! 29:19 - Advanced Concepts in Shiny Reactivity (Reactive.Effect, Reactive.Event, Reactive.Isolate, Reactive.Invalidate_Later) & Other Resources All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

Joe Cheng

Hadley Wickham - R in Production

R in Production by Hadley Wickham Visit https://rstats.ai for information on upcoming conferences. Abstract: In this talk, we delve into the strategic deployment of R in production environments, guided by three core principles to elevate your work from individual exploration to scalable, collaborative data science. The essence of putting R into production lies not just in executing code but in crafting solutions that are robust, repeatable, and collaborative, guided by three key principles: * Not just once: Successful data science projects are not a one-off, but will be run repeatedly for months or years. I'll discuss some of the challenges for creating R scripts and applications that run repeatedly, handle new data seamlessly, and adapt to evolving analytical requirements without constant manual intervention. This principle ensures your analyses are enduring assets not throw away toys. * Not just my computer: the transition from development on your laptop (usually windows or mac) to a production environment (usually linux) introduces a number of challenges. Here, I'll discuss some strategies for making R code portable, how you can minimise pain when something inevitably goes wrong, and few unresolved auth challenges that we're currently working on. * Not just me: R is not just a tool for individual analysts but a platform for collaboration. I'll cover some of the best practices for writing readable, understandable code, and how you might go about sharing that code with your colleagues. This principle underscores the importance of building R projects that are accessible, editable, and usable by others, fostering a culture of collaboration and knowledge sharing. By adhering to these principles, we pave the way for R to be a powerful tool not just for individual analyses but as a cornerstone of enterprise-level data science solutions. Join me to explore how to harness the full potential of R in production, creating workflows that are robust, portable, and collaborative. Bio: Hadley is Chief Scientist at Posit PBC, winner of the 2019 COPSS award, and a member of the R Foundation. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. His work includes packages for data science (like the tidyverse, which includes ggplot2, dplyr, and tidyr)and principled software development (e.g. roxygen2, testthat, and pkgdown). He is also a writer, educator, and speaker promoting the use of R for data science. Learn more on his website, http://hadley.nz. Mastodon: https://fosstodon.org/@hadleywickham Presented at the 2024 New York R Conference (May 17, 2024) Hosted by Lander Analytics (https://landeranalytics.com)

Hadley Wickham

How to inspire people to make a change ✨

Key takeaways from our conversation on the path to effective data stewardship: 1. Change management is hard. To inspire people to change, make it as easy as possible for them to see the path to get there by creating tools to make new tasks easy to complete. A problem not only has to exist, but that problem needs to be recognized to exist. In the data governance space, legal regulations can help push us forward because they create urgency. 2. Data stewardship and data governance enable innovation. You see more of what’s possible when you know what data exists. Core systems form the foundation that enables cutting-edge work like AI development. 3. People have good intentions and want to get their work done and impact the organization. Don’t create processes where the only way to get your job done is to break the process. In the act of governing, remember why you started pulling data together in the first place. 4. Community engagement is incredibly powerful and hearing different perspectives is how we grow. We all face similar problems regardless of our role or industry. An internal community for cross-organizational collaboration is vital for knowing what other people are working on and collaborating. This not only helps you with your data, but also with your career. 5. Implementing effective data stewardship is a journey with companies at all different stages. Those further along the journey may have teams and infrastructure dedicated to this, along with executive buy-in. Others are working to make it happen at the individual level – where it often starts with writing down the definitions of your data (who created it, when it was sourced, intended use, etc.) and recognizing that someone else will use your data output, potentially not in the way you initially intended

From Chaos to Clarity: Implementing Effective Data Stewardship

As a data scientist or data leader, data is at the center of your work. However, management of that data can feel overlooked. Many of us have asked questions like: 🤔 How do we ensure data we create is easily discoverable and reusable? 🤔 What happens when multiple people make different data transformations for similar work? The focused effort of making data accessible and secure has been called many different names: data stewardship, data enablement, data governance, etc. While it’s needed across all levels and departments within an organization, best practices for managing data remain difficult to define. We got together as a community to discuss the importance of driving data stewardship at the individual level. Jamie Warner, Managing Director, Data Science & Pricing at Plymouth Rock Assurance and Dan Boisvert, Head of Data Stewardship at Biogen kicked off the conversation by drawing from their own experiences. Resources mentioned: Follow-up Q&A Blog Post - https://posit.co/blog/the-path-to-effective-data-stewardship/ posit::conf(2024): https://posit.co/conference/ O'Reilly chapter on Data Governance over a Data Life Cycle: https://www.oreilly.com/library/view/data-governance-the/9781492063483/ch04.html Switch by Chip and Dan Heath: https://heathbrothers.com/books/switch/ To engage further with Posit on this topic: Every Thursday at 12 ET we get together for the Data Science Hangout. While there’s no set agenda for the conversation, we often talk about data science leadership, data management best practices, speaking with executives, career advice, and more. Many Posit customers also use an enterprise platform for data governance like Databricks and Snowflake to help people discover, access, and work with the right data securely and efficiently. Posit will be at the following events in June and you can use the links below to RSVP to join us at customer events there: Snowflake Summit Dev Day: https://posit.co/events/snowflake-summit/ Databricks Data + AI Summit: https://posit.co/events/databricks-data-ai-summit/

How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny)

This is a quick-start guide to Shiny for Python. It's part 1 of a multi-part series. Data scientists need to quickly build web applications to create and share interactive visualizations, giving others a way to interact with data and analytics. Shiny helps you do this. In this video, we'll walk you through the basics of setting up Shiny for Python, creating your first app, and deploying it so others can use it. We'll cover: - Installing Shiny and necessary dependencies - Writing and running your first Shiny app - Basic UI and server structure - Deploying your app online - Helpful Links Shiny for Python Homepage: https://shiny.posit.co/py/ The link to the final app can be found here: https://keithgalli.shinyapps.io/final-app/ Follow along with the code examples provided in this repository: https://github.com/KeithGalli/shiny-python-projects Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Stay tuned for part 2, where we'll dive deeper into advanced features and customization options. --- Video Timeline! 0:00 - Intro to Shiny & Video Overview 1:43 - Getting Started with the Shinylive Playground 2:44 - Building a custom visualization with Shinylive 5:18 - Easily sharing the code/application for a Shinylive app 6:12 - Building a Shiny Express App locally (VSCode) 9:40 - How to run app if you're not using VSCode 10:17 - Further customization of our app (adding title, using CSV data, dynamic input) 17:15 - Deploying our Shiny app to the web 21:20 - Conclusion & what's coming next in the series Video by @KeithGalli All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

🐙 Lunch & Learn: Let's Build An AI Assistant With GPT-4o (w/ Joe & Winston of @Posit)

---------------------------------------------------- @Posit Channel - https://www.youtube.com/@UC3xfbCMLCw1Hh4dWop3XtHg 🤖 Sign up here to get accompanying workbooks, summaries, and notifs for future lunch & learns: https://www.lonelyoctopus.com/email-signup ️ NEWSLETTER: https://tinahuang.substack.com/ It's about learning, coding, and generally how to get your sh*t together c: Lonely Octopus: https://www.lonelyoctopus.com/ Check it out if you're interested in learning AI & data skill, then applying them to real company projects! Affiliates ======================== My SQL for data science interviews course (10 full interviews): https://365datascience.com/learn-sql-for-data-science-interviews/ https://365datascience.pxf.io/WD0za3 (link for 57% discount for their complete data science training) Check out StrataScratch for data science interview prep: https://stratascratch.com/?via=tina My filming setup ======================== camera: https://amzn.to/3LHbi7N mic: https://amzn.to/3LqoFJb tripod: https://amzn.to/3DkjGHe lights: https://amzn.to/3LmOhqk Socials ======================== instagram: https://www.instagram.com/hellotinah/ linkedin: https://www.linkedin.com/in/tinaw-h/ discord: https://discord.gg/5mMAtprshX 🤯Study with Tina ======================== Study with Tina channel: https://www.youtube.com/channel/UCI8JpGrDmtggrryhml8kFGw How to make a studying scoreboard: https://www.youtube.com/watch?v=KAVw910mIrI Scoreboard website: scoreboardswithtina.com livestreaming google calendar: https://bit.ly/3wvPzHB Other videos you might be interested in ======================== How I consistently study with a full time job: https://www.youtube.com/watch?v=INymz5VwLmk How I would learn to code (if I could start over): https://www.youtube.com/watch?v=MHPGeQD8TvI&t=84s ‍⬛‍⬛About me ======================== Hi, my name is Tina and I'm an ex-Meta data scientist turned internet person! Contact ======================== youtube: youtube comments are by far the best way to get a response from me! linkedin: https://www.linkedin.com/in/tinaw-h/ email for business inquiries only: hellotinah@gmail.com ======================== Some links are affiliate links and I may receive a small portion of sales price at no cost to you. I really appreciate your support in helping improve this channel! :)

Joe Cheng | Managing long-running operations in Shiny | Posit

It’s been years since Shiny evolved to allow asynchronous operations within applications, improving scalability. The introduction of the {promises} package enabled concurrent processing between multiple Shiny sessions, a significant step forward in handling background tasks. However, this did not address the need for intra-session concurrency, where users expect to interact with the application while long-running calculations are executed in the background. Recently, we added a new ExtendedTask feature to Shiny to allow for such intra-session concurrency. This new feature provides a different approach for developers to incorporate asynchronous tasks, enabling smoother user interactions during intensive computations. Alongside ExtendedTask, this talk will also discuss newer methods for launching asynchronous tasks, besides the usual {future} package. The focus will be on the practical application and integration of these features into Shiny applications. Links mentioned in the video: ⬡ Shiny in Production: Principles, practices, and tools, https://youtu.be/Wy3TY0gOmJw?feature=shared Timestamps: 0:20 Make your slow code fast 1:43 Long-running operations are a problem 3:28 Inter-session concurrency and intra-session concurrency 4:24 Introducing ExtendedTask 5:17 Demo of a slow API using ExtendedTask 6:13 Slow code example (R) 7:16 Fix slow code with ExtendedTask (R) 8:55 Slow code example (Python) 7:16 Fix slow code with ExtendedTask (Python) 10:46 Links to get started 11:06 ExtendedTask backstory intro 11:28 ExtendedTask vs. Shiny Async 15:50 How reactive programming works in Shiny 21:31 How ExtendedTask works in the reactive process 25:38 What we’re still working on 26:35 {future} alternatives 31:47 Wrapping up

Joe Cheng

Pandas - History and Future - Talk Python to Me Ep.462

This episode dives into some of the most important data science libraries from the Python space with one of its pioneers: Wes McKinney. He's the creator or co-creator of pandas, Apache Arrow, and Ibis projects and an entrepreneur in this space. ▬▬▬▬ About the podcast ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ This video is the uncut, live recording of the Talk Python To Me podcast ( https://talkpython.fm ). We cover Python-focused topics every week and publish the edited and polished version in audio form. Subscribe in your podcast player of choice (100% free) at https://talkpython.fm/subscribe. ▬▬▬▬ Guests ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Wes' Website: https://wesmckinney.com/ ▬▬▬▬ Links and resources from the show ▬▬▬▬▬▬▬▬▬▬▬▬ Pandas: https://pandas.pydata.org/ Apache Arrow: https://arrow.apache.org/ Ibis: https://ibis-project.org/ Python for Data Analysis - Groupby Summary: https://wesmckinney.com/book/data-aggregation.html#groupby-summary Polars: https://pola.rs/ Dask: https://www.dask.org/ Sqlglot: https://sqlglot.com/sqlglot.html Pandoc: https://pandoc.org/ Quarto: https://quarto.org/ Evidence framework: https://evidence.dev/ pyscript: https://pyscript.net/ duckdb: https://duckdb.org/ Jupyterlite: https://jupyter.org/try-jupyter/lab/ Djangonauts: https://djangonaut.space/ Listen this episode on Talk Python: https://talkpython.fm/episodes/show/462/pandas-and-beyond-with-wes-mckinney Episode transcripts: https://talkpython.fm/episodes/transcript/462/pandas-and-beyond-with-wes-mckinney ▬▬▬▬ Dive deeper ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Listen to the Talk Python To Me podcast at https://talkpython.fm Over 250 hours of Python courses at https://training.talkpython.fm/courses Follow us on on Mastodon. Michael: https://fosstodon.org/@mkennedy & Talk Python https://fosstodon.org/@talkpython

MLOps for a billion pizzas a year | Zack Fragoso @ Domino's Pizza | Data Science Hangout

We were joined by Zack Fragoso, Head of MLOPs at Domino's Pizza to chat about his mission to Sell More Pizza and Have More Fun by making the business smarter with data. Speaker bio: Zack Fragoso is the Head of MLOPs at Domino’s Pizza, where he is on a mission to Sell More Pizza and Have More Fun by making our business smarter with data. His team develops ML/AI applications that supercharge the global pizza delivery leader’s e-commerce and in-store platforms. We've been able to help reward customers for ordering pizza, give them a better estimate of when their order will be ready, route orders more efficiently, and those are just a few examples! Zack is passionate about MLOPs tools that increase productivity, enhancing cross-team collaboration, and support our model life cycle management. At 11:07 we discussed two key challenges regarding MLOps: scalability and process Scalability Zack explains that while data scientists can develop accurate models on their laptops, scaling those models to handle the immense volume of Domino's business is a significant hurdle. He emphasizes that Domino's sells close to a billion pizzas annually, with a high concentration of orders between 5 pm and 8 pm. Events like the Super Bowl push the system even further, requiring the ability to process around 6,000 orders per minute. For Domino's, this means that models must be not only accurate but also highly performant to handle these spikes in demand. Process The second challenge Zack identifies is related to integrating the experimental nature of data science work with a robust and reliable production process. He acknowledges that data scientists are inherently driven to experiment with data and various modeling techniques. However, translating these experimental models into a production environment requires a more structured approach that encompasses control mechanisms, versioning, and a focus on reliability. Finding the right balance between this need for flexibility in experimentation and the demands of a structured production process is an ongoing challenge for Domino's. Resources mentioned in the chat: LinkedIn Group for the Hangout: https://www.linkedin.com/groups/12610075/ Domino's Emergency Pizza Website: https://www.dominos.com/emergency-pizza/, offering a free emergency pizza for those who order a regular pizza. Dominik Vajda shared an article from The Guardian about a Belgian brewery using AI to improve beer taste: https://www.theguardian.com/technology/2024/mar/26/ai-beer-taste-better-belgium-scientists ________________________ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software To join future data science hangouts, add to your calendar here: https://pos.it/dsh (All are welcome! We'd love to see you!) Thanks for hanging out with us!

Michael Derstine @ Wells Fargo | Data Science Hangout

We were recently joined by Michael Derstine, Vice President at Wells Fargo & Company to chat about an opinionated approach to modeling and analytics in mortgage banking. Speaker bio: Michael Derstine leads a team of analysts dedicated to the management of an asset class called Mortgage Servicing Rights (MSR). He is approaching his tenth year in mortgage finance, and has held many roles. He started as a junior analyst, compiling webs of spreadsheet reports in the early hours of the morning before the Traders started their day. Like many in this community, he was driven to make processes better (and make life easier). How do you advocate for a better way? 04:05 - Michael shared his story about waking up before 5 am as a junior financial analyst, pulling together dozens of spreadsheets with hundreds of tabs, other people’s models and recycled work Each day, he’d make sure nothing was wrong before traders started acting on these models. He was motivated to not only make his life easier, but make processes better and remove the operational risk. The spreadsheet process has since moved to data science. It’s completely automated. No junior analyst has to get up at 4:30 am. Those roles still exist but now they’re encouraging them to use languages like R and Python. Michael shared, “I’m very proud of that, but it was a lot of work technically and maybe even more work politically to get that through” If you’re changing the status quo, build a challenger processes by: 1. Building something in parallel 2. Running it side by side and obliterating the old competition to show how much better it is 3. Telling the story to your bosses ________________________ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software To join future data science hangouts, add to your calendar here: https://pos.it/dsh (All are welcome! We'd love to see you!) Thanks for hanging out with us!

How to build business reports with Quarto

How do you create the report look and feel that your leadership team expects? Christophe Dervieux at Posit joined us on Wednesday, March 27th to share how to style Quarto docs and send scheduled email updates to required stakeholders. Helpful resources: ️ Getting started with Quarto: https://quarto.org/docs/get-started ️ User guide: https://quarto.org/docs/guide ️ Github repo with this example: https://github.com/quarto-examples/quarto-business-report ️ Q&A Recording: https://youtube.com/live/bqk75igHo8M?feature=share ️ If you're interested in learning more about Posit Connect, pos.it/chat-with-us Timestamps: 02:00 - What is Quarto? 02:40 - How does Quarto work? (.md, .qmd or .ipynb as source files) 03:45 - How to get started with Quarto if you're new to it? 04:51 - Using Quarto from within RStudio 05:00 - Using Quarto within VSCode with extension & Jupyter Lab extension 05:37 - Visual Editor for Quarto 07:22 - Customer Tracker Report in RStudio IDE (using source code: https://github.com/quarto-examples/quarto-business-report) 10:39 - Making Quarto report downloadable as Excel doc (adding download button) 11:37 - Adding a table of contents to your Quarto report 12:23 - Spread Quarto graphics across page so that they go into margin 13:10 - Customizing theme in Quarto (Bootstrap 5) https://quarto.org/docs/output-formats/html-themes.html 14:45 - Increasing font size in Quarto report 17:10 - Customizing theme rules 21:16 - Publishing Quarto report to Posit Connect 22:35 - Scheduling Quarto report to automatically run 23:35 - Preview of default / non-customized email 23:58 - Customizing your Quarto email 26:52 - Customized email preview that Posit Connect can send 27:56 - Setting access controls for Quarto report on Connect and when you want emails to send Resources shared in Q&A session: Community discussion for ongoing Quarto questions: https://forum.posit.co/tag/quarto Quarto document language: https://quarto.org/docs/authoring/language.html babelquarto (for multilingual project, book, or website): https://docs.ropensci.org/babelquarto/ Quarto Manuscripts: https://quarto.org/docs/manuscripts/ Managing Execution in Quarto: https://quarto.org/docs/projects/code-execution.html Quarto Extensions: https://quarto.org/docs/extensions/ Project Profiles in Quarto: https://quarto.org/docs/projects/profiles.html Custom branding deeper dive: https://www.youtube.com/watch?v=V82BBU9ldcM Quarto Parameters: https://quarto.org/docs/computations/parameters.html Lua Development: https://quarto.org/docs/extensions/lua.html Quarto CLI Discussions on Github: https://github.com/quarto-dev/quarto-cli/discussions Data Science Hangout every Thursday at 12 ET: https://posit.co/data-science-hangout/ Get connected with others at your org using Posit: pos.it/connect-us There is no need to register; join us here on YouTube at the time above or you can add to your calendar using the link below: pos.it/team-demo We host these Workflow Demos on the last Wednesday of every month, so you can use the link above to add the recurring event as well. If you ever have ideas for topics or questions about them, you can comment below in YouTube!

Christophe Dervieux

Adam Austin @ The Hartford | Data Science Hangout

We were recently joined by Adam Austin, Director of Data Science at The Hartford to chat more about data science as a tool, but relationships as the real driver of business value and success. Speaker bio: Adam Austin is a data science leader who, over a 10-year career, has been intensely curious about why data science projects fail despite their initial potential. Invariably he has seen that it is not sophisticated models and ideas, but relationships that enable the promise of data and analytics. In addition to advocating for good workflows and development practices, Adam has turned his attention to how data scientists can become better communicators, help business partners articulate their needs, and build bridges to success amidst complex and ambiguous business backdrops -- all while having a little fun. After all, maybe the real data science is the friends we make along the way. Here are a few key takeaways from this sessions: ⭐️ Data science thrives on strong relationships. The most challenging aspects of the job often involve navigating business relationships and comprehending how data science fits into a company's broader goals. ⭐️ When presenting ideas to executives, strive for simplicity and clarity. A straightforward visual representation of data can be more impactful than a complex, difficult-to-explain model. ⭐️ Data science initiatives often involve navigating the complexities of human motivations and organizational politics. ⭐️ Data science teams should aim to be value creators, not just ticket-takers. Transitioning from a reactive to a proactive team requires strategic planning and stakeholder engagement. ⭐️ A robust intake process is essential for vetting and prioritizing data science projects. ⭐️ Data scientists can add significant value by challenging existing metrics and proposing alternative measurement strategies. ⭐️ Growing disconnect between skills emphasized in academic data science programs and the practical needs of the industry. Universities need to equip graduates with the skills to handle real-world data challenges, including data cleaning, wrangling, and understanding business context. Resources & Links Mentioned in the chat: Veridical Data Science: https://vdsbook.com/ Yes, and" is the Foundation of Collaboration: https://laderast.github.io/posts/2022-02-27-yes-and-is-the-foundation-of-collaboration/ Data Science Mishaps Night: https://datamishapsnight.com Bike Share Dataset Example: https://solutions.posit.co/gallery/bike_predict/ Data.gov: https://data.gov Student Portfolio Examples: https://sph-midterm-projects.netlify.app/ Tweet about Data Science Hype: https://x.com/PhDemetri/status/1394847400427233282?s=20 Data Science Hangout LinkedIn Group: https://www.linkedin.com/groups/12610075/ Adam Austin on Mastodon: @ataustin@fosstodon.org Missions, Visions, Maturity Models, & Strategies: https://medium.com/policygenius-stories/missions-visions-maturity-models-strategies-ce5a4a408ab2 ________________________ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!) Thanks for hanging out with us!

How to write and deploy a Python web app with Shiny Express and Posit Team

Winston Chang at Posit joined us to share a quicker and simpler way of writing Shiny applications in Python, called Shiny Express With Shiny Express you can create interactive data dashboards and data-driven applications faster than ever. Helpful resources: ️ Introducing Shiny Express: https://shiny.posit.co/blog/posts/shiny-express/ ️ Shinylive: https://shinylive.io/py/examples/ ️ Component Gallery: https://shiny.posit.co/py/components/ ️ VS Code Shiny extension: https://marketplace.visualstudio.com/items?itemName=Posit.shiny-python ️ Q&A recording: https://youtube.com/live/zg4LP4lkihM?feature=share rsconnect-python package: https://pypi.org/project/rsconnect-python/ ️ Follow-up links: Event Survey: https://forms.gle/8AcMAnjfPFSTQZJK6 Posit Team: https://posit.co/products/enterprise/team/ Request evaluation: pos.it/chat-with-us Posit Team demo resources: pos.it/demo-resources What will you learn? If you’re new to Shiny → you’ll get started writing and deploying your first Shiny Express application in Python. If you already know Shiny → you’ll see how Shiny Express can make your development experience quicker and more efficient. Happy with the way things are? No need to change what you’re doing! We think of Shiny Express and Shiny Core as complementary, and intend to support both syntaxes indefinitely. Last month’s Workflow with Posit Team Demo featured Shiny in R and used bslib for custom theming, which you can check out here in the recording: https://youtu.be/O6WLERr5bKU?feature=shared LIVE Q&A ROOM for ~11:30 am on Feb 28th: https://youtube.com/live/zg4LP4lkihM?feature=share There is no need to register; join us here on YouTube at the time above or you can add to your calendar using the link below: pos.it/team-demo We host these Workflow Demos on the last Wednesday of every month, so you can use the link above to add the recurring event as well. #python

Winston Chang

Kevin Snyder & Raju Rayavarapu @ U.S. FDA | Data Science Hangout

We were recently joined by Kevin and Raju @ the FDA to chat about democratizing data science by teaching people the basics of data wrangling, data science and the power of open source and utilizing principles of systems biology, pharmacology, toxicology, bioinformatics, and statistics to collaboratively develop open-source software solutions that support decision making and policy development. Links mentioned: FDA Digital Transformation Symposium: https://www.fda.gov/news-events/fda-meetings-conferences-and-workshops/2023-fda-digital-transformation-symposium-12042023 precisionFDA: https://precision.fda.gov/ R4DS Online Learning Community: r4ds.io/join R Validation Hub Contact: https://www.pharmar.org/contact/ Hangout LinkedIn Group: https://www.linkedin.com/groups/12610075/ Speaker bios: Kevin Snyder - Associate Director of Nonclinical Informatics at U.S. FDA Kevin received his Bachelors in biochemistry from the University of Maryland in 2008 and his PhD in neuroscience from the University of Pennsylvania School of Medicine in 2013. He currently serves as the Associate Director of Nonclinical Informatics in the Office of New Drugs in the Center for Drug Evaluation and Research at the US FDA where he manages data science and informatics initiatives to support the pharmacology/toxicology review program. These initiatives include research efforts to develop methods to optimize the regulatory use of standardized electronic CDISC-SEND-formatted toxicology study data as well as internal informatics projects to promote the development of scientifically sound, data-driven regulatory policies. Dr. Snyder also leads an agency-wide Data Science and Software Development working group that is focused on building out the organizational infrastructure necessary to support the work of data scientists across the agency and is an active collaborator with several consortia efforts, e.g. CDISC, PHUSE, and BioCelerate, to improve the implementation and use of the SEND data standard. Raju (Rama) Rayavarapu - Data Scientist at U.S. FDA Raju comes to the FDA from the great state of Pennsylvania via South Bend, IN and Memphis, TN where he did his Ph.D. and post-doctoral work. He is the lead of ODAR’s DataForward Initiative which is focused on upskilling FDA staff in data related skills and harnessing the power of the incredible existing FDA data science community to drive data literacy and the joy that comes from working with and understanding data.He loves to spend his time talking FOSS (Python/R/whatever), Natural Language Processing, artificial Intelligence (AI), and all things data science. He also spends any night he can staring into the universe with his two dogs and his telescope. ________________________ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!) Thanks for hanging out with us!

It's All About Perspective: Making a Case for Generative Art - posit::conf(2023)

Presented by Meghan Santiago Harris This talk explores how to create art in the R language while highlighting some similarities between the skills required for creating generative art and those needed to perform data science tasks in R. Because the field of data science is inherently task-oriented, it is no wonder that most people struggle to see the utility of generative art past the bounds of a casual hobby. This talk will invite the participant to learn about generative art while focusing on ""why"" people should create it and its potential place in data science. This talk is suitable for all disciplines and artistic abilities. Furthermore, this talk will aim to expand the participant's perspective on generative art with the following concepts: - What is generative art and how can it be created in R or Python - Justifications for generative art within Data Science - Examples of programming skills that are transferrable between generative art and pragmatic data science projects Materials: - Link to the talk repo: https://github.com/Meghansaha/a_case_for_genart - Link to the slides: https://meghansaha.github.io/a_case_for_genart/#/title-slide - Link to the artpack package site: https://meghansaha.github.io/artpack/ - Personal Site: https://thetidytrekker.com/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Developing your skillset; building your career. Session Code: TALK-1109

A hacker's guide to open source LLMs - posit::conf(2023)

Presented by Jeremy Howard In this deeply informative video, Jeremy Howard, co-founder of fast.ai and creator of the ULMFiT approach on which all modern language models (LMs) are based, takes you on a comprehensive journey through the fascinating landscape of LMs. Starting with the foundational concepts, Jeremy introduces the architecture and mechanics that make these AI systems tick. He then delves into critical evaluations of GPT-4, illuminates practical uses of language models in code writing and data analysis, and offers hands-on tips for working with the OpenAI API. The video also provides expert guidance on technical topics such as fine-tuning, decoding tokens, and running private instances of GPT models. As we move further into the intricacies, Jeremy unpacks advanced strategies for model testing and optimization, utilizing tools like GPTQ and Hugging Face Transformers. He also explores the potential of specialized datasets like Orca and Platypus for fine-tuning and discusses cutting-edge trends in Retrieval Augmented Generation and information retrieval. Whether you're new to the field or an established professional, this presentation offers a wealth of insights to help you navigate the ever-evolving world of language models. (The above summary was, of course, created by an LLM!) For the notebook used in this talk, see https://github.com/fastai/lm-hackers. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Notebooks+LLMs may just be the future of coding. Session Code: KEY-1107

Eric Nantz, Alex Lauer, Rich Iannone - A Pivotal Year of Milestones in R Consortium Working Groups

A Pivotal Year of Milestones: R Submissions Working Group and R Tables for Regulatory Submissions updates - Eric Nantz, Alexandra Lauer, and Rich Iannone Resources mentioned in the presentation: - R Submissions Working Group: https://rconsortium.github.io/submissions-wg/ - R Tables eBook: https://rconsortium.github.io/rtrs-wg Abstract: Within the life sciences industry, Shiny has enabled tremendous innovations to produce web interfaces as frontends to sophisticated analyses, dynamic visualizations, and automation of clinical reporting across drug development. While industry sponsors have widely adopted Shiny as part of their analytics and reporting toolset, a relatively unexplored frontier has been the inclusion of a Shiny application inside a clinical submission package to regulatory agencies such as the FDA. The R Consortium R Submissions Working Group has continued the positive momentum of previous submission pilots to achieve substantial progress in this domain. In this talk, we will share the development journey of the working group’s Pilot 2 successful submission of a Shiny application to the FDA, along with the progress on the use of novel technologies such as Linux containers and web assembly to bundle a Shiny application into a self-contained package, facilitating a smoother process of both transferring and executing the application. The R Consortium’s R Tables for Regulatory Submissions (RTRS) Working Group has released the first edition of (Tables in Clinical Trials with R)[ https//rconsortium.github.io/rtrs-wg/] as a free and openly accessible ebook. The book contributes to the development of a theory of displaying tabular information by identifying a small number of table archetypes that may be used to generate the most common tables employed in clinical submissions. Chapters in the book demonstrate how these tables may be rendered in different R Packages including flextable, gt, rtables (with and without tern), tables, tfrmt and tidytlg. All tables are generated from CDISC-compliant data. Comparing the code showcases the robustness of R for aggregating and displaying tabular information and illuminates the flexibility and design tradeoff of the various R packages. The talk will discuss the motivation for the book, present the idea of table archetypes, show some representative tables, and make the case for R as a superb language for analyzing clinical trial data. The RTRS working group expects Tables in Clinical Trials with R to become a primary resource of clinical programming teams. Speaker Bios: Eric Nantz is a director within the statistical innovation center at Eli Lilly and Company, creating analytical pipelines and capabilities of advanced statistical methodologies for clinical design used in multiple phases of development. Outside of his day job, Eric is passionate about connecting with and showcasing the brilliant R community in multiple ways. You may recognize his voice from the R-Podcast that he launched in 2012. Eric is also the creator of the Shiny Developer Series where he interviews authors of Shiny-related packages and practitioners developing applications, as well as sharing his own R and Shiny adventures via livestreams on his Twitch channel. In addition, Eric is a curator for the RWeekly project and co-host of the RWeekly Highlights podcast which accompanies every issue. Alexandra Lauer is a Senior Principal Statistical Analyst at Merck KGaA, Darmstadt, Germany, with a background in mathematics. She specializes in bridging psychometrics and biostatistics, with a primary focus on Health-Related Quality of Life evaluations. Alex is an R enthusiast, co-leading the Merck-internal R User Group. Rich Iannone: My background is in programming, data analysis, and data visualization. Much of my current software engineering work on R packages is intended to make working with data easier. I truly believe that with the right approach, tools like these can be both powerful and easy to use. Presented at the 2023 R/Pharma Conference (October 26, 2023)

Rich Iannone

Joseph Powers @ Intuit & Jen Wang | Data Science Hangout

We were recently joined by Joseph Powers, Principal Data Scientist at Intuit and Jen Wang, VP Product & Growth - formerly at thredUP to chat about transitioning from academia to tech roles, building inclusive & growth-oriented teams, and how organizations can improve their experimentation and causal inference processes to generate larger and faster insights that improve their product and marketing experiences. Featured Leader Bios: Joe Powers: Joe took a very indirect path to data science. He was a Great Books major in undergrad, a custom furniture maker for 7 years, a teacher for 3 years, then completed a PhD in Educational Psychology in his 30s, which was the first time he learned any programming or statistics. Efficient training and deep (personal) learning are lifelong interests for him. He now lives in San Diego, where his learning pursuits include surfing, cooking, and raising two young learners with his wife. Jen Wang: Jen's approach to teams and organization-building has been informed by two decades' worth of interdisciplinary expertise and leadership across sectors and cultures. Her training includes an undergrad in molecular biology and a mixed-methods PhD focused on human behavior, organizational science, and environmental decision-making, and which also included a deep dive into R and data visualization. She most recently led Product & Growth for thredUP's core secondhand clothing marketplace. Having faced many career transitions herself, she's particularly passionate about helping to spread evidence-based practices and bridging the gap when it comes to early-career and DEI issues. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!) Thanks for hanging out with us!

How to build a model annotation tool with FastAPI, Quarto & Shiny for Python

Gordon Shotwell, Senior Software Engineer at Posit walks through an end-to-end machine learning workflow with Posit Team. This demo will give you a robust pattern for hosting and sharing models on Connect before you deploy them to a customer-facing system. This includes * How and why to use an API layer to serve and authenticate internal models * Why you should wrap your APIs in Python packages * Using Shiny for Python to build a data annotation app * Using Quarto to retrain models on a schedule Agenda: 11am - Quick introduction 11:02am - Demo 11:40am Q&A Session (https://youtube.com/live/zhN8IZUBCAg?feature=share) Timestamps: 1:27 - Quick overview of text classification model used in this example 2:15 - Overview of the people that will need to use the model (modellers, leadership, data team, annotators, other systems) 4:11 - Why APIs before UIs is a good rule 5:57 - What about Python packages? 8:23 - Advantages to using an API here 9:18 - Big picture overview of the workflow 11:17 - FastAPI on Posit Connect (Swagger interface) 15:55 - The way this model will be used (authorization by validating user) 19:00 - Building a delightful user experience by wrapping API in a package 25:07 - Quarto report for leadership team showing model statistics & deploying to Connect 26:34 - Retraining the model by scheduling Quarto doc on Connect 28:37 - Shiny for Python app for Annotators (people checking if model is producing correct results & helping improve the model) 35:28 - Overview / summary of this machine learning workflow Helpful links: Github: https://github.com/gshotwell/connect-e2e-model Anonymous questions: pos.it/demo-questions If you want to book a call with our team to chat more about Posit products: pos.it/chat-with-us Don't want to meet yet, but curious who else on your team is using Posit? pos.it/connect-us Machine learning workflow with R: https://solutions.posit.co/gallery/bike_predict/ In this month’s Workflows with Posit Team session (Wednesday, November 29th at 11am ET) you will learn how to use Posit Connect as an end-to-end Python platform for hosting internal machine learning models. This will give you a robust pattern for hosting and sharing models on Connect before you deploy them to a customer-facing system. This will include: 1. How and why to use an API layer to serve and authenticate internal models 2. Why you should wrap your APIs in Python packages 3. Using Shiny for Python to build a data annotation app 4. Using Quarto to retrain models on a schedule During the event, we’ll be joined by Gordon Shotwell, Senior Software Engineer at Posit who will walk us through this end-to-end machine learning workflow with Posit Team. No registration is required to attend - simply add it to your calendar using this link: pos.it/team-demo Ps. We host these end-to-end workflow demos on the last Wednesday of every month. If you ever have ideas for topics or questions about them, leave a comment below :)

How to train, evaluate, and deploy a machine learning workflow with tidymodels & Posit Team

Helpful resources: Github: https://github.com/simonpcouch/mutagen Follow-up Q&A Session: https://youtube.com/live/vwBVOBQfc_U If you want to book a call with our team to chat more about Posit products: pos.it/chat-with-us Don't want to meet, but curious who else on your team is using Posit? pos.it/connect-us Blog post on tidymodels + Posit Connect: https://posit.co/blog/pharmaceutical-machine-learning-with-tidymodels-and-posit-connect/ Tidy Modeling with R book: https://www.tmwr.org/ Timestamps: 1:44 - Three steps for developing a machine learning model 3:35 - What is a machine learning model? 7:02 - Overview of machine learning with Posit Team 7:36: Step 1: Understand and clean data 11:05 - Step 2: Train and evaluate models (why you might be interested using tidymodels) 23:02 - Step 3: Deploying a machine learning model from Posit Workbench to Posit Connect 30:14 - Summary 31:21 - Helpful resources __________________ Machine learning models are all around us, from Netflix movie recommendations to Zillow property value estimates to email spam filters. As these models play an increasingly large role in our personal and professional lives, understanding and embracing them has never been more important; machine learning helps us make better, data-driven decisions. The tidymodels framework is a powerful set of tools for building—and getting value out of—machine learning models with R. Data scientists use tidymodels to: 1. Gain access to a wide variety of machine learning methods 2. Guard against common mistakes 3. Easily deploy models through tidymodels’ integration with vetiver Join Simon Couch from the tidyverse team on Wednesday, October 25th at 11am ET as he walks through an end-to-end machine learning workflow with Posit Team. No registration is required to attend - simply add it to your calendar using this link: pos.it/team-demo

Simon Couch

"Shiny: Data-centric web applications in Python" - Joe Cheng (PyBay 2023)

Joe Cheng https://pybay.com/speakers/#sz-speaker-7e5324de-3afa-4614-a498-562bd5eb9986 Shiny is a web framework that is designed to let you create data dashboards, interactive visualizations, and workflow apps in pure Python or R. Shiny doesn't require knowledge of HTML, CSS, and JavaScript, and lets you create data-centric applications in a fraction of the time and effort of traditional web stacks. Of course, Python already has several popular and high-quality options for creating data-centric web applications. So it's fair to ask what Shiny can offer the Python community. In this talk, I will introduce Shiny for Python and answer that question. I'll start with some basic demos that show how Shiny apps are constructed. Next, I'll explain Transparent Reactive Programming (TRP), which is the animating concept behind Shiny, and the reason it occupies such an interesting place on the ease-vs-power tradeoff frontier. Finally, I'll wrap up with additional demos that feature interesting functionality that is made trivial with TRP. This talk should be interesting to anyone who uses Python to analyze or visualize data, and does not require experience with Shiny or any other web frameworks. PyBay features the most influential speakers presenting the most crucial technologies to help beginners and seasoned developers alike get up-to-date quickly, in a single-track format. Whether you’re interested in web technologies, data, devops, Python internals, or performance, PyBay will help you stay on top of your game AND network with engineers at companies that are hiring! Working remotely and want to meet your teammates to boost team cohesiveness? Leverage the platform we’ve built. There are great talks, yummy food, fresh air, vitamin D... all the elements developers crave for these days. If there are talks that don’t interest your team, take the opportunity to talk to speakers, create your own team activities or book a tee-time at the adjacent miniature golf course! PyBay is the regional Python conference for the San Francisco Bay Area, bringing together Pythonistas from around the Bay Area and beyond. It is a volunteer-run organization dedicated to building a stronger Python community. PyBay offers deep-dive talks and networking opportunities that aim to enrich and empower the Python community. PyBay is part of BAPyA (Bay Area Python Association). BAPyA member organizations are the SF Python, Pyninsula, and BayPIGgies meetups. Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1 Sun Oct 8 18:30:00 2023 at Bungalo East

Joe Cheng

Michael Lopez @ NFL | Being forward-thinking & anticipating questions | Data Science Hangout

We were recently joined by Michael Lopez, Sr. Director of Football Data & Analytics at the National Football League to discuss using data and analytics to enhance the game of football. Timestamps: 4:29 - The process of using RFID tag data 7:31 - What kind of decision making is your work driving? 9:00 - Do you analyze player's health stats? 11:39 - How transferrable are sports analytics skills across leagues? 14:19 - Big Data Bowl and other community initiatives 19:20 - Structured vs unstructured data? 21:51 - Transition from academia to NFL 25:23 - Communication skills that have transferred over 29:22 - What data is captured from the RFID tags? 34:32 - How could someone get into the NFL space as a data scientist focused on health? 37:18 - How are you getting people to buy-in to analytics? 40:40 - How do you prioritize what your team is working on? 43:04 - Languages and tools used in analytics at the NFL 45:06 - Translator role in football analytics 49:09 - How does the NFL go about achieving the level of analytics that you do? 50:59 - Does GAIT analysis have impact on their performance? 52:59 - How do you evangelize new data points and explore things previously un-explored? 55:48 - Fantasy football 56:34 - What are you excited about in the year ahead? Diving into a specific topic on communication -- 27:38 “Sometimes we are answering the questions that we know they're going to ask before they've asked them. And I think the best presentations we give are the ones where they're asking a question and then just click Next, and it's on the next slide. Or they ask a question and I say, yep, it's in the Appendix. Click a couple slides forward and say, here it is. Because you have to anticipate the questions they're going to ask. Sometimes, we're collecting data before they've asked us to collect data because we hear something in the media or we hear something that they're asking in the replay room or their coaches are asking, and we're like, we're going to go build this. They didn't tell us to go collect data on quarterback slides, but we now know that quarterbacks are sliding more than ever before. We know quarterbacks are the most important position in football. So last year, Andrew Patton basically led an NFL data collection processing quarterback slides. Because we knew that when we got to the offseason, somebody was going to say, “hey, how are how are quarterbacks sliding?” Actually, we have that because we went out and collected it. So I would say that it's a combination of great data viz and then also being purposeful and forward-thinking with the questions that people are going to be wanting to ask. _______ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!) Come hangout with us!

Novo Nordisk's Journey to an R based FDA Submission

Abstract: Join Novo Nordisk, leading pharmaceutical company in diabetes, obesity and other chronic diseases, as we celebrate reaching our NDA milestone, having recently submitted our first submission package featuring outputs exclusively written in R, along with select ADaM datasets also written in R. In this webinar, we will provide you with an in-depth look at our computing platform and our internal R package suite, including insights into their development. We will reflect on the process and setup we have in place and provide you with a sneak peek into what the future holds for us. You'll get an exclusive look at our journey and learn how we overcame challenges, as well as how we continue to innovate and streamline our processes. Don't miss this opportunity to gain insight into our approach to NDA submissions. Speakers: Ari Siggaard Knoph (MSc): Ari is a Principal Statistical Programmer and International Lead Programmer at Novo Nordisk with a seat on the pharmaverse council. He is a driving force behind the adoption of R in clinical deliverables and submissions at Novo Nordisk. Ari is also the author and maintainer of multiple R packages and leads the Novo Nordisk pharmaverse team. Steffen Falgreen Larsen (MSc, PhD): Steffen is a Statistical Specialist with over 8 years of experience in driving the shift towards R and Python at Novo Nordisk. He has a master's degree in mathematics with a specialization in life sciences and a PhD in biostatistics and informatics. Steffen has designed and co-developed a Statistical Computing Environment for R and the internally developed package ecosystem. He is a seasoned R user and developer. Anders Ellern Bilgrau (MSc, PhD): Anders is a Statistical Specialist at Novo Nordisk and an Adjunct Associate Professor at the Department of Mathematical Sciences, Aalborg University. He holds a master's degree in applied mathematics and a PhD in biostatistics and informatics. Anders is not only a skilled statistician but also an experienced software developer, with a special focus on R development for many years. He has played a key role in co-developing the R platform and internal packages used in Biostatistics at Novo Nordisk

Hadley Wickham @ Posit | Giving benefit to people using what you build | Data Science Hangout

We were recently joined by Hadley Wickham, Chief Scientist at Posit PBC. Listen in to hear our chat about building tools (like the tidyverse) to make data science easier, faster, and more fun. 36:57 - While I'm bought into developing open source packages to help deliver better processes, any advice to those of us doing that development in getting their company bought in? You have to give some benefit to the people using (what you’re building) You’ve got to either remove pain or add pleasure in some way because if you can't do that and you're not someone's direct supervisor, it's hard to get people to change. The way I think about the tidyverse is, how do we give people some sort of quick wins so they can be motivated to do the things that are slower where they're gonna have to learn some new ideas or some new tools. You kind of build up some equity with that person. They build trust that you've helped them in the past and now they're willing to invest a little bit more time before they see the payoff. But in the early days, it's all about delivering payoffs as quickly as possible. And I think if you're doing, like, you know “my company's first R package” - the easy pain points are: make themes for your company corporate style guide, make a ggplot2 theme, make an R Markdown, a Quarto theme. Make a Shiny theme that people can just use to get, you know, something that's reasonably close to whatever your corporate style guide dictates. That just feels like an easy win for people because it makes them look good inside the corporation and because you've put in all the hard work, it's like three seconds for them to type the right function name to get the right theme. I think the other bit is making it easier to get access to data. Set up some wrappers around DBI connections to the most important data sources. Provide some conventions around authentication so that stuff just works so that they're not struggling with “What packages do I need to install? What's the password? Where's the path I need?” Just give them some, like, a list of the top ten most common data sources and people will love you by and large. *Follow-up question:* Once you identify the things that you think would be useful for people - do you have a philosophy or a way in which you approach putting things together? When you're in an environment of scarcity when you've only got so much time that you can take out of your everyday job to invest in writing a package, it's really tough to balance. Like, how do I add new stuff versus making sure the old stuff continues to work? I think, again, some of it's about building up trust. So, give people some wins so that when you inevitably break stuff, you've got some kind of cushion so people aren't going to be really angry with you right away. They're gonna be like, ok, well there’s a little bit of suffering now, but this person saved me so much time. But yeah, it's really hard. And particularly as you're starting out, like, you're going to make mistakes. That's inevitable. You’re going to do things that when you look back a year later, you're like, why on earth did I do it that way? You’ll want to rip out the whole thing and ride it from scratch. And I think that if it feels horrible, you have to remember, that's great. It means you've grown immensely as a programmer. Certainly if you have my kind of mindset, you have to resist the temptation to rip things out and redo them as much as possible and just focus on making the next generation better rather than breaking what stuff people already have. So I don't have any great answers here, but I think you just have to think about those tensions of “how do I keep my forward velocity up while getting better as a programmer and evolving over time, but also thinking about how do you make the things you did a long time ago better?” ______ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!) Come hangout with us!

Hadley Wickham

Joe Cheng @ Posit | You have to be able to reason about it | Data Science Hangout

We were recently joined by Joe Cheng, CTO at Posit PBC to chat about all things Shiny - a web framework for data scientists, career journeys, being vulnerable, and so much more. At 39:53 - Joe shared what it means when he says, "you have to be able to reason about it" When you write software or do an analysis or whatever, there's a level of “I got it to work” and then there's the level of “it works, and I can reason about it.” Complex pieces of software are among the most complicated things that humankind has ever devised. What other human-made constructs can have hundreds of millions of pieces and yet they're expected to all fit and work together so precisely that, if one token is off, rockets explode. I don't know what the limit is, but even the smartest humans can only hold some small number of variables and operations in their head at any one time. When we work on software that's non-trivial, we work on software that in its totality is more than any one human mind can hold at any one given time. The main challenge in software engineering is about, how do you take all this complexity and break it down into smaller pieces, each of which you can reason about, each of which you can hold in your head, each of which you can look at and say, “Yeah, I can fully ingest this entire function definition. I can read it, line by line, and prove to myself, this is definitely correct if the functions that it's calling don't have bugs and if it's called in the right way.” So with those caveats, if the things that this is calling are correct and are called correctly, then the result will be correct because the logic here is correct. Software engineering, at all but the most beginner level, is a lot about this. How do you break up inherently complicated things that we're trying to do into small pieces that are individually easy to reason about? That's half the battle right there. The other half of the battle is - how do we combine them in ways that can be reliable and also easy to reason about? So it's these two pieces– small pieces reliably composed– if you can achieve that, that's what I'm talking about. That's software that you can reason about. This has implications for data science as well. With data science, you're doing some kind of analysis on some data, and it starts out as, oh, I'm just doing these simple things. I'm doing this manipulation and then I'm doing this visualization. But then as you get deeper and deeper into it, it grows and grows and grows. You're at the point where you're at the end and you don't remember where these variables came from. You don't remember what's the difference between this data frame and this data frame. And you go back and hopefully start breaking it into functions or somehow dividing it into smaller pieces that each focus on a thing. Then you join those pieces together in your overall script. That's this principle of small pieces individually able to be reasoned about. When you think about other rules that you might have heard about software engineering – we know when you're writing functions, using global variables is bad. That's another one of these things where that hurts your ability to hold the entire function in your head and to prove that it'll work correctly. Because who knows who is setting that global variable to what value? You can't prove to yourself this function is definitely correct. I think if there's anything that you're working on that needs to be correct and you do care that the answer is right, I try never to stop when I have an answer but I can't reason about the code. I always try to go back and do the refactoring that's necessary just so I can prove to myself that the answer is right. The other big benefit to this is that those individual pieces– if it does turn out that there's a mistake somewhere, you can individually debug, test, unit test those individual pieces. When there are problems, you'll much more easily be able to find them. ______ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!) Come hangout with us! The Data Science Hangout is a gathering place for the whole data science community - including current and future data science leaders - to chat about data science leadership and questions you're all facing that happens every Thursday at 12 ET

Joe Cheng

Data Science Hangout featuring all of us! | Building up a data science team in a small org

Learning from our first *open week*, we decided we would continue to hold these open weeks once every 2 months or so and we were all the featured leaders for the week, asking questions to each other. There was a great discussion in the beginning of this week's hangout on building up a data science team in a smaller organization. 3:53 George shared, “As the first data scientist in HR, I was tasked with starting from scratch with everything. So building out metrics, defining metrics, doing data governance, learning a new HR system, and then from there developing a team. So I think right now it's just looking at the different tools that are out there, understanding how we might manage a data lake and trying to find the right tools. So far, my approach has been to have discussions with our HR managers - the primary stakeholders and the ones that are going to be leveraging the data - and communicating higher up or to the different managers within the organization. Also thinking about security because with HR data it's highly secure information that we share. We don't always want to share demographics, so also thinking about security policies. But just a lot of conversations with people about what their business needs are. 8:11 Eugene shared, “The first question is really what are going to be the highest impact projects and who would I hire as my first employee and then round out based on their weaknesses. But I would aim to try to cover these three bases: a programmer who understands production-level stuff, someone who understands the statistics side (probability distributions for example), and a contextual expert. If you're really going to build a whole team of four or five, six people, you really need people who are, in my opinion, really good experts at each one of those three things, because it's very rare to find somebody who's an expert in all three. You'd be paying out a ton of money if you do find someone who's an expert in all three.” 9:17, Libby shared that “In interviewing people or interviewing with people she finds that when they are building a team, they're looking for somebody to hit the ground running. They always use that term: we need somebody to hit the ground running. A lot of times what I feel like they're saying is we need somebody to do everything and we also need somebody to tell us what to do, because we don't know what to do. That feels scary because I can't be everything for everybody, but it also sounds like people are saying that person doesn't really exist. My take on that is if you have somebody who does everything and knows everything, they are now your single point of failure. If they leave, all of your expertise goes in one person. Instead of needing to replace the one person who was maybe a subject matter expert, you now have nothing.” 10:52, Katherine shared, “I feel like a lot of times, looking for people who can ‘hit the ground running’ is corporate code for we are not going to onboard you or train you at all. When you are building a data science team, you need to say these are the people that I need and the skill sets that they're going to have and then build your onboarding around what that team is going to look like. One of the things that we've been trying to get away from is, ‘oh, well this one person knows everything.’ For six months, all we did was write documentation about our processes and transcribe email chains from three years ago so that when we do bring on new people, we can onboard them not only on the technical aspect of the job, but also on the context of the data. This is especially important because we're in the medical field, where you are bringing on people who may not have had any medical experience before.” At 12:06 Coulter shared, “I was in an org where data science was uncharted territory with everything done in Excel. I tried to get buy-in on this from a selfish point of view because I was dealing with all this data and I said, “This shouldn't take this long. We're spending a ton of time trying to do all this. Luckily, I had great leadership that said,”You know what, that's right.” I was able to hire someone and immediately started converting everything to R to automate as many things as possible. The first time that we were able to show someone, hey, you know this thing that used to take you 16 hours, it takes 30 seconds now. And the joy in their faces– I will never forget that. I think that really showed leadership that this is something worth investing in even further. That was 2.5 years ago and now we are a team of 8.” _____ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!) Come hangout with us!

How to standardize access & ensure consistent data in data products with FastAPI & Posit Team

Episode 4: Standardizing access & ensuring consistent data in data products (using FastAPI and Posit Team) Led by: Ryan Johnson, Data Science Advisor Follow-up links: * Posit Team: https://posit.co/products/enterprise/team/ * Talk to us directly: https://posit.co/schedule-a-call/?booking_calendar__c=RST_YT_Demo * Follow-along blog post: https://posit.co/blog/fast-api-posit-team-demo/ * Source code for example: https://github.com/ryjohnson09/fastAPI_demo * Posit Team demo resources: pos.it/demo-resources * Q&A session link: https://youtube.com/live/nRGWO_9OovI Timestamps: 1:22 - Overview of Posit Team (Posit Workbench, Posit Connect, Posit Package Manager) 2:46 - Overview of demo workflow 3:18 - Introduction to APIs - what are they and why would you use them? 6:39 - Introduction to the data used in the example 7:31 - Building the FastAPI (data gateway in this example) 10:00 - Launching a VS Code session in Posit Workbench 13:20 - Deploying FastAPI from Workbench to Posit Connect 13:58 - Deployed FastAPI in Swagger interface on Posit Connect 15:27 - Authentication of the API 16:14 - Querying the FastAPI from Jupyter Notebook 18:02 - Querying the FastAPI from a Shiny application On the last Wednesday of every month, we host a Posit Team demo and Q&A session that is open to all. You can use this to add the event to your own calendar. Who are these monthly demos for? Everyone is welcome to join us - regardless of industry, background, or experience! We will discuss topics that will speak to: * Data scientists and administrators new to Posit Team or are looking to grow their understanding of our toolchain, * Teams searching for a new analytic platform built to support open-source data science, * And, those that are just curious about Posit Team! What you can expect from the monthly Posit Team demo: During the session, we will walk through an end-to-end data science workflow and demo the core functionality of Posit Team while highlighting some of our latest features! While each session's content will vary slightly, here are a few core topics we will address each month: * Open Source Analytics: The future of data science is open source. We'll discuss methods for leveraging open-source tools and packages in a secure and scalable way! * Deployment: How to share the amazing data science assets your Team has built, including web applications, machine learning models, APIs, and more! * Data Access: Data comes in various forms and is stored in various ways. We'll discuss best practices for accessing, reading, and writing data! * Job Scheduling: Do you have recurring data science jobs? We'll show you how to automate these processes using Posit Connect. What is Posit Team? Posit Team is a bundle of our popular professional software (Posit Workbench, Posit Connect, and Posit Package Manager) for developing data science projects, publishing data products, and managing packages. Registration is not required. The event will be streamed through YouTube Premiere

Matt Frazier @ Pie Insurance | Removing blockers for your team | Data Science Hangout

We were recently joined by Matt Frazier, Strategic Advisor and former Chief Analytics & Underwriting at Pie Insurance At 35:50 - Matt shared that being an effective leader is about being the person who makes sure that all of the conditions are set up, so that each and every one of their employees can succeed and can fully explore the domain of their competencies. Find smart people, and then set up the conditions, and get the heck out of the way. If there's a blocker that is in their way, find a way to remove that blocker. Matt adds, “Now, are there performance reviews and things like that? Yeah, every company has them. I am a strong proponent against them. I think that performance reviews should be done every single day between the individuals that are working together. I don't think it should be between a boss and that boss's employees – I think it should be the individuals and their colleagues that are working together with one another. That's where that feedback should be coming from. …I think that if we can create an egalitarian society where everyone is responsible to everyone else and everyone is giving constant feedback to everyone else on how to improve with empathy and with honesty, that is where you can create an organization or a social structure that actually allows everyone to win, and allows everyone to succeed.” On the topic of removing blockers, what should you do if you’ve identified certain roadblocks you need to get past? 38:12 - Whether it's an I or a we…sometimes there's a blocker for a whole team and sometimes there's a blocker for an individual. I don't know if any of you have taken any courses in nonviolent communication. I don't believe all of it, but I think it is a toolset where you can sort of mix and match. 1. Listen for needs and express needs. Start with, “I'm trying to do this for the company. I believe that this is going to be very valuable, do you agree?” You've got to get buy-in that what you're trying to do is valuable for the company. 2. In order to do that, I have certain needs that are not being met. This is what those needs are– it could be an individual, could be a group of individuals, a dynamic. It could be technology that isn't there or budget. 3. And then you really express that as a need. Not as so-and-so is doing something to me, and I'm mad. That's a sort of selfish communication. What you want to do is you want to try to abstract that as much as you can. Now, you can't always because sometimes there's just a person who isn't on board and so, you say, “Look, I really need this person to be on board, but I don't think they are. And I want to ask you what we can do together in order to try and identify that other person's needs and discover why it is that they appear to be a blocker. It may be in my own head, and I'm just in an echo chamber. But let's have that conversation.” And if you don't feel confident having that conversation yourself, go to someone who you think can have that conversation. It doesn't have to be a manager, It could be a colleague who knows that person really well, and can set up that meeting so that you can have that meeting and identify what those needs are. There's another book that I would really strongly recommend called Never Split The Difference by Chris Voss: https://www.blackswanltd.com/never-split-the-difference That book basically teaches you to talk to an individual, have a dialogue with them until you get to why they're saying no to whatever thing you want to get through. Yeses that aren't really yeses, don't matter. Make sure you understand what the other person or organization's needs are and then make sure that you're constructing the right conversation, and having those conversations through an expression of needs on both sides. Usually, that is the Black Swan that will bust right through that blocker. ________________ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!) Come hangout with us!

J j Allaire Jupyter Notebooks + Quarto for customizable and reproducible documents, websites and

To share our results and communicate effectively in data science, we need to weave together narrative text and code to produce elegantly formatted, interactive output. Not only does it need to look great, but it needs to be reproducible, accessible, easily editable, diffable, version controlled and output in a variety of formats, such as PDF, HTML and MS Word for convenience and often compliance. Jupyter has already made so much of this possible. By combining Jupyter with the open-source publishing platform, Quarto, built on Pandoc, you can easily create the output format and the styling that you need for any situation. With Quarto, you can author documents as plain text markdown or Jupyter notebooks with scientific markdown, including equations (LaTeX support!), citations, cross references, figure panels, callouts, advanced layouts, and more. You can also engage readers by adding interactive data exploration to your documents using Jupyter Widgets, htmlwidgets for R, Observable JS, and Shiny. In this talk, we’ll discuss authoring these dynamic, computational documents with Quarto and Python that bring code, output, and prose together, leveraging integrations with both Jupyter and the Quarto VS Code extension. Whether you’re new to Jupyter or have thousands of notebooks already, we’ll walk you through using a single source document to target multiple formats - transforming a simple document into a presentation, a scientific manuscript, a website, a blog, and a book in a variety of formats including HTML, PDF and MS Word. We’ll also show how you can change themes and styling, and publish these artifacts directly from the command line to the web, so they’re immediately available online

Katherine Gerton @ Centene | Making sure you know other teams exist! | Data Science Hangout

We were recently joined by Katherine Gerton, Manager, Data Analytics & Reporting at Centene to chat about the power of embedded teams for bridging the distance between cool data science and meaningful business insights and bringing those teams together to create a community of practice that supports each other and the bottom line. During the conversation Katherine spoke about building tools that make your own life better, and then also spreading them to other people as a really valuable way to do work. Part of being successful in this is also making sure you know other teams exist! At 32:51, Katherine shares: There's a lot of that community building and reaching out to people that I've incorporated into some of my leadership work – a lot of it is making sure you know that other teams exist. In an organization the size of Centene, this can be really difficult. For example: “Oh, I didn't know that this team was doing this kind of thing with membership. I have a need to do something with membership. Let me make sure that I know who to reach out to.” That's something that we've been really working hard on. In our internal R user group meetings – we're highlighting different teams and what they are working on. “This is how we're using the Centene tools.” It's not always R but it's just seeing how others are using our data to keep building those relationships. I think going back to something I said earlier, I did it for me. And then I'm more than happy that it's also helping other people in Centene. How do you actually build those relationships? When you're really close to a data project and there's one piece that you don't know - it's might be shooting the email to the person that you saw last accessed that database because you can check those things. You can say, “Hey, I saw you were using this data. What's going on with it?” Those kinds of small emails are important. It's also getting over that hump of self-doubt, and thinking, “Oh, am I emailing the right person? Are they going to hate me for derailing their day for emailing the wrong person?” Just send an email because the worst thing that happens is that they don't respond to you. And then you send an email to another person and build that out. As I've moved into a leadership role, it's getting plugged in with other team leaders as well, where we now know the name of each other’s team and can say, “Do you have somebody on your team who knows about this or who can help with this?” That's also something that the Women in STEM Group has helped with because women in STEM are across the entire organization. They are not just in corporate IT as your cloud engineers. Almost anyone with a data analyst title or anything like that could be in the STEM group. I've been exposed to so many more people and so many more incredible women at Centene through that other organization . It doesn't just have to be the data science community where you are meeting all these other people across different parts of the company. __ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!) Come hangout with us!

Why RStudio is now Posit (J.J. Allaire | Posit CEO) - KNN Ep. 158

Today, I had the pleasure of interviewing J.J. Allaire. J.J. is the founder of RStudio and the creator of the RStudio IDE. He is an author of several packages in the R Markdown publishing ecosystem including rmarkdown, flexdashboard, learnr, and distill, and also worked extensively on the R interfaces to Python, Spark, and TensorFlow. J.J. is now leading the Quarto project, which is a new Jupyter-based scientific and technical publishing system. In this episode, we learn about why RStudio has now repositioned itself as Posit, how it maximizes its open-source nature as a B Corp, and how J.J. as an open-source advocate views the private nature of many LLMs. I really enjoyed this conversation, and I hope you will as well! Posit - https://posit.co/ Podcast Sponsors, Affiliates, and Partners: - Pathrise - http://pathrise.com/KenJee | Career mentorship for job applicants (Free till you land a job) - Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job - 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today - Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee | Interview prep questions Listen to Ken's Nearest Neighbors on all the main podcast platforms! On Apple Podcasts: https://podcasts.apple.com/us/podcast/kens-nearest-neighbors/id1538368692 (Please rate if you enjoy it!) On Spotify: https://open.spotify.com/show/7fJsuxiZl4TS1hqPUmDFbl On Google: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS8xNDMwMDQxLnJzcw?sa=X&ved=0CAMQ4aUDahcKEwjQ2bGBhfbsAhUAAAAAHQAAAAAQAQ MORE DATA SCIENCE CONTENT HERE: My Twitter - https://twitter.com/KenJee_DS LinkedIn - https://www.linkedin.com/in/kenjee/ Kaggle - https://www.kaggle.com/kenjee Medium Articles - https://medium.com/@kenneth.b.jee Github - https://github.com/PlayingNumbers My Sports Blog - https://www.playingnumbers.com ️ 66DaysOfData Discord Server - https://discord.com/invite/4p37sy5muZ

[84] Reproducible Publications with Python and Quarto (Thomas Mock)

Join our Meetup group: https://www.meetup.com/data-umbrella Tom Mock: Reproducible Publications with Python and Quarto ## Resources - slides: https://thomasmock.quarto.pub/python-umbrella/#/ ## Full transcript https://blog.dataumbrella.org/quarto-blog ## About the Event Quarto is an open-source scientific and technical publishing system that builds on standard markdown with features essential for scientific communication. The system has support for reproducible embedded computations, equations, citations, crossrefs, figure panels, callouts, advanced layouts, and more. In this talk we'll explore the use of Quarto with Python, describing both integration with IPython/Jupyter and the Quarto VS Code extension. Users can author Jupyter notebooks or documents as plain text markdowns with code in Python, R, Julia or Observable. Quarto includes the ability to publish high-quality articles, reports, presentations, websites, blogs, and books in HTML, PDF, MS Word, ePub, Reveal.js and more. ## Timestamps 00:00 Data Umbrella introduction 03:41 Introduce the speaker, Thomas Mock 04:14 Thomas begins 05:14 RStudio is now Posit 05:55 What is Quarto? 07:13 Origins of Quarto 08:31 Goal: Computation Document 09:09 Goal: Scientific Markdown 10:03 Goal: Single Source Publishing 10:33 Simple example of what Quarto looks like (YAML, Markup, Markdown, code chunks) 12:29 Simple example: multi-format (output formats: html, pdf, docx, epub, pptx, revealjs) 13:16 List of what is possible with Quarto 14:02 So, what is Quarto: quarto is a language-agnostic command line interface (CLI) 15:27 Basic Quarto workflow 16:43 Difference between "render" and "preview" 17:16 IPython 18:43 Stored/frozen computation and reproducibility 20:36 A *.qmd is a plain text file 21:28 Quarto doesn't have to be plain text 22:12 Rendering pipeline 22:57 What to do with my existing .ipynb? 24:23 Comfort of your own workspace: JupyterLab, Visual Studio Code, 25:00 Auto-completion in RStudio + VSCode 26:01 Quarto Extensions and Visual / Live Editor 27:19 Quarto, unified document layout 29:54 Quarto, unified syntax across Markdown and code 31:11 Built-in vs Custom 33:01 Extending Quarto with Extensions 33:51 Interactivity, Jupyter Widgets (with plots, matplotlib, etc) 34:15 Interactivity, Observable 35:01 Interactivity, on the fly Observable "widgets" 36:24 Parameters - one source, many outputs 37:36 Rendering with parameters 38:27 Quarto Publish 38:57 Quarto, crafted with love and care (the team) 39:30 Quarto Resources (installation) 39:44 Quarto resources: video tutorials 40:13 Q: Can Quarto documents be shared like Overleaf docs and can users import article templates for specific journals into Quarto? 41:39 new! Manuscript option to bundle an entire project together (bundle can be shipped to a journal) 42:48 Q: Is Quarto git friendly? 43:28 Q: Has Quarto already been used in published scientific work? 44:14 publishing books with Quarto 44:22 Q: Any general suggestions for outputting to docx (Word)? 45:20 Q: Any tips on how Quarto can help conda users? 46:14 Q: Can you use GitHub Actions with Quarto? 47:18 Q: Can you have individual environments for each blog post? 49:50 Download CLI (command line interface) for Quarto 51:10 Example Gallery 51:44 nbdev project 53:14 Quarto blog, Shinylive extension 55:12 Q: How can I use Quarto to write scientific papers? ## About the Speaker: Tom Mock - Twitter: https://twitter.com/thomas_mock - GitHub: https://github.com/jthomasmock #python #quarto #rstats

How to schedule a Quarto document on Posit Connect

Episode 3: Scheduling a Quarto Doc (with custom branding) on Posit Connect Led by: Ryan Johnson, Data Science Advisor Live Q&A recording: https://youtu.be/JUgChPCa3vs Follow-up links: * Posit Team: https://posit.co/products/enterprise/team/ * Talk to us directly: https://posit.co/schedule-a-call/?booking_calendar__c=RST_YT_Demo * Follow-along blog post: https://posit.co/blog/scheduling-a-quarto-doc-on-posit-connect/ * Source code for example: https://github.com/ryjohnson09/quarto-job-scheduling * Posit Team demo resources: pos.it/demo-resources Timestamps: 1:45 - What is Posit Team? 3:31 - The data we are analyzing: R package download data from within Posit Package Manager (via experimental API) 4:44 - What is Quarto? (we are creating two Quarto docs today) 8:06 - Create a new session in Posit Workbench 8:56 - Create a new project within the RStudio IDE 10:13 - Create the first Quarto document (ETL: extra, transform, load workflow) 13:55 - Publish the first Quarto doc to Posit Connect 16:50 - Take the package download results and pin it to Posit Connect (overview of pins) 18:30 - Schedule the first Quarto doc to run every day at 7am on Posit Connect 20:49 - Create the second Quarto document (report for stakeholders) 23:41 - View the first "boring report" 24:54 - Using a custom Posit format for the report 27:39 - First look of the themed report without modifications 29:00 - Adding Posit themed colors to the gt table 31:35 - Apply code chunk options to hide code from output 32:44 - Publish the second Quarto document to Posit Connect 34:28 - View finished custom branded Quarto document 34:44 - Define specific users who have access to the Quarto doc on Posit Connect 35:10 - Schedule the second Quarto doc to read in the pinned data from the first Quarto doc 36:30 - Example emailed report from the scheduled Quarto report On the last Wednesday of every month, we host a Posit Team demo and Q&A session that is open to all. You can use this to add the event to your own calendar. Who are these monthly demos for? Everyone is welcome to join us - regardless of industry, background, or experience! We will discuss topics that will speak to: * Data scientists and administrators new to Posit Team or are looking to grow their understanding of our toolchain, * Teams searching for a new analytic platform built to support open-source data science, * And, those that are just curious about Posit Team! What you can expect from the monthly Posit Team demo: During the session, we will walk through an end-to-end data science workflow and demo the core functionality of Posit Team while highlighting some of our latest features! While each session's content will vary slightly, here are a few core topics we will address each month: * Open Source Analytics: The future of data science is open source. We'll discuss methods for leveraging open-source tools and packages in a secure and scalable way! * Deployment: How to share the amazing data science assets your Team has built, including web applications, machine learning models, APIs, and more! * Data Access: Data comes in various forms and is stored in various ways. We'll discuss best practices for accessing, reading, and writing data! * Job Scheduling: Do you have recurring data science jobs? We'll show you how to automate these processes using Posit Connect. What is Posit Team? Posit Team is a bundle of our popular professional software (Posit Workbench, Posit Connect, and Posit Package Manager) for developing data science projects, publishing data products, and managing packages. Registration is not required. The event will be streamed through YouTube Premiere

Joe Cheng - Shiny: Data-centric web applications in Python | PyData Seattle 2023

www.pydata.org Shiny is a web framework that is designed to let you create data dashboards, interactive visualizations, and workflow apps in pure Python or R. Shiny doesn't require knowledge of HTML, CSS, and JavaScript, and lets you create data-centric applications in a fraction of the time and effort of traditional web stacks. Of course, Python already has several popular and high-quality options for creating data-centric web applications. So it's fair to ask what Shiny can offer the Python community. In this talk, I will introduce Shiny for Python and answer that question. I'll start with some basic demos that show how Shiny apps are constructed. Next, I'll explain Transparent Reactive Programming (TRP), which is the animating concept behind Shiny, and the reason it occupies such an interesting place on the ease-vs-power tradeoff frontier. Finally, I'll wrap up with additional demos that feature interesting functionality that is made trivial with TRP. This talk should be interesting to anyone who uses Python to analyze or visualize data, and does not require experience with Shiny or any other web frameworks. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Joe Cheng

Brad Weiner @ CU Boulder | Using data to influence institutional decisions | Data Science Hangout

We were recently joined by Brad Weiner, Chief Data Officer at University of Colorado Boulder to talk about the intersection of data science and higher ed. At 8:53 Brad shared an example where their data team influenced an institutional-level decision. Brad shares, "Doing the data work is the easier part, getting people to actually use the data and inform policies is much trickier. I always like to point to the fact that it really does take a village. We need all kinds of people to be invested and interested in a project." One example we’ve been pointing to lately: We really want students to be successful. Not just because that’s our core mission but because we’re measured on that. All universities are measured on that in a lot of ways. Students want to go to a place where they're going to be successful. Building a college degree really is a supply-chain problem. You need to have all of these constituent parts and they need to land at the right time. There's tons of dependencies and those dependencies need to be built out years in advance. So if we don't have enough chemistry labs, that student doesn't get to take chemistry. If not enough students take chemistry, they don't have the required courses and they can't graduate. We're constantly looking for these things. One of the things that we have found from the people in the bookstore is that students found the prices of their textbooks to be something of a barrier. We're not only trying to graduate students, but we're trying to graduate everybody as equitably as possible. If the experience is one way for some students and one way for another group of students, that is not considered success. What we were finding, and what we had heard, what we had learned, was that students were potentially choosing which courses to take based on the cost of the textbooks. Students of today are used to subscription services. Nobody ever thinks, how many songs did I listen to on Spotify? Am I getting my money's worth there? They’re used to the Netflix models where you just pay one price and you get everything. It doesn't matter how much you use it or how little you use it. We’re one of the first dozen or 15 schools that have gone in on this bookstore-equity access model, which is basically, students pay one price. This gives them the financial stability, predictability, and their ability to budget on books, but also the ability to be really flexible in what classes they take. By paying that one price, they get what we call day-one digital access. There's evidence that having digital, immediate access to your books is actually a student success lever. So for a variety of reasons, we wanted to implement this program. One of the things that we were asked to do is figure out what price should the bundle be? Pricing is a really complicated problem - if the price is too high, nobody participates and the whole thing collapses on itself. And if it's too low, you don't cover your costs. We had a whole team of people who were thinking through that problem. We are happy to say that we used our super-sweet Posit Workbench set-up to simulate a whole bunch of different options. The Data Science team did that and we were able to guide them on what that price was. And it got deployed out. So next year, from what I understand, all undergraduates at CU Boulder will pay $279 a semester, and get all of their books on day one. If they drop a class, then those books go away and the new ones come in. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Natalia Andriychuk @ Pfizer | Hosting an internal data science hangout | Data Science Hangout

We were joined by Natalia Andriychuk, Statistical Data Scientist at Pfizer to discuss running an internal Data Science Hangout, building a supportive R community, discovering and learning new R packages/tools, and contributing to open-source initiatives. Resources shared: Rachael's guide for hosting your own data science hangout: https://docs.google.com/document/d/1uKgLRacVyUJA1dH4f675dKP1gnbaw--QztxwIIiLAAA/edit?usp=sharing RTP useR Group: https://www.meetup.com/rtp-r-user-group/ Data Science Hangout LinkedIn Group: https://www.linkedin.com/groups/12610075/ Shiny app submitted to FDA Pilot: https://www.r-consortium.org/blog/2022/12/07/update-successful-r-based-package-submission-with-shiny-component-to-fda 56:41 - What's something that you've learned from that process of starting your own internal data science hangout? Summarized from below: 1. Start with a list of 10 or so people you’d like to invite 2. Ask colleagues you have an established relationship with to be your first featured leaders 3. Create a calendar of events that you can invite people to 4. Use a form to collect the speaker's bio, photo, etc. 5. Schedule a 30 minute call with each featured leader the week before 6. Try to plan out 3 sessions in advance 7. Start inviting people :) Natalia’s response: It was a little bit overwhelming to think about how to start it but one thing you would need to start is a list of 10-15 people who you'd like to invite. First I would create the calendar of events. At Pfizer, we do our data science hangouts every other week. I specifically put the dates and send people an invitation asking them to be a part of hangout. I actually started the hangouts with Mike Smith and Douglas Robinson, who are part of our core team and are moving this initiative forward. For your first few hangouts, it can be easier to have colleagues join us as your co-host that you already have an established rapport with. I set up a Microsoft Form that I would send out: asking about: occupation, bio, certain questions they want me to ask, etc. I also ask them to provide their headshot. I think it makes the hangout invitation a little more colorful. For the featured leaders, I schedule a 30 min call with each guest the week prior to go over any questions that person might have if they never attended before. It helps me to know more about what they're doing, especially if this is the first time I'm having a 1:1 with this colleague. I'm pretty new to the company as well, so we would chat a little bit about their interests - and then we have the hangout session. I'll try to have at least 3 people booked in advance. So find your featured leader, and then start inviting people. ____ Having an internal hangout has been very valuable because we are able to talk more about certain topics within our organization. For example, if I come to this hangout and we discuss certain topics, sometimes I might have to say, “Well, this is a great question, but I can't talk about it because the information is proprietary.” With your internal hangout, you do not have to worry about that. This is how certain problems get solved faster. It also brings people together that wouldn't work together necessarily. I think this is a very valuable part of it. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

JD Williams @ Zoetis | 6-steps for POC to production & long-term value | Data Science Hangout

We were recently joined by J.D. Williams, Head of Business Insights and Analytics at Zoetis to discuss delivering significant business outcomes through data science solutions. Timestamps: 2:53 - Intro to Zoetis team 9:39 - Building a data science team from scratch 14:47 - Being flexible on location when hiring 17:02 - Promoting data science in the company 21:26 - Tips for building relationships with execs 23:13 - Example app showing their community growth 25:18 - Making an impact on manufacturing (where they use Posit tools the most today) 33:40 - Building up a data team at a startup 36:39 - Moving from proof of concept to production-ready apps (6-step process) 41:34 - Being open with the tools the team can use 47:22 - Designing requirements for data science and data analysts 52:50 - Keeping up with technology trends 6-step process for getting from Proof of Concept to Production & Long-Term Value. 1. Identification/prioritization. For our manufacturing and supply chain leaders, we had a small steering committee of leaders and analytics leaders that came together to identify and prioritize the highest value use cases. 2. Feasibility We took a little bit of time to gather the data and look at the data available (do we have enough data and the right kind of data to do a proof of concept? If so, we moved to next step. 3. Proof of concept Here's where we would build– we're talking the ideal would be between two and four weeks – a quick proof of concept, using the data that we have sort of in a sandbox or offline. We're not building production ready code, we're not building something that's ready to go live. We're just trying to prove the value. Two things– can we do it and does it have value? If the answer to both of those is yes, there's real business value and we're successfully able to do it. Then we go to the fourth stage, which is the pilot. 4. Pilot This is where we would make it more production ready. Here's where we were going to partner with our data engineering team to take the analytic solution and put it into production on a small scale. And think of it as maybe one manufacturing site, where you're not scaling it across many different places, but being more deliberate in how you're doing it. This is kind of a risk based approach and we were given permission to fail. A lot of the proof of concepts succeeded, there were some that did not. Or some succeeded, but maybe didn't have as much value as we anticipated. Focus on those highest value proof of concepts going on to the next stage of piloting. 5. Scale up stage Those pilots that are successful, we want to scale that to other sites, other products, other stages of the process and leverage what we learn there and scale it across other areas. 6. Sustaining How do we sustain the value long term? Those six stages are how we thought about it upfront. Within the first year or so of us getting started, the total number of use cases in our use case tracker was about 100 in various stages of those mentioned. Some were just ideas, some were being prioritized somewhere in the feasibility step, some of the proof of concept, et cetera. From there now, we've taken the most valuable of those and we're actually focusing more on those and how we can highlight them and scale them. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

How to deploy a Shiny application using clinical trial data to Posit Connect

Episode 2: Publishing a Shiny application in R to Posit Connect - Using Clinical Trial Data Led by: Ryan Johnson, Data Science Advisor Follow-up links: * Posit Team: https://posit.co/products/enterprise/team/ * Talk to us directly: https://posit.co/schedule-a-call/?booking_calendar__c=RST_YT_Demo * Follow-along blog post: https://posit.co/blog/publishing-a-shiny-app-in-r-with-clinical-trial-data-to-posit-connect/ * Source code for example: https://github.com/ryjohnson09/adam_analysis * Posit Team demo resources: pos.it/demo-resources Timestamps: 1:35 - High-level overview of Posit Team 3:30 - Overview of clinical trial data used 5:31 - Opening up RStudio session on Posit Workbench 7:51 - Creating a new directory in RStudio 9:16 - Upload the ADaM dataset to Posit Workbench 10:17 - Using packages from a validated repository on Posit Package Manager 12:37 - Install packages for your Shiny application 13:49 - Pasting the code for the Shiny application (https://github.com/ryjohnson09/adam_analysis) 16:16 - Publishing your Shiny application to Posit Connect 18:36 - Changing access controls to published Shiny application 20:25 - Using renv to record your R environment On the last Wednesday of every month, we host a Posit Team demo and Q&A session that is open to all. You can use this to add the event to your own calendar. Who are these monthly demos for? Everyone is welcome to join us - regardless of industry, background, or experience! We will discuss topics that will speak to: * Data scientists and administrators new to Posit Team or are looking to grow their understanding of our toolchain, * Teams searching for a new analytic platform built to support open-source data science, * And, those that are just curious about Posit Team! What you can expect from the monthly Posit Team demo: During the session, we will walk through an end-to-end data science workflow and demo the core functionality of Posit Team while highlighting some of our latest features! While each session's content will vary slightly, here are a few core topics we will address each month: * Open Source Analytics: The future of data science is open source. We'll discuss methods for leveraging open-source tools and packages in a secure and scalable way! * Deployment: How to share the amazing data science assets your Team has built, including web applications, machine learning models, APIs, and more! * Data Access: Data comes in various forms and is stored in various ways. We'll discuss best practices for accessing, reading, and writing data! * Job Scheduling: Do you have recurring data science jobs? We'll show you how to automate these processes using Posit Connect. What is Posit Team? Posit Team is a bundle of our popular professional software (Posit Workbench, Posit Connect, and Posit Package Manager) for developing data science projects, publishing data products, and managing packages. Registration is not required. The event will be streamed through YouTube Premiere

Alix Schmidt & Amanda Ahrens @ Dow | Creating business value with community | Data Science Hangout

We were recently joined by Amanda Ahrens & Alix Schmidt from Dow Chemical to discuss designing enterprise data architecture to enable sustainability, how data science is really all about the people, and about leading a data science community of over 500 researchers at Dow. At 10:02 in the recording, Alix and Amanda shared that one of their executive leaders at Dow asked, “How can we use this community to start creating value for the company?” They suggested, why don't you go figure out how to do a hackathon? That word can mean a lot of things to a lot of people but after really talking about it, we settled on this sort of Shark Tank- like the TV show Shark Tank - style thing where groups come up with a project idea and pitch it to this executive panel. How did you go from that one executive sponsor to a panel of eight executives in the first challenge? What really works is having the executive that's already onboard sell it to the rest. It doesn't work so much if you're out there by yourself. If you can go along with that executive that's already on board and explain the vision of why you want to do a Data Science Challenge or a Data Science Conference and what's in it for them - it’s a lot easier to get them to say yes. They felt like it was an opportunity the other executive was giving them to really help set the data science direction for the company. In all three Data Science Challenges, winning projects were implemented in some way. Even if they weren't funded directly initially, there was such high interest by the executives in moving forward with the idea. The number one project from last year actually just won an Edison Award, which is a big research and development in STEM award. That's external recognition too! With that project, pieces of it were happening already, but a lot of value is created at the interface of different functions and different organizations in a company as big as Dow, which is like 37,000 employees. One of the things we encourage folks to do is network and get out there and find people in parts of the company that you wouldn't normally talk to. That project brought three very small projects from our toxicology department, our molecular discovery research area, and our supply chain area together to make a really powerful tool for making safer and more sustainable products. That project is such an awesome example of exactly the vision of the event and to get that external recognition was really cool. A huge part of being able to bring people together across the company is making sure that you set a theme that resonates with the executives, but also, the mid-level managers because they have to be supportive of their employees actually participating in the Data Science Challenge. Our Shark Tank style challenge, wasn't just a day or two days where people were working on a problem with the same data set. It was actually over a longer period of time. Beginning to end, the challenge was more around two months. There was a lot of time spent there, so people had to feel like it was worthwhile for their team. The first theme was more general - improving EBITDA. Then the second theme was around employee experience and customer experience. Then the last one that we did in 2022 was all around sustainability and so that really resonated with everybody. Everybody at Dow is trying to prove that they're contributing to that, and that united a lot of people to come up with some great ideas cross-functionally. Did you come up with those themes with the executives– or the original executive sponsor? The latest theme was brought up to me by one of the technical principals at the company who thought it would be a really uniting theme. We pitched it to the executives that we knew were already on board and going to help us. They said, absolutely - let's go forward with it. Dow seeks to be the most sustainable material science company in the world, and so that's a very front and center theme for the company, not just the Data Science Challenge. So it's pretty easy to get executives to rally around that. They were already interested in it, it was already on their scorecard, so might as well add this to help them achieve their goals. Finding themes that are enterprise-level for the whole company is really important, because we want to make sure the theme doesn't exclude any one that has a certain type of job that just doesn't touch that area. Executive sponsors tend to have goals at this company-wide level. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Carson Sievert: Towards the next generation of Shiny UI

About the talk: Shiny, the R package for creating interactive web graphics, recently celebrated its 10th birthday. Since then, Shiny has grown tremendously in many areas (e.g., performance, functionality, extensions, etc); however, a "hello world" Shiny app still looks like it did 10 years ago. This is mostly because Shiny goes to great lengths to ensure backwards compatibility; and as a result, default Shiny UI will likely continue to be based on Bootstrap 3 (a CSS styling framework released in 2010). However, thanks to the new bslib R package, it is now easy to opt-into a modern Bootstrap 5 foundation that "just works" with Shiny, R Markdown, flexdashboard, pkgdown, bookdown, and more. In addition to upgrading Shiny's Bootstrap dependency, bslib also makes it much easier to do custom theming, leverage modern layout techniques, and create custom components (all from R without any CSS/HTML/JS required). At this point, bslib is still maturing, and does not yet provide what we'd consider a "complete UI toolkit", but it should eventually replace and/or improve upon all of Shiny UI. In this talk, I'll highlight bslib features that we're most excited about (e.g., expandable cards, accordions, (sidebar) layouts, input controls, etc.), discuss some best design practices for improving user experience with these tools, and present some real world examples of these tools in action. Speakers' bio: Carson is a software engineer on the Shiny team at Posit. He joined Posit in 2018, and in recent years, has focused primarily on Shiny for Python and improving Shiny UI. The Shiny UI work has manifested in the creation and development of many R packages such as bslib, thematic, htmltools, htmlwidgets, sass, shiny, rmarkdown, flexdashboard, and more. Carson also has a PhD in statistics, is a recipient of the ASA's Chambers Statistical Software Award, has maintained the R package plotly since 2015, authored the book "Interactive data visualization with R, plotly, and shiny", and ran a successful freelance consulting service for numerous years

Carson Sievert, Shiny Team

Josh King @ The Knot Worldwide | Creating your data and analytics roadmap | Data Science Hangout

We were recently joined by Josh King, Director of Marketing Data Science & Analytics at The Knot Worldwide. At 36:28, we discussed: What goes into creating your data and analytics roadmap? I'm actually in the midst of this right now– as in the hour before this meeting. One of the things I like to come back to is from the CTO at my last company. He had this really good framework for thinking through this, and it's to break it out into 4 things: 1. Your mission 2. Your vision 3. Your maturity model 4. Your strategy If you want to look it up, his name is Dave Kaplan and he has a great article on Medium: https://medium.com/policygenius-stories/missions-visions-maturity-models-strategies-ce5a4a408ab2 This gets into a good framework for building out and identifying the different aspects of a strategy that you should consider and having a clear alignment for what that will look like. Generally, a mission is like a single sentence for why your team or your department or your company exists. Your vision is what it means to succeed within that. So, if I'm thinking through marketing data science & analytics - I'm thinking what does it mean to have a best in class marketing data science organization across relevant brands? Your maturity model is, where are you along that map from very basic early foundational building into “I am best in class”? Then your strategy is how you get from where you are now to where you want to be in your vision. So, this really aligns a good framework for what it is that you're building out. And to the other part of your question, does it cover all data work? I have data scientists and analytics that report into me, but there's also some of a dotted line from all of our data engineers that focus on marketing problems so that can include BI, data operations, data warehouse, tracking architecture. As we're thinking through a bit more holistically in terms of all of our data functions that are supporting marketing, I identify, what do we need to drive decision making for our marketing organization and not only how does data science and analytics fit into that, but what is the infrastructure we need from a data engineering perspective that would enable us to be able to do this work? Yes, it's not necessarily my vision or my strategy that will get us there but working really closely with our Head of Data Platform so that my vision and his vision are really aligned together and we’re thinking through a bit more holistically as we're building that out. How long is it? My current focus is on building out a 1 year, 3 year, and a 5 year roadmap to really have some intermediate stages along the way that we're getting towards. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

posit::conf(2023) Workshop:Enhancing Communication & Collaboration with Quarto and Jupyter Notebooks

Register now: http://pos.it/conf Instructor: Hamel Husain Workshop Duration: 1-Day Workshop This workshop is for you if you: • have some experience with Python and Jupyter and want to learn how Quarto can support and enhance your workflows • want to learn about turning your notebooks to websites and publications • want to learn how to write python packages with Jupyter notebooks and Quarto with the help of nbdev The workshop will assume some prior experience with Python and Jupyter Notebooks. Sharing knowledge through writing is a critical aspect of scientific activity, including data science. It allows researchers to communicate their findings and insights to a wider audience, build upon existing work, and collaborate with others in their field. However, until recently, there have been limited options for publishing long-form writing and expository analyses authored in Jupyter Notebooks, a popular medium for data scientists. Enter Quarto - an innovative, open-source scientific and technical publishing system compatible with Jupyter Notebooks and other popular mediums. Quarto provides data scientists with a seamless way to publish their work in a high-quality format that is easily accessible and shareable. With Quarto, researchers can turn their Jupyter Notebooks into professional-looking publications in a variety of formats, including web pages, books, and slides. In this workshop, we will demonstrate how Quarto enables data scientists to turn their work products into professional, high-quality publications, websites, blog posts, and other shareable artifacts. As a bonus, we will also discuss how you can create and document Python packages using Jupyter notebooks and Quarto with the help of nbdev. The learning outcomes for the workshop include: • examine case studies where sharing scientific knowledge has greatly improved the efficacy of data science teams • author documents in plain text markdown or Jupyter notebooks with equations, citations, crossrefs, figure panels, callouts, and advanced layouts • learn how to author content in IPython/Jupyter and the Quarto VS Code extension • leverage Quarto for creating different types of publications, including personal blogs, knowledge management for teams, notes, books, websites, and presentation slides • extend Quarto with notebook filters and extensions • host websites and publications on platforms like GitHub Pages, QuartoPub, and Netlify • test notebooks and documentation with Quarto’s execution options • create and document Python packages with nbdev and Quarto

posit::conf(2023) Workshop: Tidy time series and forecasting in R

Register now: http://pos.it/conf Instructor: Rob J Hyndman Workshop Duration: 2-Day Workshop This course is for you if you: • already use the tidyverse packages in R such as dplyr, tidyr, tibble and ggplot2 • need to analyze large collections of related time series • would like to learn how to use some tidy tools for time series analysis including visualization, decomposition and forecasting It is common for organizations to collect huge amounts of data over time, and existing time series analysis tools are not always suitable to handle the scale, frequency and structure of the data collected. In this workshop, we will look at some packages and methods that have been developed to handle the analysis of large collections of time series. On day 1, we will look at the tsibble data structure for flexibly managing collections of related time series. We will look at how to do data wrangling, data visualizations and exploratory data analysis. We will explore feature-based methods to explore time series data in high dimensions. A similar feature-based approach can be used to identify anomalous time series within a collection of time series, or to cluster or classify time series. Primary packages for day 1 will be tsibble, lubridate and feasts (along with the tidyverse of course). Day 2 will be about forecasting. We will look at some classical time series models and how they are automated in the fable package, and we will explore the creation of ensemble forecasts and hybrid forecasts. Best practices for evaluating forecast accuracy will also be covered. Finally, we will look at forecast reconciliation, allowing millions of time series to be forecast in a relatively short time while accounting for constraints on how the series are related

posit::conf(2023) Workshop: Teaching Data Science Masterclass

Register now: http://pos.it/conf Instructor: Dr. Mine Çetinkaya-Rundel Workshop Duration: 1-Day Workshop This course is for you if you: • you want to learn / discuss curriculum, pedagogy, and computing infrastructure design for teaching data science with R and RStudio using the tidyverse and Quarto • you are interested in setting up your class in Posit Cloud • you want to integrate version control with git into your teaching and learn about tools and best practices for running your course on GitHub This masterclass is aimed primarily at participants teaching data science in an academic setting in semester-long courses, however much of the information and tooling we introduce is applicable for shorter teaching experiences like workshops and bootcamps as well. Basic knowledge of R is assumed and familiarity with the tidyverse and Git is preferred. There has been significant innovation in introductory statistics and data science courses to equip students with the statistical, computing, and communication skills needed for modern data analysis. Success in data science and statistics is dependent on the development of both analytical and computational skills, and the demand for educators who are proficient at teaching both these skills is growing. The goal of this masterclass is to equip educators with concrete information on content, workflows, and infrastructure for painlessly introducing modern computation with R and RStudio within a data science curriculum. In a nutshell, the day you’ll spend in this workshop will save you endless hours of solo work designing and setting up your course. Topics will cover teaching the tidyverse in 2023, highlighting updates to R for Data Science (2nd ed) and Data Science in a Box as well as present tooling options and workflows for reproducible authoring, computing infrastructure, version control, and collaboration. The workshop will be comprised of four modules: • Teaching data science with the tidyverse and Quarto • Teaching data science with Git and GitHub • Organizing, publishing, and sharing of course materials • Computing infrastructure for teaching data science Throughout each module we’ll shift between the student perspective and the instructor perspective. The activities and demos will be hands-on; attendees will also have the opportunity to exchange ideas and ask questions throughout the session. In addition to gaining technical knowledge, participants will engage in discussion around the decisions that go into developing a data science curriculum and choosing workflows and infrastructure that best support the curriculum and allow for scalability. We will also discuss best practices for configuring and deploying classroom infrastructures to support these tools

Mine Çetinkaya-Rundel

posit::conf(2023) Workshop: Steal like an Rtist: Creative Coding in R

Register now: http://pos.it/conf Instructors: Ijeamaka Anyene Fumagalli & Sharla Gelfand Workshop Duration: 1-Day Workshop This workshop is for you if you: • are comfortable with R and RStudio, experience with tidyverse and ggplot2 • are interested in applying data visualization skills more creatively, but may not know where to start or how to develop style/inspiration • are an artist interested in exploring code as another medium for creating their work R is a tool for data analysis but also can be used for self-expression. This workshop will be an introduction to creative coding in R in order to make visual art. We will take an inspiration-first approach, using compelling pieces to discuss and learn the techniques that shape the work. This workshop takes guidance from its namesake, the book “Steal Like An Artist” by Austin Kleon - once we have identified and learned to recreate existing works, we will cover how to take this inspiration and transform, remix, or reinterpret it in the pursuit of developing our own work and artistic styles. This workshop is hands-on and will cover color theory and manipulation, a reintroduction of the data frame as the foundation for creating art (instead of just for analyzing data!), using ggplot2 as an artistic canvas, creating basic and specialized shapes, tiling and pattern making, developing your own functions and using iteration. We will also discuss how to use controlled randomness to convert a standalone piece into a generative art system that can produce many distinct outputs. Creative coding may seem a world apart from data analysis, but we see a large overlap and intersection of the skills used in both, not to mention the creative muscles that are already used in data visualization

posit::conf(2023) Workshop: Machine Learning and Deep Learning with Python

Register now: http://pos.it/conf Instructor: Sebastian Raschka Workshop Duration: 1-Day Workshop In this workshop, you will learn the machine and deep learning fundamentals using a modern open-source stack. We’ll start with a brief introduction to Python’s scientific computing libraries, including NumPy, Pandas, and Matplotlib, which provide the foundation for data analysis and visualization. From there, we will dive into the scikit-learn API, a user-friendly, open-source library for machine learning in Python. You will learn how to use it to create machine learning classifiers and apply tree-based models like random forests, gradient boosting, and XGBoost. In the second part of this workshop, we will also cover deep learning concepts and introduce PyTorch, the most widely used deep learning research library. You will also learn about training multi-layer neural networks efficiently using multi-GPU and mixed-precision techniques. Finally, we will explore how to use a pretrained large language transformer with scikit-learn and fine-tune it on a custom downstream task using PyTorch. By the end of this workshop, you will have a good understanding of the fundamental principles of machine learning and be able to construct advanced classification pipelines for tabular data using scikit-learn. Additionally, you will gain experience in image classification and natural language processing techniques using PyTorch and be able to implement them in your own predictive modeling projects effectively

posit::conf(2023) Workshop: Fundamentals of Package Development

Register now: http://pos.it/conf Instructor: Andy Teucher Workshop Duration: 1-Day Workshop This workshop is for you if: • You have written several R scripts and find yourself wondering how to reuse or share the code you’ve written • You know how to write functions in R • You are looking for a way to take the next step in your R programming journey We will be demonstrating some workflows using Git and GitHub. Knowledge of these tools is not required, and you will absolutely be able to complete the workshop without them, but some of the lessons will be more rewarding to you if you are prepared to try them out. If you are looking to get started with Git and GitHub, we recommend you register for the “What they forgot to teach you about R” workshop on Day 1, and join us for this workshop on Day 2. We are often faced with the need to share our code with others, or find ourselves writing similar code over and over again across different projects. In R, the fundamental unit of reusable code is a package, containing helpful functions, documentation, and sometimes sample data. This workshop will teach you the fundamentals of package development in R, using tools and principles developed and used extensively by the tidyverse team - specifically the ‘devtools’ family of packages including usethis, testthat, and roxygen2. These packages and workflows help you focus on the contents of your package rather than the minutiae of package structure. You will learn the structure of a package, how to organize your code, and workflows to help you develop your package iteratively. You will learn how to write good documentation so that users can learn how to use your package, and how to use automated testing to ensure it is functioning the way you expect it to, now and into the future. You will also learn how to check your package for common problems, and how to distribute your package for others to use. This will be an interactive 1-day workshop, and we will be using the RStudio IDE to work through the materials, as it has been designed to work well with the development practices we will be featuring

posit::conf(2023) Workshop: Engaging and Beautiful Data Visualizations with ggplot2

Register now: http://pos.it/conf Instructor: Cédric Scherer Workshop Duration: 1-Day Workshop This course will be appropriate for you if you: • already know how to create basic graphics with the ggplot2 package • aim to improve the design of your ggplot outputs • want to learn how to create more complex charts which feature multiple layers, annotations, text styling, custom themes, and more Creating effective and easily accessible data visualizations of high quality in an efficient and preferably reproducible way is an essential skill for everyone working in a data-related field. Luckily, by leveraging the functionality of ggplot2, the most famous package for data visualization with R, and related extension packages one can create highly customized data visualization without the need for post-processing. This workshop provides everything one needs to know to create and customize numerous chart types with ggplot2. Participants will learn the most important steps and helpful tips to create visually appealing and informative graphics with a code-only approach. The power of ggplot2 and related extension packages will be illustrated with advanced real–life examples that help to understand useful coding tricks and the process of creating engaging and effective visualizations. The workshop will particularly focus on more advanced tasks with ggplot2 such as styling labels and titles, customizing themes and visual aesthetics, and using less-common chart types

posit::conf(2023) Workshop: Designing Data Visualizations to Successfully Tell a Story

Register now: http://pos.it/conf Instructor: Cédric Scherer Workshop Duration: 1-Day Workshop This course is for you if you: • want to understand and learn the art of communicating data with impactful visualizations • aim to improve your data visualization design to create effective and informative graphics • are willing to spend a bit more time to choose the right chart, proper color palettes, and suitable fonts along with additional elements to guide the viewer Communicating data through meaningful and easily accessible visualization is a critical competence for most data-related roles including data scientists, analysts, scientific researchers, and managers. A well-designed graphic is able to inform, spark engagement, explain patterns, and drive decisions and actions. At the same time, poor choices in the design process can complicate interpretation or even, intentionally or unintentionally, mislead the audience. The aim of this course is to demystify the creative processes of data visualization design to turn data into a meaningful story. Participants will learn helpful tips and tricks to create appealing and informative data visualizations that are not “just showing the numbers” but successfully tell a story. We will cover principles of good data visualization design, explore different options to display data, and discuss ways to guide and engage viewers with the aim to create impactful graphics. The course also features sessions on picking suitable yet beautiful colors, what to consider when choosing and pairing typefaces, and the layout of graphics, also in the context of dashboard building

[79] Create a Python Web App Using Shiny (Gordon Shotwell)

Join our Meetup group for more events! https://www.meetup.com/data-umbrella ## Resources - website for presentation: https://shiny.rstudio.com/py/ - https://shiny.rstudio.com/py/docs/reactive-mutable.html - https://www.shinyapps.io/ - https://huggingface.co/new-space - https://shiny.rstudio.com/py/docs/shinylive.html - https://shiny.rstudio.com/py/api/reactive.poll.html#shiny.reactive.poll ## About the Event Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. If you want to develop a python web application you usually need to choose between simple, limited frameworks like Streamlit and more extensible frameworks like Dash. This can cause a lot of problems if you get started with a simple framework but then discover that you need to refactor your application to accommodate the next user request. Shiny for Python differs from other frameworks because it has tremendous range. You can build a small application in a few minutes with the confidence that the framework can handle much more complex problems. In this workshop we will go through the core limitations of Streamlit, and build a Shiny app which avoids those limitations. ## Timestamps 00:00 Welcome 00:23 Reshama introduces Data Umbrella 03:45 Reshama introduces Gordon Shotwell 04:21 Gordon Shotwell begins 04:29 The motivation to develop Shiny for Python 06:05 The main strength of both the R and Python library 06:56 What Gordon Shotwell will build during his presentation 07:25 Shiny documentation website 08:01 QuickStart for R users showing differences between the R and Python libraries 08:44 All the function reference in Shiny 09:08 Demo starts 09:50 Virtual environment 10:36 How to start shiny app in the terminal 11:15 Install shiny extension in VS Code which makes it easier to preview the web app 11:36 How the output function works on the preview app to execute 12:22 Penguin dataset description for the demo 12:45 Modules/submodules shiny app is built on 13:04 How to add a sidebar layout (sidebar, panel sidebar and panel main) 13:43 How to read in the data and the output functions 14:31 How to define some server logic 14:59 The conventional shiny rule 16:30 Use of slide input 17:50 Where the reactive magic comes in 19:30 Important note on what can really slow down your shiny app 20:14 Importance of Python data copy method when using external dataset 21:01 Important note to avoid dependency inside the render function 21:30 Q&A 29:35 Adding a plot to the output: The UI sides 30:12 Adding a plot to the output: The render sides 32:16 The core principle of reactivity in which you do not want to repeat yourself 33:26 Reactivate calculation concept which allows you to store intermediate values in one place 37:24 Q&A 38:53 Reactive calculations and rendering functions 39:30 Side-effects or user effect. Another class of interactions 41:18 How to tell reactive effect what it should respond to or what events to watch before executing 41:53 How to update the data filter in the side-effect function 42:22 The second important pattern for shiny 43:00 One of the important things to pay attention to once you start learning/using shiny 44:45 Series of Q&A until the end of the video. Some response includes live demo 01:01:03 Gordon Shotwell ends his presentation 01:01:17 Reshama closes the session ## About the Speaker Gordon Shotwell is a Software Engineer at Posit. He's been using Shiny to solve business problems for the past ten years. - LinkedIn: https://www.linkedin.com/in/gshotwell/ ## Key Links - Transcript: https://github.com/data-umbrella/event-transcripts/blob/main/2023/78-gordon-shiny.md - Meetup Event: https://www.meetup.com/data-umbrella/events/292848290/ - Video: https://youtu.be/pXidQWYY14w #python #deployment

Emily Riederer @ Capital One | Explicit design at the start of a project | Data Science Hangout

We were recently joined by Emily Riederer, Senior Manager - Customer Management Data Science & Analytics at Capital One. We discussed how a strong foundation in high quality data infrastructure and reproducible tools sets the stage for innovation in modeling, causal inference, and analytics, and so much more. Diving into a question asked at (42:38): What is your thought process for solving a problem that you don’t know how to solve immediately? One thing that I think is a really undervalued part of that process is thinking about how you will know a good solution when you find one? Also, how would you know if there was a good solution staring you in the face and you already had it? I think the more unstructured and complicated a problem can be, it can almost be a little deceptive of what's good-- which can have one or two bad outcomes. You find a good solution, but you don't realize it's good so you keep going You spend a lot of time chasing after an outcome, and only then do you realize, I solved the problem I was trying to solve but it wasn’t the problem I wanted to solve. Something I've really been experimenting with in my own work is having a lot more of an explicit design stage at the beginning of a project and thinking, how can you do a pilot? If I'm trying to predict some target, can I take those two values of that target and plug them into a downstream problem I actually thought that I was going to solve, and make sure that's actually what I want to solve? Almost like frontloading model evaluation with even a fake solution is the first step versus last step. Then, I'll check on one other point. I think the other aspect of that – going back to that level of abstraction – is figuring out how to take the context out of my problem to make it something more Googleable. So I mean thinking, not being like, “oh, this experiment, the random seeds were wrong, so I don't have a control population - what do I do?” Backing that into more of a general question - “how do you sample a synthetic control through observational data?” which is something you can Google and then find a ton of resources about. I think pushing myself on what I want, and then finding the right framing at which to ask for help. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

How to deploy a Dash application from VS Code to Posit Connect

Episode 1: Publishing a Dash application to Posit Connect Led by: Ryan Johnson, Data Science Advisor Follow-up links: * Posit Team: https://posit.co/products/enterprise/team/ * Talk to us directly: https://posit.co/schedule-a-call/?booking_calendar__c=RST_YT_Demo * Follow-along blog post: https://posit.co/blog/deploying-a-dash-application-to-posit-connect/ * Source code for example: https://github.com/sol-eng/python-examples * Posit Team demo resources: pos.it/demo-resources Timestamps: 00:53 - What is Posit Team? (Posit Workbench, Posit Connect, Posit Package Manager) 3:25 - JumpStart Examples in Posit Connect 4:00 - Opening up a VS Code Session in Posit Workbench 8:03 - Downloading the Dash JumpStart example in Posit Connect 9:40 - Open up the JumpStart example in VS Code in Posit Workbench 11:18 - Install the required Python packages using pip 12:55 - Python packages on Posit Package Manager 15:12 - Defining the packages you want to install (latest, specific date, etc.) 19:12 - Creating an API key in Posit Connect 20:29 - Deployment process from VS Code to Posit Connect 21:36 - Dash application hosted on Posit Connect 22:23 - Access controls in Posit Connect (sharing with the people who need to see your app) 24:10 - Adjusting the runtime of an application in Posit Connect (controlling compute resources) 26:35 - Tags in Posit Connect 26:48 - Vars (if your content requires privileged access) 27:15 - Recap of what was covered in the demo On the last Wednesday of every month, we host a Posit Team demo and Q&A session that is open to all. You can use this to add the event to your own calendar. Who are these monthly demos for? Everyone is welcome to join us - regardless of industry, background, or experience! We will discuss topics that will speak to: * Data scientists and administrators new to Posit Team or are looking to grow their understanding of our toolchain, * Teams searching for a new analytic platform built to support open-source data science, * And, those that are just curious about Posit Team! What you can expect from the monthly Posit Team demo: During the session, we will walk through an end-to-end data science workflow and demo the core functionality of Posit Team while highlighting some of our latest features! While each session's content will vary slightly, here are a few core topics we will address each month: * Open Source Analytics: The future of data science is open source. We'll discuss methods for leveraging open-source tools and packages in a secure and scalable way! * Deployment: How to share the amazing data science assets your Team has built, including web applications, machine learning models, APIs, and more! * Data Access: Data comes in various forms and is stored in various ways. We'll discuss best practices for accessing, reading, and writing data! * Job Scheduling: Do you have recurring data science jobs? We'll show you how to automate these processes using Posit Connect. What is Posit Team? Posit Team is a bundle of our popular professional software (Posit Workbench, Posit Connect, and Posit Package Manager) for developing data science projects, publishing data products, and managing packages. Registration is not required. The event will be streamed through YouTube Premiere

Natalie O'Shea @ BetterUp | Focus on relationships first | Data Science Hangout

We were recently joined by Natalie O’Shea, Analytics Consultant at BetterUp. In this hangout we discussed developing communities of belonging in data science, building a business case and so much more! (43:02) On building procedures and standards for your data science team: Focus on relationships first. Inevitably, what you define as the best practice in the first case, you're not going to be doing that best practice in 6 months. Not always - there are certain things that will stay, and teaching your team to use Git branches and merge into main and those things are going to be pretty stable. But there are other more thorny questions that are really hard to build best practices around. For example, in our team, we support a lot of different organizations with different data analyses. Do we put everything into one big GitHub repo? Do we make a repo for every organization for every project? It's always in development and it's constantly iterating, so being comfortable with that is a key part of developing best practices, because it's never going to be just like a set it and forget it. It's going to be constantly in building relationships and those best practices together. We run an R users office hours every two weeks and I often don't have an agenda or anything. It's a great way to just first and foremost build community. But that's also where people come to say, "I'm really struggling with this; how do you approach this?" And now we're starting to build those best practices as we build out the whole Posit onboarding infrastructure. Our hope is that we have internal packages. I think building that into vignettes, like Confluence documents (I did see that Quarto is supporting Confluence documentation, which is awesome) Be comfortable with iterating and make sure that you're always communicating with each other and building strong relationships. __________________________________________________________________________ (52:06) On building a business case for data science tools: We did a survey of people in terms of how much time are people spending building a single slide deck? Where are all the places that you have to go? On average, you spend, say, 10 hours building this slide deck. Then hey, “if all of these data points were populated automatically for you, how much time would be remaining?” We used that and factored in people's salaries and all of these things, and then came up with a rough estimate of the time-cost savings that building out this app would have. The app I've built now, I don't think anyone would have been able to see that if I just started with that. Building MVP– minimally viable products - like little pieces of the pie is important. I started off doing parameterized reporting with R Markdown, and people were like, "That's amazing!" But the problem is that people aren't going to have the kind of flexibility on the end user side. So I'm like, “this is great - this is how we actually populate slide decks with data insights, but if you want the flexibility to change the branding, alter the wording, all of this stuff like, this is our next product.” Then you just kind of work your way up - where now I have a production-grade Shiny app that does all of this for you. Build in little chunks that get you there to your end result. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Oscar Baruffa @ IDH | Hiring for skills you don't have | Data Science Hangout

We were joined by Oscar Baruffa, Senior Analytics Manager at IDH. We discussed the "soft skills" side of data science and how we manage ourselves, our teams and our projects - along with hiring. Specifically at 34:14, Oscar talked about the difference in hiring to replace you versus hiring for skills you don't have. "When you're getting into a management position, at some point you need to start hiring people and a team around you. Hiring is actually very different depending on different points in your career. I thought that it had been the same, because I had done hiring before, even when I wasn't a manager. There comes this split that I think takes some people by surprise the first time. So before you're, let's say, a “manager manager”, if you are growing in your career and you do happen to be hiring someone, you are hiring someone most likely to replace you - like a more junior version of yourself. You're going to train them and you are an expert of some level, and you're going to bring them up to your level or something like that. You feel, I suppose, in a way, quite secure in what it is you're doing because you've already got much more knowledge than them and you are bringing them up to your level. You feel very confident in answering the questions and mentoring them and all of that. But it becomes very different if you're starting to hire for skills you don't have. For example, I don't have a stats background. I don't have a data engineering background. I don't have even that much, actually, of an analyst background. I was a market analyst, not a data analyst, per se. When you start hiring these team members into your team, these are for skills I don't have. It becomes much more difficult for me to also assess - what are they good at or not? I first started - like I think most people start - trying to learn as much data engineering and data science as I could to try and just stay at least up with them. But there's no way that you can do that. It clicks and you're like, OK, wait, my job is not to be the expert of all of the people in my team. I am the facilitator of their work. If I know more than them, that's probably actually a problem. We can't have me spend 10% of time on all of these different vast fields while still being the expert in everything. That's not going to be great and that's quite a different switch. I think it takes a bit of getting used to - to start not understanding bits of what it is. Then the tables turn a little bit. I'm in some ways the non-technical manager of technical people, although I'm kind of technical myself. They'll talk about things, and I also have to be like, whoa, whoa. Then I start also clicking a lot of what my directors and execs feel like sometimes. I'm like, yeah, you're getting into way too much detail. I can't do anything with what you're telling me. My role mostly is to try and not just prioritize work but try and help them uncover where there's work that needs to be done. Or if there's a dilemma, try and break down, well, what if we go this direction? How bad will it be if this happens? Or how good will it be if this happens? Can we weigh risk like this? So I can convey to them as well where things just aren't a priority. Because no matter what they do or stress about here, it isn’t going to matter later. That's quite a different ballpark. I think it probably happens the first time you hire someone that's not a replacement for you, it's an addition to your team. It can catch you by surprise." ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Leveraging Pins with Posit Connect

Leveraging Pins with Posit Connect Ryan Johnson, Data Science Advisor at Posit You might find this helpful if: * You have reports that need to be regularly updated so you want to schedule them to run with the newest data each week * You reuse data across multiple projects or pieces of content (Shiny app, Jupyter Notebooks, Quarto doc, etc.) * You’ve chased a CSV through a series of email exchanges, or had to decide between data-final.csv and data-final-final.csv * You haven’t heard of pins yet! For some workflows, a CSV file is the best choice for storing data. However, for the majority of cases, the data would do better if stored somewhere centrally accessible by multiple people where the latest version is always available. This is particularly true if that data is reused across multiple projects or pieces of content. With the pins package it’s easier than ever to have repeatable data. Timestamps: 0:17 - install the pins package and load into your environment 0:32 - register a board 0:59 - connecting to your Posit Connect instance from Posit Workbench or RStudio IDE 1:43 - define the Connect instance as your board 2:01 - pin the mtcars dataset to your Connect instance 2:38 - a pinned dataset on Posit Connect 2:50 - reading a pinned dataset Additional resources: * Example workflow that involves Quarto, pins, plumber API, vetiver and shiny: machine-learning-pipeline-with-vetiver-and-quarto/ * Connect User Guide - Pins for R: https://docs.posit.co/connect/user/pins/ * Connect User Guide - Pins for Python: https://docs.posit.co/connect/user/python-pins/ * 9 ways to use Posit Connect that you shouldn’t miss: https://posit.co/blog/9-ways-posit-connect/ Learn more: If you haven’t had a chance to try Posit Connect before or you’d like to learn how your team can better leverage pins, schedule a demo with our team to learn more! https://posit.co/schedule-a-call/?booking_calendar__c=RSC_Demo On the last Wednesday of every month, we host a Posit Team demo and Q&A session that is open to all. You can use this to add the event to your own calendar: pos.it/team-demo

Dan Negrey @ MarketBridge | Creating a framework for consistent measurement | Data Science Hangout

We were joined by Dan Negrey, Director, Analytics at MarketBridge. At (15:11) we asked Dan about a tip for impacting the business with data science. So I think every business is going to have KPIs (Key Performance Indicators), and there's going to be other metrics besides KPIs, things that lead into that. As crazy as it sounds, some organizations struggle to measure those and to do so in a consistent and repeatable way. Maybe they measure something that just comes from one person sitting at a desk, and they've done it for six years, and they leave. All of a sudden, who knows how they do that? Creating a framework for consistent measurement is huge for an organization. The measurement is consistent and the outcomes are measured consistently. Then taking action to improve those outcomes can be thought of as more reliable because the measurement process is consistent. So that would be one thing for sure. Another – on that note, is decision making. Every company makes decisions. A lot of us are here because we like to do this kind of work, but most of our companies exist because they like to make money, and they like to grow. So we find a balance between doing what we do to help our company to achieve their goals. Find ways to help your company optimize cost, reduce waste and increase growth. All of that is through measuring and looking at decisions that have been made in the past and thinking about how they could have been made differently. This could be through historical analysis or building models to help make those decisions more effectively. That’s a huge win for any organization. ____ There was also lot of love for repeatable data with the pins package at this Data Science Hangout. Dan Negrey shared: "Pins has been a huge package that we've started using a year or so ago...if you've never used pins, it's definitely worth checking out." Helpful resources on pins: Pins for R: http://pins.rstudio.com/ Pins for Python: https://lnkd.in/ghmxiEHV Great repo that uses pins: https://lnkd.in/ezvBkav Workflow that involves Quarto, pins, plumber API, vetiver and shiny: https://lnkd.in/e6gnMXfD Link to Ryan's video & stepping stones: https://lnkd.in/erR-Mjr9 Other resources: MarketBridge career page (with open data science roles): https://marketbridge.applytojob.com/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Nick Pylypiw @ Cape Fear Collective | Building up trust with stakeholders | Data Science Hangout

We were joined by Nick Pylypiw, Chief Data Officer at Cape Fear Collective. Nick shared that one thing he’s been successful at in his career is building up trust with stakeholders. How do you build that trust? First off, people like data and analytics if it confirms their preconceived biases and beliefs A lot of people are fans of analytics and data science if it's confirming what they already believe. One of the things working in pricing and marketing analytics at Lowe's, is if I went into a room with a bunch of people who are marketing lumber and I said, “Hey, the promotion that you did last week did really well.” They may think, yeah, we really like this guy. If I said, “Hey, but it missed these markets and these markets” …if it counters their beliefs then that's where they start poking holes and wanting to find fault in it. Find some quick wins. What are some small projects we can work on together that are low stakes projects that get people to buy into it and see, “Ok, now I can see why there's value in this way of thinking.” When that inevitable cognitive dissonance comes, then you've built up some trust and some capital with that partner. There has to be a boots on the ground connection. For the same reasons we talked about building trust and capital in a Fortune 500 company, if you think about the state in that same way, if you're from the beach town and you try to go up into the mountains and tell them what they don't know about their public health crises, you're going to get run out of there. You still have to build these connections organically. The same way we talk about building capital in a big company - especially with this line of work, you'd be surprised at how much buy-in you can get if you show up and you actually know how to pronounce the town name and the county name correctly. Do you go into Beaufort, North Carolina and you say “Buford”. That's South Carolina by the way. Spelled exactly the same, Beaufort in South Carolina and Beaufort in North Carolina. If you pronounce it the other way, you've lost them before even the first slides hit the screen because it's just some outsider coming in here who knows nothing about us. Learn about their area (of work) One of the things that our data science team has done is when you're learning about a new area, one of the first tasks that we start doing is putting together a deck of the five worst areas of poverty in this area. I want to know the five areas where there's great employment parity or great unemployment rates, all these different things. Through that exploration, you understand, oh, there's this town here and there's this county and you learn about that area. So now when you are in a conversation, you can intelligently talk about that region. It doesn't sound like you're just some person looking at a chart and saying, you need to do this and this. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Dara Straussman @ Stripe | Partnership-model when forecasting | Data Science Hangout

We were joined by Data Straussman, Data Science Manager at Stripe. Dara's team focuses on forecasting, machine learning, and analytics in the Finance and Operations spaces. We were able to get a glimpse into their financial modeling methodology at Stripe after a question from Alan at 12:59 Question: How do you get buy-in from business stakeholders who historically would have been setting targets (like salespeople and their leaders) that the forecasting you're doing seems like a realistic enough picture of the world? Answer: It's an ongoing evolution for how we do this interaction because it's very important to get the input from stakeholders. There might be a big sales deal coming down the pipeline that no statistical model will know about. There might be some product launch plans that we're just not going to be able to learn from history. So we find that interaction very important. When we started doing the – we call it the statistical forecasting and the adjustments — it was all done in spreadsheets by finance folks. We did really have to prove our value and show how a statistical model will actually help. So our current system: 1. We have the statistical model that's all done by data science. 2. We do backtesting and reports to be able to build confidence in each level of the hierarchy so that people can understand where do they expect accuracy, where do they expect less accuracy. 3. We have a whole process with the finance and strategy team, where they make adjustments to the forecast and are pretty clear about where they are doing adjustments and how it's affecting the total. 4. Then we track both - the statistical versus the adjusted forecast. What we've learned over time is that making adjustments in certain ways or certain places is very important and very effective. We sort of encouraged the finance and strategy team to focus their efforts there. Whereas in other places, the statistical model is quite accurate, and it's best to spend effort elsewhere. So that's the baseline of how we think about it– again, ever evolving. We also have an embedded model so we sit with finance and strategy. We work really closely with them and really understand their problems. It's very much a partnership-type model rather than a "throw a model over the wall" type model. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Art Steinmetz, (formerly) @ Oppenheimer Funds | Communicating with Execs | Data Science Hangout

We were joined by Art Steinmetz, private investor and former Chairman, CEO and President of OppenheimerFunds. During our conversation, Art shared his perspective on open source data science and how these tools helped contribute to the company's bottom line. He also shared a few tips on communicating with executives - from an executive's point of view, included below: Two key points (20:31) 1. Data visualization is the most powerful way to get a narrative across quickly. 2. Don't use a PowerPoint presentation when you should be using a memo that people can read in a narrative On point one: Spend time learning good visualization techniques. Even if you're a math whiz and a coding whiz, understand good visualization. It's an aesthetic issue. It's an art. There’s lots of blogs, lots of literature that talk about good visualization and bad visualization. For example: Don't use a linear scale when you should be using a log scale, simple stuff like that because I've seen so many bad charts. At best, you're going to lose your audience quickly. At worst, your audience will draw the wrong conclusions and make bad decisions. One point two: The physicist Richard Feynman was on the Challenger disaster panel. One of the big takeaways in that inquiry was shortcomings of PowerPoint for technical work.. So the other point I would make is, don't use a PowerPoint presentation when you should be using a memo that people can read in a narrative. All of you are familiar with the problem of seeing a PowerPoint slide. You've got landscape orientation and you've got this incredible amount of 12 point text filling up a slide. Sometimes you're subjected to sitting in a conference room, having that thing with 2 minutes worth of text on a slide that you're supposed to process. Of course, nobody looks at that at all as they're listening to, hopefully, what the speaker is saying. It's a complete waste of everyone's time to create and display this giant PowerPoint, this massive quantity of text on a PowerPoint slide. So don't use PowerPoint when a pre-read memo would do the job. And yeah, some people aren't going to read the memo. But treat your audience with respect anyway and tell them you're going to assume people have read the materials. Then if you're going to have a PowerPoint or a slide deck: have three, four bullet points at most on a slide with compelling visualizations that tell your story. That's going to earn you far more respect than showing that you've crammed your PowerPoint with a million words. _____ Example of Shiny generating discussions and helping investors: (37:30) So for instance, at Invesco right now– one of their folks has created a Shiny app that if one of our customers (our brokers) shares their customer’s (end investor) portfolios with us - we can run an analysis on that to help them optimize their tax situation– when they should take capital gains, when they should take capital losses, what they should sell to realize a loss or a gain. Then if they want to keep the portfolio complexion the same, what should they then buy on the other side to keep the portfolio complexion– the overall risk or aggressiveness - the same. He built a Shiny app that does that. All of the Invesco salespeople can look at all of the advisors that they serve (in that Shiny app) and that generates discussions. Salespeople love to have opportunities to call their customers up and have an interesting discussion. To say, “look, here's an issue you have that I think I can help you solve.” Then if the discussion goes further, with one click, they generate a detailed PDF report on exactly what the gains and losses are across the portfolio. And the salespeople just love that tool. ____ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Data Science Hangout | Regis James, Regeneron | Achieving scalability & showing value of community

We were joined by Regis James, Senior Manager (Biopharmaceutical Data Science) at Regeneron Pharmaceuticals. Achieving scalability through standardization (19:04) In order to achieve scalability I do have to standardize things a bit. We use Confluence, so on the wiki that I've made, I have a lockdown page where I have some standardized messages that I send out to people at different phases of things. I do customize and read what people say about themselves and I'm thinking, “this could be useful here, that could be useful there.” One of the things that I do is– I have this whole ingestion pipeline that I maintain to identify new members of the community and who can help the community learn more about the blockers to current responsibilities they have for their job. All companies have a directory where you can learn about who exists at the company and see people's job titles. I can look into the directory for data or data science or whatever and see if the people that come out in the results are already in my Microsoft Teams group because they could be potential new people who could benefit from being in the community. They might be able to do their job better and also help other people do their job better, if they can connect to others who have similar needs or can offer similar things. I do this on a semi-regular basis, so I can really help onboard people who have just joined the company or maybe moved into a data science type role. Then I reach out to these people, and use my standardized message like, “Hey, I just want you to know this group exists. This community was built just for people like you. This is what we do and here's a link to the wiki that has the links of all the recordings” - because we have a growing library of all the conversations just like the Hangouts. I only send one email and ask people to let me know if they’re interested in it. I don't spam people. Of the subset, who answer - sometimes it's like 20% that respond, they say, “Yes, this is amazing. Thank you for letting me know. I just came to the company. I didn't even know if there was a community.” I'm also doing it from my own perspective of when I joined the company. When they respond and then they say they're interested, I add them to the Teams group, to the Active Directory group, and then forward the invitation to the ongoing gatherings every other Friday. In the invitation text I paste in, “Welcome to the group. Please introduce yourself in the introduction section" and include a link that takes them directly into not just the Team for the group, but the channel of introductions. Since doing this, I've gotten a lot higher percentage of people who have been added to the group to start introducing themselves and say what they work on. I see what people are working on, and integrate that with my understanding of what people need help with from the Shiny app (the poll for what talks people are interested in.) Then I reach out to them and say, “Hey, would you be interested in being one of the guests for an upcoming event?” I send them the link and they can see there's no pressure because I'm a year out. I've got someone every other week. So it's like, Hey, would this be something you’re interested in and then I am gently having a conversation over time. Sometimes people drop out and I just reach out to those people and ask if they would be able to switch. ______ How do you show the value of community? (40:14) To prove the contribution, the benefit of a community – I use the words stuck and unstuck because that's really the only thing the business, or any organization cares about. There's a bunch of stuff that the organization has to do. There are obstacles in the way, they are stuck. How do they get unstuck? That's what data science is for, really. You can argue that it’s also for going from stuck to unstuck. The non-computational people who have the power to bestow upon you promotions, more money, or more influence over the company to make other decisions, really care about the value of the institution. If you can show how the existence of that community propagates through the network that exists in the institution so that something goes from stuck to unstuck and you can mark that, you can measure it, then you can say this only happened because of me. If you're a community that's gathered around pulling thorns out of your colleagues arms, you can say, we pulled 15 thorns out or we taught another group how to pull 100 thorns out because we were force multipliers. If you say, “what do you hate about your job?" to the person with power that I need to prove the community's value to, think about how you’re reducing those pain points for those people and enabling them to go from stuck to unstuck. Collect those things and showcase them. From the get go, you should be thinking about how to indicate success to leadership. To join future hangouts: pos.it/dsh (All are welcome! We'd love to see you!)

Data Science Hangout | Namrata Shetty-Anderson, UPS Store | Professional development for data teams

We were joined by Namrata Shetty-Anderson, Advanced Analytics Manager at The UPS Store. Right from the beginning of the conversation with Namrata, we dove into the art of data storytelling (7:20) ...In my mind, data science is as much a science as it is an art. To be able to tell your story, you could write hundreds of lines of code and you could build amazing models, but at the end of the day if you can't translate it to a non-data person as a data story, all that is a waste. You're not adding any value. Businesses don't care about your code. My boss doesn't care about my hundreds of lines of code. He wants value. So I call it the art of data stories. I actually took a course by Kevin Hartman from Google, which talks about this. One thing I learned from that course is that you want to start with a solution for the problem in a tool agnostic environment. Don't start with your tool when the problem is posed in front of you because what happens is the tool is going to limit your ability to tell your story. If I say I'm going to use Power BI, then I'm going to be limited to the visuals that are available in Power BI. So what I started doing, literally, was use a blank page and a pen. What do I want my outcome to look like? Once I know what that story is going to look like on a blank sheet of paper, then I decide what tool I'm going to use. Now the tool is going to depend on a few things: How quickly do I need to turn this around? How complex is this task? How good am I with this tool? Those are the things you need to look at. People say, Python's better than R, R is better than Python. No, Power BI is the way to go. Tableau is the way to go. No, everybody's right and everybody is wrong. Your tool of choice will depend on the problem you're trying to solve. Don't fall in love with anything you're doing. Today you're doing it one way, you're going to find a better way to do it as you keep learning. *The course mentioned above was Business Analytics, Communicating With Data by Kevin Hartman: https://www.coursera.org/learn/intro-business-analytics &/or https://elvtr.com/course/storytelling-with-data _____ Namrata also shared thoughts on approaches to professional development and skill building - (22:58) Managing a data science team is different from managing a team that's operations driven, product driven, or even a software engineering team. It's different because problems that come to a data science team are not always well defined and the solution is not cookie cutter. There's no right answer, there's no wrong answer. You need a research oriented approach to what we do and a drive to keep learning. So here's what we have been doing. 1. My whole team has the option of picking their learning platform. Some pick Coursera, some pick a virtual summit or buy a book, whatever. You tell me what your best learning approach is and you can have it. 2. The team is also encouraged to block off time on your calendar. X hours a week or X hours a month, whatever you like to focus on learning and put it on your calendar. No one's going to bother you. Do we do it? Sometimes. They'll block it off and sometimes won't do it. It happens to the best of us. 3. We have a week long group camp Every year we have kind of a week long group camp, as I call it, where we bring in a certified instructor (we've been focusing on R for the last two years at least) and just work on little projects with the team. For this week, the team is not bothered by anyone just to focus on learning. (more on how they block that time at 31:51) 4. We started having monthly sessions. We started with best practice sessions where one of the team members would say, hey look, this is a project I worked on and here's how I approach the problem. This helps someone that's new on the team, someone that's learning to get some exposure to it. You can learn everything online, you can learn from instructors but then when you have to apply it in the real world, it's not a one to one translation. Real world data is not normally distributed. Real world data has noise in it. When we maxed out our best practice sessions, we started doing a micro assignment. My team member Liz puts together micro assignments for the team with real work data - like online reviews, customer data, sales data - and perform these operations in R. 5. We put together a central repository Because my team does so many things, we have a repository where we have reference guides, best practices, and a lot of documentation. If someone new joins my team, I tell them you're going to spend your first week just reading everything that's on that repository. Documentation, process documentation plays a very important role and it allows us to cover for each other. We want to rely on processes that make our people's lives easier. ____ To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Data Science Hangout | Emi Zambada, Nationwide Financial | Building a story for the C-suite

We were joined by Emi Zambada, Assistant Vice President, Annuity Distribution Intelligence at Nationwide Financial. In talking with Emi about data science in insurance technology and managing teams through digital transformations, we also learned more about building a story for the C-suite in our own organizations. (40:26) Storytelling is critical. I go back to thinking about how we speak to little kids. You need to speak their language. They are not going to speak your language. If you spoke in Shakespearean language to a kid, they’re not going to understand you and they don’t care. You’re going to lose them. Listen to your leaders. Listen to the earning calls or whatever ways that they present to the organization. Pay attention to the words they use. Pay attention to how they describe things and the metrics they share. Use the words they use. Words are important. You can say the same thing with different words. We talk about data foundation, but if you say data integrity - you might say, well data integrity and data foundation are the same. If they use the word data foundation, use that. Use their words so they can understand you. Keep it consistent. Think about the old times - and the old fashioned newspaper. The New York Times always looks the same. They have the same structure, the same format. The articles and the layout are similar every day. Imagine if they tweak things and move things around - you will spend a lot of energy and time trying to figure it out. It takes mental energy to figure out the format and the structure that you don’t care about. You want to read the news. All your energy is to understand the content of the news. The same thing with storytelling and dashboards. Find a way to use the words, the structure, and the metrics that they use and are familiar with so that they don’t spend time and energy trying to understand your dashboard - your story. For example: the metric that you’re tracking is going this way, we might need to take action on this. Be very specific. Don’t say a lot of things. Less is better. Be clear, concise, and very specific on the things that you really care about. Repeat the things you care about. Silence doesn’t mean they agree. Watch them, look at their expressions and repeat. A mistake that we can make is that we don’t recap what we talked about last month. You can’t walk into a meeting thinking that they remember everything that happened last month. They don’t. They have done so many things since then. Don’t feel offended, it’s normal. Go back to that and say, last time we spoke we did this. Now we’re going to do this and let’s bring it to the present. Speaker bio: Emi leads strategic business intelligence for Nationwide Annuity distribution by leveraging data, analytics and technology and partnering with senior leadership to identify trends and measure efficiency and effectiveness of distribution. Emi is recognized as a thought leader and was recognized by MIT Chief Data Officer Magazine´s “40 Under Forty Data Leaders in 2022” and is a recipient of the Hispanic Association on Corporate Responsibility’s “2022 Young Hispanic Corporate Achievers Award”. A proud Mexican immigrant, Emi serves as an advocate for minority groups in AI and tech. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Data Science Hangout | Yu Cao, Exeter Finance | Impacting business with data science

We were joined by Yu Cao, VP Data Science at Exeter Finance. Speaker bio: Data science professional, EMBA student, amateur biologist, rock'n'roll zealot. Not only viewing business problems from the perspective of data science, but also from the perspective of the business. Yu shared his thoughts on creativity as a data scientist, working with the business, growing in your career, and much more. A few snippets from our conversation on building relationships with the business. (15:32) It all started with networking. I work with a very analytical team, so a lot of the people there just want to code and build a fantastic model, not spend time networking with people from other business lines. But actually, this is not the right attitude. If you want to collaborate with other people, first you need to get to know them. You need to understand their business – and you need to become their friends. Usually I reach out to them and do a self-introduction to let them know what I do and what my team can do. Let's go out for lunch, let's grab a cup of coffee and talk for 15 minutes to see what kind of business difficulties you are having now. If you are a sales team, are you worrying about how to increase the volume of sales? If you are a loan origination team, are you worried about how to increase the capture rate? Let's talk about that. Then I will see, how can I connect my data set knowledge with the problem you are facing now? I can see whether I can build a model to help you solve that problem you have. Usually, the collaboration starts with this. _____________ If you want to build a data science team from scratch (25:51) : 1. Figure out what your team can do for the business. If data science has little to do for the business, it will create very little value there and it doesn’t make sense for you to build a team. You probably need to go to a new company. 2. If you determine that data science can provide value to the business, you need to do some public relations. You need to let everybody, all the leaders in the company, know and understand this. 3. Reach out to the leaders in your organization and ask them to provide feedback for your work. Tell them “My goal is to build a data science team and become a data science leader in this company. What do I need to do to make this happen?” Everything starts from the people, the buy-in from other leaders. 4. If you are a data scientist wanting to move into leadership, you need to show ambition to leadership. The job market for data science is hard. This is the right time for you to show the company that you want to invest in yourself - that you don’t want to just code and build models, but also increase your influence, learn more about the whole business, and contribute to the entire organization. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Data Science Hangout | Satish Murthy, Janssen | Creating a validated environment for reproducibility

We were recently joined by Satish J. Murthy, Senior Manager, Pharma R&D IT at Janssen. During the hangout we dove into the topic of validation and what it actually means to have a validated environment. Snippets from our conversation with Satish: In regulated industries like pharma and finance, any time a health reporting agency comes in and asks for proof, we have to be comfortable in saying that what we ran a couple of days ago, weeks ago, months ago, or years ago – we need to be in a position to replicate that entire environment as is. So this goes through a formal validation/verification process. And this, in traditional parlance, is known as a GXP platform. [GxP is a general abbreviation for the "good practice" quality guidelines and regulations.] X will stand for clinical practice, manufacturing practice, lab practice, etc. So the verification validated environment is often referred to as a GxP environment. It is to build confidence for the end users that we serve in pharma. It is for folks who want to help deliver patient care, which is crucial. One of the key components of this is containerization. Because the ask in validation is to ensure repeatability and reproducibility, the only logical choice at this time is containers. We have to containerize. I have a responsibility for the R part of it so I'm going to concentrate a little bit more on that. First: Users would want to basically run through their studies using R. We are using the Posit Workbench as our launch pad. This is deployed on a traditional EC2 instance. This is a production platform but there is a logical grouping where our users would first want to come and test it in a development area, and then move it into the traditional QA and prod. The way they would first test in the development area is, number one, the entire Posit Workbench is containerized. We always try to keep up with the latest release of Posit. There are some unique challenges around it - mostly about the IQOQ, and the time that it takes. It’s not just Posit, there are other vendors [like Atorus] who are helping us with this effort. We have to go through a formal process of vetting, validating, and verifying. So all of this takes time. That's one challenge that we have. Second: Because we have containerized, it gives our users the capability to test something that is locked down and be ensured that, again, from a repeatability perspective, containers are the only way that will help them with that. So first, we will go through defining the process and specifying the requirements as to what really needs to go into the container. For example, if our users have identified a set of workflows that they would want to help them with the clinical studies that they are looking into. Traditionally, it will map to a set of R packages - the packages that need to make it into this container. This is where we are using standard technologies and tools that are used in the industry. We have a Jenkins pipeline and we are using the Posit Package Manager to lock down versions of our packages that we want to go into the container. There are some business processes that are defined as to what should go into this container. Once we have defined, we run through the build process to create the container and we will verify it in the traditional Posit Workbench. Once our users are comfortable that, yes, this is the workflow, this fits our model and we don't see any issues, that container is then locked. Then it moves into the QA/Prod area and that is what is deemed as a validated run. Anything that the users would try out in their development environment is a prerequisite, but it is not deemed as a validated run. It is only when it is run through the production that it is deemed as validated. In order to do that, all of these validated runs behind the scenes will run as a service account user. Behind the scenes, we also have the traditional EKS cluster where we scale up and launch these containers. _ Thank you to the Atorus team for their help in planning today's session! Resources shared during the call: R Validation Hub: https://www.pharmar.org/ riskmetric: https://pharmar.github.io/riskmetric/ Atorus Resources: https://www.atorusresearch.com/resources/the-power-of-a-trusted-partner/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Justin Elszasz @ City of Baltimore | Data governance to better serve citizens | Data Science Hangout

We were joined by Justin Elszasz, Chief Data Officer at the City of Baltimore. Justin shared about the collaboration happening among other US governmental data organizations and Michael Bloomberg's theory of change that if you really want to make an impact on people's lives, local government is the place to be. At the Federal level, you've got a very wide, broad impact but it is very hard to see this trillion dollar budget trickle down to see how it impacts someone's lives. Local government is where that money eventually comes into your programs and projects. Justin shared two organizations he is involved in: 1. Bloomberg Philanthropies City Data Alliance: https://lnkd.in/en_-f4nS 2. Civic Analytics Network: https://lnkd.in/e2imBP3k As a group, we also shared portals for public data across various cities: ⬢ Baltimore: https://lnkd.in/eGm_AKZd ⬢ San Francisco: https://lnkd.in/e66UeK2w ⬢ LA: https://data.lacity.org/ ⬢ Chicago: https://lnkd.in/eMHpdb3P ⬢ Washington, DC: https://opendata.dc.gov/ ⬢ Toronto: https://open.toronto.ca/ ⬢ Ottawa: https://lnkd.in/e8yhMzMk Part of the hangout touched upon legacy systems in government and the City of Baltimore’s direction in regards to hardware and cloud infrastructure. Justin shared that: Chief Data Officer roles run the spectrum in terms of responsibilities, and where they are situated organizationally. I have a little bit less control over architecture. That said, yes there are tons of legacy applications and infrastructure to deal with. Luckily, like we have an IT department that's on it right now, and has plans to mitigate a lot of that. As those things get replaced, we will only go up in terms of data quality. Just over the last 2 years, we have rolled out Workday as our new ERP (enterprise resource planning) system. That in and of itself, has addressed a lot of Finance and HR data quality issues. We're making moves in that direction. Yes, there's legacy systems but I challenge you to find a Chief Data Officer in another city who doesn't reckon with that sometimes. As far as cloud goes, our GIS team is working hard on moving a lot of our GIS layers and infrastructure over to cloud with Esri - as well as our Baltimore open data platform. I think it's picking your battles and where you're going to get the most bang for the buck at the moment for us. I'm looking to pilot a couple of tools to move to the cloud. One example I'll mention is that over the summer, we partnered with a group at Carnegie Mellon called Data Science for Social Good. You may know this already, but Baltimore has a lot of vacant buildings and when a building sits vacant for long enough, rooftops tend to become an issue. Back in January of last year, we had 3 firefighters killed when a rooftop was in a fully involved fire. We partnered with Carnegie Mellon to develop an AI tool that will detect collapsed roofs based on our aerial flyover imagery. Every year we get new flyover imagery, which means every year we'll want to refine and update the model and get new predictions, so we can find any new buildings that might have rooftop issues. We can't run that on someone's local machine, so that's a good opportunity to start pushing the bounds of what we can do and moving to the cloud. We're looking for a couple of places to definitely move that way. Other resources shared in the chat: ⬢ An article on Data Engineers before Data Scientists by Keith McNulty: https://lnkd.in/evTpT7ee ⬢ A citizen data science analysis of Baltimore Waterbill system: https://lnkd.in/eXGm6Ek4 ⬢ Book recommendation - "Change: How to Make Big Things Happen": https://lnkd.in/eVH4NHSC We were joined by Justin Elszasz, Chief Data Officer at the City of Baltimore. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Data Science Hangout | JJ Allaire, Posit PBC | Making data science more open and collaborative

We were recently joined by J.J. Allaire, Founder and CEO of Posit PBC. What made you so interested in open source initially? It was really that the first big software projects I worked on were proprietary software, and I found it disappointing that proprietary software products kind of get very tied up with the company that sponsors them – the fate of that company, the other products that that the company might offer, or who that company gets acquired by really end up affecting the fate of the projects. I also found that while some proprietary software is either cheap or low cost, there's a certain impediment to adoption associated with the price. As a creative person, I want my work to be available as broadly as possible. I like to make the work available to as many people as possible, but also there's a dimension of the durability of the work. Is the work going to be around in 30 years or 40 years? What are the things that would make it? So that's what made open source appealing to me. What are you most excited about at Posit in the next year ahead? It's going to be related to Quarto, of course, because that's what I'm spending all my time on. I would say that the thing I'm excited about is that we recognized Quarto is really powerful and flexible, and easy to use for a certain subset of users who are very technical and very motivated, but we actually want to make Quarto available more broadly. So working on tooling that lets both technical and non technical people collaborate over documents, and also lets some less technical people participate in using Quarto. (You can sort of see some of this work in the visual editor that's in RStudio) Those are the kind of things that I'm focused on for the next year, and I'm excited to see those getting realized. In hindsight, what is one of the best decisions that you ever made for your career or your education? I think this actually has a lot of commonality with talking to a lot of people about their careers. I think when I was about in my late 30s, I had been involved in both developing software and also starting companies. I really learned about myself, that the company part of things – the management and entrepreneurship – I really did not like it all. I didn't enjoy it. So I kind of said, I'm happy to be involved in starting companies but I'm absolutely not going to do that part, and I'm going to have to find a partner or other people who are excited about doing that part. What I really want to do is focus on engineering and product development. It's very easy as a company founder to get pulled into all kinds of other things and that's what happened to me the first 2 times. Just being super clear about that, and saying I won't even do it unless I can satisfy this condition. I think that's pretty broadly applicable in that a lot of us accumulate a lot of responsibility in our careers. Some of it is necessary and important for a given role or company, but then eventually being clear about, what do I really like to do and really want to do? What do I feel like I should do or what is put upon me? I know a lot of people now who are in their 40s who have actually managed dozens of people and are like, yeah, I don't really want to do that. I don't ever want to have anyone work for me anymore. So I'm going to be really clear about that. I'm going to walk in, and people are going to try to give me people and try to make me a manager, and I'm not going to do it. I think it applies pretty broadly, but just knowing yourself and setting pretty rigid constraints about what you're willing to do in the workplace and not. Everybody's different, and both can be really rewarding. I know a lot of people who find management very rewarding, but I know a lot of people who find it really alienating. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

JJ Allaire

Data Science Hangout | Javier Orraca-Deatcu, Centene | Excel to data science to lead ML engineer

We were joined by Javier Orraca-Deatcu, Lead Machine Learning Engineer at Centene. Among many topics covered, Javier shared how his background in finance and consulting led to his interest in data science to automate some of his work - and how he helped get other data scientists together in his organization. (26:31) How did you organize and recruit people for the data science community group at Centene? So I sort of piggybacked from a general data science community chat that we had at the company. There were several hundred people on this of varying backgrounds and expertise levels so there was a lot of conversation happening. There was already a Python group that was meeting– I think every other month. So three weeks after I started, I got really excited about the possibility of potentially creating something similar for R users. 1. It started by just trying to figure out who owns that already existing data science chat and see if they could help support the idea of creating an R user group, something to meet once a month or once every two months. At larger companies especially, getting that type of top-level executive stamp of approval and support can go a long way, especially if that individual is part of the already existing IT or data science function. 2. At the time, I created a Blogdown site. For those of you who are familiar with R Markdown, Blogdown is a package that allows you to create static websites and blogs with R Markdown. Now with Quarto you can do the same thing and create websites. I love the syntax of Quarto. 3. We had partnerships with Posit, so we were able to get some people to come in and do workshops as well. 4. We also had reticulate sessions, where it was a co-branded Python & R workshop where we were looking at ways in which we can actually communicate between teams of different languages a lot easier. I had a great experience with it. Everyone was so collaborative and it was such a great way to see the excitement around what you could do with both R and Python. I think what started as 13 users the first month, jumped to about 100 - 125 monthly users on this monthly meetup. ...And on the journey to machine learning engineer, what was the hardest part? (49:10): Because of SQL, I had a really good understanding of at least how tabular data could be joined and the different transformations that could be done to these data objects. I think I would have really struggled without that basic understanding. But having said that, I think the part where I really struggled at first was function writing. Function writing was not intuitive to me. Basic function writing was but in general, I found it to be very complicated and it took a solid three to six months of practice to feel actually comfortable with it. Even when I started building Shiny apps– basic Shiny is quite easy but large functions underpin the entirety of a Shiny app. Everything you do within Shiny is effectively writing functions. The process of learning Shiny and becoming more comfortable with Shiny was very difficult and something that just took a lot of repetitions but it all sort of played together. While people may think of Shiny more as a frontend type of system, it did make me a much better programmer in the way I thought about actual functions and function writing. Other things that I found hard, looking back, I'm sort of embarrassed to say this, was reproducibility of machine learning – being able to reproduce a code set and get the exact same predictions every time. I wasn't quite sure why this wasn't working or how to create these fixed views, setting a seed or whatever you need to do to ensure that someone else downstream could replicate your study or analysis and get the exact same findings themselves. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Data Science Hangout | Christina Fillmore, GSK | Open source community collaboration in Pharma

We were recently joined by Christina Fillmore, Data Science Leader at GSK. Christina shared some lessons learned from the pharmaverse and some insight into the community building going on in pharma. For more information on the pharmaverse: https://pharmaverse.org/ "Pharmaverse: Packages for clinical reporting workflows" blog: https://posit.co/blog/pharmaverse-packages-for-clinical-reporting-workflows/ A few tips shared from Christina: I don't personally believe you need to have a big splash in order to start. I think if you wait for a big, beautiful website and everything to be working before you try and get into anything you're gonna just be waiting. Find some friends and start developing packages with them. It doesn't have to be a huge number of people. I wasn't necessarily around for the whole origin story of the pharmaverse but, part of it grew out of Michael Rimmler and Mike Stackhouse saying we should build some packages together and I built a package for them with Atorus. We built a package or two together, and then started having more conversations with other people. And all of a sudden there was a beautiful website, and things were starting to come together. To some extent, it’s just talking to people and starting to build stuff. Eventually you'll have enough stuff that you will have your own “verse.” I highly recommend starting. When things aren’t standard, what do you do? And how do you start? Sometimes a nice starting place is a package that helps standardize stuff. Metadata is not really in a standard format in pharma. Everyone has their own way of holding this metadata. One of the first packages that I built as part of the pharmaverse was a package called Metacor. Part of the reason was that we wanted to build automation off metadata, but everyone held it in a different excel format typically, or it was in a database. It's different for everybody so we needed to have a thing that we could all use. It's the most boring package in the whole wide world, but that enabled additional audit automation to be run off. From there, we then created a different package called Metatools that does a lot of this automation. But the first step was getting that initial thing we could all agree on. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Data Science Hangout | Eric Nantz, Eli Lilly | Innovation in clinical trials with open source

We were recently joined by Eric Nantz, Director at Eli Lilly and Company. Eric shared his view on the biggest changes in pharma over the past 12 years: A lot of change that happened was an initiative many years ago where we tried to minimize the time from a treatment's discovery in the lab to when we can actually get that treatment out to patients to benefit from. To do that, we had to do a lot of things differently. There have been reorgs in terms of how trials are designed and more emphasis into other functionalities but within statistics, it actually empowered us quite a bit to push new cutting-edge algorithms for designing, for simulation, and to not just do the traditional analysis for a clinical outcome. The fact that we got this mandate to shorten the time that we're in the research phase, then we had to really put all hands on deck to put out new solutions. That brought innovation out of necessity but it also opened the door for those in my group at the time to really think differently of how we're leveraging R in the design space, and become one of the industry leaders in clinical simulation. We definitely have relied on a lot of the functionalities that my colleagues have made. I'm not going to come here and say I know all the ins and outs of every statistical method. That's why we have a team. Everybody can specialize in their key areas. But we've all been able to pitch in and be able to bring that innovation to more novel designs that can shorten that time that a patient has to be in a trial. The industry itself still has a ways to go, but just that emphasis on getting these treatments out to patients sooner organically then brought some new innovations that we were doing in statistics and it really gave us a voice at the table that we could bring some really innovative change to how we were doing things in the past. So certainly that's been one of the higher-level changes I've seen. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Quarto with the Quarto Team | An Open-Source Chat

Join Al Manning, Carlos SchIidegger, & Charles Teague, members of the Quarto Team, as they take our questions. Quarto is an open-source tool for scientific and technical publishing. Create dynamic content with Python, R, Julia, and Observable. Author documents as plain text markdown or Jupyter notebooks. Publish high-quality articles, reports, presentations, websites, blogs, and books in HTML, PDF, MS Word, ePub, and more. Author with scientific markdown, including equations, citations, crossrefs, figure panels, callouts, advanced layout, and more. Key Resources: ⬡ Learn more and get started with Quarto at quarto.org Contact ⬡ Bug reports and feature requests - https://github.com/quarto-dev/quarto-cli/issues ⬡ Need help? Github discussions - https://github.com/quarto-dev/quarto-cli/discussions Introduction Videos for Quarto ⬡ Mine and Julia talk, https://www.youtube.com/watch?v=p7Hxu4coDl8 ⬡ Quarto Series, 1️⃣ Welcome to Quarto Workshop led by Tom Mock: https://www.youtube.com/watch?v=yvi5uXQMvu4 2️⃣ Building a Blog with Quarto led by Isabella Velásquez: https://www.youtube.com/watch?v=CVcvXfRyfE0&feature=youtu.be 3️⃣ Beautiful reports and presentations with Quarto led by Tom Mock: https://www.youtube.com/watch?v=hbf7Ai3jnxY&feature=youtu.be Timestamps 00:00:00 Introductions 2:55 Why open source? 6:20 Can we expect to see Quarto available to R-users via CRAN any time soon? 9:10 Quarto and Google Docs? 9:49 Lua filters/shortcodes. Advice for a good development environment for prototyping and debugging? 14:59 Is there a single documentation page for ALL the quarto-specific YAML options? https://quarto.org/docs/reference 16:15 Navigating Quarto's documentation. 18:00 Is there something like Observable SQL cells on the roadmap? 20:10 Is there something closer to {bookdown} for Quarto? What is the best way to retain data and environment objects in a quarto book? Is there any path to enabling this? See Includes, https://quarto.org/docs/authoring/includes.html 24:20 Flexdashboard? Coming soon. 26:30 A big challenge in the adoption is that Quarto is competing with ipython notebooks for mindspace, what does the Quarto team think about that? Quarto and Jupyter Notebooks will hopefully be thought of as complementary to one another, with Quarto helping a lot with narrative, layout, and appearance for publication and sharing. 30:10 Where should I go to contact you about an issue? What if the issue isn't just Quarto, say, Quarto + Jupyter? 31:50: What is the Quarto team hoping to see the community produce? Feedback, reporting in github issues. Quarto Extensions. 34:05 Custom styling, configuring grid options. Any tips or anything in the roadmap that will help users finetune the look and feel of their output? 38:40 Terminology question; what do we call a published Quarto doc? (or webpage, blog, etc.?) 40:00 How do I stay up to date with Quarto? Getting the latest release and learning about what is new? See what's up on quarto.org. Look under get-started and under downloads for pre-releases

Meetup | The Looma Project | Why I Stopped Worrying and Learned to Love the API

Why I stopped worrying and learned to love the API Led by Davis DeRodes, Director of Data Science at The Looma Project Link to slides: https://github.com/RStudioEnterpriseMeetup/Presentations/blob/main/Looma_LearnedToLoveAPIs_PositMeetup.pdf Source code: https://github.com/DavisDeRodesLooma/positconnect-demo-api Helpful resources: ⬢ Brief intro to APIs blog post: https://rigor.com/blog/what-is-an-api-a-brief-intro/ ⬢ Isabella Velasquez, Intro to APIs blog post: https://posit.co/blog/creating-apis-for-data-science-with-plumber/ ⬢ Plumber package: https://www.rplumber.io/ ⬢ Public APIs: https://github.com/public-apis/public-apis ⬢ Aviation API that Davis used: https://docs.aviationapi.com/# ⬢ Posit Connect: https://posit.co/products/enterprise/connect/ APIs are the ticket to getting easy access to new data sources and creating automated ways to share data externally. Even the most stats-oriented data scientist with just R and Posit Connect can do some super useful and cool things with APIs. This talk will go over some best practices and interesting use cases for querying, posting to, and creating APIs using R packages, Shiny, Plumber and the Posit Connect ecosystem. Speaker bio: Davis DeRodes is the Director of Data Science at the retail media start-up Looma whose work focuses on the intersection of human-centric content and sales. Davis’ focuses are full stack data science, causal inference, data quality, explainability and R. Davis is an ATL-ien who currently resides in Durham, NC and is an alumni of Columbia’s Data Science Institute. Please note this event will be live streamed through YouTube Live and the recording will be available at the same link immediately following the event. During the event you can ask questions through this link as well: pos.it/meetup-questions Full playlist of meetup recordings: https://www.youtube.com/playlist?list=PL9HYL-VRX0oRKK9ByULWulAOO5jN70eXv All upcoming events: pos.it/community-events

R at AstraZeneca: upskilling our workforce through education, experience, and exposure

We were joined on November 29th at 12PM EST by Gabriella Rustici & Guillaume Desachy, who shared their experience about the R journey AstraZeneca is currently on. Resources: ⬢ R @ AZ: Building a Community in the Pharmaceutical Industry Blog Post: https://www.rstudio.com/blog/building-a-community-in-the-pharmaceutical-industry/ ⬢ R in Pharma YouTube videos: https://www.youtube.com/c/RinPharma ⬢ Posit Pharma Site: https://posit.co/solutions/pharma/ Timestamps: 4:53 - Start of session 5:41 - Paradigm shift in the pharmaceutical industry (many people are multilingual) 6:40 - Profile of R users at AstraZeneca (varied across data science, clinicians, medical director) 8:28 - Meet the R&D Learning & Development Team 10:03 - 3E Framework: Education, Exposure, Experience 12:11 - Bridging the science community and data science audience 13:11 - We all learn differently (solutions that suit different needs & styles) 17:31 - Index of learning (self-led index, synchronicity index) 12:20 - Experiential Learning 20:47 - The community of R users at AstraZeneca 21:05 - The early days (April 2021) 21:52 - azTidyTuesday: a playground to hone data viz skills 24:15 - internal R conference 25:38 - R function of the month 27:04 - Lunch & LeaRn 28:20 - R @ AZ 10:1 29:44 - Communication expanded from internal social media to R @ AZ Monthly Newsletter 31:57 - Workshops with Posit 32:11 - AZRHotdesk - come with your questions and someone will help you solve it 35:02 - Wish list for 2023 38:38 - Start of Q&A section Abstract: The use of R continues to become more and more important at AstraZeneca. It is a true paradigm shift that we have embarked on! This shift has required upskilling our workforce to make them proficient R users. To do so, we are leveraging the 3Es of learning: education, experience and exposure. Learn more about their team's Data Science Educational Program and how the team at AstraZeneca has built their own strong community of R users - where learning takes place through experience and exposure. Speaker bios: Gabriella is Data Science Learning Senior Director in Astrazeneca’s R&D Data Science & AI where she is responsible for developing a strategy for, and creating a centralised approach to, data science learning for R&D. Gabriella completed her PhD at the Wellcome Sanger Institute and previously run bioinformatics training programs at the University of Cambridge and the European Bioinformatics Institute, in the UK. She is passionate about designing, implementing and evaluating effective and scalable solutions to educate scientists and data science practitioners at all career stages. Guillaume is passionate about helping bring new medicines to patients by leveraging the power of statistics and precision medicine. Since October 2020, he has been doing so at AstraZeneca where he works as a Statistical Science Director. In addition, since March 2022, he have been leading a team of 15 collaborators focusing on building the community of R users at AstraZeneca, called R @ AZ. During the event you can ask questions anonymously through slido here as well: rstd.io/meetup-questions Blog post on R @ AZ, Building a Community in the Pharmaceutical Industry: https://www.rstudio.com/blog/building-a-community-in-the-pharmaceutical-industry/ Please note the recording of this session will be shared at the same YouTube Live link

Data Science Hangout | Alan Schussman, W. L. Gore & Associates | Data science is not one thing

We were recently joined by Alan Schussman, Data Team Leader at W. L. Gore & Associates, Medical Products. "Data science is not one thing." This was a recurring theme among comments and conversations in today's data science hangout with Alan. Data science is about problem-solving I don't think specific technical skills are the only thing that gets you in the door. We have people who come from analyst backgrounds and tools backgrounds of all different kinds — and can be successful here. There's an element of being competitive that is rooted in some core technical foundations. However, you can be really technical but not a good problem solver. We need people who can learn technical things but also know what solving problems looks like. If you're inflexible in how you solve problems, you are not going to be successful. Thinking of interviews I've been part of, you see people light up when they explain a problem they solved with different kinds of cooperation and creativity. Seeing this is really fun and helps you stand out. Data science is about compromise There's a whole lot of what we do in our work that has to do with satisficing — getting to a good enough solution that folks can work with. A lot of technical training is a little too black and white for being able to compromise about, “This is good data that my customers can work with, and I'll come back and improve it for the next cycle.” You've got to be able to compromise. Data science is about communication You've got to be able to talk through problems and feel embedded with your customers, so that you're a partner to them and not just a technical service. Data science is more than machine learning I don't think we do a service to people the way we set them up for what the data science industry “tends to look like.” I don't know if it's the right expectation for people going through data science training to have their eyes on the machine learning engineer kind of career, for example. There aren't a ton of them. The applications are pretty specific. We're a pretty sophisticated business, and we're trying to figure out what some of those things mean to us, and how to effectively use them. Don’t get me wrong, I'm really excited for us to be at that place but without the business need it's not fair for me to tell people: “Come join my team and be a data scientist the way that the world says data science looks.” It's going to be technical. It's going to be interesting. It's going to be rewarding, but it's different from that other picture. There isn't any reason we can't make people really excited about that kind of work. It's really complex and the problems to solve are really fun. The platforms we get to work with are cool, but it looks a little different. We've got a lot of runway for folks with a broad set of skills. We have to be realistic about what we promise the work is going to look like and when, so that we're not bringing in people who have a very particular vision of data science. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Data Science Hangout | Marcos Huerta, CarMax | Translating Academia Experience to Data Science

We were joined at the Data Science Hangout by Marcos Huerta, Manager of Data Science at CarMax. A few snippets from the conversation with Marcos at 36:40: What was the most effective asset that you had that led to your transition to your current position? ⬢ A willingness to just try and teach myself new things. In graduate school, I'd done a ton of data analysis, but it was all in an obsolete language that wasn't going to help me. I had to teach myself Python and R. I think the openness to trying that was key. ⬢ I gave myself a project to figure out how to use Python and understand classes and object-oriented programming, which I did not understand 10 years ago. ⬢ I do think that my work experience -- because I had done a lot of non-technical stuff -- helped as well. I had this record of professional accomplishment that maybe wasn't technical but people knew I could think and I had this track record. How do you do the mental folding to translate the many years you spent getting a PhD in astrophysics to a different position? ⬢ The first step for me was not to data science, it was to the science policy world. I think I always had this interest in politics and the government. That first transition came because of these talks at Rice that happened once a month about non-academic careers. Someone who had done science policy and worked at the National Academies of Science and as a Congressional Science Fellow came and gave a talk about what she had done with her physics or astrophysics degree. That really fascinated me. ⬢ From science policy, data science was more practical. There's a ton of jobs at the entry level for science policy but as you start to work your way up I was running out of things to do. There become fewer and fewer job openings. ⬢ Turns out when I started itching that part of my brain again, I really enjoyed it. I enjoyed the Data Incubator. I enjoyed trying to do the swirl lessons in R, building some Shiny apps, etc. Once I got back into doing technical stuff, I found it was still very satisfying. *non-verbatim transcription, summary of a few insights ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

Data Science Hangout | Jessie Pluto, Comcast | Taking Initiative with an Idea

We were joined at the Data Science Hangout by Jessie Pluto, Senior Manager of EBI Strategic Analytics at Comcast. Question: As an analyst when you're working with business partners, you're just trying to deliver value and insight in a speedy way. So I'm trying to convince analysts to maybe take a bit of a step back and learn and implement something new that can help both them and their business partners in the future. Do you have any advice for how I, as a person trying to get these things up and running, can help to create more of that culture of let's go ahead and take a step back and do something now that will help me in the long term versus just trying to check boxes as quickly as possible? Answer: It is a constant battle. I feel like it is the place where it requires some strategy because you can't do it all the time. You have to really recognize when is the right time to pounce on an idea? You have to have the ideas first. Sometimes you have to sit on them for a little while and then you have to just be able to recognize, I think, that opportunity. It's OK to make mistakes. Sometimes you push something too soon and it might hurt a little bit, but don't let that kill your motivation. I hate PowerPoint. I hate copy and paste so much. I might have said copy and paste 80 times already. But I hate it. For a long time, some of the reports that I was doing– just to give you an example, Paramount is, like, 30 individual networks - and so sometimes we're repeating views for 30 networks times five different views at the bare minimum. It's too many slides, too much copy and paste. I had made a push for, oh, we should convert this to R Markdown or Shiny or something. And same thing-- it's like, well, our stakeholders don't know how to use that and it's a server. It's very private and customer information. And COVID hits, and sports viewing kind of just went away. We had so many questions coming in about, what do we do? What's going on? What are customers doing? What are they watching? All sports just kind of went completely off the air immediately. And I realized, this is my moment. I created (we were in Hadoop at the time, we've since moved but we were in Hadoop at the time) some shell scripts, bash scripts to run at certain days and scheduled it and created this pipeline that I had been messing around with for a while, created an automated R Markdown report. And basically, I was able to give the information that the business was asking for very, very quickly. And they learned how to use it because they really wanted it. And it would have been too much for my very small team to be reporting every single week on what is happening this week. It'd be a huge load on our team to do that every single week from scratch. So basically investing-- and I will admit that I spent a little bit of night time on this project - but it was a passion project where I was like, all right, this is my moment to prove that this is a feasible solution, and show that-- because we had so many questions coming in. This was one of a huge backlog of questions. I was able to show that, OK, this is done. We've automated it. We can use this for other things, for other questions that business owners want every single week and still have time leftover for those ad-hoc questions. Those one-off things that are really important, we'll have time for that, too, and not miss the report. I think there's just this strategy that you have to really always be thinking about of when is the right moment to maybe not necessarily ask for approval– to just do something and show the value. Hopefully, you have leaders who will support you in those decisions If it's the right one and the right moment you can push some of the different tools that were not really supported in the beginning of my career here. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We'd love to see you!)

With a little help from my friends: Tools for developing & deploying algorithms in the hospital

The Data Science and Advanced Analytics (DSAA) team at Unity Health Toronto has successfully deployed prediction models to the hospital. This would not have been possible without trial-and-error, open source tools, and help from our friends at Posit. In this talk, we will share the various tools and approaches used to develop and deploy an early warning system in the hospital. Speaker bio: Chloé Pou-Prom is a data scientist with the Data Science and Advanced Analytics (DSAA) team at Unity Health Toronto. The DSAA team uses high quality healthcare data in innovative ways to catalyze communities of data users and decision makers in making transformative changes that improve patient outcomes and healthcare system efficiency. Timestamps: 2:37 - Start of presentation 4:41 - Projects their team works on 7:39 - Team is split into 4 teams (data integration & governance, advanced analytics, product development, and support) 8:00 - Successful deployment example (CHARTwatch - early warning systems for detecting patient deterioration) 12:09 - How this was running before (one person's laptop, no logging, no dev environment) 13:23 - Moving from before to now - What do you need for development? 13:40 - Connections to databases 16:25 - Reproducible environment 17:50 - Package based development 19:30 - Dev, staging, production environments 20:24 - What do you need for deployment? 30:21 - end of fire alarm break :) 33:07 - Authentication with Posit Connect (with hospitals Active Directory) 34:30 - Scheduling scripts on Posit Connect 35:35 - Receiving alerts when there's downtime (internal package for monitoring applications) 38:07 - Using internally developed packages with Posit Package Manager 40:58 - Implementation plan 49:29 - Monitoring 54:25 - Summary of development and deployment tools needed 56:12 - Start of Q&A Helpful resources: ⬢ Slides: https://lnkd.in/ePEH5k6Z Tools for Development: ⬢ Database connections [https://lnkd.in/eFDHR4cF] ⬢ Reproducible environment that lets multiple people collaborate on a project: [Renv: https://lnkd.in/e_DHddeA] ⬢ Package-based development [Functions and utilities that you re-use: https://r-pkgs.org/] ⬢ development, staging, production environments [https://lnkd.in/ezczn2Zz] Tools for Deployment ⬢ Authentication: [Posit Connect: https://docs.posit.co/rsc/ ] ⬢ Downtime protocols ⬢ A secure way to download internally-developed packages [Posit Package Manager: https://docs.posit.co/rpm/] ⬢ Implementation plan ⬢ Monitoring ["Monitoring Model Performance": https://lnkd.in/eSUgRYNM & "MLOps with vetiver in Python and R": https://lnkd.in/efN4vcrg] About Data Science and Advanced Analytics at Unity Health Toronto: ⬢ https://unitynet.unity.local/departments-programs-services/corporate-services/data-science-and-advanced-analytics/ About CHARTwatch ⬢ "Preparing a Clinical Support Model for Silent Mode in General Internal Medicine": https://lnkd.in/eJAWmYWk ⬢ "Implementing Machine Learning in Medicine": https://lnkd.in/eFesPijY ⬢ "From Compute to Care: Lessons Learned from Deploying an Early Warning System into Clinical Practice": https://lnkd.in/e-D7aDDf Want to join us at a future event? pos.it/community-events

Data Science in People Analytics | Led by Elizabeth Esarove, AT&T

People are the face, heart, and hands of a company. In people analytics, we analyze data to reveal actionable insights that provide evidence for decisions regarding employees, work, and business objectives. This talk will cover the use of data science for people analytics projects such as workforce planning, improving employee engagement, and retaining talent. Speaker bio: Elizabeth Esarove is a data scientist in People Analytics at AT&T. In her role, Elizabeth is part of a larger team focused on embedding data and analytics into the root of decision-making and transforming insights into actionable solutions that improve employee outcomes and drive business value. Timestamps: *Q&A timestamps listed further below 3:42 - Start of session 5:14 - What is People Analytics 6:26 - Opportunities for Data Science in People Analytics 7:10 - Using Predictive Models to Reduce Attrition 11:10 - Segmenting Your Population 18:55 - Communicating with Leaders 20:11 - Time Series Forecasting for Workforce Changes 24:41 - Analyzing Employee Survey Comments Helpful Resources Below: *more follow-up to come with a Q&A blog post in the works People Analytics Books Mentioned today: Handbook of Regression Modeling in People Analytics: with examples in R, Python and Julia by Keith McNulty https://lnkd.in/eBFgniFG Excellence in People Analytics: How to Use Workforce Data to Create Business Value by Jonathan Ferrar and David Green https://a.co/d/bJrMRuW People analytics books shared in a previous data science hangout: Predictive HR Analytics: Mastering the HR Metric: https://a.co/d/5Hx05mw Inclusalytics - How Diversity, Equity and Inclusion Leaders Use Data to Drive Their Work: https://lnkd.in/g48tdrMu Other links shared by Liz: Time Series Models Forecasting: Principles and Practice by Rob Hyndman and George Athanasopoulos https://otexts.com/fpp3/ Text Analytics Text Mining with R by Julia Silge & David Robinson https://lnkd.in/emawveZd Additional resources shared: R Gov Conference: https://lnkd.in/ePfN7jru (David Meza is presenting on the RStudio (Posit) Ecosystem as a Critical Part of NASA Analytics Capabilities) People analytics for getting to the moon | Data Science Hangout with David Meza, NASA: https://lnkd.in/eDirbgCF For LATAM and Spanish Speaking people, Sergio Garcia Mora shared the R4HR community which has developed lots of free access content: https://data-4hr.com/ John Kelly IV shared the Human Resources Science LinkedIn Group: https://lnkd.in/eEMpYAfk Adrian M. Pérez shared the People Analytics Handbook: https://lnkd.in/ecsWy-dA Data Science Hangout: pos.it/dsh All upcoming #Posit community events: pos.it/community-events Q&A Timestamps: *the following timestamps are approximate. 16:00 - What are the most important people analytics KPIs @ AT&T? Can you share how your team/HR acts on these predictions (for optimal policy) both experimentally and ethically? do you implement new policy in smaller groups? 23:00 - How have you validated the predictive models? Looking backwards, how precise were they? 25:00 - Do you work with your HRBPs to segment your population? 25:00 - What languages are you using to build your predictive models? 31:00 - Do you include demographic information (gender, race, age) in your models? 31:00 - Are your surveys anonymous? 32:00 - How would you get the ROI from HR attrition modeling? 34:00 - Are most data scientists from a Psychometrics background? 35:00 - Is there a kind of "critical mass" to apply People Analytics? (just for big companies?) 36:00 - Looking at positive / negative comments, do you quote verbatim comments in your reports? (e.g. "here is one of the very positive / very negative comments we received") 37:00 - Do you use something like Snowflake to store and model your data? And do you deploy these models automatically or manually update them? 38:00 - R user here. How do you balance between people-ops focused analytics tools from outside vendors (often very expensive, but helpful) with custom in-house analytics (often time-consuming)? 41:00 - How much of your work is driven by HR leadership, by HR business leaders, or by the HR analytics team pushing modeling and insights to those groups? 42:00 - What was your journey into learning data science and getting into people analytics? 44:00 - Do you have a role in education business units? to improve their questions, etc.? 45:00 - What is the HR tech stack at AT&T? Does your team have a data engineer solely for people data since they're more sensitive? 47:00 - How do you present your results? (an application, report, power point) and how important is it to learn other languages (javascript, css, sql)? If you were to start a people analytics team in a company (+1000), how do you start? 50:00 - Do you use an internal tool for surveys? Do you use thresholds to maintain anonymity? 53:00 - Does AT&T have remote workers? If so, does people analytics segment on remote vs hybrid vs on-site?

Julia Silge

Open Source Chat - {gt} with Rich Iannone

Join Rich Iannone, maintainer of the {gt} package, as he takes questions from the community about the latest in {gt} v0.7.0, and building great looking data display tables with R. Key Resources: ⬡ Get started with {gt} - https://gt.rstudio.com Reach out: 38:48 - How do I ask Rich about {gt}, feature requests, bug reports, how to solve a problem via {gt}? Rich and the {gt} team would love to hear from you. ⬡ Feature requests & bug reports with GitHub Issues, https://github.com/rstudio/gt/issues ⬡ GitHub Discussions, https://github.com/rstudio/gt/discussions ⬡ Ask the community a question, https://community.rstudio.com/tag/gt ⬡ Follow {gt} on Twitter, feel free to reach out and ask questions, https://twitter.com/gt_package Timestamps Rich Iannone Introduction. 03:52 - Why {gt}? - What does {gt} bring to the table? Why so much effort into static, data display tables? 05:50 - Why open source? Why is {gt} open source and why have you dedicated your career to develop open source software? 08:30 - {gt} v0.7.0, Tell us about those new vector formatting functions in {gt}. Why did you include them? Could you show us some examples? {gt}'s vector formatting functions help you customize the styling, look and feel of your values. Converting the output values R gives you, and making them look exactly the way you want them to can be tricky. A lot of work was put into {gt} to give nice value formatting options. You can now access all these outside of a gt table; e.g. in text, in a plot, etc. 22:35 - Could you provide an example or two with the new styling function called `opt_stylize()`? What kinds of tables can you make with that? Can you extend that with your own tweaks? 28:15 - Can you make your own themes and share them? "How do I create my own custom theme for my table? A theme I can share with the rest of my organization?" 31:58 - What is the distinction between tab_options and the opt_* functions? Why would a function be in opt_* and not tab_options? 34:00 - sub_values() function, to find and replace certain values in your table. 36:50 - What is the current support for latex in {gt} at the moment? "Personally, I much prefer HTML, but for scientific publications, we are asked to provide a LaTeX file." 42:50 - "In my work, I often produce A4 output in PDF, mainly with ggplot2 content. It would be nice to be able to combine ggplot + gt tables in a similar way {patchwork} works. Having the plot and the table next to it is very useful sometimes." 44:30 - Interactive Tables with {gt}? 47:45 - "Any plans to make applying of same style to several columns easier? Unless I'm mistaken, the locations argument of tab_style requires one to specify an individual column. See here: https://gt.rstudio.com/reference/tab_style.html#examples." Yes, supply a vector of columns or use tidyselect functions. 49:15 - "Excel output with {gt}? Would be a huge improvement. I often have to produce tabular output that can be easily reused. Usually it means Excel tables. So far I have mainly done this with Python and openpyxl or PyWin32 (through COM). A simple solution in R would be great." 50:20 - Support for additional output formats with {gt}? Excel, PowerPoint, etc.? 50:25 - {pointplank}, a package to methodically validate your data whether in the form of data frames or as database tables., https://rich-iannone.github.io/pointblank/. Check out the workshop materials at https://github.com/rich-iannone/pointblank-workshop 55:50 - "Are there ways to have grouped rows? I mean when repeated rows have same characters can we merge them to one?" 58:00 - "Is there an ability to add 'battleship coordinates' (e.g. column letters & row numbers) to a gt object? This is a standard for table across my org and I’ve been trying to figure out how to implement it." 59:59 "Do you have suggestions or examples of building out & applying corporate formatting to gt tables (e.g. adding a company logo, company colors, etc.)?" 01:04:30 - "With PDF/LaTeX output for wide tables, it does not shrink the table."

Rich Iannone

RStudio Pro Product Lightning Series Meetup ⚡️

Recording from our October 11th meetup: a lightning series with our RStudio Product Managers to hear what's new, ask questions, and provide feedback. Lightning talks: 3:16 - Sharing Internal Packages with RStudio Package Manager 18:07 - Running RStudio workloads in the Cloud with Amazon SageMaker 40:02 - Content execution in Kubernetes with RStudio Connect Resources and links shared during the meetup: A Package Manager demo tutorial on GitHub: https://github.com/rstudio/package-manager-demo Remote API Quickstart: https://docs.rstudio.com/rspm/admin/getting-started/configuration/#quickstart-remote-cli Differences between RStudio Workbench and RStudio Workbench on SageMaker: https://docs.aws.amazon.com/sagemaker/latest/dg/rstudio.html#rstudio-differences RStudio Workbench release notes: https://www.rstudio.com/products/rstudio/release-notes/ Remote Content Execution with RStudio Connect and Kubernetes Conference talk: https://www.rstudio.com/conference/2022/talks/remote-content-execution-rstudio-connect/ Product Links: Package Manager, control and distribute packages throughout your organization: https://www.rstudio.com/products/package-manager/ Workbench, premiere development environment for data science professionals: https://www.rstudio.com/products/workbench/ Connect, easily share your insights: https://www.rstudio.com/products/connect/ Timestamps: 22:43 - Demo of RStudio Workbench on Amazon SageMaker Sharing Internal Packages with RStudio Package Manager | Presented by Joe Roberts Many know that RSPM can be used to mirror public packages from CRAN or PyPI, but it can also be used to share your private, internally developed packages. We'll explore the latest features to make internal packages easier to deploy within your organization. Running RStudio workloads in the Cloud with Amazon SageMaker | Presented by James Blair RStudio Workbench on SageMaker enables users to “right-size” their environment for any given analysis. We’ll showcase how this flexibility enables users to effectively meet the workload demands of various analyses. Content execution in Kubernetes with RStudio Connect | Presented by Kelly O'Briant New and interesting ways to configure RStudio Connect: A quick introduction to off-host content execution in Kubernetes We're looking forward to learning from you and hearing your feedback as well! Meetup recordings are always shared here: https://www.youtube.com/playlist?list=PL9HYL-VRX0oRKK9ByULWulAOO5jN70eXv

Data Science Hangout | Matthew Montero, Gen Re | Bringing a Vision to Life

We were joined at the Data Science Hangout by Matthew Montero, Head of Enterprise Data Services at Gen Re. At 4:23, Matthew started us off with sharing the time that focusing on a vision and defining the strategy around that really clicked for him "When I worked in pharmaceuticals, I had the pleasure of having a manager who was very focused on vision. So that's when I really got a feel for once you have a vision and you can sell that vision, then you have the ability to build as big a team you need. You have to be able to back that up with either ways to improve the business or ways to even improve the organization as a whole. With the knowledge I gained through the experience of dealing with getting consultants, onboarding new hires, and defining the strategy for that vision, I moved on to my current company, Gen Re. It fostered from there, let me take this vision and further define the way I feel it should be at this company and build a full strategy around that. This was about figuring out, from day one when you get the data in ️ all the way to building applications that use that data ️ the self-service route of other people wanting to use that data for their analytical purposes. Let me build that strategy around there. Being able to both build out strategy and communicate that to executives, to different business units, the heads of different business units, moved me up relatively fast in a sense. It's being very confident in that vision and in the strategy and even the principles around that as well. It's really laying out the whole vision of how everything is going to be. You don't go into the technicals yet of how you actually accomplish that, it's just how you view what it all looks like. And that's the first thing you got to sell because at least if you have a vision, you have something you can communicate to everyone. And then that's when you break down now what's that strategy to accomplish that vision because you can't do all that in one day. It's going to take some time. So you have to have individual steps or a strategy to accomplish that. When I was earlier in my career, my boss asked - “You know my vision. How are you going to make that vision happen?” Once I was given that, I sat down and actually really thought through that. That's the moment it clicked in my head. Like, this is my next step. I want to be able to both define that vision, define the high level strategies to it, and then work with teams to actually make that happen. That was a couple years back and the time it really clicked for me." ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio To join future data science hangouts, add to your calendar here: rstd.io/datasciencehangout (All are welcome! We'd love to see you!)

Model Monitors and Alerting at Scale with RStudio Connect | Adam Austin, Socure

Deploying a predictive model signals the end of one long journey and the start of another. Monitoring model performance is crucial for ensuring the long-term quality of your work. A monitor should provide insights into model inputs and output scores, and should send alerts when something goes wrong. However, as the number of deployed models increases or your customer base grows, the maintenance of your monitor portfolio becomes daunting. In this talk we’ll explore a solution for orchestrating monitor deployment and maintenance using RStudio Connect. I will show how applications of R Markdown, Shiny, and Plumber can unburden data scientists of time-consuming report upkeep by empowering end-users to deploy, update, and own their monitors. Timestamps: 2:01 - Start of presentation 3:43 - About Socure 6:12 - Model performance matters, deployment isn't the end of the story 7:26 - What is a monitor? 8:55 - What is an alert? 11:00 - Monitor example 18:09 - Firing an alert from RStudio Connect 19:00 - Why monitor from RStudio Connect? 24:33 - How monitoring drives success at Socure 30:00 - Git-backed deployment in RStudio Connect 36:00 - Shiny app that their account managers see 46:00 - Architecture of a monitoring system 56:00 - Connect hot tip System-wide packages 57:00 - Why did we try Connect for monitoring 59:02 - Why do we keep using it for that :) Resources shared: Blastula package: https://github.com/rstudio/blastula connectapi package: https://github.com/rstudio/connectapi rsconnect package: https://rstudio.github.io/rsconnect/ Intro to APIs blog post: https://www.rstudio.com/blog/creating-apis-for-data-science-with-plumber/ Speaker Bio: Adam Austin is a senior data scientist and RStudio administrator at Socure, a leading provider of identity verification and fraud prevention services. His work focuses on data science enablement through tools, automation, and reporting

Data Science Hangout | Mythili Krishnaraj, AXA XL | Platform Governance With a Shared Vision

We were joined by Mythili Krishnaraj, Global Delivery Lead - Pricing and Analytics Platform at AXA XL. Mythili provided a really helpful view into the IT/technology perspective and shared the steps to platform governance at AXA XL. At (24:14) Mythili shares some guidance on how their team manages 200 users: With onboarding of users, they are put into different AD Groups. Some users might only use Workbench and are not publishing content to Connect so they are put in different AD Groups. Something that was initially lacking for us is that we didn’t know who was successfully onboarding or when someone left and we needed to revoke access to reallocate the license. The governance was thought about as what can we do in terms of each steps? First, with the license: 1️⃣ How do you get them onboard? 2️⃣ How do you keep an audit trail of what they are doing? 3️⃣ How can we tell when they are leaving? One other consideration was - what if someone is not able to use the license for 5-6 months? We started putting a timeline around it. If they don’t use the platform for 6 months, we get in touch with them and find out whether they need the license. Otherwise, we take it back. The team has also started their own R training to support new users. We have helped them with a sort of Wiki to put together all of our R resources so that they know who to go to for a request, how to connect to a data source, etc. That sort of knowledge sharing was a requirement. In terms of governance, we have also segregated the environments now: we have separate development and production. In development, we also have staging and non-prod. When they are ready to publish a model, they come through a ticket. We go through it to see what they are deploying, which are the data sources, have they used any hard coded passwords, etc. There is also a validation framework which we are using. Are they using licensed libraries? Are they using proper packages? A few tips also shared at (17:06) for IT and data science teams to successfully work together: Help the IT team understand the value of what you’re doing. Maybe your eventual output takes a week, a month, or even six months. It’s very important for them to be able to understand that value of what you’re going to bring in. Explain to IT the flexibility that you need. Analytics and data science is all about the speed that comes with any platform. It’s always a two-way street. Let them know that you’re here to support the governance, security and stability as well. Say, “I’m going to be really supportive of your guardrails but at the same time I’m expecting XYZ from your team.” Collaboration is required. Each team knows how we work now and there are sometimes tradeoffs for both sides, but we are able to deal with that because we want to do what’s best for the business. This objective is the same both for technologists and the data science world. Where the conflict comes in is when there is no shared vision. To get that shared vision, both teams need to talk. This conversation also reminded me of the meetup with Gordon Shotwell at Socure on Creating Secure Systems for Growth: https://www.youtube.com/watch?v=UnLpB4IDpZU Follow-up blog post on that meetup above as well, “How Data Scientists and Security Teams Can Effectively Work Together” https://www.rstudio.com/blog/how-data-scientists-and-security-teams-can-work-together/ Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the weekly Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Beautiful reports and presentations with Quarto | Led by Tom Mock, RStudio

Quarto is a powerful tool for authoring reproducible computational documents in R, Python or Julia. Quarto can also help with sharing your results to business stakeholders across your company. This talk will provide an overview of Quarto's implementation of revealjs for interactive presentations and HTML/PDF documents for static reports. Content website: rstd.io/quarto-reports Timestamps: 2:55 - Start of session 4:10 - Visual editor in RStudio 6:20 - Parameters to create different variations of a report 15:30 - Unified syntax across different output formats 18:01 - Pandoc fenced divs 20:10 - Tabsets 22:22 - Pandoc bracketed spans 24:30 - Footnotes 26:30 - Layout image inline with paragraphs / image into "gutter" column margin 29:23 - Hide all code 29:50 - Code tools (Fold code, source code) 34:12 - Code highlighting 37:03 - HTML Appearance 38:00 - Bootswatch themes 38:43 - PDF Articles 42:05 - Presentations (revealjs (HTML), PowerPoint (MS Office), beamer (LaTeX, PDF)) 45:06 - Creating slides 47:53 - Multiple columns 48:28 - Secret Tip (Alt + Click to Zoom in to a section) 49:24 - Absolute Position 51:04 - Presentation themes 52:44 - Footer/Logo 54:01 - Slide Background 57:01 - Custom classes 58:35 - End slide with helpful links (all shared here: rstd.io/quarto-reports) This meetup is Part 3 in our Quarto series: Part 1: Welcome to Quarto Workshop: https://www.youtube.com/watch?v=yvi5uXQMvu4 Part 2: Building a Blog with Quarto: https://youtu.be/CVcvXfRyfE0 For more about Quarto: quarto.org Resources discussed: Visual editor: https://quarto.org/docs/visual-editor/ Parameters: https://quarto.org/docs/computations/parameters.html Tabsets: https://quarto.org/docs/interactive/layout.html#tabset-panel Fenced Divs and Bracketed spans: https://quarto.org/docs/authoring/markdown-basics.html#divs-and-spans Footnotes: https://quarto.org/docs/authoring/footnotes-and-citations.html Figures and figure layouts: https://quarto.org/docs/authoring/figures.html#complex-layouts Code execution options: https://quarto.org/docs/computations/execution-options.html Code chunk format options: https://quarto.org/docs/reference/formats/html.html#code Code appearance: https://quarto.org/docs/output-formats/html-code.html#appearance Code highlighting light/dark: https://quarto.org/docs/output-formats/html-code.html#appearance Function links in code chunks with downlit: https://quarto.org/docs/output-formats/html-code.html#code-linking HTML Themes: https://quarto.org/docs/output-formats/html-themes.html PDF formatting options: https://quarto.org/docs/reference/formats/pdf.html#title-author PDF journal templates: https://quarto.org/docs/journals/templates.html Presentations: https://quarto.org/docs/presentations/index.html Revealjs Options: https://quarto.org/docs/presentations/revealjs/ Advanced Revealjs (absolute positioning, layout helpers like r-stack): https://quarto.org/docs/presentations/revealjs/advanced.html Revealjs themes and customizing: https://quarto.org/docs/presentations/revealjs/themes.html Revealjs footer & logo: https://quarto.org/docs/presentations/revealjs/index.html#footer-logo Inline span text formatting: Emil Hvitfeldt’s Slidecraft 101: Colors and Fonts, https://www.emilhvitfeldt.com/post/slidecraft-colors-fonts/ Meghan Hall’s Quarto Slides, https://meghan.rbind.io/blog/quarto-slides/ Andrew Heiss’ Quarto slides on APIs and webscraping with R, https://github.com/andrewheiss/2022-seacen Speaker bio: Thomas is the Customer Enablement Lead at RStudio, helping RStudio’s customers be as successful as possible. He is deeply involved in the global data science community, sharing tips on RStats Twitter (find him at @thomas_mock), as co-founder of TidyTuesday, a weekly Data Science learning challenge, and presenting on various Data Science topics on YouTube or at conferences. For upcoming meetups: rstd.io/community-events

Emil Hvitfeldt

MLOps with vetiver in Python and R | Led by Julia Silge & Isabel Zimmerman

Many data scientists understand what goes into training a machine learning model, but creating a strategy to deploy and maintain that model can be daunting. In this meetup, learn what MLOps is, what principles can be used to create a practical MLOps strategy, and what kinds of tasks and components are involved. See how to get started with vetiver, a framework for MLOps tasks in R and Python that provides fluent tooling to version, deploy, and monitor your models. Blog Post with Q&A: https://www.rstudio.com/blog/vetiver-answering-your-questions/ For folks interested in seeing what data artifacts look like on Connect, we have these for R: ⬢ Versioned model object: https://colorado.rstudio.com/rsc/seattle-housing-pin/ ⬢ Deployed API: https://colorado.rstudio.com/rsc/seattle-housing/ ⬢ Monitoring dashboard: https://colorado.rstudio.com/rsc/seattle-housing-dashboard/ ⬢ Create a custom yardstick metric: https://juliasilge.com/blog/nyc-airbnb/ ⬢ End point used in the demo: https://colorado.rstudio.com/rsc/scooby Our team's reading list (mentioned in the meetup) Books: ⬢ Designing Machine Learning Systems by Chip Huyen: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/ Articles: ⬢ “Machine Learning Operations (MLOps): Overview, Definition, and Architecture” by Kreuzberger et al: https://arxiv.org/abs/2205.02302 ⬢ “From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors” by Bayram et al: https://arxiv.org/abs/2203.11070 ⬢ “Towards Observability for Production Machine Learning Pipelines” by Shankar et al: https://arxiv.org/pdf/2108.13557.pdf ⬢ “The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction” by Breck et al: https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf Web content: ⬢ _How ML Breaks: A Decade of Outages for One Large ML Pipeline_ by Papasian and Underwood: https://www.youtube.com/watch?v=hBMHohkRgAA ⬢ _MLOps Principles_ by INNOQ: https://ml-ops.org/content/mlops-principles ⬢ _Google’s Practitioners Guide to MLOps_ by Salama et al: https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf ⬢ _Gently Down the Stream_ by Mitch Seymour: https://www.gentlydownthe.stream/ Speaker bios: Julia Silge is a software engineer at RStudio focusing on open source MLOps tools, as well as an author and international keynote speaker. Julia loves making beautiful charts, Jane Austen, and her two cats. Isabel Zimmerman is also a software engineer on the open source team at RStudio, where she works on building MLOps frameworks. When she's not geeking out over new data science techniques, she can be found hanging out with her dog or watching Marvel movies

Isabel Zimmerman, Julia Silge

Data Science Hangout | JD Long, RenaissanceRe | Empathy When Integrating with Other Tools

We were recently joined by JD Long, VP - Risk Management at RenaissanceRe. JD is a financial risk manager, analytical polyglot, and purveyor of colorful metaphors. As people have recently asked more about building communities, I’ll share this snippet here from 45:29 in his hangout. 🤝 I started the Chicago useR Group 13-14 years ago and the design pattern for that group was, I think, a really good one: 1️⃣ We had about four or five people who were going to get together, drink beer, and talk about R anyway, and what we effectively said was, "let's get in the types of presentations the four or five of us would like to hear, and then invite other people." And if they don't show up, we don't really care because we were going to drink beer and listen to this anyway because we're interested in this. And that community grew wildly with me running it like as a benevolent dictator just getting the presentations I wanted to hear. 2️⃣ And then one of the things I observed is we were getting lots of new users of R. So I decided once a quarter we would have a beginner's meetup-- And at the beginner's night, we would make sure we had two things: 1. Topics appropriate for beginners: like grouping and summing, and getting environments set up or updating packages. All the normal stuff people have friction with. Focus on that. 2. Then we would make sure we had--I would call it now like a learner salon or something. We had a handful of people who were more experienced who said, I will sit at a table and anybody that brings their computer with questions, I will answer. We would have really senior people with lots of experience there doing Q&A, and people could bring their own problems, literally eat pizza, drink beer, and look at code. Those beginner nights were super helpful. So anyway, when people ask about building communities, I recommend those two things. Resources shared in the chat: ► Esquisse package: https://github.com/dreamRs/esquisse ► Shinyuieditor: https://rstudio.github.io/shinyuieditor/ ► Shiny Developer Series episode 7 (talking about esquisse): https://shinydevseries.com/interview/ep007/ ► Another "bridge" tool for business analytics is radiant: https://radiant-rstats.github.io/docs/ ► Norm Conf: https://normconf.com/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio To join future data science hangouts, add to your calendar here: rstd.io/datasciencehangout (All are welcome! We'd love to see you!)

RStudio Sports Analytics Meetup: NFL Big Data Bowl 2022 Winners discuss the Math behind the Path

RStudio Sports Analytics Meetup: NFL Big Data Bowl 2022 Winners discuss the Math behind the Path Led by Robyn Ritchie, Brendan Kumagai, Ryker Moreau, Elijah Cavan Helpful Links: Kaggle: https://www.kaggle.com/code/robynritchie/punt-returns-using-the-math-to-find-the-path Github: https://github.com/ritchi12/punt_returns_using_the_math_to_find_the_path Optimal Path Generator Shiny app: https://rstd.io/meetup-shiny-app Related blog post, Tips for Getting Started With the NFL Big Data Bowl: https://www.rstudio.com/blog/tips-for-getting-started-with-the-nfl-big-data-bowl/ Timestamps: 3:02 - Start of presentation 4:07 - Meet the team 5:25 - Quick overview of 2022 NFL Big Data Bowl subject 7:00 - Finding the best path to the end zone 9:12 - Penalized Expected Arrival Time (PEAT) 13:10 - Take the PEAT to find the safest route (A-star algorithm) 15:11 - Comparing the optimal path to the observed path 18:30 - Key R functions for this project 23:37 - Optimal Path Generator Shiny app demo 33:38 - Comparing optimal paths (risk/reward) 35:44 - "Aiming for the side won't provide" 37:54 - The precision of a good decision 38:38 - How do you have a good submission for the big data bowl 40:40 - Elements of a good big data bowl submission Talk Abstract: Simon Fraser University has been a force in the NFL's Big Data Bowl for years. Focusing on punt returns, this team used tracking data to develop an algorithm to find the optimal path for a successful return, quantify player formations on the field and predict yards remaining on any frame of the return. This year's winning team will discuss their entry, the R code behind the optimal path and advice for this year's entrants. Speaker Bio: Robyn, Brendan, Ryker and Elijah are all graduate students in statistics at Simon Fraser University in Vancouver, Canada. They each have a passion for sports analytics and enjoy applying their stats knowledge to the game. Q&A Timestamps: *rough estimate :) 15:00 - Do you think being in a general Statistics graduate program was beneficial to your work? As opposed to more specific like "Data Science" or "Sports Analytics"? 19:00 - Did you calculate PEAT at just a single point in time (at catch), or at multiple points during the return? 30:00 - Is it possible to share the code for future reference? 43:00 - What were the biggest challenges when going through the data? 45:00 - In terms of all your data inputs, do you see this extending to performance data like expected speed, fatigue etc., perhaps from practice etc.? 46:00 - At any point during the project, did you feel like you had hit a cul-de-sac or impasse? 48:20 - How do you get started working with player tracking data? 56:00 - Sounds like you were running the code locally? Were there any specific roadblocks to offloading that to the cloud, or was that just not really necessary? Upcoming community events: rstd.io/community-events Feedback & Suggestions: rstd.io/meetup-feedback

Data Science Hangout | Tiger Tang, CARFAX | Quantifying the Hours Saved

We were joined by Tiger Tang, Manager, Data Science at CARFAX. Tiger (Chongtai) Tang is dedicated to building the Data Science team specializing in NLP and forecasting. He is a big fan of Shiny and he has a passion for the Data Science community. We recommend checking out Tiger's 2022 RStudio Conference Talk as well, "Saving 1000 hours with RStudio:" https://www.rstudio.com/conference/2022/talks/saving-1000-hours-rstudio/ How do you sell RStudio in your workplace? ️ Build a work automation process How do you build an automation process? 1️⃣ Look at a typical report & identify all the manual steps 2️⃣ These manual steps can usually be tied into 3 portions: getting the data, wrangling/analysis/visualization, and communication 3️⃣ You can replace these portions with R code, with the help of various R packages What are the three types of automation? 1️⃣ Attended automation - reports that still need human involvement: use R code in RStudio 2️⃣ Unattended automation - don’t need human input, but need to happen at the same time: use RStudio + RStudio Connect 3️⃣ Hybrid - combination of the previous 2, human input will come from the stakeholder & they can kick off processes to get answers: use Shiny + RStudio Connect Ok, time to sell it to decision makers What are the benefits? Reproducibility Less human error Cost benefit (hours saved) Why weren’t they interested when you shared these? The benefits listed above are great for selling to an R user who is concerned about the day to day workflow, but not decision makers who are more concerned about ROI. Update your strategy in highlighting the benefits: Cost benefit (hours saved) - If we go through with this we might be able to save 1,000 hours per year Less human error Reproducibility (as a free add-on travel insurance) It’s still the same benefits, just in a slightly different order. Start with one that does not require too much context to understand. What do you need to do the actual automation? Understand the current process and document it. This includes understanding: ⬢ the business reasons ⬢ the occurrence (daily, weekly, ad-hoc) ⬢ how you will communicate ⬢ how often to update the process so it will not become obsolete ⬢ you are not always the original report owner, so you need to know when to stop and call for additional help. From this you will understand the complexity, impact, and stability & can help you decide which project to start with as well. Top 3 recommendations for automation: 1️⃣ Always start with components: For ex, if you have a process that involves: SQL, Excel, and Outlook - code them individually because the same team will need this again and you can reuse the code. 2️⃣ Test, Test, Test: Capture all the scenarios possible. 3️⃣ Be practical and stay on target: Not everything needs to be fully automated. It’s not about building something cool with R but building something impactful with R. Structure for you: 1️⃣ Identify tasks in your workplace 2️⃣ Build a proposal with the benefits that matter to decision makers in your workplace 3️⃣ Build a requirement doc that identifies the right task to start with 4️⃣ Code by component and do plenty of tasks, while staying on target 5️⃣ Share the progress from time to time Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Posit Meetup | Ryan Derickson, VA | Collecting & Acting on Employee Engagement with Shiny

Led by Ryan Derickson, Veterans Affairs Shiny Executive Dashboards in VA 1:46 - Start of presentation 3:34 - The data used for the application 5:18 - Executives need to know how to help and what to watch out for (AES Exec Dashboard briefs ~2,000 people) 9:08 - Shiny app (Exec dashboard) demo 12:20 - Storytelling within the application 19:22 - Q&A: Is the url available? This is an internal app for VA behind our firewall, so external users aren't able to access 19:33 - Q&A: My employer would like their data source reference daily, is Shiny the recommended method? A: We have large single efforts (annual survey) rather than continuously updated data, but I've seen really effective use cases of that. It depends a lot on how the data is stored and that you can access it through API or database. 20:24 - Q&A: Is there any concern from IT regarding open source tools? A: There is inertia to overcome. You need executive sponsorship to be able to say why the ultimate outcome matters. IT cares about security and maintainability, not the details of the app. A leader who has sway with IT leadership can help them in seeing the value. In VA, there is a really detailed process to go through to get on our federal Azure space, which is slow but you do get there eventually. 21:51 - Q&A: What has been your experience with R and VA? 23:22 - Q&A: Are there limitations on the packages used? 23:44 - Employee engagement & burnout communicated through the app 29:01 - Q&A: How many people are using the app at one time? 30:15 - Q&A: Is the UI done with bslib or javascript and css? 30:49 - Q&A: Where can we learn more about the click and drill-down interface function? 41:20 - Q&A: How long did it take to get the app up and running? 43:22 - Q&A: What was the process for designing the UI? 45:38 - Q&A: What is the return on investment for the VA? 47:00 - Q&A: What would you recommend for learning Shiny? 50:27 - Q&A: For other public sector employees making the case for open source, is there other advice you have? Abstract: Data analysis and visualization is becoming increasingly accessible, yet the difficulty in translating data into action often remains persistent. VA has adopted an integrated approach to collecting & acting on employee engagement data, with a (robust) Shiny app as the centerpiece for the executive-focused portion of that approach. This talk will cover that approach, demonstrate the app, and discuss organizational and service improvements that have followed. Speaker Bio: Ryan Derickson leads VA's quantitative approach to improving employee engagement, primarily through feedback and related outcome data

Communicating the value of data science | Led by Merav Yuravlivker

Communicating the value of data science Led by Merav Yuravlivker, CEO & Co-Founder of Data Society Four years ago, Merav sent out a small survey to better understand the challenges of data professionals. It quickly spread and hundreds of responses poured in from Frustrated Data Scientists, who didn't feel understood or have the support they needed. From that, Data Society released the Data Science Communicator Toolkit to help alleviate their pain points. Now, they've relaunched the survey to see how they're doing today - what's better, what's worse, and what's next. Merav will release the preliminary results of the updated survey and talk through how data professionals today can bridge the communication gap to foster better insights and increase their impact. Survey link: https://bit.ly/3pxrejT Data Science Communicator Toolkit: https://www.rstudio.com/champion/business-case (under the section that says build your case with a presentation) Speaker bio: Merav Yuravlivker is the CEO and co-founder of Data Society. She and her team train up thousands of professionals every year across Fortune 500 companies, large organizations, and federal agencies to help them use data better and solve tough challenges. Merav is passionate about delivering the impactful combination of education and data to transform how industries run today. What is a Champion Chat? The champion chat is an informal meetup where we can ask questions to the community and share best practices with each other for advocating for data science at your organization. During these calls, we may also feature a short presentation from a community member who has championed data science and has a few tips to share with us all. *Please note, any presentation portion of this meetup would be recorded, but the open discussion/ Q&A will not. Who? All are welcome - no matter your role/industry/experience No need to register for anything It's always okay to join for part of a session You can just listen-in if you want You can ask anonymous questions too! Helpful info on advocating for data science: rstudio.com/champion

Data Science Hangout | Ivonne Carrillo Domínguez, Bixal | Transitioning to data engineering

We were joined by Ivonne Carrillo Domínguez, Data Engineering Manager at Bixal. Ivonne is passionate about storytelling and empowering data professionals to jump to the cloud. Here's a snippet (55:00) including a few thoughts that both Ivonne and Brittany shared at the hangout with regards to some people being overlooked for data engineering roles based on their experience: When I am hiring people for a data engineer position - I focus more on the programming skills. If you are a good engineer, you will learn Spark or MapReduce in time. You will end up using certain tools, but in my opinion I wouldn’t say no to someone because of a specific technology. If you find a good candidate, you can teach them or mentor them in specific frameworks. When I started my first job as a data engineer, I didn’t know about big data or Spark. I had a lot of experience with Java and that’s why they hired me. I think knowing how to program is more important - and that’s the philosophy I use. I think what they are looking for when they ask about certain tools is if you have experience with concurrency or parallel jobs. Sometimes you need to think a bit differently with distributed computing frameworks, but if you start playing around and reading about it, it will also help give you an opportunity to land a job. Sometimes when you’re working through a third party like a hiring recruiter, they may not know all of the things that are needed for the role. They’re given the job description and they may not know “I really need someone who has this skill, but if they don’t have this particular one - I’m willing to train on that.” The recruiter might not know that information and this may even change depending on how long the role is open for. What’s best for the candidate in that case is to get as close to the actual hiring manager as possible. Give them your resume and let them know your experience because sometimes they’ll look at your resume and would absolutely love to give you an interview, where the recruiter might say, “oh, you don’t meet X percent of the job description, therefore I’m not going to pass it on.” This is one of the disconnects between having a third party do that interfacing. Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Posit Meetup | Afshin Mashadi-Hossein, Bristol Myers Squibb | Framework for Data Collaboration

Led by Afshin Mashadi-Hossein, Sr Principal Scientist at Bristol Myers Squibb Github link: https://github.com/amashadihossein/daapr Other pharma use cases: rstudio.com/champion/life-science RStudio for Clinical Reporting: rstudio.com/solutions/pharma Chat with RStudio: rstd.io/chat-with-rstudio Abstract: For data science teams, data preparation takes substantial investment of time, data science expertise and subject matter proficiency. However, as the name implies, data preparation is typically viewed merely as a means to an end, encouraging creation of expensive but often single-use and fragile elements in data analysis workflows. Rather than seeing data preparation as an obstacle to be removed, we propose a framework that recognizes the time and expertise invested in data preparation and seeks to maximize the value that can be derived from it. Viewing analysis-ready data as a multi-purpose, modularly built product that should lend itself to collaborative development and maintenance, the framework of Data-as-a-Product (DaaP) aims to remove barriers to version tracking and collaborative data development and maintenance. Specifically, the framework, which is entirely implemented in R, enables joint code and data versioning based on git, standardizes metadata capture, tracks R packages used, and encourages best practices such as adherence to functional programming and use of data testing. Collectively, the patterns established by the DaaP framework can help data science teams transition from developing expensive, single-use “wrangled” datasets to building maintainable, version-controlled, and extendable data products that could serve as reliable components of their data analyses workflows. Bio: Afshin is a data scientist who is passionate about putting engineering and computational tools to work to realize the potential of biomedical data in service to human health

BioC 2022 - Hello, Quarto!

Mine Çetinkaya-Rundel, PhD., Professor of the Practice at Duke University, Data Scientist and Professional Educator at RStudio, Inc., gives her keynote presentation at the Bioconductor Conference 2022. Dr. Cetinkaya presents Quarto, an open-source scientific and technical publishing system built on Pandoc. Dr. Mine began her presentation by introducing Quarto and her personal experience using and teaching it to others. She then continued by giving an overview of the R Markdown ecosystem which includes packages like xaringa, Distill, Blogdown, Rmarkdown, and more. She explained the use of Quarto with these packages along with some other Quarto highlights. Afterward, Dr. Mine gave her first demo of Quarto which included setting up and sharing some handy features. Dr. Mine also gives another demonstration on publishing with Quarto and an overview of the features of collaboration. Dr. Mine then gives a second demonstration of collaborating and teaching with Quarto. To wrap up the presentation, Dr. Mine shared about reimagining open source and the work done by Openscapes and their mission for open practices accelerating data-driven solutions, and increasing diversity, equity, inclusion, and belonging in science. The presentation ended with a questions and answers session from the audience. Main Sections 0:00 Introduction 4:29 Quarto! 6:12 Share 10:57 The R Markdown ecosystem 11:59 Quarto highlights 13:40 Demo of Quarto 21:40 Demo Quarto Publishing 22:58 Quarto rundown 25:23 Collaborate 28:43 Demo 2 34:07 Teaching with Quarto 40:39 Reimagine 44:03 Q&A and Resources More Resources Bioconductor Conference Site: https://bioc2022.bioconductor.org/ BioC2022 Github: https://github.com/Bioconductor/BioC2022 Main Site: https://www.r-consortium.org/ News: https://www.r-consortium.org/news Twitter: https://twitter.com/Rconsortium LinkedIn: https://www.linkedin.com/company/r-consortium

Mine Çetinkaya-Rundel

Intro to Functional Data Analysis - Part 2 | Matthew Malloure, Dow Chemical

RStudio Meetup: Functional Data Analysis (Part 2) Led by Matthew Malloure, Dow Chemical Link to slides: https://github.com/MatthewMalloure/RStudioMeetup_FDA Intro to Functional Data Analysis (Part 1) https://youtu.be/nA9fVOCD8yM Timestamps: 3:00 - Start of presentation 7:50 - Recap, what is functional data analysis (FDA)? 12:00 - Why do we need FDA? 16:40 - Initial step in FDA applications - smoothing 22:21 - What made you first interested in FDA? 25:38 - What is your decision framework for which basis function to choose? 27:50 - Screening additives compared to control 41:27 - Specific FDA problems can pop up at work and I'm not sure which analysis is right. Is there a recommended resources for selecting approaches? 44:40 - Can you define "functional" as it is used in the FDA context? 49:30 - Functional regression 58:50 - Alternative modeling approaches Abstract: The primary purpose of this presentation is to continue the introduction of functional data analysis methods that was kicked-off during the March RStudio Energy Meetup by Santiago Rodriguez. During that session’s Q&A, two specific methods were frequently mentioned: Functional Principal Components Analysis (FPCA) and Functional Regression. In this talk, both methods are introduced in more detail, applied to a simulated example from the chemical industry, and compared to their univariate/multivariate analogues. Though no code will be shown in the presentation, commented R code used to produce all data, analyses, and figures will be provided. Speaker Bio: Matt is an associate research data scientist supporting new product development within the Packaging and Specialty Plastics business at The Dow Chemical Company. His specialty areas include functional data analysis, Bayesian hypothesis testing, computational statistics, and experimental design. Prior to joining Dow he earned a BS in Statistics and MS in Biostatistics at Grand Valley State University and a PhD in Statistics from Texas A&M University

Data Science Hangout | Lindsey Dietz, Federal Reserve Bank | Focus on the impact of the output

We were joined by Lindsey Dietz, Stress Testing Production Function Lead at the Federal Reserve Bank of Minneapolis. Lindsey leads a team of quants & data scientists implementing and analyzing stress testing models in the Federal Reserve System. She loves diving into the nitty gritty of data and using R code to better understand the statistical dynamics we observe in the banking sector. Loved kicking off the session with tips for communicating technical results to the business. ⬢ It’s key to think of pretty much every audience as a non-technical audience, even when you have people you know are technical. It’s best not to assume that your audience ever knows the things you know because it’s unlikely that anyone will be as deeply immersed in a problem as you are. ⬢ Focus on the impact of the output first. I like to tell my team that what you might think is the smallest piece of work, someone else is going to think that is magic. I was actually just telling someone this earlier today. They were talking about the coding they had done and I said, “you know, the thing that is going to really impress people is that you generated this automated piece of documentation from your code basically for free.” ⬢ I remember someone at the RStudio Conference talking about “minimizing time to magic” and that is really the impact driver for most people. ⬢ Focus on the impact in your regular words and remove the jargon. ⬢ Trust yourself that you’ve done all the checking, the assumptions, and the technical details. Write that down for yourself and have a good appendix or technical paper to go with your work. ⬢ Non-technical audiences may ask you questions that you didn’t even think about that are really helpful in being proactive for future analysis. ⬢ Practice your translation skills, even if you know they’re not going to understand your world and you’re not necessarily going to understand theirs. For decision makers - bottom lines are important, right? How does this save us time, how does it save us money? These are key concepts that you should keep in mind for non-technical spaces. Resources shared: Stress Testing Publications: https://lnkd.in/egXfajBF Info on Stress Testing data submitted by banks: https://lnkd.in/eqh5AckU Where to download some useful public bank data: https://lnkd.in/eRiceXaU Upcoming R Ladies meetups around the world! https://lnkd.in/dzcpRT-D Upcoming R user groups around the world as well! https://lnkd.in/esNku4Ei Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Welcome to Quarto Workshop! | Led by Tom Mock, RStudio

Welcome to Quarto 2-hour Workshop | Led by Tom Mock, RStudio Content website: https://jthomasmock.github.io/quarto-2hr-webinar/ FULL Workshop Materials (this was from a 2-day workshop): rstd.io/get-started-quarto Other upcoming live events: rstd.io/community-events Double-check: Are you on the latest version of RStudio i.e. v2022.07.1 or later? Packages used: tidyverse, gt, gtExtras, reactable, ggiraph, here, quarto, rmarkdown, gtsummary, palmerpenguins, fs, skimr ️ Pre-built RStudio Cloud with workshop materials already installed: https://rstudio.cloud/content/4332583 For follow-up questions, please use: community.rstudio.com/tag/quarto Timestamps: 7:16 - What is Quarto? 8:28 - How does R Markdown work? 9:40: Quarto, more than just knitr 13:56 - Quarto can support htmlwidgets in R and Jupyter widgets for Python/Julia 14:18 - Native support for Observable Javascript 19:28 - Quarto in your own workspace (Jupyter Lab, VSCode, RStudio) 20:26 - RStudio Visual Editor mode 23:30 - VS Code YAML 26:02 - Quarto for collaboration 26:55 - How do you publish Quarto? (Quarto Pub, GitHub Pages, RStudio Connect, Netlify) 28:44 - What about Data Science at Work? 29:59 - Formats baked into Quarto (basic formats, beamer, ppt, html slides, advanced layout, cross references, websites, blogs, books, interactivity) 32:13 - What to do with my existing .Rmd or .ipynb? 33:16 - Why Quarto, instead of R Markdown? 40:50 - Text Formatting 41:30 - Headings 41:51 - Code (also merging R and Python in one document) 43:29 - What about the CLI? 44:55 - Navigating in the terminal 57:56 - PART 2: Authoring Quarto 1:00:22 - Output options 1:04:46 - Quarto workflow 1:12:06 - Quarto YAML intelligence 1:13:20 - Divs and Spans 1:22:13 - Figure layout 1:34:40 - Code chunk options 1:41:00 - Quarto and R Markdown (converting R Markdown to Quarto) This 2-hour virtual session is designed for those who have no or little prior experience with R Markdown and who want to learn Quarto. Want to get started with Quarto? - Install RStudio v2022.07.1 from https://www.rstudio.com/products/rstudio/download/#download - this will come with a working version of Quarto! - Webinar materials/slides: https://jthomasmock.github.io/quarto-2hr-webinar/ - Workshop materials on RStudio Cloud: https://rstudio.cloud/content/4332583 What is Quarto? Quarto is the next generation of R Markdown for publishing, including dynamic and static documents and multi-lingual programming language support. With Quarto you can create documents, books, presentations, blogs or other online resources. Should I take this? As with all the community meetups, everyone is welcome. This will be especially interesting to you if you have experience programming in R and want to learn how to take advantage of Quarto for literate data science programming in academia, science, and industry. This workshop will be appropriate for attendees who answer yes to these questions: Have you programmed in R and want to better encapsulate your code, documentation, and outputs in a cohesive “data product”? Do you want to learn about the next generation of R Markdown for data science? Do you want to have a better interactive experience when writing technical or scientific documents with literate programming? For more info on Quarto: quarto.org

Getting Started with {shinytest2} Part I || Example + basics || RStudio

00:00 Introduction 00:48 Overview of the demo Shiny app 03:00 Running record_test() 04:44 Results from record_test() 07:18 A note on .png files created during testing 08:52 Debugging with shinytest2 09:32 Using app$view() to open a visual representation of a headless browser Part 2 - Exporting values: https://youtu.be/7KLv6HdIxvU Part 3 - Using shiny.testmode: https://youtu.be/xDxa_mDwN04 Manually testing Shiny applications is often laborious, inconsistent, and doesn’t scale well. Whether you are developing new features, fixing bug(s), or simply upgrading dependencies on a serious app where mistakes have real consequences, it is critical to know when regressions are introduced. shinytest2 provides a streamlined toolkit for unit testing Shiny applications and seamlessly integrates with the popular testthat framework for unit testing R code. shinytest2 uses chromote to render applications in a headless Chrome browser. chromote allows for a live preview, better debugging tools, and/or simply using modern JavaScript/CSS. By simply recording your actions as code and extending them to test the more particular aspects of your application, it will result in fewer bugs and more confidence in future Shiny application development. Read up on shinytest2 here: https://rstudio.github.io/shinytest2/ Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Motion design and editing: Jesse Mostipak (@kierisi) Theme song: Brad PKL by Blue Dot Sessions (https://app.sessions.blue/browse/track/113507)

Barret Schloerke

Getting Started with {shinytest2} Part 3 || Using shiny.testmode in {shinytest2} || RStudio

00:00 Introduction 00:15 Testing production apps Part 1 - Getting started: https://youtu.be/SS1Na3c8lhk Part 2 - Exporting values: https://youtu.be/7KLv6HdIxvU Manually testing Shiny applications is often laborious, inconsistent, and doesn’t scale well. Whether you are developing new features, fixing bug(s), or simply upgrading dependencies on a serious app where mistakes have real consequences, it is critical to know when regressions are introduced. shinytest2 provides a streamlined toolkit for unit testing Shiny applications and seamlessly integrates with the popular testthat framework for unit testing R code. shinytest2 uses chromote to render applications in a headless Chrome browser. chromote allows for a live preview, better debugging tools, and/or simply using modern JavaScript/CSS. By simply recording your actions as code and extending them to test the more particular aspects of your application, it will result in fewer bugs and more confidence in future Shiny application development. Read up on shinytest2 here: https://rstudio.github.io/shinytest2/ Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Motion design and editing: Jesse Mostipak (@kierisi) Theme song: Brad PKL by Blue Dot Sessions (https://app.sessions.blue/browse/track/113507)

Barret Schloerke

Getting Started with {shinytest2} Part 2 || Exporting values || RStudio

00:00 Introduction 00:29 Exporting reactives 03:28 Using exportTestValues() Part 1 - Getting started: https://youtu.be/SS1Na3c8lhk Part 3 - Using shiny.testmode: https://youtu.be/xDxa_mDwN04 Manually testing Shiny applications is often laborious, inconsistent, and doesn’t scale well. Whether you are developing new features, fixing bug(s), or simply upgrading dependencies on a serious app where mistakes have real consequences, it is critical to know when regressions are introduced. shinytest2 provides a streamlined toolkit for unit testing Shiny applications and seamlessly integrates with the popular testthat framework for unit testing R code. shinytest2 uses chromote to render applications in a headless Chrome browser. chromote allows for a live preview, better debugging tools, and/or simply using modern JavaScript/CSS. By simply recording your actions as code and extending them to test the more particular aspects of your application, it will result in fewer bugs and more confidence in future Shiny application development. Read up on shinytest2 here: https://rstudio.github.io/shinytest2/ Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Motion design and editing: Jesse Mostipak (@kierisi) Theme song: Brad PKL by Blue Dot Sessions (https://app.sessions.blue/browse/track/113507)

Barret Schloerke

Data Science Hangout | Rebecca Hadi, Lyn Health | Transparent & Visible Work

We were joined by Rebecca Hadi, Head of Data Science at Lyn Health. Rebecca is passionate about improving healthcare using data science and machine learning. At 19:11, Rebecca shared her approach to communication with transparent and visible work. Here are 4 things they do on their team to create transparent & visible work: 1. Starting off with an open Data Science channel (different from their team channel) that’s open to the company. For example, if we do an analysis for the sales team - we will post it in that channel and tag the sales team. That way anyone in the company can see the work. We keep conversations in the channel, rather than DMs. A lot of times someone else may be interested in that work. 2. We also have a Google sheet that we call our insight repository. We have that pinned to our channel so when we post analysis, we also put them in our insight repository with one row per analysis. For example: in-patient mortality model, a headline, the link to the PDF, and the GitHub link. The insight repository has been very well received as a place that someone can start when they have a question. 3. I also host a weekly data science prioritization meeting and we use Monday as our project management tool. This has been really helpful because in the past I’ve had some challenges with balancing requests from our clinical team versus sales team and understanding the priority. It can be hard to be that person in the middle. It’s a lot more productive when we can all have that conversation live. The sales team might say, “I need this for XYZ meeting” and you might have to say, “Ok, you know that’s going to push off these additions to the clinical dashboard.” Then the clinical team can weigh in, “We’re okay with that because of XYZ reason” 4. I’m also a really big advocate of a monthly all-employee call and I try to get air time there pretty frequently. Initially it was level setting on “this is what data science is and how my team operates.” That has been successful for setting this idea of, “you are going to ask me something and I’m going to ask you the why behind it.” This helped set the precedent ahead of time where I’m not challenging an idea, but it will help my team produce better work if we understand why you care about this and how you are going to use it because that gives us context in decisions we need to make throughout the process. A few resources shared too: Javier shared: A resource that I just came across last week (def not something I do daily). if you're wanting to collect / manipulate JSON data with R, Tom Mock has a super-easy-to-follow writeup with several different approaches: https://lnkd.in/gwGxJpcW Luke shared: Data is Plural newsletter is a decent off-the-beaten-path dredger of data sources: https://lnkd.in/grRGqQRA Rebecca shared this book on management, The Hands-Off Manager: https://lnkd.in/gg2TU2mB Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Alec Campanini, Walmart | Using Shiny to make business decisions

We were joined by Alec Campanini, Senior Manager, Merchandising Operations Omnichannel Business Analytics at Walmart. Alec loves to standardize and scale data in a targeted and meaningful way without sacrificing speed of development. (5:12) - Alec shared an awesome example of the way their team is using Shiny at Walmart: - Their team's main scope is “rest of market” - trying to figure out every item that exists in the rest of the market, no matter if it’s in the store, online, through TikTok trends, social listening, whatever it may be. - We have a lot of market share data, sales information, pricing over time, and bestseller trends that are popping up. We do a lot to find out where merchants would be lacking inside of a brand. - Today I own a Shiny app (which has been alive for about a year and a half now) that is pushed to all merchandising. We have about 12,000 users on it today across all of the merchants and merch-ops. What does it do? - Starting off it was insights on 28-30 different retailers in the e-commerce space. With so many items, there are a lot of different contracted data sets coming in. The biggest thing is normalizing all of those to the Walmart hierarchy. We need to have some way to tell a merchant that something from the rest of the market on a different website makes sense for them to look at. - Our Shiny app and workflow allows us to quickly identify what percentage we have of a certain brand and drill down into things like: 1. What are the top items being sold? 2. Do I want stable items or trending items? Do I want something that I’ve never seen before? 3. Do I already have the item but need to increase what I’m giving the customer? 4. Do we need to lower the price? 5. Is it shipping too slow? 6. Maybe Amazon has 10 images for the item and we only have 5? An example of this Shiny app helping make business decisions: - We released a line of new golf tech products for Father’s Day that came from the Shiny app - We had a lot of the sports & fitness merchants find out that we sell a lot more golf technology rather than golf balls online. These are merchants that used to be in the store, but they got into the e-commerce space as a new merchant. - They think the trends are going to be the same in-store as online, but they found a different trend inside of our app and were able to launch a whole slew of things this past week, which was really cool. Other timestamps: - When making the case for code-first data visualizations over BI tools (25:58) Packages shared in the chat: - bs4Dash: https://lnkd.in/gPUBDW72 - Rhandsontable: https://lnkd.in/gtYedxzY - Bigrquery: https://lnkd.in/gtPM6Pn2" Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | David Meza, NASA | People analytics for getting to the moon

We were joined by David Meza, AIML R&D Lead, People Analytics at NASA. We had a great chat about people analytics for getting to the moon and beyond Here are a few snippets: What areas do you focus on in people analytics? (5:19) A lot of our focus is around natural language processing, graph algorithms, and graph databases. We create a knowledge graph & apply various algorithms to be able to deliver answers. An example of this is around our skills and competency capabilities. Trying to get back to the moon and onto Mars, we need to identify the various skills and competencies that we have in our individuals. From a human perspective, that’s very labor intensive. We’re trying to use some of the concepts I’ve mentioned to be able to extract NASA-specific competencies from individuals or people applying for jobs to see how they align to our workforce. We’re starting to develop pipelines and methods to say: • We’ve got gaps here • Or here’s your career path • Here’s your training path • Here are the number of individuals we have that meet 70% of our skills and we need to upscale them to the next 30% People analytics can be broken into so many different areas: • Attrition • Time to hire • Recruitment capabilities • Learning/ development With regards to skills for people analytics: • NLP is a good tool to have because a lot of the data we have is unstructured. We need to be able to pull from that. • Understanding various classification models, how you’re grouping individuals & understanding various metrics within the HR area. How much of people analytics is data science and predictive modelings versus reports and business intelligence? Right now everybody does a little bit of everything When I first got here, they were primarily focused on reporting and creating some type of visualization Over the last couple of years, we’ve been trying to break into a group that focus on reports in general and a group looking more at the data analytics & modeling, creating the various algorithms that we can utilize for extracting skills and competencies or looking at our attrition models, for example. Ideally, I would break it down to 5-6 visualizers, 10 data scientists and 4-5 data engineers or architects. Resources shared: Joe shard an example of Shiny at NASA: https://lnkd.in/gRJjFX6B David shared Predictive HR Analytics: https://lnkd.in/gPPjjzPZ David shared Inclusalytics: https://lnkd.in/g48tdrMu Rachael shared the rstudio::conf workshop for admins: https://lnkd.in/eB9FZt-c Rachael shared the RStudio workshop for people analytics: https://lnkd.in/e4DdhFAV George shared an article on writing a resume for AI: https://lnkd.in/g3cF_vtD Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Posit Meetup | Jake Riley, Children's Hospital of Philadelphia | Translating Facts to Insights

RStudio Healthcare Meetup: Translating facts into insights at Children's Hospital of Philadelphia Led by Jake Riley, data analyst at The Children's Hospital of Philadelphia Abstract: {headliner} is a new R package to add dynamic, insightful text to plots and reports. {headliner} generates useful talking points that users can string together using {glue} syntax. This makes it easy to write an informative sentences without adding a lot of technical debt to a project. Learn how to get started with {headliner} and ways we have used it at The Children's Hospital of Philadelphia. Speaker Bio: Jake Riley is a data analyst at The Children's Hospital of Philadelphia. He is the author of several R packages related to data visualization and automated exploratory analysis. You can find his published work [simplecolors] and [shinyobjects] on CRAN with more packages on the way. Timestamps: 0:49 - Start of talk 1:25 - Dashboards focused on facts vs. insights 2:56 - What's a good title for a chart? 5:09 - Intro to headliner package 7:41- using glue() under the hood 14:04 - helpers for working with data frames: compare_conditions() 18:41 - using ggtext 21:27 - example using pixar_films 23:40 - how they've used it at CHOP 28:05 - Next steps for headliner package 29:32 - Start of Q&A session Questions: 29:32 - Can you use any package you want in your organization? 31:13 - How do you load previous datasets to compare to current datasets? 32:48 - When you mentioned a front page on RStudio Connect (with the headlines), what is that? 33:25 - Is anyone using this for manuscripts at CHOP now? 36:24 - What has the adoption of R or Python been within the hospital analytics team? 37:28 - My manager is very leery of R because of technical depth. Any suggestions for convincing her of R's value? 42:22 - How does CHOP use R for non-clinical analysis? 43:36 - How do you train new people to use R? 46:28 - How do you compare last week's analysis to this week's? 49:37 - Were there any major challenges in creating the hospital's internal package? Resources/links shared: Jake's LinkedIn: https://www.linkedin.com/in/jake-riley-70736a3/ headliner package: https://github.com/rjake/headliner waldo package: https://www.tidyverse.org/blog/2020/10/waldo/ Examples of R in Life Science & Healthcare: https://www.rstudio.com/champion/life-science Chris Bumgardner's talk on building an R-based analytic practice at Children's Wisconsin: https://youtu.be/pHZ8dsc0PhY simplecolors package to generate hex codes using uniformly named colors: https://rjake.github.io/simplecolors/ R Packages book by Hadley Wickham & Jenny Bryan: https://r-pkgs.org/ Meetup Links: Future events: rstd.io/community-events-calendar If anyone's interested in speaking at a future meetup, we’d love to hear from you too! rstd.io/meetup-speaker-form

Hadley Wickham, Jenny Bryan

Data Science Hangout | Tanya Cashorali, TCB Analytics | Saving millions with a Shiny app

We were joined by Tanya Cashorali, CEO & Founder at TCB Analytics. Tanya is passionate about playing with data but using it for good causes whenever possible. Energized by working with ambitious people on difficult problems. I'm a huge advocate of rapid prototyping data products using Shiny and can talk about it for hours! What was your biggest win for a client? (23:30) We’ve had a lot of wins moving someone from Excel into a reproducible R pipeline and then displaying all the results in Shiny. One example is a pharmaceutical company that was analyzing clinical trial data super manually, generating reports in Excel and putting together a PowerPoint weekly. It probably took about 20 hours of multiple people’s time. We made a Shiny app and that turned this into pretty much no time at all. They sent us the data and it was updated monthly. They were able to then take that Shiny app to senior management and clinical trial managers to make decisions based on data very quickly, helping them understand which clinical trial sites were having problems. Another similar one at another pharmaceutical company was focused on drug manufacturing, where there are a lot of things that can go wrong. When there’s a contaminant in a batch, it can basically cause millions and millions of dollars in company loss because they have to shut down manufacturing completely until they identify the problem. This involves going up and downstream of these different drug products to identify the issues. It was taking a team of 5-10 people sometimes 6 months to identify the problem. Meaning for 6 months, you’re not able to manufacture drugs. We built a Shiny app that built out a D3 directed graph. This enabled one person to go and type in the drug compound and see everything up and downstream. It took that one person now maybe a week or several days to identify the problem now. That’s an instance where you could say this is literally millions of savings from a Shiny app. To me that just shows the power of R and the ability to really streamline manual processes. Resources shared: Rachael shared Libby’s officerDemo: https://github.com/LibbyHeeren/OfficerDemo Daren shared the xaringan package for producing super impressive presentationshttps://github.com/yihui/xaringan Tanya shared Jesse’s Twitter as a great resource for learning Shiny: https://twitter.com/kierisi Javier shared xaringanthemer: https://pkg.garrickadenbuie.com/xaringanthemer/ and xaringanExtra: https://github.com/gadenbuie/xaringanExtra Tanya shared Hugo’s podcast: https://hugobowne.github.io/ Tanya’s Twitter: https://twitter.com/tanyacash21 Tanya’s presentation on “How to Hire and Test for Data Science Skills” https://tcbanalytics.com/2017/11/07/tanya-cashorali-presents-strata-2017-nyc-hire-test-data-skills/ Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Enabling Citizen Data Scientists at Dow Chemical with Posit Academy

Led by James Wade, Associate Research Scientist at Dow Chemical Timestamps: 2:46 - Start of presentation 5:25 - Goal: "apply science and engineering technical expertise along with data science tooling to innovate in the materials science arena." 6:36 - What does citizen data science mean? 8:05 - Data science as an interdisciplinary endeavor - looking to build a community of innovators 9:30 - Translating data to decisions 11:03 - Guidelines for success (data organizations, data access, data analysis, value preservation) 13:30 - Welcoming new users in an approachable, collaborative, and secure workspace with RStudio Team 14:25 - Making sure you can rapidly deploy your insights to others 16:25 - What is RStudio Academy? 20:55 - What do you need for academy? (Academy learners: 5-7 per cohort, cohort mentors from RStudio & your group, and a project - the closer to your work the better) 22:15 - Who is a good candidate? 23:55 - Who might not be the best candidate? 26:00 - What makes a good cohort? (similar work group, time zone, and skill level) 27:27 - Feedback (Are they still using the content they learned? 16 out of the 17 survey respondents were still writing code 6 months after) 31:42 - Community building (want to have a landing zone for people to continue to learn) 32:31 - RStudio Academy success story at Dow 35:30 - Start of Q&A portion Questions: 36:00 - How do you help someone who knows coding would be useful but can't motivate to take 5 steps back to take 10 steps forward? 37:55 - How can more advanced users participate in developing curriculums? 39:44 - Does Academy also teach good coding style and version control? 41:00 - If you're trying to "sell" Academy to the individual who would fill the group mentor role, what level of commitment and bandwidth do they need to have? 42:13 - Is the type of data you work with relevant to the work you do at Dow? or random / set datasets regardless of which company you're with? 43:00 - What other ways of teaching R have you tried (or considered) at Dow? How does Academy compare? 44:55 - What is the duration of RStudio Academy? 46:15 - Can you have multiple cohorts go through at the same time? What if we want to up-skill hundreds of people? 48:20 - How did you find out who might be interested and get the word out? 50:08 - Advertising that you help learners up-skill in coding seems like a good way to set your company apart from others, are you hiring? 51:25 - After the RStudio Academy 10 week training is the Academy team still available for questions, support or consult? 53:01 - Is Academy only for R? 53:38 - How can a data science student participate in RStudio Academy? (https://www.rstudio.com/conference/2022/workshops/intro-to-tidyverse/) 54:44 - How do you collaborate with others outside of Dow? 57:20 - How does RStudio Academy handle sensitive data? 1:00:20 - Do you have statistics on how many graduates are still using R? Abstract: In chemistry and materials science research, data is messy, unstructured, and scattered. Solving this problem requires researchers to deeply embed within data generation and analysis workflows. We are on a multi-year journey to equip scientists and engineers with guidance and tools to extract insights from their data. To this end, we have developed a set of 15 guidelines designed to move our organization toward a collaborative, reproducible work process in a dynamic data-diverse environment. In this talk, I will share our lessons from this journey learned through teaching, community building, and collaboration with a particular focus on the integration of language agnostic RStudio tools, products, and programs. I will especially be focusing on our experience with RStudio Academy. Speaker Bio: James is a research scientist working in the chemicals manufacturing industry as part of a research and development team. James applies materials characterization and data science with a special interest in sustainable materials design to develop new capabilities for research. His current focus is on augmenting materials characterization innovations with statistical analysis, machine learning, and data visualization. For more information on RStudio Academy: rstudio.com/academy Link to speak with RStudio: rstd.io/chat-with-rstudio

RStudio Team

Rich Iannone || New features in {gt} 0.6.0! || RStudio

00:00 Introduction 00:18 sub_missing() 03:51 Markdown formatting in sub_missing() 04:51 sub_zero() 07:34 sub_small_vals() 13:08 sub_large_vals() 16:25 final thoughts A new version of the R package {gt} has been released! We are now at version `0.6.0` and there are now even more features that'll make your display/summary tables look and work much, much better. Let's run through some of the bigger changes and see the benefits they can bring! New functions for substituting cell data We now have four new functions that allow you to make precise substitutions of cell values with perhaps something more meaningful. They all begin with `sub_` and that's short for substitution! sub_missing() (formerly known as fmt_missing()) Here's something that's both old and new. The sub_missing() function (for replacing NAs with... something) is new, but it's essentially replacing a function that is old (fmt_missing()). The missing_text replacement of "---" is actually an em dash (the longest of the dash family). This can be downgraded to an en dash with "--" or we can go further with "-", giving us a hyphen replacement. Or, you can use another piece of text. If you're using and loving fmt_missing(), it's okay! You'll probably receive a warning about it when you upgrade to {gt} 0.6.0 though. Best to just substitute fmt_missing() with sub_missing() anyway! sub_zero() The sub_zero() function allows for substituting zero values in the table body. sub_small_vals() Next up is the sub_small_vals() function. Ever have really, really small values and really just want to say they are small? With sub_small_vals() we can reformat smaller numbers using the default threshold of 0.01. Small and negative values can also be handled but they are handled specially by the sign parameter. Setting that to "-" will format only the small, negative values. You don't have to settle with the default threshold value or the default replacement pattern (in small_pattern). This can be changed and the "x" in small_pattern (which uses the threshold value) can even be omitted. sub_large_vals() Okay, there's one more substitution function to cover, and this one's for all the large values in your table: sub_large_vals(). With this you can substitute what you might consider as too large values in the table body. Large negative values can also be handled but they are handled specially by the sign parameter. Setting that to "-" will format only the large values that are negative. You don't have to settle with the default threshold value or the default replacement pattern (in large_pattern). This can be changed and the "x" in large_pattern (which uses the threshold value) can even be omitted. Final thoughts We are always trying to improve the gt package with a mix of big features (some examples: improving rendering, adding new families of functions) and numerous tiny features (like improving existing functions, clarifying documentation, etc.). It's hoped that the things delivered in gt 0.6.0 lead to improvements in how you create and present summary tables in R. If there are features you *really* want, always feel free to: File an issue: https://github.com/rstudio/gt/issues) Talk about your ideas on the Discussions page: https://github.com/rstudio/gt/discussions Learn more about the gt package here: https://gt.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) Motion Design & editing: Jesse Mostipak Music: Nu Fornacis by Blue Dot Sessions https://app.sessions.blue/browse/track/98983

Rich Iannone

Tom Schenk & Bejan Sadeghian | Making Microservices Part of Your Data Team

Making microservices a part of your data science team Led by Tom Schenk & Bejan Sadeghian at KPMG Timestamps: 1:09 - Start of presentation 4:00 - Challenges and trade-offs of a growing team (how I stopped worrying about hiring) 8:14 - What are microservices? (help separate out the different layers of an app) 9:36 - Hosting other web technologies on RStudio Connect (ex. React) 12:25 - Simple Hello World example of microservices 16:00 - Reason to separate out logging 17:15 - How to design & plan microservices (moving from a monolithic Shiny app) 17:51 - Challenges to getting started with microservices 21:02 - How do you address getting started? (domain driven design) 23:17 - Applying cloud design patterns 25:37 - Separation of development duties 27:22 - Addressing any risks that come with microservices 29:17 - Considering costs and benefits 30:11 - Microservices in action: demo of KODA app (making changes to the organization) 36:21 - PowerBI interacting with the same microservice from Connect 38:30 - Growing teams face a trade-off of complexity and simplicity (KPMG's path) Questions: 43:28 - Can you use a Shiny front end together with a microservice backend? 44:09 - Do you hire separately for back-end data science development and front-end Shiny UI development? 46:00 - Are all microservices managed by a centralized unit? 47:02 - Who can access RStudio Connect in your organization? 48:29 - When you decided to go the microservice route, what was your first step? 50:47 - What roles are you hiring for? 52:20 - Might you suggest some web service servers that host R-based or Python services? 53:33 - Are apps build with microservices as responsive as those that adopt a monolithic architecture or do microservices introduce a lag? 55:09 - Can you show the back-end response data through developer tools? 56:45 - Can you speak more about the logging microservice? Did you build it ground-up or did you adopt an off-the-shelf package or app? Abstract: Whether or not you’ve heard of microservices architecture, you may want to know how microservices can help you scale R-based applications across an enterprise. As data science teams—and their applications—grow larger, teams can experience growing pains that make applications complex, difficult to customize, or challenging to collaborate across large teams. This meetup will discuss what microservices are, how it compares to Shiny, how it can help a data science team, and how you can deploy microservices using your RStudio Connect environment. This meetup will help you understand several key items: • The basic concept of microservices and benefits, such as making your code modular, domain-driven design, and reducing the complexity of application development, and facilitate larger development teams. • How to use the Plumber package to deploy APIs as part of a microservices architecture. • How you can work with front-end development teams using their preferred framework (e.g., React, Angular, Vue) using RStudio Connect. We will show a widely-used application built using a microservices architecture and hosted in RStudio, including before-and-after comparisons to show the strengths of a microservices framework leads to a better-looking and better-functioning application. Our team will discuss the journey and growth to arrive at the new approach to make development easier within a quickly growing group. Speaker Bios: Tom Schenk Jr. is a researcher and author on applying technology, data, and analytics to make better decisions. He's currently a managing director at KPMG. He has previously served as Chief Data Officer for City of Chicago. Bejan Sadeghian is a director of analytics at KPMG and leads data science development, which spans from advanced analytics to machine learning engineering. For upcoming events: rstd.io/community-events-calendar Info on RStudio Connect: https://www.rstudio.com/products/connect/ To chat with RStudio: rstd.io/chat-with-rstudio

Data Science Hangout | Travis Gerke, PCCTC | Tips for Very Remote Work

We were joined by Travis Gerke, Director of Data Science at The Prostate Cancer Clinical Trials Consortium. Travis is an enthusiastic proponent of the use of R for data science in the clinical trials space and an advocate for productive remote work environments. Tips for very remote work 1. Ok, first things first - how do you ensure you have internet? - I use a 5g Netgear hotspot and have redundant phone plans with T-Mobile, AT&T, and Verizon. It depends where we are and what kind of service is going to be best, but I’ve never had a challenge streaming videos or with Zoom. - If you do get a hotspot, $20 boosters can be tremendously helpful too and help funnel the signal into your hotspot a bit better. - If you want to geek out on this, the best resource is rvmobileinternet.com. - Even in the remotest places we’ve been, I was still able to maintain Zoom meetings. On a week where I am furthest from civilization, I will plan ahead and focus more on learning and heads down work. It’s actually kind of good it gives you a little bit of space from the usual grind of meetings.. 2. How do you balance the time you can be available for conversations or when you’re traveling - It’s something that one has to be very intentional about and communicate broadly. I do work odd hours, because I’m West Coast now and most of my colleagues are East Coast. I try to wake up early and stick to their schedule. - It really comes down to communication. I will let people know if I’m traveling and block time on my calendar. - I think async is the future of most work environments for data science. I’d recommend checking out content from Chris Herd on Twitter too: https://twitter.com/chris_herd - If I send an email at a time like 2am, I make sure to communicate that if I’m sending an email, it doesn’t mean I expect anyone to see or respond at that time. - A few people mentioned putting something in an email signature to let people know. Here’s an example: “I work on a flexible work schedule and across a number of time zones so I’m sending this message now because it works for me. Feel free to read, act on or respond at a time that works for you.” 3. What tools are there for async work to work through something complicated without meetings? A few ideas and tips shared from the group: - We lean into github whenever we can. The process of writing down where you’re stuck and/or how you solved something is good. - When working with non-data scientists, sometimes you just have to have the meeting and that’s fine. It can be a lot more efficient that way but the drawback is that you don’t end up documenting. Writing things down reduces those institutional knowledge silos. - People mentioned using: Slack, video messages on Slack, Teams, Loom for sharing video screen shares, snagit, Discord, Fuze, Zoom whiteboard Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Alice Walsh, Pathos | Improving an Interview Experience

We were joined by Alice Walsh, PhD, VP of Translational Research at Pathos. Alice works in drug development, where she is excited about the potential of computational research to yield breakthroughs for patients. Loved that Alice also asked this question back to the audience: How do I make an interview a good experience for a candidate? Or have you had any nightmares that’d be helpful to share? A bunch of thoughts shared from the group: ⬢ I’ve had way more success not giving a technical interview, and having the technical interview be more of a discussion where I'm not even asking them to whiteboard anything or it's just talking. ⬢ If I asked them, “how do you develop a shiny app”, I'd much rather someone tell me I’ve never developed a shiny app in my life but I use R Markdown every day. That tells me a lot about their ability to actually jump in and learn something new and their transparency. ⬢ I’m much more interested in the process. How do people approach a problem and solve challenges that they encounter versus the specific project they worked on because they're not going to work on that project ever again with me. It’s going to be a new project so they will need to learn something anyway. ⬢ I’ve had success hiring from meetups or hackathons. Seeing people here and the way they problem solve gives you a lot of insight about these individuals. ⬢ My company actually does do a technical interview and we give candidates a data set while they're on site or in a Teams meeting. We give them an hour to see what sorts of insights they find with a few very specifically directed questions. What we're often looking for is not someone to have perfect answers to those questions - it's really about understanding how they looked at the data set, what other information they want, and what do you wish you had more time to do. You get to see how people work through something and it's okay if they don't have a perfectly polished presentation. ⬢ I’ve had a nightmare interview that became a pop quiz on R stuff. What are all the packages in the tidyverse (and at the time I didn't use tidyverse I was base R) ⬢ A nightmare one that sticks in my head was, please describe in detail the differences between Python 3 and Python 2. ⬢ I think, “this is something I would Google” is a valid answer sometimes because even if I don't know this, I know where to find it and am really confident in my ability to Google this. ⬢ Honestly if I ask somebody a question and they said this is something that I know I could find the answer to, that would be a perfect answer to me. Not knowing but knowing where to find the information great. ⬢ I went on 3 interviews and they each had a technical part where for every single one of them the answer was: dynamic programming. They must have gone somewhere and decided that was the algorithm to ask about. I found that a bit ridiculous because it wasn’t relevant to what they were working on and it was off-putting. Now, when I interview people I try to make it more of a conversation around the data and what we might actually be doing. ⬢ I have an optional take home: “here’s a data set, take an hour and tell me something. Use whatever tools you want: Excel, R, Python, an abacus.” The key thing I want to see is a written output of what you did. I'm still on the fence, though, because I know a lot of people are anti-technical component. I'm still trying to figure out if it is really helping us make the best hiring decisions. Also wanted to share a link to a job posting on Alice's team! She has simplified to just one posting, but they have a couple of openings. They are hiring both for more experienced scientists and folks transitioning from academic research and no drug development experience: https://www.indeed.com/viewjob?t=senior+computational+biologist+data+scientist&jk=204065f326f6e803&_ga=2.142078395.801277873.1653586940-640826182.1637348361 Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Lindsey Clark, Healthcare Bluebook | Measuring success of data science

We were joined by Lindsey Clark, Director of Data Science at Healthcare Bluebook. Evolving and Leading a Data Science/AI First Organization. One of the topics of discussion (33:46) was measuring the success of a data science team and calculating ROI. How do you measure success for a data science team? Lindsey shared a few tips from her experience at two different organizations: One measure of success was the conversion rate of projects. How many projects come to us and what percent are actually converted to an actual deployment? Our target was 5% which may sound low, but the idea was that we want to fail fast. It can be like the drug discovery mentality - if things aren’t going to work, we want to know early on. We also have targets around the number of specific models we wanted - what we called our data science portfolio library. They didn’t need to be really fancy machine learning, but a lot of them were business logic sort of ranking models. Another measure was product value and financial targets around that. Could we pinpoint that our model went into the data product and it resulted in more clients contracting with us or greater revenue because we were able to upsell a data product due to another feature we added. In my position now, we have recently been acquired so we are working on the success measures and what that looks like. We do utilize the Outcomes and Key Results (OKR) Strategy from the Measure What Matters book. We plan OKRs quarterly and try to plan projects that are really no longer than three month increments in order to keep things going and satisfy the business. Data Science ROI can of course be a difficult thing to measure. Data science can be a capital expense at times and sometimes you have to look at this team that’s going to do high risk projects as research. Things are going to fail and that’s okay. We’re willing to invest the money for our team and absorb that cost for what we potentially get out of the team in terms of deployed solutions, specific insights, and research that are valuable to the business. Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Using Python with RStudio Team

Using Python with RStudio Team Led by David Aja, Solutions Engineer at RStudio 00:30 - Overview of RStudio Team - RStudio Workbench, RStudio Connect, RStudio Package Manager 2:38 - Use case of RStudio Package Manager with Python packages 4:29 - RStudio Workbench (ability to use RStudio, Jupyter Notebook, JupyterLab, VS Code) 5:48 - VS Code running on RStudio Workbench 11:50 - Deploying a Streamlit app to RStudio Connect 14:00 - Pause for questions 18:55 - Jupyter on RStudio Workbench 19:41 - Extension in Jupyter that gives you push-button publishing from RStudio Workbench 20:35 - Publishing with source code vs. publishing only the finished document 21:10 - Hiding the code that is generated when publishing Jupyter to RStudio Connect 23:30 - Creating a custom url in RStudio Connect for your content 24:30 - Adding viewers / collaborators to your content in RStudio Connect 25:18 - Scheduling your Notebooks to run repeatedly 25:54 - Stepping back to describe the differences between RStudio open-source IDE and RStudio Workbench *please note that this meetup will cover our enterprise product, RStudio Team but all who are interested in joining are welcome! Many Data Science teams today are bilingual, leveraging both R and Python in their work. While both languages have unique strengths, teams frequently struggle to use them together: ⬢ Data Scientists constantly need to switch contexts among multiple environments. ⬢ Data Science Leaders wrestle with how to share results consistently and deliver value to the larger organization, while providing tools for collaboration between R and Python users on their team. ⬢ DevOps engineers and IT Admins spend time and resources attempting to maintain, manage and scale separate environments for R and Python in a cost-effective way. Join David Aja in this meetup, which will highlight how other data science teams are able to solve these problems with RStudio Team by: ⬢ Combining R and Python in a single data science project. ⬢ Launching and managing RStudio, Jupyter Notebooks, JupyterLab, and VS Code from the RStudio Workbench environment ⬢ Sharing Jupyter Notebooks, Python APIs via Flask, Dash, Streamlit, Bokeh, FastAPI, Shiny, R Markdown, etc. with the business through RStudio Connect. ⬢ Controlling and distributing Python and R packages with RStudio Package Manager. A few helpful links shared and mentioned during the call: ⬢ Examples David used: https://lnkd.in/g8bdbj7Q ⬢ Example usage patterns of Using Python with RStudio: https://lnkd.in/gek3BhgW ⬢ Helpful place for questions about using Python in RStudio: https://lnkd.in/gWRV2rbG ⬢ Model Management with Python example: https://lnkd.in/gyX2YVvi Product / Conference Questions: ⬢ More info on RStudio Team: https://lnkd.in/gv4YQj2G ⬢ If you’re starting to champion data science at your company: rstudio.com/champion ⬢ If you’d like to chat about RStudio Team: rstd.io/chat-with-rstudio ⬢ RStudio Conference: https://lnkd.in/dcrz79y ⬢ System Admin Workshop at RStudio Conference: https://lnkd.in/eB9FZt-c

RStudio Team

Veerle van Leemput | Analytic Health | Optimizing Shiny for enterprise-grade apps

Can you use Shiny in production? A: Yes, you definitely can. Link to slides: https://github.com/RStudioEnterpriseMeetup/Presentations/blob/main/VeerlevanLeemput-OptimizingShiny-20220525.pdf Packages mentioned: ⬢ shiny: https://shiny.rstudio.com/ ⬢ pins: https://pins.rstudio.com/ ⬢ plumber: https://www.rplumber.io/ ⬢ blastula: https://github.com/rstudio/blastula ⬢ callR: https://github.com/r-lib/callr ⬢ shinyloadtest: https://rstudio.github.io/shinyloadtest/ ⬢ shinycannon: https://github.com/rstudio/shinycannon ⬢ shinytest2: https://rstudio.github.io/shinytest2/ ⬢ feather: https://github.com/wesm/feather ⬢ shinipsum: https://github.com/ThinkR-open/shinipsum ⬢ bs4Dash: https://rinterface.github.io/bs4Dash/index.html Timestamps: 2:44 - Start of presentation 5:41 - What qualifies as an enterprise-grade app? 10:46 - UI first / user experience / prototyping 13:20 - Separating code into separate scripts and creating code that's easy to test 17:15 - Golem 19:28 - Functionize your code 20:50 - Rhino package, framework for developing enterprise-grade apps at speed 22:33 - Infrastructure, how do you bring this to your users? (lots of ways to do this. They do this with R, pins, plumber, rmd, blastula, and Posit Connect on Azure) 31:17 - Optimizing Shiny (process configuration, cache, callR, API, feather) 47:35 - Testing your app (shinyloadtest and shinycannon) 50:23 - Testing for outcomes (shinytest2) 52:15 - Monitor app performance & usage (blastula, shinycannon, usage metrics with Shiny app) Questions: 57:38 - What's the benefit of using pins rather than pulling the data from your database? 59:30 - Are there package license considerations you had to think about when monetizing shiny applications? 1:00:45 - Do you use promises to scale the application? (they use CallR) 1:01:49 - For beginners, golem or rhino? 1:02:50 - The myth is that only Python can be used for production apps, what made you choose to use R? 1:05:12 - Is feather strictly better than using JSON? 1:06:38 - Where do you see the line between BI (business intelligence) and Shiny for your applications? 1:08:36 - Any tips for enterprise-grade UI development? Making beautiful apps (bs4Dash app) 1:10:25 - Have you found an upper limit for users? 1:12:19 - Any tips for more dynamic data? (optimizing database helps here) 1:13:50 - Where do you install shinycannon? (on our development Linux server) 1:15:00 - Can you share other resources or examples of code? (Slides here with resources: https://github.com/RStudioEnterpriseMeetup/Presentations/blob/main/VeerlevanLeemput-OptimizingShiny-20220525.pdf) For upcoming events: rstd.io/community-events-calendar Info on Posit Connect: https://www.rstudio.com/products/connect/ To chat with Posit: rstd.io/chat-with-rstudio

Data Science Hangout | Wayne Jones, Shell | Thinking Empathetically & Using Your Initiative

We were joined by Wayne Jones, Principal Data Scientist at Shell. Wayne has been using R since its first inception and is passionate about open source solutions. During the hangout with Wayne, he brought up the point of thinking empathetically when communicating. Here are a few other tips shared among the group: 1️⃣ If you spot someone who you think is really good at communicating and you’re impressed by them - buddy up. Tell them you like the way they did that, can you give me a few tips and hints? 2️⃣ With data science, you can focus on a very small area sometimes and get obsessed by the details. Step back, step back, and step back some more so that when you’re presenting a problem you give the bigger picture and narrow into the data science details. 3️⃣ If you think more empathetically, this will also go a long way. Instead of thinking about it from a data science standpoint, put yourself in the customers or client’s shoes. With more empathy, you make yourself a better consultant. 4️⃣ Try using your team meetings (scrum meetings for example) to practice presenting in front of people, even something for 5 minutes where you need to learn to be succinct about it. 5️⃣ Think about when you were a child and bringing something in for show and tell. When they get to bring something they really care about, they are thinking more about the toy that they brought in rather than presenting. 6️⃣ There are lots of R meetup groups that are always looking for volunteers to speak at events! Lots of opportunities to practice there! 7️⃣ Learn about everyone else's role so that you can talk their language - “BE RELEVANT” to your teams Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Michael Chow, Posit | Exploring Team Structure w/ Data Scientists & Engineers

We were joined by Michael Chow, Data Scientist and Software Engineer at RStudio. Michael also previously led a team at the California Integrated Travel Project. On this week’s hangout there were a lot of thoughts shared on structuring a data science team from both Michael and the broader group: ⬢ Jacqueline Nolis also shared thoughts on this on a data science hangout that there were virtues to different ones, but ended up sold on the decentralized model where data scientists are embedded in teams: https://youtu.be/CcPE29bYGVo?t=325 ⬢ Michael agreed that data scientists and analysts should be sitting with the teams that they’re pushing out reports for. Otherwise, I would be trying to send people into those teams to figure out their priorities. ⬢ A data scientist should work with a Project Manager or whoever’s leading the team to push up metrics but also help change the roadmap. ⬢ It leaves a tricky question of where data engineers should be and how they should interact with the team. Today data engineers are often doing more tooling empowerment, so it can be okay to have them a bit more centralized and connect to the data scientists to enforce best practices or enable new pieces for them. ⬢ I think a nice model is for data scientists/analysts to live in the teams and data engineers to be like spokes of a wheel where then the data scientists connect with them and work closely to enforce better best practice and enable new important things. ⬢ Tatsu shared that in thinking of the structure, it’s also important to find your translators and to use the power of feedback. Reach out to those people to start to put that feedback into action. ⬢ George shared that insurance companies have come from a really traditional landscape where they have lots of actuaries working on lots of excel spreadsheets and there can be a lack of knowledge sharing and tool sharing. This is where the data science element comes in. To me, within the organization, you need to have this team which is a mini-spoke if you will, because they are central to the actuarial team. If they are too far removed and they’re back with the IT team, you end up with the old problems because they may not get the business concept communicated back. It's all about getting enough skills, so they can get stuff done, especially proof of concepts. Maybe after that you can take a step back and then start to look at the centralized model again. ⬢ A central team can help converge to what they see as best practice, but if you’re pushing out something new, exploring a new line of work or area it can be important to set the data engineer there to actually do whatever they need to. Make sure that the converging doesn’t stifle creativity or prevent a team from doing the right thing. ⬢ Manny jumped in to share the perspective from data science being with IT as well, data science is a new field for their company (in real estate) and there’s an identity of where does data science fall. The IT team is fantastic and they’re very structured. Data science is so fluid and creative and non structured at the moment, so you kind of have to look at where it actually should fall. * please note that some of the points above are summarized and not 100% actual quotes. Resources shared: ⬢ Tatsu shared in the chat, a few projects that Michael is working on: vetiver: https://vetiver.tidymodels.org/articles/vetiver.html, siuba: https://github.com/machow/siuba ⬢ Libby shared a helpful tip on creating a 2 minutes YouTube video with a cover letter, to get the attention of a hiring manager ⬢ Javier shared an example Shiny app used in an interview: https://javierorraca.shinyapps.io/Bloomreach_Shiny_App/ ⬢ Michael mentioned David Robinson’s screencasts: https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ ⬢ Michael mentioned an article on “What data scientists really do according to 35 data scientists”: https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists ⬢ Rachael shared a blog post link where Jacqueline Nolis talked about team structure as well: https://www.rstudio.com/blog/building-effective-data-science-team-answering-your-questions/#Structure ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout ► View the Data Science Hangout site here: rstudio.com/data-science-hangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

Michael Chow

Kelly O'Briant | Building a business case for data science & advocating for analytic infrastructure

3:10 - Start of Kelly's presentation 5:05 - Identifying where you are in your organizational culture when asking for change 9:30 - What is analytic infrastructure: the how, where, and with what that goes into your daily data science work 10:55 - Production looks different at different organizations 12:20 - Don't get caught up in the hype - there is no perfect deployment pipeline 14:00 - Tactical metrics for communicating devops - code deployment lead time 15:55 - Your analytic infrastructure is what enables you or teams to deliver value through decreasing that code deployment lead time 20:49 - What is an analytic administrator / R Admin? (A data scientist who onboards new tools, deploys solutions, support existing standards. They work closely with IT to maintain and scale analytic environments. They influence others in the organization to be more effective, and are passionate about making data science a legitimate analytic standard) 24:04 - Building a business case (introducing rstudio.com/champion) Join us for future champion chats the 4th Monday of every month (*not the date I said in the recording*) rstd.io/champion-chats Let’s admit it. Getting to use the tools you want sometimes needs a little convincing. We’ve had the opportunity to meet so many wonderful people from the community. People that: organize meetups in their own communities, spend time after-hours teaching their co-workers, solve business problems at work with Shiny, write blog posts to help others, and so much more.. People are doing amazing things with data science, yet so many are still in a position where they are not able to make the case for it at their own organizations. Hear from Kelly O’Briant on how you can start to navigate the internal process and advocate for great analytic infrastructure as a data scientist. Whether you’re getting pushback about using open-source, being told to use a BI tool instead, or just unable to find the other data scientists at your company - we want to help make this process less frustrating. Come hang out and chat with us! *Please note, only the presentation portion of this meetup was recorded, the open discussion and Q&A was not. Resources shared this week: ⬢ rstudio.com/champion ⬢ John shared a typology of organisational cultures: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1765804/pdf/v013p0ii22.pdf ⬢ Kelly shared The Phoenix Project book: https://www.amazon.com/Phoenix-Project-DevOps-Helping-Business-ebook/dp/B078Y98RG8 and The Unicorn Project book: https://www.amazon.com/gp/product/B07QT9QR41 ⬢ Kelly shared Benjy’s talk on conveying data science findings to non-experts: https://www.youtube.com/watch?v=UUIda4-CSXQ ⬢ Nick shared: The pkgdown package can be used to build informational web pages about R packages, which can themselves be deployed to RStudio Connect: https://pkgdown.r-lib.org/ ⬢ Kelly shared Nathan Stephens RViews blog on Analytics Administration for R: https://rviews.rstudio.com/2017/06/21/analytics-administration-for-r/

Data Science Hangout | Daren Eiri, Arrowhead General Insurance | Building a DS Playbook

We were joined by Daren Eiri, Director of Data Science at Arrowhead General Insurance. Daren is passionate about building creative solutions within the insurance industry using data, statistical modeling, and cloud technology. What do you do to ensure your ideas or solutions are well received by internal customers? Daren shared that they have a data science playbook for building out models. This generally follows the path of: initial kickoff, understanding what their business problem is, and if we do build out a solution - what is it going to look like? Frequently meet with the business stakeholders. Whenever you’re making major decisions or have questions about something, make sure that you reach out to them about that. Make sure that it’s part of their business workflow. If they’re adding this new feature, how is the business going to use it and continue using it so it doesn’t get left in the dark. In terms of making sure that they’re onboard, we talk about their data and what it looks like. This is a service to the team because they may not have looked at the data at that grand of a scale. They may be more used to looking at it quarter by quarter or year over year. We look at the data and provide the perspective that we’re seeing. We always ask for their hypothesis and what they think will lead to what we’re trying to predict. This helps us understand what their problem is and include their own ideas into the project as well. That results in some ownership on their side too because they are partnered with us. When we have the model built out, we evaluate it and see the accuracy. We provide them with several examples and the variation of that data to show the limitations of what that final product will look like. When you’re spending the time walking through the process and they see it working, this helps getting them onboard. Resources shared: ⬢ Javier shared this article on packages to help work with business stakeholders more comfortable with Excel: https://lnkd.in/eqXTP82c ⬢ Daren shared, The Making of a Manager by Julie Zhuo: https://lnkd.in/epjdHGgc Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Jennifer Listman, Statespace | Culture that Helps Avoid Burnout

We were joined by Jennifer Listman, Director of Research at Statespace. Jenny likes mining multidimensional datasets to find valuable unknown unknowns. One of the questions that had a lot of people sharing in the chat as well was: ""How do you ensure a work-life balance to avoid burnout?"" ️ A few thoughts: The culture of the team matters. Make it okay to take personal time in the middle of the week to go to doctors, take children somewhere, etc. If it’s a family member's birthday, let people do what they need to help them celebrate that person. Having that freedom is important. As a manager, take time off yourself to set a good example. Managers have to be really careful and be on top of that with your employees. Understand who has to take responsibility for certain things and if they are at risk of experiencing burnout. A good manager recognizes when the team needs a break is key. Know when they've had a crazy week & let them allocate mental health days accordingly Especially for companies that have nebulous working hours and lack of specific vacation time, ensure that people are taking vacation. HR may keep tabs on if people are taking enough time off, and will make people take off. Try quarterly check-ins to make sure that people are on-track to take enough time off. Some people make the choice to not have any work stuff on my phone at all or delete those apps from your phone on the weekend. Links shared about burnout ⬢ https://lnkd.in/g5GssAck ⬢ https://lnkd.in/g5DUVj8r Speaking of culture, Statespace is hiring! ⬢ Statespace Data Scientist (Remote) - Data & Analytics team: https://lnkd.in/ddxpp3Ep ⬢ Statespace Manager of Marketing Analytics and Data Science (Remote) - Data & Analytics team: https://lnkd.in/da-9J9aw ⬢ Statespace Biostatistician / Data Analyst, Digital Health (Remote) - Digital Health team: https://lnkd.in/d2ni-7vu Other resources shared: ⬢ Jenny shared a paper Statespace recently published (all of my analyses were done in R!): https://lnkd.in/dWFE-Cqx ⬢ Rachael shared Resume Review Club form: https://lnkd.in/gjtQDS9D ⬢ Antti shared a thread on R-related podcasts: https://lnkd.in/gTNtZhdg ⬢ Book on importance of sleep: https://lnkd.in/gUtWb8YF ⬢ Bala shared a link on keeping your mind young: https://lnkd.in/g9XiGmmb ⬢ Antti shared this post on LinkedInHardMode (content challenge): https://lnkd.in/giKWzeYh ⬢ Upcoming Shiny conference (April 27-29): https://shinyconf.com/ ⬢ Bala shared podcasts he enjoys: Datacast (https://lnkd.in/gJtSURsh), AI Podcast (https://lnkd.in/gVddYDT2) and Data Science Imposters (https://lnkd.in/gMEUMUWN) Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Joseph Korszun, ProCogia | Encouraging People to Learn to Code

We were recently joined by Joseph Korszun, Manager of Data Science at ProCogia. There was a great discussion on mentorship and helping teach people to code. 🤔 How do you encourage people to learn to code? (15:27) ⬢ At every point in your career, you’ve written a formula in Excel so just lending that to translate into you know more usable scripts is helpful. ⬢ It starts with identifying what their determined career path is from their own perspective. Some people want to be more statistics oriented, or more ML engineering oriented. You have to understand what their focal point is. ⬢ Help identify someone’s pain point that they have right now and with that - what’s the quickest way to up-skill them in that? ⬢ Have people do some hands-on labs. This can be more beneficial than watching videos or reading textbooks and allows you to get into the code. You’re going to have errors along the way but that’s ok! ⬢ Let new coders know that it’s okay to google and copy/paste code. This can unblock new coders, so that you don’t have to code everything from scratch. Adapt existing code to your problem. ⬢ You can pair someone with somebody else who has that skillset to help mentor them. ⬢ Remember that junior colleagues often have a lot to teach to senior team members as well. Things are changing so quickly, and there are so many ways to do things. Interns may teach you something new technically and senior colleagues may then mentor them in the soft-skills. Resources shared: ⬢ Working with IT: https://lnkd.in/dzU-uzzW ⬢ Security courses here give practical tips for how to follow good security practices as an R programmer: https://lnkd.in/dmb2nmGV ⬢ Manager tools podcast: https://lnkd.in/dXY6hK7V ⬢ Breaking Math podcast: https://lnkd.in/dEJNu64F Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Jorge Valente Hernández Castelán | Algunas formas de ver a Uber en datos: Shiny en acción

*Please note, this meetup was hosted in Spanish. Algunas formas de ver a Uber en datos: Shiny en acción Jorge Valente Hernández Castelán App: https://lnkd.in/evpSjden ¿Cuál es el límite verdadero de la programación en R? Esta pregunta me rondó la cabeza cuando conocí este lenguaje por primera vez hace unos dos años. En aquel momento imaginé que era un lenguaje algo corto de posibilidades, que estaba orientado a estadística y, únicamente, obtener resultados de algunos cálculos. No pude haber estado más equivocado en aquel momento. Cuando comencé a trabajar con Dashboards, probando primero cosas simples como Flexdashboard, me di cuenta de que el presentar información que se encuentra dentro de una base de datos, archivo de excel, etc, no solo se limita a crear estadísticos y un simple gráfico. Las aplicaciones web hechas con shiny no solo presentan información estadística, te cuentan una historia mediante los datos, la cuál puede ser presentada de muchísimas maneras. La aplicación “Uber App” de hecho tiene ese objetivo: Contar mediante estadística y geografía una historia, conocer la lógica y la tendencia de los viajes en Uber dentro de la ciudad de Nueva York, pero ¿Cómo surgió la idea de hacer esta app? En realidad fue una experiencia interesante, ya que un día me encontraba visualizando ejemplos de apps hechas dentro de Python, por lo que una en específico me pareció bastante interesante y en ese momento me hice la pregunta: ¿Es posible realizar algo similar en R? La respuesta a la pregunta anterior fue la siguiente: No solo es posible, también es posible hacerla mejor, mucho mejor. Abstract in English: What is the true limit of programming in R? This question was on my mind when I first came across this language about two years ago. At that time I imagined that it was a language somewhat short of possibilities, that it was oriented to statistics and, only, to obtain results of some calculations. I couldn't have been more wrong at the time. When I started working with Dashboards, first trying simple things like Flexdashboard, I realized that presenting information found in a database, excel file, etc, is not just limited to creating statistics and a simple graph. Web applications made with shiny not only present statistical information, they tell you a story through data, which can be presented in many ways. The application "Uber App" in fact has that objective: To tell a story through statistics and geography, to know the logic and the trend of Uber trips within the city of New York, but how did the idea of making this app come about? It was actually an interesting experience, since one day I was viewing examples of apps made within Python, so one in particular seemed quite interesting to me and at that moment I asked myself the question: Is it possible to do something similar in R? The answer to the previous question was the following: Not only is it possible, it is also possible to do it better

Data Science Hangout | Mike Smith, Pfizer | Building an R Center of Excellence

We were joined by Mike Smith, Senior Director, Pfizer R&D UK Ltd at the Data Science Hangout - a weekly, free-to-join open conversation for the data science community. If you'd like to join us live, you can add it to your calendar here: rstd.io/datasciencehangout Mike shared with us all that they are building up a Center of Excellence at Pfizer to help teams across the business build reproducible workflows and use analytics tools effectively & efficiently. What led to the creation of the CoE within Pfizer and how could we do something similar? Mike: ⬢ Last year before R/Pharma, we did a poll & found that 1,500+ colleagues had downloaded R. I wanted to service & build up that community to find out what other people are doing and share that. (2:45) ⬢ We’re a very decentralized disparate team, so there are subject matter experts (SMEs) throughout the organization. The Center of Excellence is focused on building connections between SMEs and helping the teams where there isn’t an SME available. ⬢ What we saw was that it’s hard to sometimes get an effective strategy across people in such a big company. We also saw that there were other places within the organization that wanted data science work but they didn’t have an R subject matter expert there. We want to be able to help them solve their problems and set them up with a proof of concept that they can tweak. 33:52 - Ok so how to do this? ⬢ Find out how many people are using the tools and who you could help. ⬢ Be that translator role between the business people who need solutions with the technical side - folks who are building things. Communicate the value: ⬢ We may have a bunch of people trying to write the same function or access the same data. We could solve this problem once and then make that into a package and serve that out to everybody and streamline their workflow for the future. ⬢ There’s a benefit in being able to solve problems strategically. We’re trying to build the lego pieces so that the next time we see a problem like this, we can use that. We can also offer this as a package or via something that allows other people to solve that problem for themselves. Talk to someone who has experience in this, other community builders ⬢ Doug Robinson helped start this at Pfizer because he had set-up something like this at Novartis before as well. Talking with someone who has done this before is really helpful because they have the experience of : who do we need to tell, what do we need to tell them, what’s our purpose for being, who do you have to speak to and convince. That has to be ready to go. Find a champion in leadership: ⬢ We went to the head of Statistical programming and said we’d like to do something like this. Fortunately, she was 110% supportive here. How did they phrase this CoE at Pfizer? ⬢ Check out this description from the job post: https://lnkd.in/g776nYVF Resources shared: Ethan shared: I saw on RStudio blog the other day the {sassy} system for SAS programmer transitioning to R: https://sassy.r-sassy.org/index.html Tatsu shared: For folks that have RStudio Connect and Tableau, there’s now a supported integration https://www.rstudio.com/blog/dynamic-r-and-python-models-in-tableau-using-plumbertableau/ Tatsu shared the Working with IT section of the champion site: https://www.rstudio.com/champion/working-with-it Mike’s Bandcamp: https://mikeksmith.bandcamp.com/ R Consortium Pharma Working Groups: https://www.r-consortium.org/projects/isc-working-groups R in Pharma Conference: https://rinpharma.com/ Upcoming Pharma meetup with Merck: https://youtu.be/RBVqKi3FV30 Question about style guides: Jesus shared: Tidyverse Style Guide: https://style.tidyverse.org/ Jesus shared: One guide overall guide on better clean R code is the contributing.md of the ggplot2 package: https://github.com/tidyverse/ggplot2/blob/main/CONTRIBUTING.md Sam shared: Efficient R Programming book that Colin wrote: https://csgillespie.github.io/efficientR/ Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Erin Pierson, Charles Schwab | Advocating for the Role you Want

We were joined by Erin Pierson, Sr Manager of Trading Operations at Charles Schwab. There was a great conversation about networking within your organization on the hangout with Erin and also tips for the question: How do you have the conversation with your manager that...this isn't the job that I was hired to do? 🤔 11:57 - ⬢ Talk to people and let them know what you’re capable of doing and what you’re interested in doing. Hopefully you have someone who’s in your corner that will help you get to where you want to be. ⬢ Start talking about what your skills are and what you had discussed your position being. ""You have technically hired me for this job and I’m not doing that job."" ⬢ You have to have a little finesse about it and can’t be super negative but you can say, “You know, I’m really interested in doing X, Y, Z. I appreciate this job. I’m doing this and I’m learning this but I could also be doing more. I’m interested in doing more.” ⬢ Try phrasing it as what can I do for your leadership, even if it’s really more for you. ⬢ Make your manager aware of where you are and what you want to do. You need to have a good manager to have this happen but in some cases they may even be thinking about hiring someone for that role you actually want to do. ⬢ The bottom line is, if you’re not happy in your job you’re not going to stay in your job. They don’t want you to be unhappy because they don’t want to lose you. If you’re not happy, you’re not going to be doing good work. Be honest and let them know how you’re feeling about it. Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Joe Gibson, de Beaumont Foundation | Collaboration Across a Team

We were joined by Joe Gibson, Senior Project Director at de Beaumont Foundation. At the hangout, there was a great conversation on building a code archive database/using snippets ️ 38:16 - Sharing a bit of the conversation below: Stephanie asked: How do you build a code archive database? I’ve worked places with Word documents and need something more user-friendly. Joe: Each time that we create a work product, we log into our database - it creates a new ID for that project and we add some basic information then create a folder for it. We then store it in our structure we have in our network to make it easier for people to find things. In addition to having the library of all our code, we have some folders that have handy code snippets. Steve: We’re developing an internal package for getting data and doing common tasks is a more standard way, which is nice. Nothing too fancy but it streamlines things. Ethan: If you're using GitHub, Gist is also a great way to share snippets of code. I have a snippet that set up the header/documentation structure for a script. On of the first bits is library(tidyverse) Mike asked: Has anyone developed R snippets and distributed them across a team? I don’t know if people are familiar with the snippets within RStudio but they are cool because you can use template frameworks and it jumps you to the next thing you need to tailor for your own situation. It’s essentially a function. Tatsu shared: https://lnkd.in/gwWCB3T2 Javier: These are super helpful, Mike. I recently learned about them and was shocked. I have all kinds of snippets for myself and my team now. Jordan: I've saved snippets inside a core package and have a function that updates your RStudio snippets. Saved a snippet update gist here: https://lnkd.in/gEcmNViN requires a snippets folder in your package/inst. You can have this .onLoad() if you're feeling lucky Resources shared: ◘ Rachael shared the new champion site: https://lnkd.in/gaHt_8Br ◘ Jen & Michelle shared the National Syndromic Surveillance Program Community of Practice: https://lnkd.in/gHE3C94s ◘ Joe shared Harold’s GitHub for NSSP projects: https://lnkd.in/gFRsezTM ◘ Joe mentioned, Mozilla Foundation to ensure the internet remains a public resource that is open and accessible to us all. https://lnkd.in/gbvdQXwH ◘ Rachael shared AI Inclusive https://lnkd.in/gt8cQUUX ◘ Cris shared Fairlearn to improve fairness of AI systems:: https://fairlearn.org/ ◘ Angela shared openscapes, helping teams develop collaborative practices that are more reproducible, transparent, inclusive, and kind: https://lnkd.in/gs-6_-ZA Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Rich Iannone || {gt} Intendo Game Data Project Walkthrough || RStudio

00:00 Introduction 00:11 Setting up our environment 01:21 Importing data 01:56 Data preparation using the tidyverse 14:12 Basic gt table 16:25 Specifying row order with row_group_order() 17:20 Formatting currency with fmt_currency() 18:10 Formatting missing values with fmt_missing() 18:55 Creating row groups with tab_options() 19:50 Relabel column names with cols_label() 20:41 Creating tab spanners with tab_spanner() 23:00 Creating a table title and subtitle with tab_header() 24:40 Aligning table title and subtitle with opt_align_table_header() 25:16 Creating a stubhead label with tab_stubhead() 26:00 Format all table cell text using tab_style() 27:25 Automatically format data color based on value using data_color() 30:45 Creating Markdown-friendly source notes using tab_source_note() 32:45 Creating Markdown-friendly footnotes using tab_footnote() 39:28 Adjust table column width using cols_width() 40:55 Adjust cell padding using opt_horizontal_padding() and opt_vertical_padding() 42:22 Change row group headers using tab_style() 43:40 Convert all table text to small caps using opt_all_caps() 43:58 Change all table text font using opt_table_font() 44:28 Changing table, table heading, footnotes, and source notes background color using tab_options() 46:41 Add a table "cap" at the top and bottom using table.border.top.width() and table.border.bottom.width() 47:23 Use multiline formatting with footnotes using footnotes.multiline() 47:34 Change line style using table_body.hlines.style() 47:55 Change table title and subtitle font sizes using heading.title.font.size() and heading.subtitle.font.size() 48:11 Checking out our final table! Code to recreate the table from the video: https://github.com/kierisi/rstudio_videos/blob/main/gt/rich-intendo-project-walkthrough/intendo-30032022.R Learn more about the gt package here: https://gt.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) Motion design and editing: Jesse Mostipak (@kierisi) Music: Nu Fornacis by Blue Dot Sessions https://app.sessions.blue/browse/track/98983

Rich Iannone

Santiago Rodriguez | Intro to functional data analysis | RStudio Meetup

Energy Meetup - Intro to functional data analysis Presented by Santiago Rodriguez 2:49 - Start of talk 12:15 - First Q&A point 23:23 - Second Q&A point 31:56 - Third Q&A point 40:44 - Final Q&A point Abstract The focus of this talk is to introduce functional data analysis (FDA) and to showcase some of its applications in the utility space. A primary source of data in a utility are meter reads. This data is periodic and appears discrete, but energy consumption is continuous, which makes meter reads a perfect use case to apply FDA. The talk will highlight two applications: load profiles and segmentation. The talk will be non-technical - no math or code - because the goal of the talk is to persuade you to investigate FDA on your own. Speaker Bio Santiago is a data scientist in the marketing department at Consumers Energy, a Michigan-based, public utility. He focuses on data engineering, data science, and MLOps. Santiago has about a decade of experience working in the analytics space across energy, aviation, automotive, and contact centers. He has a bachelor’s in finance from Florida State University and a master’s in statistics from Texas A&M. Resources shared in the chat: FDA descriptive statistics blog post: https://lnkd.in/gZjauTdt Intro to FDA Blog Post: https://lnkd.in/gM5NCbHe Refund package is introduced in 'Introduction to Functional Data' by Kokoszka and Reimherr: https://lnkd.in/gd5Bt3tE Gaussian Process Regression Analysis for Functional Data by Shi & Choi: https://lnkd.in/gAig-AZa GAMs in R, free course - Noam Ross: https://lnkd.in/gput2QbK Jiguo Cao - Intro to FDA on YouTube: https://lnkd.in/ga4qfGDr Feedback: rstd.io/meetup-feedback Talk submission: https://lnkd.in/gJ7EUSCk If you'd like to find out about upcoming events you can also add this calendar: rstd.io/community-events Packages shared in the chat: Task view https://lnkd.in/gBqtuV2b FDA: https://lnkd.in/ge85i6UE Refund package: https://lnkd.in/gXGcT79t FDA.USC: https://lnkd.in/ggHdUaEe

Data Science Hangout | Kristi Angel, Stitch Fix | How to Break into Data Science

On March 10th, we were joined by Kristi Angel, Data Scientist - Experimentation @ Stitch Fix. This week featured a lively discussion on breaking into data science: Try to get around the applicant tracking systems by networking - we can call it something different though. Utilize LinkedIn as a professional social media. Find people who are generating content that you find interesting. Engage and people will start to know who you are by your comments and feedback. This will help you build a reputation for yourself. Try to connect directly with the hiring manager. There are a lot of hiring managers advertising for roles on LinkedIn right now. Take a look at your resume and see if you can get some reviews from peers you trust and see if there's a way that might be more effective. Your resume needs to tell your story in less than a 5 minute glance. A lot of us mentioned we would be willing to help review here - let's think of ways to connect people through this group too :) For some people hitting HR roadblocks, where large businesses have decided that there are minimum requirements for education and experience and there is absolutely no budging from it - looking at smaller companies without those rules has helped lots of people I know. On the topic of interviewing and hiring people with degrees vs no degrees or not a relevant degree ️ A lot of us shared in the chat that we have different degrees than the job we’re doing today :) Looking past the degree is a really good idea in many cases because you need diversity of thought and background. If you want the company as a whole to have empathy for customers, it’s important to have a really varied set of life experience backgrounds. We still acknowledge, it can be hard because the degree and experience gives a little bit of a certification, especially for junior people. In startups especially, the time spent hiring can be a lot of drain on the organization so degrees are often used to quickly filter. If you’re on the side of the company interviewing, something that can also help is to make sure that all interview loops have a diverse representation. Links shared: Frank shared: Kahnaman’s book Noise has some great frameworks for interviewing and how we make decisions on candidates. One example, stack rank your candidates instead assessing each individually because (people) are typically bad at that: https://readnoise.com/ Melody’s team is hiring several Data Scientist positions and a Data Architect position. Here are a few links: https://lnkd.in/gf9nqqWV https://lnkd.in/gKwEcqC2 Data Architect position: https://lnkd.in/g3dRxKMG Liz shared: Manager Tools has a great podcast about resumes https://lnkd.in/gsRDKrqF" Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Stephen Bailey, Whatnot | From Academia to Industry

We were joined by Stephen Bailey, Data Engineer at Whatnot and former Director, Data & Analytics at Immuta at the Data Science Hangout. A few topics from the discussion: being the first data scientist working with real-time data bringing data science skills to data engineering getting people to feel ownership of data 49:43 - Towards to end of the hangout, there were a lot of thoughts on moving from academia to industry. Tatsu asked: As a fellow recovering academic, I’d love to hear your views of where we are in terms of the process for PhDs to go into industry in data related role? In psychology, there was minimal visibility into how to apply my skill set. A few thoughts from the discussion: ‣ With a PhD program, it’s almost like you have a relationship with the field and you’re very invested in the research. You’re part of the scientific community. One of the things that’s very jarring about leaving, is that it feels like a divorce - severing that relationship and that community. It is almost two different worlds. ‣ If anyone’s thinking about transitioning or doing something similar, talking to people and building relationships before that transition can really help. ‣ Intentionally think about branding yourself. ‣ The biggest thing in industry is that you can’t fall in love with a problem. It makes finding a job much harder. You have to fall in love with your position in some way, how you are solving problems. Apply the mindset of a scientist into this business context. ‣ The skillsets that everyone’s learning in graduate school - no matter what the subject area is, are very helpful. My biggest advice would be don’t be afraid to reach out to people (whether on LinkedIn or in community events.) Most people are very willing to talk to you about what their journey was no matter where they’re at in their career. Once you realize that, you will see that your room is much bigger. ‣Finding a mentor outside academia is really helpful. ‣ Certain departments within universities do a really nice job connecting with industry with the community and have great collaboration. How can that carry over to other institutions? How can companies better connect with colleges to help them understand how they can help more students? Other resources shared: Santiago shared: I watched this recently. It explains zero knowledge proofs: https://lnkd.in/g6iS-97i (source = Wired) Ian shared: PAWS is a very useful package for interacting with AWS: https://lnkd.in/g_Fh9t6X ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: https://www.addevent.com/event/Qv9211919 Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstu... Twitter: https://twitter.com/rstudio

George Mount | R for Excel Users - First Steps | RStudio Meetup

Abstract: Excel's built-in programming language has served as an entry point to coding for many. If you’re a data analyst steeped in Excel, chances are you could also benefit from learning R for projects of increased scope and complexity. This presentation serves as a hands-on introduction to R for Excel users: How R differs from Excel as an open source software tool How to translate common Excel concepts such as cells, ranges, and tables to R equivalents Example use cases that you can take and apply to your own work How to enhance Excel and Power BI with R By the end of this presentation, you will have a clear path forward for building repeatable processes, compelling visualizations, and robust data analyses in R. Speaker Bio: George Mount is the founder of Stringfest Analytics, a consulting firm specializing in analytics education and upskilling. He has worked with leading bootcamps, learning platforms and practice organizations to help individuals excel at analytics. George regularly blogs and speaks on data analysis, data education and workforce development. He is the author of Advancing into Analytics: From Excel to Python and R (O’Reilly). Link to George's white paper “Five things Excel users should know about R”https://stringfestanalytics.com/five-things-r-excel/ Working group sign-up for those interested! Within many organizations Microsoft Excel is a preferred tool for working with data for non data analytics users. In order to build a data driven organization, source data and analytical models must be accessible to all data users (technical and non-technical) within their preferred tool. Let’s rally the R community to welcome Excel users into our data driven culture by building an Excel add-on to access data and models available within RStudio. If you're interested in continuing this conversation and joining a working group, let us know! rstd.io/excel-r-community Links shared at the meetup! George's GitHub/ Presentation Resources: https://github.com/stringfestdata/rstudio-mar-2022 Packages? Where to find them & recommendations: CRAN Task Views: https://cran.r-project.org/web/views/ Mark shared: for folks who primarily use excel to present formatted tables, the `gt` package is a great way to start doing this programmatically in R: https://gt.rstudio.com/ Ivan shared: In addition to regular Google, I'd recommend https://rseek.org/, given that the character 'R' is sometimes not search friendly :) Jeff shared: Fpp2 is great for forecasting and time series analysis - https://otexts.com/fpp2/ Floris shared: https://otexts.com/fpp3/ Ivan shared: If you're into tidyverse, there's an equivalent for time-series: https://tidyverts.org/ George shared: https://dplyr.tidyverse.org/ Ryan shared: This can be a helpful package for dynamically editing tables, like in excel https://github.com/DillonHammill/DataEditR Ryan shared: This is a great package for making and learning ggplot visualizations: https://cran.r-project.org/web/packages/esquisse/vignettes/get-started.html Other resources: Monaly shared: There is a R help group: r-help@r-project.org George shared: Helpful book/site on statistics: https://moderndive.com/ Ryan shared:Harvard has a good online source (free options) that has a number of classes, the following for stats: https://www.edx.org/professional-certificate/harvardx-data-science George shared: R for Data Science free book: https://r4ds.had.co.nz/ Fernando shared: big book of R https://www.bigbookofr.com/index.html Floris shared: Advanced R Book: https://adv-r.hadley.nz/ Pedro shared: The R for Data Science Slack channel is a great learning resource! r4ds.io/join (we just made a channel there called #chat-excel_to_r Ivan shared: For teams who are deeply entrenched in Excel (like my old team), this tool may be useful - https://bert-toolkit.com/. It allows running R code in .xls, so you can learn R while doing .xls :) Re: Glossary of terms: Ivan shared: inner_join() is like VLOOKUP in .xls. Dan shared: Here's one cheat sheet (glossary of Excel to R) that I just found; https://paulvanderlaken.com/2018/07/31/transitioning-from-excel-to-r-dictionary-of-common-functions/ Extra Meetup Links Feedback: rstd.io/meetup-feedback Talk submission: rstd.io/meetup-speaker-form If you'd like to find out about upcoming events you can also add this calendar: rstd.io/community-events RStudio conference/submit a talk: https://www.rstudio.com/conference/ Recordings of all meetups: https://www.youtube.com/playlist?list=PL9HYL-VRX0oRKK9ByULWulAOO5jN70eXv

Mara Averick & Maya Gans | Data Visualization Accessibility | RStudio Meetup

2:55 - A11Y in R: Adapting Sarah L. Fossheim's 10 dos and don'ts to keep in mind when designing accessible data visualizations | Maya Gans 30:11 - Adventures with {highcharter} and the Highcharts accessibility module | Mara Averick 58:07 - Q&A A11Y in R: Adapting Sarah L. Fossheim's 10 dos and don'ts to keep in mind when designing accessible data visualizations | Presented by Maya Gans Abstract: This talk will use R based visualizations and walk through examples of accessibility considerations when making plots and applications. Speaker Bio: Maya Gans is a Data Visualization Engineer at Atorus Research where she develops custom applications using R and JavaScript. As an RStudio intern she designed TidyBlocks, a visual block based programming language. Maya also co-wrote JavaScript for Data Science. Maya uses `ggplot2` and `d3.js` to create music related infographics for JamBase.com. When Maya's not coding, she's climbing mountains. Adventures with {highcharter} and the Highcharts accessibility module | Presented by Mara Averick Abstract: Lessons learned about accessibility in data visualization through using the {highcharter} R package and the Highcharts visualization library's accessibility module. Speaker bio: Mara is a developer advocate at RStudio. She is the author of neither the highcharter package nor the Highcharts charting library, but enjoys using both to make interactive, accessible data visualizations. So many amazing resources shared yesterday on data visualization accessibility: Sarah L Fossheim's blog - intro to designing accessible data viz: https://lnkd.in/dAAXfE35 Coblis - Color Blindness Simulator: https://lnkd.in/dJT-hJE4 Web Content Accessibility Guidelines (WCAG): https://lnkd.in/dJKmvFTq Color Contrast Accessibility Validator: https://color.a11y.com/ Google lighthouse (automated tool for improving quality of web pages): https://lnkd.in/d8xjSN5i A11y Project Checklist: https://lnkd.in/diFM_TBd Chartability (questions) for ensuring data visualizations, systems, and interfaces are accessible: https://lnkd.in/d_7wk3zx Accessible {highcharter} GitHub repo (Mara's charts, and source .Rmds): https://lnkd.in/dhvBwQ-f Mara's blog post series: https://lnkd.in/d9xz6VZ6 10 Guidelines for DataViz Accessibility by Øystein Moseng: https://lnkd.in/dS-XsKxw Accessible visualization via natural language descriptions by Alan Lundgard and Arvind Satyanarayan: https://lnkd.in/dpdN4skK DataViz Accessibility Advocacy and Advisory Group: https://lnkd.in/d336ACn3 Alt-texts: The Ultimate Guide by Daniel Göransson: https://lnkd.in/dsHcvPs2 JooYoung Seo's Talk on non visual interactions with R packages: https://lnkd.in/dcid56BT Accessible Data Science for the Blind Using R: https://lnkd.in/dTWZbau8 Maya's blog on skip links: https://lnkd.in/dFTYFxTk Twitter alt text: https://lnkd.in/d9bKqiPU Twitter account to follow: @alttextreminder Silvia Canelón's blog posts: https://lnkd.in/drwbE2Rf Packages shared: ggpattern: https://lnkd.in/dyTBvvz4 gglabeler: https://lnkd.in/dumA8Um8 gghighlight: https://lnkd.in/d_m25j7x sonfiy: https://lnkd.in/dyPwHimP tuneR: https://lnkd.in/dWi2WZH8 brailleR package - https://lnkd.in/d_75cdnQ Caleb brought up a great point that visualizing data isn't new, so it cam be helpful to look at adjacent disciplines to see ways people have solved these before as well. For example, cartographers have had really creative ways to make things visible. Sarah Belle, cartographer who makes fonts / typography really legible on maps: sarahbellmaps.com/belltopo-sans-font-by-sarah-bell/ Cynthia Brewer's work on color palettes. https://colorbrewer2.org/# cartographers who use 3d printing for tactile maps: https://touch-mapper.org/en/

Shiny Usage Tracking in Posit Connect

🤔 Who actually used my Shiny app and for how long? ↗️ Is viewership increasing? Did my CEO use it?! Did you know? Posit Connect can record event-style usage information which is intended to answer questions like this and is accessed via the Posit Connect Server API. Please note: during the meetup you can ask questions here anonymously as well: rstd.io/connect-meetup-questions We will walk through several examples for getting started with the Posit Connect usage data designed to help you answer questions like: Who is visiting my content? What reports are most common? Has viewership increased over time? During/after the meetup, you can use the GitHub repo to create the dashboard in your own Connect environment as well and schedule the report to be distributed through email (with inline graphics). The goal for this code is that it is generic enough so that you can copy/paste it into your own R session and run it successfully. In order for the code to work in your environment, you need two pieces of information unique to your enterprise: Posit Connect's server path A Posit Connect API Key We’d love to hear your feedback and learn how you have taken this API and created dashboards for your own organizations as well. Helpful Links: Follow-up thread on community.rstudio.com: https://community.rstudio.com/t/rstudio-connect-usage-data-thread-to-discuss-ideas-improvements/130581 Contribute to open-source examples: https://github.com/sol-eng/connect-usage Posit Connect Server API: https://docs.rstudio.com/connect/api/ Cookbook: https://docs.rstudio.com/connect/cookbook/ R Client: https://pkgs.rstudio.com/connectapi/ Python Client (mostly deployment): https://github.com/rstudio/rsconnect-python/ Cole's slides: https://github.com/RStudioEnterpriseMeetup/Presentations If you ever have questions about Posit Connect or any of our professional products, you can use this link to chat with our team: rstd.io/chat-with-rstudio

James Blair || Getting Started with {plumbertableau} || RStudio

00:00 Introduction 01:19 Setting up the problem - capitalizing text with a custom function 02:18 Using Plumber to create an API for our function 04:08 Using Run API + Swagger from the RStudio IDE 05:44 Giving Tableau access to the function with PlumberTableau 09:16 Reviewing what we've done so far 09:47 Comparing results between Plumber and PlumberTableau 10:12 Overview of what PlumberTableau does 14:27 Centralized hosting with RStudio Connect 15:17 Looking at our API in RStudio Connect 18:14 How to access the deployed API from Tableau 21:03 Overview of RStudio Connect, Tableau, and PlumberTableau process 21:52 More in-depth example using sample sales data 22:36 Example with the Python equivalent of PlumberTableau, FastAPITableau 25:15 Overview of how these Tableau extension packages work 27:21 Setting up a connection between Tableau and RStudio Connect Read more about the plumbertableau package here: https://rstudio.github.io/plumbertableau/ And learn about the fastapitableau package here: https://rstudio.github.io/fastapitableau/ If you’re unfamiliar with Plumber, this Quickstart guide gives a good overview of the package: https://www.rplumber.io/articles/quickstart.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: James Blair (@Blair09M) Design and editing: Jesse Mostipak (@kierisi) Music: Borough by Blue Dot Sessions https://app.sessions.blue/browse/track/89821

Data Science Hangout | Katie Schafer, Beam Dental | Building a Data Science Portfolio

We were recently joined by Katie Schafer, VP of Advanced Analytics at Beam Dental, to discuss the most important things going on with data science leadership. Great tips & resources shared for this question: What can I do to be able to present my skills and build a portfolio if I’m not able to share my work? Many people expressed that they are in this position. Katie: Is it possible to simulate something akin to some of the data you would use? For project portfolios, submitting the actual work is not something that I’ve seen often. In interviews, you need to be able to discuss the work. Describing the projects is really tough because you want to say something meaningful but you don’t want to be too specific. Having a role play conversation with yourself on questions you’re getting & practicing answers that are more generalized can be helpful. Take one specific problem, translate that to an abstract problem that you tackled with statistics and programming. Javier: I’ve been in a similar boat. There’s a package called synthpop: https://lnkd.in/g_paFGNB, which helps take actual data points and simulates them to make a completely synthetic data set. When I was interviewing for my job now I built a shiny app that I deployed to a free shinyapps.io. I styled the whole app to be consistent with their brand. I don’t attribute that alone to getting the job, but the interviewers were super impressed. The ability to change out the theme is so easy, so for anyone who’s trying to get a job this could be an easy way for you to have something that you can change whenever you interview with a new company. Rick: If you’ve ever worked with a designer - they have a template and they’re not just creating everything from scratch. If you have a nicely organized project as a data scientist, you’re showing your skills as a software engineer. Zac: I do a lot of personal projects because I don't use R at work. For these projects I focus the topic on what skills I want to show off. I’m looking to start my career, so I write a blog & include reasons why I did something. I think this makes it a lot easier to share what I’ve done https://lnkd.in/gzhwuCqw Libby: For me with portfolio work, it's been difficult because sometimes my contributions are one algorithm inside a larger shared project, so I stick to descriptions, talking about what I did and why I did it, what the result was. Larry: I describe the topic and present the deck. Sometimes I just make fake data, but talk about the steps I took. Katie: Some of my richest connections have also come from speaking. R-Ladies has been a great starting point for that. A lot of meetups have gone virtual so you can now join a chapter even if you don't have one in your city. Jiwan: Maybe start with a 5 min lightning talk with a topic you're excited about! Other resources shared during the chat: Prabha: Technical debt in ML systems - https://proceedings.neurips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf Rachael: R community explorer app: https://benubah.github.io/r-community-explorer/rugs.html Javier: GitHub for the {bslib} app, feel free to clone as you see fit: https://github.com/JavOrraca/bslib_demo_shiny Jobs shared during the chat: Niklas: Several data science roles at my company (sustainability SaaS). 2 data engineers, 1 data analyst, 1 data scientist - https://higg.com/careers/ Jill: We are hiring for a Data Analyst role here at Viemed. We are a healthcare company and we LOVEEE DATA. https://viemed.apscareerportal.com/jobs/1338665/apps/new?embed=1"

Data Science Hangout | Asmae Toumi, PursueCare | Data Science Team Culture

We were recently joined by Asmae Toumi, Director of Analytics at PursueCare, to discuss the most important things going on in data science leadership. There was a great discussion during this session starting from the question, "Is it ok to say that we are data analysts and data scientists interchangeably?" Javier: Some of the best data scientists have the title of financial modeler or senior actuary. Some of the best programmers and package developers I know, have the title of data analyst. I think it depends on the organization. We have these guidelines that are more of an ideological function of a data scientist vs data analyst but I think in practice it’s all over the place. Asmae: Jacqueline Nolis and Emily Robinson have good thoughts about this: https://jnolis.com/book/ (Short rules from the book I saw: An analyst creates dashboards and reports that deliver data. A decision scientist creates analyses that produce recommendations.) Mike: I think to anyone that is OUTSIDE of Data Science / Data Analytics, the distinction is not worth discussion. If you want funding you're a Data Scientist. If you HAVE funding it doesn't matter. Frank: No right answer (DS vs. DA)… only different ways to think about it at different times in different contexts. Jordan: I've usually been working in a ""Science"" (science/research) domain at companies. To this end, I've sort of been the ""Data"" Scientist, maybe not ""Data Scientist"" *What about business intelligence?* Asmae: I use data analyst and data scientist interchangeably. I think there is a meaningful difference there between a business analyst and a data scientist. *What about data engineering?* Asmae: We have 3 data engineers. Data scientists all want a data engineer. As a healthcare company, it’s complex to deliver the care that we do. With that complexity comes interfacing with different data vendors - different data sources so it’s really important to invest in data engineering so the reconciliation of that data across the many vendors is ensured. Data quality is also super important. Bryan: Data engineering is the foundation of it all — need a solid data engineer to do anything Falak: Data scientists understand the distribution of the data and figure out what class of algorithms would be suitable to handle a particular problem. Data engineers can solve the optimization problem. Other resources shared during the hangout: Allissa: An NIH prize challenge on maternal health that may be of interest to some - https://lnkd.in/gbtxTTcF Bala: Top 10 Roles for your Data Science Team: https://lnkd.in/g_SVzxDcAsmae Toumi, Director of Analytics at PursueCare, to discuss the most important things going on in data science leadership

Carson Sievert || Customizing Navigation Items in Shiny using {bslib} || RStudio

00:00 Introduction 00:15 Linking inside navbarPage 01:19 Replacing tabPanel with navbarPage, and navbarMenu 02:32 nav_spacer() 03:41 Adding header and//or footer content 04:07 Replacing tabsetPanel with navs_tab and navs_pill 04:32 navs_tab_card() and navs_pill_card() variants 04:40 Demo of all of the nav_*() functions The bslib R package provides tools for customizing Bootstrap themes directly from R, making it much easier to customize the appearance of Shiny apps & R Markdown documents. bslib’s primary goals are: - To make custom theming as easy as possible. - Custom themes may even be created interactively in real-time. - Also provide easy access to pre-packaged Bootswatch themes. - Make upgrading from Bootstrap 3 to 4 (and beyond) as seamless as possible. (Shiny and R Markdown default to Bootstrap 3 and will continue to do so to avoid breaking legacy code.) - Serve as a general foundation for Shiny and R Markdown extension packages. (Extensions such as flexdashboard, pkgdown, and bookdown already fully support bslib’s custom theming capabilities.) You can read more about bslib here: https://rstudio.github.io/bslib/articles/bslib.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Carson Sievert (@cpsievert) Design & editing: Jesse Mostipak (@kierisi)

Carson Sievert

Carson Sievert || Custom Theming with {bslib} in Shiny and R Markdown using bs_theme() || RStudio

00:00 Introduction 01:15 Jumping right in with the theme argument in Shiny 01:31 Shiny's classic navbarPage using bs_theme() 01:46 Specifying your Bootstrap version 02:31 Using the theme argument in R Markdown 03:17 Custom theming in R Markdown using bs_theme() 04:10 bslib templates provided by RStudio 05:33 Review of common arguments for the theme parameter 08:47 Tips for working with dark themes 10:34 Introduction to the thematic package, which styles plots 12:04 How thematic handles fonts 13:09 Using fonts with bslib in R Markdown 14:36 Moving a theme from an R Markdown document into a Shiny app 16:51 Setting warnings for contrast ratios 18:42 A quick tour of Bootstrap 4 and Bootstrap 5 Sass variables 20:35 A quick overview of writing custom HTML in Shiny 22:15 How bslib automatically handles color contrast ratios bs_theme() allows you to creates a Bootstrap theme object, where you can choose a (major) Bootstrap version, choose a Bootswatch theme (optional), customize main colors and fonts via explicitly named arguments (e.g., bg, fg, primary, etc), and customize other, lower-level, Bootstrap Sass variable defaults via .... You can read more about the bslib package here: https://rstudio.github.io/bslib/ And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Carson Sievert (@cpsievert) Design and editing: Jesse Mostipak (@kierisi)

Carson Sievert

Barret Schloerke || Maximize computing resources using future_promise() || RStudio

00:00 Introduction 01:45 Setting up a multisession using the future package 02:05 Simulation using two workers 04:14 Simulation using 10 workers 05:20 What happens when we run out of workers? 05:35 How Shiny handles future processes like promises 07:16 Introduction to future_promise() 07:45 Demo of the promises package 09:21 Setting the number of workers 10:40 Demo of processing without future_promise() 14:11 Wrapping a slow calculation in a future() 14:53 Demo of processing using Plumber 16:25 Considerations on the number of cores to use 17:21 What happens if we run out of workers? 19:44 Decrease in execution times using future_promise() In an ideal situation, the number of available future workers (future::nbrOfFreeWorkers()) is always more than the number of future::future() jobs. However, if a future job is attempted when the number of free workers is 0, then future will block the current R session until one becomes available. The advantage of using future_promise() over future::future() is that even if there aren’t future workers available, the future is scheduled to be done when workers become available via promises. In other words, future_promise() ensures the main R thread isn’t blocked when a future job is requested and can’t immediately perform the work (i.e., the number of jobs exceeds the number of workers). You can read more about the promises package here: https://rstudio.github.io/promises/articles/shiny.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Design and editing: Jesse Mostipak (@kierisi)

Barret Schloerke

Nick Strayer || Part IV: Styling a Shiny Wordle App with CSS || RStudio

00:00 Introduction 00:44 Switching from verbatimTextOutput to uiOutput 01:42 Switching from renderText to HTML DOM elements 03:17 In-line styling with divs 07:30 Converting individual letters from block elements to adjacent grids with CSS grid 08:56 Adding CSS at the head of the UI variable in Shiny with tags$head (and wrapping with HTML!) 10:36 CSS targeting of the background color 12:24 Link: Complete Guide to CSS Grid 14:05 Moving text position within each individual div using CSS classes 16:48 Creating a gap between grid elements 17:13 Rounding border edges for letter grids 19:00 Formatting letter grid background color to indicate result "correctness" 21:30 Increasing font size 23:37 Updating the legend to use color, not text indicators 26:40 Adjusting padding to improve app aesthetic 28:08 Formatting the app UI with justified centering 31:56 Adjusting the text input and Go button 34:07 Why Flexbox is the right tool for this task 35:09 Exploring Flexbox Dev Tools in Chrome 39:14 Adjusting the colors of letter grids using Inspect Element 40:40 Making text bold with font-weight 41:04 Hint on how to approach formatting the keyboard In final installment of this four-part series, RStudio's Nick Strayer walks through using CSS to stylize our Shiny Wordle app. Code + word list: https://github.com/wch/shiny-wordle Check out the full Shiny app here: https://winston.shinyapps.io/wordle/ You can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Nick Strayer (@NicholasStrayer) Animation, design, and editing: Jesse Mostipak (@kierisi) Music: Lakal by Blue Dot Sessions Wordle: https://www.powerlanguage.co.uk/wordle/

Nick Strayer

Barret Schloerke || {reactlog} Rundown || RStudio

00:00 Introduction to Reactlog 00:44 Viewing Reactlog using an Old Faithful Shiny app 02:07 The Reactlog interface 04:31 Walking through a reactive graph with Reactlog 05:14 Downstream dependency invalidation in Shiny 06:43 How Shiny "grabs" data 09:41 How the Reactlog timeline works 10:46 Switching between idle states in Reactlog 11:58 Reactlog interactivity - clicking a single item 13:21 Reactlog with the Pythagoras Theorem app 15:45 Adding a UI and server value to add Reactlog to your Shiny app 18:05 Walking through the reactive graph using the Pythagorean Theorem app 21:07 Append-only behavior of Reactlog 21:18 Marking a time point in Reactlog 23:17 Using Reactlog to debug reactivity 26:55 Resetting our app and testing logic changes 28:01 Reactlog with a large Shiny app, CRANwhales 34:10 Freezing reactive values 36:19 Calculating click count in a Shiny app 37:10 Click the button, render the plot is bad - see why Shiny is an R package from RStudio that makes it incredibly easy to build interactive web applications with R. Behind the scenes, Shiny builds a reactive graph that can quickly become intertwined and difficult to debug. reactlog provides a visual insight into that black box of Shiny reactivity. After logging the reactive interactions of a Shiny application, reactlog constructs a directed dependency graph of the Shiny’s reactive state at any time point in the record. The reactlog dependency graph provides users with the ability to visually see if reactive elements are: - Not utilized (never retrieved) - Over utilized (called independently many times) - Interacting with unexpected elements - Invalidating all expected dependencies - Freezing (and thawing), preventing triggering of future reactivity There are many subtle features hidden throughout reactlog. Here is a short list quickly describing what is possible within reactlog: - Display the reactivity dependency graph of your Shiny applications - Navigate throughout your reactive history to replay element interactions - Highlight reactive family trees - Filter on reactive family trees - Search for reactive elements You can read more about reactlog here: https://rstudio.github.io/reactlog/articles/reactlog.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Design & editing: Jesse Mostipak (@kierisi)

Barret Schloerke

Dmitri Adler & Merav Yuravlivker | The shift to data: Industry trends in finance | Posit

Posit Finance Meetup The shift to data: Industry trends from banks to hedge funds to federal agencies How has the finance industry shifted towards data in the past 5 years? Will all analysts need to program in Python in order to have a job in the future? Join Data Society co-founders Merav Yuravlivker and Dmitri Adler as they discuss the trends that they're seeing in financial institutions from banks to hedge funds to federal agencies. By the end of the session, you'll be able to speak about specific industry uses and walk away with concrete steps you can take to ensure you're riding the data wave. Speaker Bios: Dmitri Adler is the Chief Solution Architect and co-founder of Data Society. He has deep expertise in building predictive models and algorithms for forecasting macroeconomic conditions, healthcare outcomes, trade flows, and business performance for a variety of government and private sector clients. Prior to starting Data Society, he advised the U.S. Treasury Department on the structure of the mortgage market after the financial crises while he was at J.P. Morgan and developed expertise in applying machine learning to financial modeling and investing. Dmitri has worked with large financial institutions and agencies to build custom software, assess financial risk, and integrate machine learning applications into their operations. Merav Yuravlivker is the Chief Executive Officer and co-founder of Data Society. She has deep expertise in developing effective professional development programs and assessments to maximize the capability of an organization and empower the workforce. Prior to starting Data Society, she built her career at educational institutions that include Teach for America, Kaplan, and the International Baccalaureate Organization. Over the past seven years, Merav and her team have saved organizations millions of dollars by incorporating data analytics skills and best practices that educate, equip and empower an organization’s workforce to achieve its goals and expand its impact. Data Society specializes in providing industry-tailored data science training and AI/ML solutions that enable Fortune 500 companies and government agencies to educate, equip and empower their workforce. Since 2014, the company has trained thousands of professionals with the skills needed to solve complex challenges, realize new opportunities, and take their careers to the next level. Data Society was recognized as an Inc. 5000 2021 fastest-growing company and named a top EdTech Company to watch by Forbes. For more information, visit www.DataSociety.com. Link to slides: https://github.com/RStudioEnterpriseMeetup/Presentations/blob/166452d28d61ef33faf8980c8f0f43426e72926b/The%20shift%20in%20data.pdf

Sjoerd Wierenga & Job Spijker | Public Health | Shiny in Production | Posit

R in Public Sector: Organizational & Technical Aspects of Shiny in Production with the Dutch National Institute for Public Health and the Environment. 00:00 - Introductions 2:47 - Organizational aspects of Shiny in production 32:52 - Technical aspects of Shiny in production 52:33 - Ask us everything / Open Discussion Questions: 29:00 - When you first introduced Shiny, what other tools were you comparing it to? How did you explain the difference to your leaders? 30:00 - What were the most important aspects of your prototype app to create buy-in? 52:33 - As Clusterbuster began to be used by more people, did you face any performance issues? How did you adjust your app to deal with more concurrent users? 56:10 - Can you say anything about the update frequency of the data? 57:15 - Which model was used to define the clusters? 58:23 - Did you ever consider not using a database? 1:01:50 - What's the communication with the data engineering team? 1:03:51 - How often do you collect feedback from users and update your app? 1:05:10 - Was your data loaded into Docker in a form of some aggregates? How did you create them? 1:06:26 - What is the main advantage of keeping it all in R with Shiny? Did you feel at any point you were sacrificing simplicity? 1:08:14 - Did you use any specific methods to increase the performance of your app? Did you scope your data, or load it all in the global file? 1:12:03 - How did you make sure regions and users felt comfortable using your app? 1:13:25 - What types of businesses are hotbeds for covid clusters? Has this info informed policy changes? 1:14:50 - How did the data quality issues improve over the rollout? 1:16:47 - Did you use CI/CD? 1:17:38 - Did you have any functionality within your apps to send individual-level data to municipalities? 1:19:47 - For huge amounts of data, have you tested out different file types to store your data set within your containers? 1:20:54 - For people just starting to use Shiny, what is one piece of advice you would give them? Proof on Concept with fictitious data: https://rivm.shinyapps.io/clusterbuster/ Blog post from the team as well! https://www.rstudio.com/blog/how-the-clusterbuster-shiny-app-helps-battle-covid-19-in-the-netherlands/ Code-first blog post mentioned: https://www.rstudio.com/blog/code-first-data-science-for-the-enterprise2/ How the "Clusterbuster" app provides actionable information to 300 health professionals Presented by: Sjoerd Wierenga In this talk we want to give an overview of what it took to create the Clusterbuster from an organizational perspective. We will go into detail on how we got from an abstract question to an application that is user-friendly, safe, and valuable. Furthermore, we will offer a glimpse of what is yet to come, and where we see possibilities to turbocharge a more data-driven public policy approach. How to build a production shiny app within the context of public health governance. Presented by: Job Spijker This presentation goes into the more technical details about the production environment of the Clusterbuster application. We will show how we deployed the application, how we ensured security and mitigated the risks in case of a security breach, and how we organized our code for maintainability and refactoring. Presenter Biographies: Sjoerd Wieringa: As the son of two healthcare professionals, with a background in Public Administration, and a passion for technology, it is no surprise that Sjoerd Wierenga now works at the National Institute for Public Health and the Environment leading a team of highly skilled Data Scientists that created an application to support the battle against COVID-19. After having worked as a healthcare manager for several years, he decided he wanted to learn how to program. Which he has been doing now since 2016 in different capacities. Job Spijker: Job Spijker is a senior research and data scientist at the Dutch National Institute of Public Health and the Environment. He has a PhD in Earth Sciences with a focus on computational and statistical methods of spatial data. He is currently involved in projects about how the institute’s environmental and health data can be leveraged to create insightful actionable information to assist policy makers at local, regional, and national level

Data Science Hangout | Prabha Thanikasalam, Flex | Calculating ROI with the Business

The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. rstudio.com/data-science-hangout We were recently joined by Prabha Thanikasalam, Senior Director, Analytics and Supply Chain Solutions at Flex, to discuss the most important things going in data science leadership. There was a great conversation live and in the chat that followed this question. 𝐇𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐬𝐭𝐚𝐲-𝐟𝐨𝐜𝐮𝐬𝐞𝐝 𝐰𝐡𝐞𝐧 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐨𝐧 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬? (From two sides: when learning data science and also when working on projects for the business) Think about it as a marathon, not a sprint, you won’t be an expert in 6 months or a year. Build a good foundation over time by spending 45 minutes a day learning something. Don’t keep your email inbox open all the time. This won’t work all the time, but it helps! Try to separate the time you devote to learning new skills rather than "learning while doing" all the time. This can allow you to work faster with imperfect solutions and set aside learning a better way of doing it in dedicated time. Although it's important to keep delivering quality analysis and product, it helps me move away from perfectionist tendencies. Getting “side-tracked” isn’t necessarily bad - sometimes we’re asked to solve the wrong question and find that out half way through Give yourself a deadline, or if you’re working with the business meet with them to discuss the success criteria and deadlines Encourage teams to explore rabbit holes and not be mindless “task completers.” Some of the best insights come out of curveballs we explore. Curiosity is also key to being a great data person Try using a pomodoro timer to keep focused time Check out Jacqueline Nolis & Emily Robinson’s book/companion podcast, "Build a Career in Data Science", which talks about using projects as a motivator for learning new skills. Sometimes, it's better to start with a problem you want to solve rather than start with a skill you want to learn! When starting out, try to use code as frequently as possible, and also find a project you can get really excited about. Passion can help with the late nights and tying in some of the side track thoughts to one topic or outcome. It’s helpful to realize there are still plenty of skills that require dedicated time to do more focused learning to exclusively understand the math, methodology, and technology. For example, with Natural Language Processing, starting with the technology and then moving onto the documentation can be an order that keeps you motivated. 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐬𝐡𝐚𝐫𝐞𝐝 𝐚𝐬 𝐰𝐞𝐥𝐥: Prediction Machines: https://lnkd.in/gSft3XRK & Measure what Matters: https://lnkd.in/g6xdSNci Building a Career in Data Science: https://jnolis.com/book/ Lessons learned from the smartest Software Engineer I’ve met: https://lnkd.in/gDScE7gH Inside Intel: https://lnkd.in/gKyTWZj4

Data Science Hangout | Ian Anderson, Philadelphia Flyers | Moving into Leadership & Managing a Team

The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. posit.co/data-science-hangout We were recently joined by Ian Anderson, Director of Hockey Analytics at the Philadelphia Flyers, to discuss the most important things going in data science leadership. One of the topics that there were a lot of thoughts on during this hangout was balancing that shift into leadership and maintaining technical skills. Question: 𝐀𝐬 𝐲𝐨𝐮 𝐦𝐨𝐯𝐞 𝐮𝐩 𝐭𝐡𝐞 𝐥𝐚𝐝𝐝𝐞𝐫 𝐢𝐧𝐭𝐨 𝐥𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐚𝐧𝐝 𝐦𝐚𝐧𝐚𝐠𝐢𝐧𝐠 𝐚 𝐭𝐞𝐚𝐦, 𝐚𝐫𝐞 𝐲𝐨𝐮 𝐰𝐨𝐫𝐫𝐢𝐞𝐝 𝐚𝐛𝐨𝐮𝐭 𝐥𝐨𝐬𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐜𝐨𝐝𝐢𝐧𝐠 𝐬𝐤𝐢𝐥𝐥𝐬? Here are a few of the thoughts shared live from Ian and the community: * You make time for things that are important to you. If exercise and health is important to you, you make time for it. If coding is something that you want to continue to maintain, you can find the time for that. * Think about blocking designated focus time to do your individual work * Be curious. Office hours as a manager to answer questions from your group can also be an opportunity to learn new things/packages/methods together * Organize opportunities for the team to share any topics that they would like (coding, techniques, etc.) In sharing topics, you can also try limiting the time to ~4min to share. Two benefits of this fast moving, high energy meeting: 1. People will inspire each other with passion and creativity 2. The team gets to practice communicating their ideas with precision. * Becoming more comfortable with what it means to lead people, it's about enabling them to be really successful. If you measure your success by other people's success, it can also help rationalize that. If you can chop down barriers for them, then they will be more successful so that means the technical time I sacrifice enables them further. * It's still very relevant to keep your skills at some type of level just for street cred in your team. Bringing in new people, if you can talk that language it just really helps your team and you can have those conversations." ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstu... Twitter: https://twitter.com/rstudio

Daniel Petzold || RStudio Team: Building and Sharing Jupyter Notebooks || RStudio

Learn more about RStudio Team here. https://www.rstudio.com/products/team/ Find the code for this example here. https://github.com/danielpetzold/space-tracker Read our blog post here. https://www.rstudio.com/blog/build-and-share-jupyter-notebooks-on-rstudio-team/ Timecodes 0:00 - Intro 0:07 - Build Jupyter Notebooks to analyze and visualize data 2:47 - Publish directly from RStudio Workbench to your content hub 5:13 - Share With Your Stakeholders on RStudio Connect Jupyter Notebooks are interactive documents for code, outputs, and text. However, they’re often stuck in data scientists’ local computing environments. Collaborating can be difficult and sharing can be tedious. To live up to their fullest potential, data science teams need a way to scale their development securely and efficiently — while providing stakeholders easy access to their output and visualizations. RStudio Team, made up of RStudio Workbench, RStudio Connect, and RStudio Package Manager, brings everything together to help data scientists create, reproduce, and share insights from their Jupyter Notebooks. Let’s dive into a real-life example by exploring data from NASA’s Center for Near-Earth Objects (NEOs). Daniel Petzold walks us through his data analysis and reporting. Want to explore the report yourself? Check out the published report on RStudio Connect here. https://colorado.rstudio.com/rsc/space-tracker/space_tracker.html On RStudio Workbench, you have a choice of editors: the RStudio IDE, JupyterLab, Jupyter Notebook, or VS Code. Choose your preference. From here, you can explore your dataset, embed HTML directly in your document, create visualizations, and more. Once you've run your analyses and created insightful visualizations, you want to be able to share them with your team. RStudio Workbench allows you to publish to RStudio Connect, the content platform from RStudio. You have multiple options: push-button deployment from Jupyter Notebook or using terminal commands from JupyterLab. It’s not enough to publish your work. Once on RStudio Connect, you can share with end-users. Make your analysis accessible to specific users or more generally with different authentication measures. In addition, you can schedule the document to run at a certain time and send out an email with refreshed data. Click the links below to learn more about these offerings. RStudio Workbench: https://www.rstudio.com/products/workbench/ RStudio Connect: https://www.rstudio.com/products/connect/

RStudio Team

Capacity Planning for Microsoft Azure Data Centers | Using R & RStudio Connect

Capacity Planning for Microsoft Azure Data Centers | An Explainable Data Science Workflow using R & RStudio Connect | Presented by Paul Chang 2:12 - Start of presentation 47:43 - Start of Q&A session Thank you for watching! Here are a few helpful links: * Link to Paul's slides: https://lnkd.in/gh-hGScE * More information on RStudio Connect: https://www.rstudio.com/products/connect/ * How to open an Azure account: https://azure.microsoft.com/en-us/ * Getting started with SAML authentication on RStudio Connect: https://support.rstudio.com/hc/en-us/articles/360022321494-Getting-Started-with-SAML-in-RStudio-Connect * pins package: https://pins.rstudio.com/ * plumber package: https://www.rplumber.io/ * Upcoming events: rstd.io/community-events * Chat with our team to start an RStudio Connect evaluation: rstd.io/chat-with-rstudio Abstract: The Long Range Capacity Planning team at Microsoft is responsible for producing plans for expanding Microsoft Azure Data Centers around the world. These are multi-billion dollar plans that enable the full suite of IaaS and PaaS cloud offerings for our customers, over a 5+ year time horizon. In this talk, we will present the data science software stack that we have built using RStudio Connect and Azure, for producing these data center capacity plans. We will discuss how RStudio Connect has empowered our data scientists to connect more directly with internal stakeholders and decision makers, and how RStudio Connect has enabled us to streamline our data science and business processes. Speaker Bio: Paul Chang, Senior Data & Applied Scientist, Microsoft Paul Chang is the Systems Architect of the Long Range Capacity Planning team for Microsoft Azure Data Centers. He received his Applied Math PhD from Simon Fraser University and has worked in a variety of fields including Applied Functional Analysis, Hydrogen Fuel Cell modeling, and A.I. Applications in Vehicular Traffic Engineering. He was also a software engineer in SQL Azure for a couple of years. Thank you for joining us! * If you ever have suggestions or general feedback, please let us know! Here's an anonymous google form: rstd.io/meetup-feedback * We'd love to hear from you too! Here's a talk submission form as well: rstd.io/meetup-speaker-form * If you'd like to learn more about RStudio Connect: https://www.rstudio.com/products/connect/ * If you're just starting to advocate for data science in general or RStudio tools: rstudio.com/champion

Rich Iannone || Making Beautiful Tables with {gt} || RStudio

00:00 Introduction 00:37 Adding a title with tab_header() (using Markdown!) 01:47 Adding a subtitle 02:48 Aligning table headers with opt_align_table_header() 03:48 Using {dplyr} with {gt} 06:03 Create a table stub with rowname_col() 07:35 Customizing column labels with col_label() 09:45 Formatting table numbers with fmt_number() 12:10 Adjusting column width with cols_width() 15:39 Adding source notes with tab_source_note() 16:55 Adding footnotes with tab_footnote() 18:55 Customizing footnote marks with opt_footnote_marks() 19:10 Demo of how easy managing multiple footnotes is with {gt} 23:41 Customizing cell styles with tab_style() 27:07 Adding label text to the stubhead with tab_stubhead() 28:15 Changing table font with opt_table_font() 29:25 Automatically scaling cell color based on value using data_color() With the gt package, anyone can make wonderful-looking tables using the R programming language. The gt philosophy: we can construct a wide variety of useful tables with a cohesive set of table parts. These include the table header, the stub, the column labels and spanner column labels, the table body, and the table footer. It all begins with table data (be it a tibble or a data frame). You then decide how to compose your gt table with the elements and formatting you need for the task at hand. Finally, the table is rendered by printing it at the console, including it in an R Markdown document, or exporting to a file using gtsave(). Currently, gt supports the HTML, LaTeX, and RTF output formats. The gt package is designed to be both straightforward yet powerful. The emphasis is on simple functions for the everyday display table needs. You can read more about gt here: https://gt.rstudio.com/articles/intro-creating-gt-tables.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) Design & editing: Jesse Mostipak (@kierisi)

Rich Iannone

Isabella Velásquez | Building a Blog with R | RStudio

Building a Blog With R Presented by: Isabella Velásquez Here are a bunch of resources Isabella shared ⤵️ Slides from the presentation: https://lnkd.in/gqGFmHMf Internal Blog Example: https://lnkd.in/gaFPxN5F Other resources from the talk: https://lnkd.in/gjXxeMaa Distill Resources: 1️⃣ Distill for R Markdown: https://lnkd.in/gWsEBXfN 2️⃣ Building a blog with distill by Tom Mock: https://lnkd.in/gQiE8PC2 3️⃣ (Re-)introducing Distill for R Markdown: https://lnkd.in/gzidDpV2 4️⃣ The distillery: https://lnkd.in/gwDAg_7G 5️⃣ Postcards package: https://lnkd.in/geT6uB9t Blogdown Resources: 1️⃣ blogdown: Creating Websites with R Markdown: https://lnkd.in/gGQ-fCWw 2️⃣ Hugo Themes: https://themes.gohugo.io/ 3️⃣Hugo Apéro: https://lnkd.in/g8U9tfvq 4️⃣ A Blogdown New Post Workflow with Github and Netlify: https://lnkd.in/gYNwsKTm The R programming language is known for its applications to data science, but one of its best assets is the inviting community. Folks from around the world share their lessons learned, best practices, and code to support and inspire others. One tool that helps contribute to the thriving community is the blog. A blog is a wonderful opportunity to record your data stories, gain exposure for your expertise, and support others in their R journey. Thanks to the advancement of tools like R Markdown, you can quickly get up and running with a blog and focus on customization and style. In this talk, we will discuss possible reasons for creating a blog, the pros and cons of a blog, and how to decide on topics. We will then explore tools for creating your blog that make it easy to showcase your R skills, such as blogdown and distill. At RStudio, we are always looking for stories of how you are using R for your work, community, or for fun. If this talk inspires you to start writing, we would love for you to contribute to the RStudio blog: https://www.rstudio.com/blog/ Speaker Bio: Isabella Velásquez: Isabella is a content strategist, author, and active member of the R community. Currently, she works at RStudio as a Sr. Product Marketing Manager with the goal of driving engagement around all the awesome things happening at RStudio. In her previous role, she conducted data analysis and research, developed infrastructure to support use of data, and created resources to engage technical and non-technical audiences. She channels these experiences to illuminate what is possible with great products

Ralph Asher & Laura Darby Rose | R in Supply Chain Management | RStudio

Two talks! 3:24 - Start of meetup 3:24 - Intro to Supply Chain Design - Ralph Asher 33:38 - Forecasting Demand with R - Laura Darby Rose Intro to Supply Chain Design - Ralph Asher Abstract: COVID-19 has moved supply chain management from the back office to front-page news. And along with it, the discipline of supply chain design – the strategic evaluation of deciding where to locate manufacturing sites, warehouses, and other supply chain facilities – has gone from a little-known niche to a C-suite priority. In this talk, I will introduce the field of supply chain design to the R community. Drawing upon my decade of experience in supply chain design and R, I will give a short example of how to design a warehouse network to support future customer need. This example will be drawn directly from my experience in the corporate world and my consulting business. Speaker Bio: Ralph Asher is the founder of Data Driven Supply Chain LLC, a Minnesota-based consulting firm that uses data science and AI to evaluate, design, and optimize supply chains. (www.datadrivensupplychain.com) Prior to founding Data Driven Supply Chain, Ralph worked as an Operations Research Scientist in corporate supply chain functions for 8 years at Target, designing e-commerce supply chain networks, and at General Mills, designing warehousing networks. Ralph has used R for supply chain analytics for a decade and can be reached at ralph@datadrivensupplychain.com or via LinkedIn. Forecasting Demand with R - Laura Darby Rose Abstract: Over the course of about a year, Mallinckrodt Pharmaceuticals’ Specialty Generics division replaced an expensive SaaS (Software as a Service) with an R script and alternate forecasting process. Using an RStudio Enterprise solution, they have found a more flexible and cost-effective way to forecast demand and analyze data with R. This discussion will detail the process of replacing a SaaS with R, as well as challenges and next steps in the project. Speaker Bio: Laura Darby Rose is Manager of Demand at Mallinckrodt Pharmaceuticals, where she is responsible for statistical forecasting, forecast visualization, and forecast accuracy measurement for the Specialty Generics division. She has a M.A. in Economics from the University of Missouri-St. Louis, and enjoys using R and SQL for time-series analysis, creating Shiny apps, and data wrangling/cleaning. Are you a data scientist or data analyst working in supply chain management? Are you interested in joining a group of fellow practitioners and taking a leadership role in developing and promoting open source solutions in your field? Join us! At this meetup, we also proposed the formation of a community working group focused on developing and popularizing open source solutions for data scientists and analysts working in supply chain management. We'd love to start by creating a home – initially a website – which hosts resources for supply chain data scientists. More to come. Form to be part of the working group: rstd.io/supply-chain-community-org

An inclusive solution for teaching and learning R during the COVID pandemic

The COVID pandemic has shaken our teaching and learning approaches in many different ways all over the world. Nonetheless, it has also provided opportunities for bringing creativity into the classroom. In this talk, I will discuss how I have used RStudio Cloud in my teaching during the pandemic and how I capitalized on the opportunities that RStudio Cloud offers to deal with the crucial issues of software installation. Introducing RStudio Cloud in the units has allowed me to work effectively in an online environment to engage, motivate and empower students through their learning process while removing the troubles and hurdles of software installation which is generally particularly challenging in first-year cohorts without prior coding experience. I used RStudio Cloud in a data science introductory unit at Monash University and as a tool to present the usage of R and RStudio for reproducible reporting in another unit on Reproducible and Collaborative Practises. In the latter, I introduced RStudio Cloud during the first few weeks to get the students up to speed before transitioning to using R and RStudio Cloud in their own local machines while using the command line interface, Git, and GitHub as a version control tool for reproducible reporting. I will also discuss how I organized and managed the unit's RStudio Cloud account so that my research associates were also an integral part of the unit delivery to ensure the success of the units. Read Dr. Menéndez's guest post on the RStudio Blog: https://www.rstudio.com/blog/rstudio-cloud-an-inclusive-solution-for-learning-r/ Read more in the follow-up blog post: https://www.rstudio.com/blog/teaching-data-science-in-the-cloud/

To Our Community: Thank You | RStudio Open Source (2021)

This year, among many challenges, we have been so grateful for our community who have continued to show up in so many different ways. As the year comes to a close, we wanted to say thank you. We know folks have adjusted where they spend their time and energy, and we just want to say thank you for continuing to be here and learn together, wherever you are right now. Thank you to our maintainers. Our package maintainers not only contribute code, but also steward projects, welcome new contributors, and answer many questions. Thank you for continuing to set a positive tone and fostering a positive community for our contributors and users! Thank you to our contributors. Package contributors contribute code, ideas, conversation, documentation, and tests. You are the ones trying things out, figuring out what’s wrong, and sharing ideas and fixes. Thank you for pushing our code to grow and evolve and to help ever more people! Thank you to our educators. Educators work in classrooms, with friends and colleagues, and respond to email lists from someone around the world. Thank you for sharing your enthusiasm for data science with others and expanding the group of people who make sense of data with code! Thank you to everyone who’s used R to solve a problem, create aRt, write a blog post, share insights or any of the thousands of ways we can create with R. Thank you to all of you who have contributed in some way, no matter how big or small. Without you, there would be no point to writing open source code. Thank you for all the work you do in the world, using data to improve our lives and our knowledge of the world in such incredibly diverse ways. Music: Basketliner by Bitters, published on Blue Dot Sessions - https://app.sessions.blue/browse/track/81258 Editing & motion design: Jesse Mostipak

Leveraging the Cloud for Analytics Instruction at Scale: Challenges and Opportunities

Data science and programming languages like R and Python are some of the most in demand skills in the world. Students interested in analytics and professors facilitating curriculums deserve to use industry-leading tools to acquire these skills. However, it’s challenging to enable this experience in an educational setting, especially at scale. The traditional tools to facilitate learning analytics simply aren’t great. Students and professors often spend way too much time troubleshooting systems and software, things that are a complete waste of time and detract from the learning experience. Additionally, there are seemingly endless IT hurdles and requirements. That’s part of the reason we created RStudio Cloud, a brilliantly simple but powerful solution for teaching and learning analytics, especially at scale. RStudio Cloud solves many of the technical and financial challenges associated with teaching analytics. It’s also a joy to use for professors, students, and IT administrators. In this presentation, Dr. Brian Anderson will discuss the challenges and opportunities associated with leveraging the cloud to deliver analytics instructions at the undergraduate and graduate levels at scale. Our hope is that you walk away inspired to think about ways you can leverage RStudio and the Cloud to enhance your students' experiences with learning analytics. Read more in the follow-up blog post: https://www.rstudio.com/blog/teaching-data-science-in-the-cloud/

Leveraging R & Python in Tableau with RStudio Connect | James Blair | RStudio

Leveraging R & Python in Tableau with RStudio Connect Overview Demo / Q&A with James Blair Tableau combines the ease of drag-and-drop visual analytics with an open, extensible platform. RStudio develops free and open tools for data science, including the world’s most popular IDE for R. RStudio also develops an enterprise-ready, modular data science platform to help data science teams using R and Python scale and share their work. Now, with new functionality in RStudio Connect, users can have the best of both worlds. Tableau users can call R and Python APIs from Tableau calculated fields, getting access to all the power and analytic depth of these open-source data science ecosystems in real-time. For Tableau users, this makes it easy to add dynamic, advanced analytic features from R and Python to a Tableau dashboard, such as scoring predictive models on Tableau data. They can leverage all the great work done by their organization’s data science team and even call both R and Python APIs from a single dashboard. Data science teams can continue to use the code-first development and deployment tools from RStudio that they know and love. Using these tools, they can build and share R APIs (using the plumber package) and Python APIs (using the FastAPI framework). Speaker Bio: James is a Solutions Engineer at RStudio, where he focuses on helping RStudio commercial customers successfully manage RStudio products. He is passionate about connecting R to other toolchains through tools like ODBC and APIs. He has a background in statistics and data science and finds any excuse he can to write R code. A few other helpful links: Tableau Integration Documentation: https://docs.rstudio.com/rsc/integration/tableau/ Tableau / RStudio Connect Blog Post: https://blog.rstudio.com/2021/10/12/rstudio-connect-2021-09-0-tableau-analytics-extensions/ Embedding Shiny Apps in Tableau using shinytableau blog: https://blog.rstudio.com/2021/10/21/embedding-shiny-apps-in-tableau-dashboards-using-shinytableau/ James' slides: https://github.com/blairj09-talks/rstudio-tableau-webinar

RStudio Team Demo | Build & Share Data Products Like The World’s Leading Companies

You probably know that RStudio makes a free, open-source development environment for data scientists. It’s made with love and used by millions of people around the world. What you might not know is that we also make a professional platform, called RStudio Team. Learn How RStudio Team Can... - Help you scale your data science work - Seamlessly manage open-source data science environments - Automate repetitive tasks - And, rapidly share key insights and data science products securely to your entire organization. Timecodes 0:00 - Intro 4:18 - Hard truth of data science 10:22 - Serious Data Science 16:46 - Model management with R and Python 18:48 - Live Demo / RStudio Workbench 23:09 - RStudio support for Jupyter Notebooks 24:40 - Live Demo / RStudio Connect 28:01 - RStudio support for VS Code 30:05 - R and Python within RStudio 32:33 - Scale and share data science results 36:55 - Sharing previous versions of presentations 38:16 - Data Science team knowledge sharing 40:36 - Scheduling snd emailing data science content 43:55 - Live demo / RStudio Package Manager 48:09 - Data Science stories 49:37 - RStudio Team 52:59 - What makes RStudio different? 55:12 - Q/A - Learn More Leading organizations like NASA, Janssen Pharmaceuticals, The World Health Organization, financial institutions, government agencies and insurance organizations around the globe use RStudio’s professional products to tackle world-changing problems and we’re inviting you to learn how. You’ll learn how RStudio Team gives professional data science teams superpowers, with all of the bells and whistles that enterprises need. You can try RStudio Team free here: https://www.rstudio.com/products/team/evaluation2/ If you'd like to access presentation slides, sign up for future events, provide feedback and/or ask additional questions we've bundled everything together for you here: https://docs.google.com/document/d/1HGt7LSohhyxpCvETvVEFHugrdaSnTcZaXbI0jV5g9ok/edit?usp=sharing

RStudio Team

GA Tech || "Communicating with 8 Million People through Shiny" || Posit

Georgia Institute of Technology faculty, scientists, GIS specialists, and graduate students launched a tool that provided real-time, localized information on the estimated risk of COVID-19 exposure by attending an event. In talking with their team it was clear that their empathetic perspective of the audience and communication-focus helped successfully share their insights with event planners, policy makers, various news outlets and individuals - adding up to ultimately over 8 million unique users around the world. Lessons learned from the GA Tech team about their experience building the COVID-19 Event Risk Assessment Planning Tool can apply to visualizations across many different industries and use cases - whether you are communicating to a handful of executives at your company or out to the world: 1. As a Shiny developer, make sure that you have a specific question in mind. What is the problem that you have that your app is helping them solve? Think about who your audience is going to be and what they would use this for. For the COVID-19 Event Risk Assessment Planning Tool, this question was “what is the risk level of attending an event, given the event size and location?” 2. View your audience through a lens of empathy. Think about metrics that people can really get a grip on and visualize. For example, the risk of attending a local event with 100 people in your own town vs. communicating this as cases per 100,000 people. If you want to communicate something that’s critical to the public, put it in the right terms. 3. Balance the straightforwardness of your visualization. You don’t have to anticipate every single question. With every feature or piece of information included, ask yourself if this supports your overall point? Importance of continued communication. 4. Keep the lines of communication open with your users. If you share a visualization, make sure that people have a clear way to contact you (email, Twitter, LinkedIn) with questions or feedback. Their team made an intentional effort to be available for local news particularly. They were responsive to the kinds of decisions people were making and adjusted the app to match their needs with event sizes for example. Check out their Shiny application here: https://covid19risk.biosci.gatech.edu/ Read the related blog post here: https://blog.rstudio.com/2021/09/14/how-do-you-use-shiny-to-communicate-to-8-million-people/

Teaching and learning with RStudio Cloud | RStudio

Learn about RStudio Cloud and most recent developments, particularly with respect to teaching with it. Slides are posted at https://rstd.io/tl-rscloud. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

Data Science Hangout | Frank Corrigan, Target | Understanding the Impact of Data Science

We want to help data science leaders become better. The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. An accomplished leader in the space will join us each week and answer whatever questions the audience may have. We were recently joined by Frank Corrigan, Director of Decision Intelligence at Target. 6:38 - Problem formation - trying to find the unknown unknowns and bringing them to the business 8:20 - Integrating your data science team into your company's business objectives (ex. newsletter) 10:50 - What is the divide between a business analyst and a data scientist, in your eyes? How business analysts and data scientists differ 15:32 - What is the biggest mistake you’ve made in your role and what did you learn from this mistake? 19:15 - Onboarding new team members effectively 25:00 - The importance of motivating non-data scientists 26:42 - Resources for data scientists 32:30 - Challenges when using different tools across a data science team 49:28 - Analytical thinking vs critical thinking skills 57:30 - Embracing the 80/20 Rule & the importance of Focus Time 1:02:48 - Two frameworks to be more effective with stakeholders 1:06:47 - Rebranding to "decision intelligence" 1:08:35 - Quantitatively measuring impact from data science insights ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Join the Data Science Hangout Live every Thursday from 12-1 ET: https://www.addevent.com/event/Qv9211919 Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

RStudio Cloud | Viewing Learning Work | Instructor View

As an Admin (or Moderator) of your course space, you and your fellow instructors have access to all projects in your space. You can open student projects from any projects listing, or to see all the projects of a given student, go to their profile page by clicking on their name from the Members page. Note that if you do open a student's project while they also have it open, they will be temporarily disconnected from the project. You can also view how much time your students have spent using Cloud. Simply visit the Usage area of your space, where you can see aggregate usage data for the entire class, or for an individual student and their projects. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

RStudio Cloud | Tuning Resources | Instructor View

By default, each project is allocated 1 GB of RAM and 1 CPU, and can execute in the background for up to 1 hour. If your plan allows it, you can increase the memory, CPU or background execution time allocated to a project. To do so, open Project settings, go to the Resources panel and adjust the allocation. Note that copies of a project will inherit its resource settings. However, the effective allocation for the copy will be limited by the maxiumum allowed by the account that "owns" the new copy. The usage hours consumed by a project in a given amount of time depend on the resources allocated to the project, according to the following formula: (RAM + CPUs allocated) / 2 x hours. On disk, each project is allocated up to 20GB for its files, data, and packages. The maximum size of a file that can be uploaded to a project is 500MB. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

RStudio Cloud | Troubleshooting | Instructor View

Relaunch Project If you are working on a project, and it fails to load or becomes completely unresponsive, you can relaunch the project. Depending on the underlying problem, relaunching the project may fix it and let you continue. Allocate Additional Memory memory-gaugeRunning out of memory while working on a project may be the cause of a variety of errors. The project memory gauge will give you an idea of what percentage of available memory your project is currently using. The gauge updates roughly every ten seconds, so it may not show the exact usage at the current moment. Note that reclaiming unused memory is controlled by R and the operating system - you may see uncanny fluctuations in the gauge as the system manages memory. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

RStudio Cloud | Setting Up a Base Project | Instructor View

You can make all projects in a space begin with a default set of files and packages. You do this by defining a Base Project for the space. Create a new project and add any packages or files you want all projects created in the space to start with. Set the project's access so that everyone in the space can view the project. Go to the Space Settings page and select the project as the Base Project. Once you select a project as your Base Project, it will no longer be included in the projects listing for the space. To access it, choose the Edit command from the Space Settings page. Changes to the Base Project are not retroactive. Changes will not be applied to any projects already created - the changes will only apply to future projects created via the New Project action. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

RStudio Cloud | Roles | Instructor View

Each member of a space is assigned a role to determine what they are able to do within the space. The available roles are: Admin: can manage users, view, edit and manage all projects and view space usage data. Moderator: can view, edit and manage all projects, and view space usage data. Contributor: can create, edit and manage their own projects. This is the default. Viewer: can view projects shared with everyone in the space. A viewer cannot create or save copies of projects. Members are assigned an initial role when they are invited to or join a space, but roles can be changed by an Admin at any time. When you invite an individual member to a Invitation Required space, you set the initial role in the Add Member dialog box. When someone joins via a sharing link, their initial role is set to the current Initial Role setting in the members options panel. Admins can update a user’s role via the role selector in the members list. Changing permissions lets you fine-tune the Contributor and Viewer roles. The permissions are: Contributors can see the members list: enables Contributors to see who can access the space. Contributors can make their projects visible to all members: enables Contributors to share their projects with everyone in the space. Contributors can change project resources: enables Contributors to change RAM and CPU settings on their projects. Viewers can see the members list: enables Viewers to see who can acess the space. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

RStudio Cloud | Reusing a Workspace | Instructor View

One approach to reusing a space is to remove the current cohort of students from the space, and reuse the same space with the next cohort. If you are an Admin of a space, you can remove any member from that space. Go to the Members area of the space. For the member you would like to remove, press the Delete icon. You will be prompted to confirm that you would like to remove them from the space, and to choose whether to leave their projects behind in the space, or move them to their personal space. You can also remove members programatically via the Cloud API using the rscloud package. Another option is to create a new space from your current course space using the Copy Space action. If you'd like to re-use materials from a space, e.g. to teach a new cohort of students, use the Copy Space command. All projects shared with everyone in the original space will be copied over to the new space. Members of the original space are not copied over - you will be the only initial member of the new space. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

RStudio Cloud | Project Types | Instructor View

A project is the fundamental unit of work on RStudio Cloud. It encapsulates your R code, packages and data files and provides isolation from other analyses. If you are familiar with projects in the desktop RStudio IDE, an RStudio Cloud project is the same thing, plus some additional metadata for access and sharing. To create a new project from scratch, simply press the New Project button from the Projects area. Your new project will open in the RStudio IDE. To create a new project from an existing git repository, press the down arrow on the right side of the New Project button, and choose ‘New Project from Git Repo’ from the menu that appears. Note that your git credentials need to be entered each time you create a new project and are only cached for 15 minutes by default. To create a Jupyter Project, press the down arrow on the right side of the New Project button, and choose ‘New Jupyter Project’ from the menu that appears. A new Jupyter project will be created and deployed. Once deployed, you will see the Jupyter hub tree view with a welcome.ipynb notebook that contains information about getting started with Jupyter. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

RStudio Cloud | Overview | Instructor View

ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

RStudio Cloud | Inviting Co-instructors and Teaching Assistants | Instructor View

After creating a space, go to the Members area to manage its membership. You can add members to your space in three ways: To add members one by one, choose the Invitation Required option, and send invitations to each person you’d like to add to the space via the Add Member button. Note that for security reasons, the invitation is good for 7 days from the time that it is sent. To allow many people to join the space, choose the Sharing Link option, copy the sharing link and then share that link with all the people you’d like to join the space, either via an email or by posting the link on a web page. Keep in mind that anyone with that link will be able to join your space. If you would like to disable a link you previously shared, choose Reset Sharing Link; this will prevent any additional people from joining the space using that link. Space membership can also be managed programatically. See API Access from R in the Advanced Topics section below for details. Note that you can switch the access option at any time - a common approach is to initially set the access option to Sharing Link, post the sharing link, then after all the people you want to join the space initially have done so, switch access to Invitation Only. The original sharing link will no longer allow additional people to join the space, but you can add any new members individually. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

RStudio Cloud | Invite Learners | Instructor View

As you did to invite your co-instructors, go to the space's Members area to invite your students to your course space. The easiest approach is to enable access via a sharing link. - Click on the Sharing link option in the Access section - Set the Initial Role to Contributor - Click on the Copy Sharing Link action You can then distribute the sharing link to all your students via a course web page or email. Once all your students have joined the space, you can either reset the sharing link (which will disable access via the previous sharing link), or set Access to "Invitation Required" to ensure that nobody joins the class later without your permission. See the Members section in Shared Spaces above for more details and alternate methods for adding students to your space. Note that each student must have their own RStudio Cloud account. When they attempt to access your course space for the first time, they will be prompted to log in, or to sign up for an account if they don't already have one. If your organization is using SSO, account creation will happen automatically when they log in the first time. At this point, you and your students should be all set. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

RStudio Cloud | Exporting Projects | Instructor View

The contents of a project can be downloaded without opening the project. Press the Export action to the right of the project that you wish to download. A dialog box will appear showing the progress of your export. The process can take anywhere from a few seconds to a couple of minutes, depending on the size of the project, and how recently it was opened. Once the export is complete, press the Download button in the dialog box to download your project. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

RStudio Cloud | Creating an Assignment | Instructor View

A good way to create assignments for your students is to make a project for each assignment, and populate it with the files and packages you would like each student to have when they begin the assignment. When you are ready to reveal an assignment to your students, open the project and do the following: - Click on the Project Settings button (the gear in the upper right) - Select the Access panel - Set the project access so it can be viewed by everyone in the space - Check "Make this project an assignment" When the student opens an assignment project (either via the projects listing, or via a direct link to the project), RStudio Cloud will automatically make a copy for them. To convey this special behavior, assignments are displayed a bit differently, both for you and for your students. You can see the students’ copies of the assignment by clicking on the "View n derived projects" link that will appear with your original project. Note that changes you make to the original assignment will not be applied to any student copies already created. The changes will only apply to future copies of the assignment. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

RStudio Cloud | {rscloud} Package | Instructor View

You can access RStudio Cloud's API to manage space members programatically using the rscloud package. You will need to create client credentials to use the package. To do so, click on your icon/name in the header to reveal the User panel, then click on Credentials. This will take you to the Credentials page of RStudio User Settings, where you can create and manage your client credentials. {rscloud} package repo: https://github.com/rstudio/rscloud ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

Posit Cloud | Creating a Shared Space | Instructor View

A shared space is an area where a group of people can collaborate together - only the members of a shared space can access the space and its contents. To create a shared space, go to the navigation sidebar (click the menu icon at the upper left if needed) and choose New Space, then follow the on-screen instructions. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

Data Science Hangout | Jonathan Regenstein, Truist | Relationships with IT and Non-Data Scientists

We want to help data science leaders become better. The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. An accomplished leader in the space will join us each week and answer whatever questions the audience may have. We were recently joined by Jonathan Regenstein, Head of Data and Quantamental Research at Truist Securities. Working with IT and building relationships was a focus in our conversation with Jonathan and he included a few tips for building relationships with non-data scientist colleagues. Find a partner within the IT organization and talk to that person at least once a week. IT can help you communicate value proposition along the way as well. "It sounds crazy to say this in the world of data science, but relationship building was critical to what we did, especially at a bank. Thousands of request for new technology. There's no way to avoid going through all the security scans and check marks that we have to go through. We want to make sure we have a good partner who is going to help us do that" 0:48 - Start of session 10:52 - How should data science leaders work with IT? 46:20 - How far out Data Science Leaders should be planning projects with IT 48:20 - How do you become a champion of data science within your organization? 1:02:11 - Your responsibility as a data science leader is to work cross functionally 1:04:17 - Data Science Leaders: Your business cares about the value, not how you got there. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

Yu-Hung Chang, Phillip Lear & Brendan Scully | R in Manufacturing & Consumer Products | RStudio

Presentations from: Pratt and Whitney - Brendan Scully Kellogg Company - Phillip Lear AGCO - Dr. Yu-Hung Chang Abstract #1 In this talk, Brendan will showcase two of the tools developed at Pratt using R and hosted on RStudio Connect: a forecasting model for repair shop performance and a real-time dashboard for work management on the repair shop floor. In addition to these tools, he will talk about how to approach some common development and implementation challenges for decision analytics tools at large companies like Pratt. Bio: Brendan Scully is the Decision Analytics Strategy Lead for Pratt and Whitney’s aftermarket operations business. In this role, he is focused on how decision analytics tools can be used to improve the maintenance, repair, and overhaul of aircraft engines. Before this, Brendan helped other companies make the most of their data as an actuarial consultant at Milliman and Deloitte, and as data scientist at the African Leadership University. Abstract #2 Phillip will discuss his organization’s journey in transitioning a set of mixed analytic technologies to using a standardized analytic content platform. This talk will offer real life lessons learned in overcoming organizational barriers, getting stakeholders on board, and running a successful POC. Bio: Phillip is a Principal Data Scientist at the Kellogg Company working with global sales, marketing and ecommerce teams. He also teaches R to economics students at Grand Valley State University and sits on the Advisory Board for the Economics department. He lives in Kalamazoo (yes there really is a Kalamazoo!) with his wife Jamie and their two corgis, Penny and Winston. Abstract #3 Dr. Yu-Hung Chang is an Advanced Analytics Specialist at AGCO. She’s going to showcase an interactive app developed at AGCO using R and hosted on RStudio Connect, which provides comprehensive insights for global manufacturing quality analysis. The real-time dashboard provides not only forecasting failure rate based on different levels of parts, but also varieties of diagrams of cost and claims in different aspects. In this talk, Yu-Hung will share what are the challenges for development of analytics tools and how to establish some common development in agricultural manufacturing world. Dr. Yu-Hung Chang is an Advanced Analytics Specialist working for AGCO’s Global Field Quality group. Her multi-disciplinary background combines technical fundamentals with leading-edge research and big data applications, using the most advanced technologies. She has authored numerous articles, published in inter-disciplinary journals, covering topics from Statistics and Computational Physics to Aeronautics and Automotive

Chris Bumgardner, Children’s Wisconsin || Healthcare Meetup || Posit

Cultivating an R-based Analytic Practice in Healthcare Supporting the advanced analytic needs of an active academic healthcare organization requires tools and practices that enhance the application of statistical and algorithmic approaches. To positively impact care, system operations, or even well-being at the community level, these tools need to support solutions that can be rapidly deployed and communicated as well as reproduced when studying longitudinal trends. At Children’s Wisconsin, we use R and Posit's suite of tools to enable forecasting, modeling, and data mining among other data science activities. We communicate the results of our efforts using interactive applications built with Shiny as well as reports and push analytics created using RMarkdown. This talk will discuss how we have developed this capability and provide a few examples of the applications that have been created to support our vision that the kids of Wisconsin will be the healthiest in the nation. Agenda 1) Children’s Wisconsin Introduction 2) Data Science Tools and Supporting Infrastructure 3) Example R-based Projects [Community: Missing Youth, System-wide: COVID-19 Response, Operational: Patient Placement Planning and Optimization] 4) Challenges and Future Plans Speaker Bio: Chris Bumgardner leads the data science efforts at Children’s Wisconsin and works with teams across the health system to improve decision-making. He is focused on applying statistical methods to data sets large and small to discover and visualize insights that will help ensure Wisconsin’s kids are healthy, happy, and safe. Chris can often be found awake far too early thanks to an insubordinate rescue dog named Dutch. R in Healthcare Slack Group: https://join.slack.com/t/rinhealthcare/shared_invite/zt-sc7lc4k6-K9zb~kX826dOXMcaj~Wt~w RStudio Enterprise Community Meetup for future events: https://www.meetup.com/RStudio-Enterprise-Community-Meetup

Managing COVID vaccine distribution in West Virginia | RStudio

With a little help from open source software Learn more: Data Driven West Virginia: https://business.wvu.edu/research-outreach/data-driven-wv DDWV PPE forecasting: https://wvutoday.wvu.edu/stories/2020/04/27/wvu-business-experts-partner-with-the-national-guard-to-forecast-ppe-needs DDWV inventory management system: https://wvutoday.wvu.edu/stories/2021/03/22/a-different-kind-of-science-wvu-chambers-college-data-scientists-propel-west-virginia-s-acclaimed-vaccine-strategy-with-digital-inventory-management-system West Virginia National Guard: https://www.wv.ng.mil/ Shiny: https://shiny.rstudio.com/ West Virginia leading nation at start of vaccine rollout: https://www.vox.com/first-person/2021/3/4/22313540/covid-19-vaccine-west-virginia *** In the United States, approximately 2.5 million doses of COVID vaccines are being delivered each day, and how these doses go from the manufacturer to a shot in someone’s arm varies by state, often with mixed results. But early on in the vaccine distribution process, one state led the pack in terms of using the majority of vaccine doses it had been allotted. That state? West Virginia. Part of what has made West Virginia successful is the creation of an inventory management system using Shiny, an open source framework for building interactive web applications. The system was built by Data Driven West Virginia, part of the John Chambers College of Business and Economics at West Virginia University, in collaboration with the West Virginia Army National Guard. Using Shiny has provided visibility into each component of the vaccine supply chain, leading to the creation of distribution plans that are able to quickly and efficiently match supply with demand, getting vaccines to the right people in the right location at the right time. *** RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

Katherine Kopp | COVID vaccine distribution Shiny app walkthrough (mock data) | RStudio

Learn more: Data Driven West Virginia: https://business.wvu.edu/research-outreach/data-driven-wv DDWV PPE forecasting: https://wvutoday.wvu.edu/stories/2020/04/27/wvu-business-experts-partner-with-the-national-guard-to-forecast-ppe-needs DDWV inventory management system: https://wvutoday.wvu.edu/stories/2021/03/22/a-different-kind-of-science-wvu-chambers-college-data-scientists-propel-west-virginia-s-acclaimed-vaccine-strategy-with-digital-inventory-management-system West Virginia National Guard: https://www.wv.ng.mil/ Shiny: https://shiny.rstudio.com/ West Virginia leading nation at start of vaccine rollout: https://www.vox.com/first-person/2021/3/4/22313540/covid-19-vaccine-west-virginia *** To understand just how hard it is to get vaccines to the population, it helps to understand where it can go wrong. This starts with how vaccines are packed into containers. To fill up a container, Pfizer places 195 vials into a tray, and up to 5 trays into a single container. Moderna puts 10 vials into a small box, and then combines a minimum of 10 small boxes into a single container. In most states Pfizer and Moderna ship directly to the organization that will be administering the vaccine to the population. This could be a hospital, a pharmacy, or any place where trained professionals will be putting shots into arms. But what happens when a pharmacy receives a full container from Pfizer, 975 vials, but only needs 600? West Virginia has removed this complication by shipping directly to five hubs strategically located throughout the state. Within each of these hubs, containers of vaccine vials are broken down into smaller components and then either picked up or shipped directly to the hospital, pharmacy, or organization that will be administering the vaccine. These hubs are managed by the Joint Interagency Task Force (JIATF), a team of teams composed of public, private, and governmental organizations as well as the National Guard. The Joint Interagency Task Force is responsible for drawing up a weekly distribution plan for each hub, in alignment with CDC allocations, and matching vaccine supply with demand. By using a statewide system managed by a central organization, there’s a level of agility and fluidity that allows each hub to adjust to a variety of changes in order to maximize the number of vaccines that are being administered to the population each week. *** RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com. *** Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

Webinar Summary | Avoid Dashboard Fatigue | RStudio (2020)

0:00 Introduction 0:07 The Problem 1:05 The Solution 3:20 Real Life Success Stories 5:27 Demo (with code) Don't have an hour to watch a webinar? We've made a summary video that covers the main points of our "Avoid Dashboard Fatigue" webinar from Sean Lopp and Rich Iannone. The full webinar covered: Data science teams face a challenging task. Not only do they have to gain insight from data, they also have to persuade others to make decisions based on those insights. To close this gap, teams rely on tools like dashboards, apps, and APIs. But unfortunately data organizations can suffer from their own success - how many of those dashboards are viewed once and forgotten? Is a dashboard of dashboards really the right solution? And what about that pesky, precisely formatted Excel spreadsheet finance still wants every week? In this webinar, we’ll show you an easy way teams can solve these problems using proactive email notifications through the blastula and gt packages, and how RStudio pro products can be used to scale out those solutions for enterprise applications. Dynamic emails are a powerful way to meet decision makers where they live - their inbox - while displaying exactly the results needed to influence decision-making. Best of all, these notifications are crafted with code, ensuring your work is still reproducible, durable, and credible. We’ll demonstrate how this approach provides solutions for data quality monitoring, detecting and alerting on anomalies, and can even automate routine (but precisely formatted) KPI reporting. Webinar materials: https://rstudio.com/resources/webinars/avoid-dashboard-fatigue/ About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native. About Rich: My background is in programming, data analysis, and data visualization. Much of my current work involves a combination of data acquisition, statistical programming, tools development, and visualizing the results. I love creating software that helps people accomplish things. I regularly update several R package projects (all available on GitHub). One such package is called DiagrammeR and it's great for creating network graphs and performing analyses on the graphs. One of the big draws for open-source development is the collaboration that comes with the process. I encourage anyone interested to ask questions, make recommendations, or even help out if so inclined!

Rich Iannone

Mike Garcia | R in Pharma: Intro to Shiny | Posit

Slides: https://garciamikep.github.io/rstudioglobal-2021-shiny-slides/slides.html#1 From rstudio::global(2021) Pharma X-Sessions, sponsored by ProCogia: in this introduction to Shiny app development, we begin with a quick review of visualization with {ggplot2} and then cover core concepts in app structure and reactive programming. After building several Shiny apps of increasing complexity, we wrap up with a demonstration of how to include your Shiny app in a dashboard using the {flexdashboard} package. About Mike Garcia: Mike is a Data Science Consultant with ProCogia, with a background in Biostatistics and experience in clinical trial design and public health research. If not geeking out on data with a cup of coffee and spreading his passion for R, he's probably out enjoying the outdoors. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/ _______________________________________________________________________________________________ To hear more about how other major pharmaceutical companies are transitioning to open source data science you can watch talks from this year’s R in Pharma conference: https://www.youtube.com/@RinPharma/playlists At Posit, we have a dedicated Pharma team to help organizations migrate and utilize open source for drug development. To learn more about our support for life sciences, please see our dedicated Pharma page where you can book a call with our team. (https://posit.co/solutions/pharma)

Lou Bajuk & Kevin Bolger | Why Data Science in the Cloud? | RStudio (2020)

As business and organizational needs expand, a centralized ecosystem such as the cloud is needed to securely store and access data, conduct analyses, and share results. We’ll share some examples of what it means to do data science in the cloud and discuss some problems that users may face along the way, and the solutions that RStudio products can provide. We’ll also discuss best practices for migrating to a cloud environment. What you’ll learn: - What are the benefits of working in a cloud environment? - What are the different cloud environments available? - How do I learn which is the best fit for my organization? - What should I consider when migrating my data science infrastructure to the cloud? Webinar materials: https://rstudio.com/resources/webinars/why-data-science-in-the-cloud/ About Lou: Lou is a passionate advocate for data science software, and has had many years of experience in a variety of leadership roles in large and small software companies, including product marketing, product management, engineering and customer success. In his spare time, his interests includes books, cycling, science advocacy, great food and theater. About Kevin: After finishing his education in the University of Limerick, Ireland – Kevin’s passion for data science was cemented. Focusing primarily on data analytics and modelling, he went on to spend the first years of his career working at a biopharmaceutical company, where he led the data team on multiple products. Since moving to Seattle with his Washington native wife, Kevin has spent his spare time enjoying the beautiful PNW and playing ‘hurling’, an ancient gaelic field sport with the Seattle Gaels. He now leads the Data Science team at ProCogia as the Director of Data Solutions – where he works with clients from Biotech to Telecom

Andy Nicholls & Michael Rimler | Using R to Drive Agility in Clinical Reporting | RStudio (2020)

The R language is used extensively throughout the pharmaceutical industry. But its use within the tightly regulated clinical reporting workflows has remained limited. GSK Biostatistics has embarked upon a journey to embed R as a primary statistical analysis tool for clinical reporting. Enabling R within a global department of over 600 Statisticians, Programmers and Data Scientists is challenging! It requires planning, patience, and a strong foundation that enables consistency across the enterprise. We invite you to learn more about how we achieved this at GSK. You'll learn about our tidyverse-centric training program, a future-ready Working Area for R Programming (WARP) environment, and a leading-edge R for Clinical Reporting (R4CR) initiative. The goal: help embed R in every-day clinical reporting output. About Andy: Andy Nicholls has a long history with the R language and in Data Science, authoring the book ‘R in 24 Hours’. He is currently Head of Statistical Data Sciences within GSK’s Biostatistics department. One of his team’s main objectives is to embed the R language within Biostatistics; developing training materials, overseeing various adoption initiatives and provisioning a world-class environment for R . He is also the lead for the cross-industry R Validation Hub initiative that aims to support the use of R for regulatory work. About Michael: Michael Rimler is a clinical programmer in GSK Biostatistics and passionate about influencing the evolving role of open source technologies and data science capabilities on clinical data analytics. He is involved with numerous internal initiatives aimed at moving the organization in this direction, including leading the effort to fully integrate R into the clinical reporting process. Michael is a co-lead of the Open Source Technologies in Clinical Research PHUSE working group project, has chaired a PHUSE US Single Day Event on Data Visualization, and will serve as a co-chair for the 2021 PHUSE US Connect

Hadley Wickham | testthat 3.0.0 | RStudio (2020)

In this webinar, I'll introduce some of the major changes coming in testthat 3.0.0. The biggest new idea in testthat 3.0.0 is the idea of an edition. You must deliberately choose to use the 3rd edition, which allows us to make breaking changes without breaking old packages. testthat 3e deprecates a number of older functions that we no longer believe are a good idea, and tweaks the behaviour of expect_equal() and expect_identical() to give considerably more informative output (using the new waldo package). testthat 3e also introduces the idea of snapshot tests which record expected value in external files, rather than in code. This makes them particularly well suited to testing user output and complex objects. I'll show off the main advantages of snapshot testing, and why it's better than our previous approaches of verify_output() and expect_known_output(). Finally, I'll go over a bunch of smaller quality-of-life improvements, including tweaks to test reporting and improvements to expect_error(), expect_warning() and expect_message(). Webinar materials: https://rstudio.com/resources/webinars/testthat-3/ About Hadley: Hadley Wickham is the Chief Scientist at RStudio, a member of the R Foundation, and Adjunct Professor at Stanford University and the University of Auckland. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. You may be familiar with his packages for data science (the tidyverse: including ggplot2, dplyr, tidyr, purrr, and readr) and principled software development (roxygen2, testthat, devtools, pkgdown). Much of the material for the course is drawn from two of his existing books, Advanced R and R Packages, but the course also includes a lot of new material that will eventually become a book called "Tidy tools"

Hadley Wickham

Mine Çetinkaya-Rundel | Teaching R online with RStudio Cloud | RStudio (2020)

RStudio Cloud is a lightweight and easy to set up / use solution to teaching R online, in the browser. In this webinar we will walk you through the steps of setting up your course on RStudio Cloud, highlighting the various functionalities for teachers and students. We will also discuss best practices and provide an opportunity for the audience to experience the setup first hand. Additionally, we highlight a suite of ready to use resources for teaching an introduction to data science and statistical thinking course using R. Webinar materials: https://rstudio.com/resources/webinars/teaching-r-online-with-rstudio-cloud/ About Mine: Mine Çetinkaya-Rundel is Professional Educator and Data Scientist at RStudio as well as Senior Lecturer in the School of Mathematics at University of Edinburgh (on leave from Department of Statistical Science at Duke University). Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. Mine works on integrating computation into the undergraduate statistics curriculum, using reproducible research methodologies and analysis of real and complex datasets. She also organizes ASA DataFest and works on the OpenIntro project. She is also the creator and maintainer of datasciencebox.org and she teaches the popular Statistics with R MOOC on Coursera

Mine Çetinkaya-Rundel

A quick tour of RStudio 1.4 | RStudio

HD version here: https://youtu.be/oCR_LB3H73M 0:00 Introduction 0:20 R Markdown Visual Editor 0:46 Insert citations in R Markdown 1:09 Python support in Environment pane 2:05 Python environment selection 2:25 Rainbow parentheses 2:43 Monospace font support 2:54 Support for multiple source columns 3:10 Command palette 3:27 Customize data and configuration storage (users and servers) 3:55 RStudio Pro edition features 4:08 Authenticate RStudio Server Pro using SAML 4:25 Project sharing with Launcher 4:48 Request a GPU with SLURM 5:00 Run Visual Studio Code sessions (beta) What's new with RStudio 1.4: A visual markdown editor that provides improved productivity for composing longer-form articles and analyses with R Markdown. New Python capabilities, including display of Python objects in the Environment pane, viewing of Python data frames, and tools for configuring Python versions and conda/virtual environments. The ability to add source columns to the IDE workspace for side-by-side text editing. A new command palette (accessible via Ctrl+Shift+P) that provides easy keyboard access to all RStudio commands, add-ins, and options. Support for rainbow parentheses in the source editor (enabled via Options, then Code, then Display). New citation support that allows you to include document citations from your document bibliography, personal or group libraries, and several other sources. Integration with a host of new RStudio Server Pro features including project sharing when using Launcher, Microsoft Visual Studio Code support (currently in beta), SAML authentication, and local launcher load-balancing. Read more on our blog: https://blog.rstudio.com/2021/01/19/announcing-rstudio-1-4/

Alison Hill & Desirée De Leon | How to Get Your Materials Online With R Markdown | RStudio (2020)

Full title: Sharing on Short Notice: How to Get Your Teaching Materials Online With R Markdown Educators create a lot of files for teaching- slides, exercises, solutions, assignments, data, figures- that all ultimately need to be shared with other people. Having a link for sharing your teaching materials can save you time and pain, but it is hard to get started if you’ve never shared your resources online before. In this webinar, we’ll give a tour of the R Markdown ecosystem for educators that you can start to use right away. We’ll show how it can help you make your teaching more shareable, reproducible, and resilient. About Alison: I studied psychology and quantitative methods, receiving my Ph.D. from Vanderbilt University (2008). For eight years, I was a professor and scientist at Oregon Health & Science University, where my research was funded by the National Institutes of Health, the Oregon Clinical and Translational Research Institute, and Autism Speaks. I have written numerous scientific journal articles and book chapters on autism and neurodevelopmental disorders. I have developed and delivered workshops, graduate-level courses, and curricula based on teaching R, the tidyverse, and literate programming. You can follow my current work for RStudio Education on GitHub. About Desirée: I am neuroscience PhD student at Emory University and also a former summer intern at RStudio. This past summer, I worked with Alison Hill to develop a handbook filled with practical advice and resources for educators who teach with R and RStudio. I enjoy spending my time on collaborative projects that involve coding, teaching, and illustration

Roche & Novartis: Effective Visualizations for Data Driven Decisions || Posit (2020)

Effective visual communication is a core task for all data scientists including statisticians, epidemiologists, machine learning experts, bioinformaticians, etc. By using the right graphical principles, we can better understand data, highlight core insights and influence decisions toward appropriate actions. Without it, we can fool ourselves and others and pave the way to wrong conclusions and actions. While numerous solutions exist to analyze data, these often require many manual steps to convert them into visually convincing and meaningful reports. How do we put this in practice in an accurate, transparent and reproducible way? In this webinar we will introduce an open collaborative effort, currently undertaken by Roche and Novartis, to develop solutions for effective visual communication with a focus on reporting medical and clinical data. The aim of the collaboration is to develop a user-friendly, fit for purpose, open source package to simplify the use of good graphical principles for effective visual communication of typical analyses of interventional and observational data encountered in clinical drug development. We will introduce the initial visR package design which easily integrates into a typical tidyverse workflow. The package provides guidance and meaningful default parameters covering all aspects from the design, implementation and review of statistical graphics. Webinar materials: https://posit.co/resources/videos/effective-visualizations-for-data-driven-decisions/ About Charlotta: Charlotta is a computational biologist by training and works as a data scientist in the Personalized Healthcare department at Roche where she uses R to untap the wealth of information coming from healthcare data collected in real-world settings to support the development of new medicines. About Diego: Diego is a data scientist specializing in applied machine learning at Roche Personalized Healthcare since March 2019. He has developed models to perform various tasks and analyze diverse data sources. Currently, his main applications of interest are in onocology and clinico-genomics. About Mark: Mark is a methodologist supporting the clinical development and analytics department at Novartis. He has a focus on data visualization working on a number of internal and external initiatives to improve the reporting of clinical trials and observational studies. About Marc: Marc is a biostatistics group head at Novartis. He is interested in advancing the methods and practice of clinical development, for instance through effective use of graphics. https://graphicsprinciples.github.io/

Sean Lopp & Lou Bajuk | R & Python: A Data Science Love Story | RStudio (2020)

Many Data Science teams today leverage both R and Python in their work, but struggle to use them together. Data Science leaders and their business partners find it difficult to make key data science content easily discoverable and available for decision-making, while IT Admins and DevOps engineers grapple with how to efficiently support these teams without duplicating infrastructure. Even experienced data scientists familiar with both languages often struggle to combine them without painful context switching and manual translations. In this webinar, you will learn how RStudio helps organizations tackle these challenges, with a focus on some of the recent additions to our products that have helped deepen the happy relationship between R and Python: - Easily combine R and Python in a single Data Science project using a single IDE. - Leverage a single infrastructure to launch and manage Jupyter Notebooks, JupyterLab, VSCode and the RStudio IDE, while giving your team easy access to Kubernetes and other resources. - Share and manage access to R- and Python-based interactive applications, dashboards, and APIs, all in a single place. Webinar materials: https://rstudio.com/resources/webinars/r-python-a-data-science-love-story/ About Lou: Lou is a passionate advocate for data science software, and has had many years of experience in a variety of leadership roles in large and small software companies, including product marketing, product management, engineering and customer success. In his spare time, his interests includes books, cycling, science advocacy, great food and theater. About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native

Sean Lopp & Rich Iannone | Avoid Dashboard Fatigue | RStudio (2020)

Data science teams face a challenging task. Not only do they have to gain insight from data, they also have to persuade others to make decisions based on those insights. To close this gap, teams rely on tools like dashboards, apps, and APIs. But unfortunately data organizations can suffer from their own success - how many of those dashboards are viewed once and forgotten? Is a dashboard of dashboards really the right solution? And what about that pesky, precisely formatted Excel spreadsheet finance still wants every week? In this webinar, we’ll show you an easy way teams can solve these problems using proactive email notifications through the blastula and gt packages, and how RStudio pro products can be used to scale out those solutions for enterprise applications. Dynamic emails are a powerful way to meet decision makers where they live - their inbox - while displaying exactly the results needed to influence decision-making. Best of all, these notifications are crafted with code, ensuring your work is still reproducible, durable, and credible. We’ll demonstrate how this approach provides solutions for data quality monitoring, detecting and alerting on anomalies, and can even automate routine (but precisely formatted) KPI reporting. Webinar materials: https://rstudio.com/resources/webinars/avoid-dashboard-fatigue/ About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native. About Rich: My background is in programming, data analysis, and data visualization. Much of my current work involves a combination of data acquisition, statistical programming, tools development, and visualizing the results. I love creating software that helps people accomplish things. I regularly update several R package projects (all available on GitHub). One such package is called DiagrammeR and it's great for creating network graphs and performing analyses on the graphs. One of the big draws for open-source development is the collaboration that comes with the process. I encourage anyone interested to ask questions, make recommendations, or even help out if so inclined!

Rich Iannone

Lou Bajuk & Sean Lopp | RStudio: A Single Home for R & Python| RStudio

Many Data Science teams today are bilingual, leveraging both R and Python in their work. While both languages are tremendously powerful, teams frequently struggle to use them together. We’ve heard from our customers how even experienced data scientists familiar with both languages often struggle to combine them without painful context switching and manual translations. Data Science leaders and business stakeholders find it difficult to make key data science content easily discoverable and available for decision-making, and IT Admins and DevOps engineers grapple with how to efficiently support these teams. In this webinar, you will learn how RStudio helps Data Science teams tackle all these challenges, and make the Love Story between R and Python a happier one: - Easily combine R and Python in a single Data Science project - Leverage a single infrastructure to launch and manage Jupyter Notebooks and JupyterLab environment, as well as the RStudio IDE - Organize and share Jupyter Notebooks alongside your work in R and your mixed R and Python projects This webinar will show examples of all these capabilities, and discuss the benefits of leveraging R and Python. About Lou: Lou is a passionate advocate for data science software, and has had many years of experience in a variety of leadership roles in large and small software companies, including product marketing, product management, engineering and customer success. In his spare time, his interests includes books, cycling, science advocacy, great food and theater. About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native

Podcast | Not So Standard Deviations Episode 100 | RStudio (2020)

Featuring: Hilary Parker & Roger Peng In episode 100 of Not So Standard Deviations, the first ever episode prepared in advance, Hilary and Roger discuss creativity, its role in data science, and how it can be fostered through conversation. Also, follow up on coffee and oat milk. About Hilary: Hilary Parker is a Data Scientist on the styling recommendations team at Stitch Fix, a personal styling service that uses a combinations of human stylists and algorithmic recommendations to help people find what they love. At Stitch Fix, she focuses on what sorts of data to collect from clients in order to optimize clothing recommendations, as well as building out prototypes of algorithms or entirely new products based on new data sources. She is also a co-founder of the Not So Standard Deviations podcast, a bi-weekly data science podcast with Roger Peng that has over half a million downloads. Their topics of discussion include the R ecosystem, recent developments in the data science and statistics field, reproducibility and the "how" of how data scientists and statisticians work. Hilary recently authored the paper Opinionated Analysis Development based on discussions from the podcast. Prior to her career in the tech field, Hilary received her PhD in Biostatistics from Johns Hopkins School of Public Health. She lives at the San Francisco Zen Center with her partner, a Soto Zen Priest. In her free time, she enjoys exploring her home of 2 years, San Francisco. About Roger: Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health where his research focuses on the development of statistical methods for addressing environmental health problems. He is the author of the popular book R Programming for Data Science and nine other books on data science and statistics. He is also the co-creator of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui. Roger is a Fellow of the American Statistical Association and is the recipient of the Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health. He can be found on Twitter and GitHub at @rdpeng

Panel: Career Advice for Data Scientists | RStudio (2020)

Featuring: Gabriela de Queiroz, David Keyes, Sydeaka Watson, and Jen Hecht This panel will be focused on how you build a career around R! Our panelists are all passionate about R and have each taken a different path to build a career around that passion. We'll be touching on topics like the different stages of career growth and how you find a community you can go to for support. If you're just getting started in your career, looking to make a transition, or interested in learning some great career-building skills, be sure to join us! About Gabriela de Queiroz: Gabriela de Queiroz is a Sr. Engineering & Data Science Manager at IBM where she manages and leads a team of developers working on Data & AI Open Source projects. Her team contributes to projects such as TensorFlow, PyTorch, Apache Arrow, Apache Spark, and several other open source projects inside IBM. She works to democratize AI by building tools and launching new open source projects. She is passionate about making data science available to everybody and is actively involved with several organizations to foster an inclusive community. She is the founder of AI Inclusive, a global organization that is helping increase the representation and participation of gender minorities in Artificial Intelligence. She is also the founder of R-Ladies, a worldwide organization for promoting diversity in the R community with more than 180 chapters in 50+ countries. About David Keyes: David Keyes is the founder of R for the Rest of Us, which teaches people and individuals to use R through online courses, custom trainings, and public workshops. With stops as an elementary school teacher, PhD social scientist, and program evaluator, David brings his unconventional career trajectory to his current role, helping those with who don't think of themselves as typical R users embrace the power of R. About Sydeaka Watson: Dr. Sydeaka Watson is a native of New Orleans, Louisiana and currently lives in Dallas, Texas. She is Founder and Owner of Korelasi Data Insights, LLC and a Senior Data Scientist at Elicit Insights, LLC. In these roles, Sydeaka uses predictive analytics and visual tools to draw insights from diverse datasets. Sydeaka earned a Ph.D. in Statistics from Baylor University and has several years of teaching experience. In her 5 years as Research Assistant Professor in The University of Chicago Biostatistics Laboratory, she consulted with over 110 biomedical research teams in The University of Chicago Medical Center, specializing in statistical analysis and experimental design for clinical research studies. Her current research interests include applications of (1) image recognition for computer vision and (2) data science for social justice. Sydeaka currently serves as Organizer of the R-Ladies Dallas Chapter. She also volunteers in the Dallas chapters of Girls Who Code and Black Girls Code. About Jen Hecht: Jen Hecht is the VP of People Operations at RStudio. She was first introduced to R in 2013, as a non-programmer seeking better ways to manage analytical projects - a quest which was aided both by the RStudio toolchain and the welcoming support of R Ladies, R meetup groups, and other wonderful open resources. Ever since, she has been captivated by open data science tools and the communities that build them. Before joining RStudio in 2018, Jen held HR and People Analytics roles in a variety of industries, including financial services, biotech, and used record shops. Outside of work, Jen loves books and music of all kinds, and is a novice fly caster

Fernanda Viegas & Martin Wattenberg | Data, visualization, and designing AI | RStudio (2020)

Recent progress in machine learning has raised a series of urgent questions: How can we train and debug deep learning models? How can we understand what is going on inside a neural network? And, perhaps most important, how can we design systems that serve people best? We'll show a series of examples from the People+AI Research (PAIR) initiative at Google--ranging from data visualizations for researchers, to tools for medical practitioners, to guidelines for designers--that illustrate how thinking carefully about data can lead to better tools, more effective design, and help humans and AI work together. About Fernanda: Fernanda Viégas and Martin Wattenberg co-lead Google’s PAIR (People+AI Research) initiative, part of Google Brain. Their work in machine learning focuses on transparency and interpretability, as part of a broad agenda to improve human/AI interaction. They are well known for their contributions to social and collaborative visualization, and the systems they’ve created are used daily by millions of people. Viégas and Wattenberg are also known for visualization-based artwork, which has been exhibited in venues such as the Museum of Modern Art in New York, London Institute of Contemporary Arts and the Whitney Museum of American Art. Their artwork has influenced contemporary design practice: for instance, the techniques in their wind map are now used by many major media companies to display the weather. About Martin: Fernanda Viégas and Martin Wattenberg co-lead Google’s PAIR (People+AI Research) initiative, part of Google Brain. Their work in machine learning focuses on transparency and interpretability, as part of a broad agenda to improve human/AI interaction. They are well known for their contributions to social and collaborative visualization, and the systems they’ve created are used daily by millions of people. Viégas and Wattenberg are also known for visualization-based artwork, which has been exhibited in venues such as the Museum of Modern Art in New York, London Institute of Contemporary Arts and the Whitney Museum of American Art. Their artwork has influenced contemporary design practice: for instance, the techniques in their wind map are now used by many major media companies to display the weather

Marie Vendettuoli | Lessons learned developing a library of validated packages | RStudio

Full title: Towards an integrated {verse}: lessons learned developing a library of validated packages Developing R packages as a unified {verse} – a set of packages that work well together but with each focusing on individual tasks – is an efficient strategy to structure support for complex workflows. The ongoing challenge becomes managing the growth of related packages in a holistic manner. This is especially problematic in industries with a heavy emphasis on stability, for example if packages need to be validated prior to use in production. In this talk, I will discuss a paradigm for developing and maintaining validated R packages, emphasizing the following areas: 1. Strategies for organizing packages to prevent excessive re-work 2. Facilitating responsive, iterative development and 3. Empathy for developer and user experiences About Marie: Marie Vendettuoli is a Senior Statistical Programmer at Statistical Center for HIV/AIDS Research and Prevention (SCHARP - https://www.fredhutch.org/en/research/divisions/vaccine-infectious-disease-division/research/biostatistics-bioinformatics-and-epidemiology/statistical-center-for-hiv-aids-research-and-prevention.html) @ FredHutch. She holds a PhD from Iowa State University in Human Computer Interaction and started developing R packages for use within regulatory frameworks while working as a Data Scientist at USDA Center for Veterinary Biologics (https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/veterinary-biologics/sa_about_vb/ct_vb_about). Before discovering R, Marie worked in a CBER (https://www.fda.gov/about-fda/fda-organization/center-biologics-evaluation-and-research-cber)-regulated laboratory. Her main interest is developing analytical infrastructure to facilitate scientific analysis for fellow data scientists working in a regulatory environment

John Helveston | Using formr to create R-powered surveys with individualized feedback | RStudio

This talk demonstrates how the formr study framework extends the power and flexibility of R to surveys. Using R and RMarkdown code, researchers and teachers can use the formr platform to generate both simple surveys and complex studies with individualized feedback. The platform is built on a web-based application programming interface for R via OpenCPU, enabling complex features such as automated email and text message reminders, adaptive testing, graphical and interactive feedback, and integration with external data sources. In this talk, I introduce some of the formr basics and showcase two examples of how I have used it, including making conjoint surveys with randomized images and timed, randomized quizzes for my students. About John: John Paul Helveston is an Assistant Professor in the Engineering Management and Systems Engineering Department at the George Washington University. He studies technological change, with a particular interest in accelerating the transition to environmentally sustainable and energy-saving technologies. His research centers around how consumer preferences, market dynamics, and policy affect the emergence of critical technologies, such as electric vehicles and solar energy. He is an expert on China's rapidly emerging electric vehicle industry as well as the critical relationship between the US and China in developing and mass producing low carbon energy technologies. He applies an interdisciplinary approach to research, with expertise in discrete choice modeling and conjoint analysis as well as interview-based case studies. He has conducted extensive fieldwork in China, collaborating with colleagues at Tsinghua university, Beijing Normal University, and China's State Information Center on past projects. He is a fluent speaker of Mandarin Chinese and also an award-winning swing dancer. John holds a Ph.D. and M.S. in Engineering and Public Policy from Carnegie Mellon University and a B.S. in Engineering Science and Mechanics from Virginia Tech

Dean Marchiori | A retrospective on a year of commercial data science projects in R | RStudio

Full title: How reproducible am I? A retrospective on a year of commercial data science projects in R Reproducibility is a critical aspect in science to enable trust & communication. In R, many tools exist to bring in the best practices of reproducibility into the hands of data scientists. However, outside of a research setting, how does reproducibility hold up in commercial data science projects? In this talk I take an honest retrospective of my own commercial R projects in the last year. I look at the various types of analyses completed, and which workflows were selected and why. Through this process we can learn how workflow choices may help in the short term but hinder in the long term. More importantly what can be done strike the balance between progress and perfection when doing data science in the wild? About Dean: Dean Marchiori is a Statistician based in Sydney, Australia. He currently works with Endeavour Energy as a Senior Data Scientist modelling bushfire and vegetation risk on the electricity network. Dean's career started in finance as an equities trader before moving into advanced analytics, where he has worked with some of Australia’s largest organisations. His professional interests are in geospatial analysis, time series modelling and the R programming language. In 2019, Dean was named one of the top 10 analytics leaders in Australia by IAPA. He is also recognised as an Accredited Statistician with the Statistical Society of Australia. He holds a Bachelor of Science in Mathematics (awarded with University Medal) and a Masters degree in Applied Finance. Outside of work Dean enjoys bodysurfing, running and spending time with his wife and two boys

Danielle Smalls-Perkins & Dorris Scott | Reflections on creating the Mi-R community | RStudio

Full title: Your R is My R too: Reflections on creating the Mi-R community While the R community has made strides in increasing the representation and participation for women and users from underrepresented regions, there are still members of the R community that have expressed desires for a more inclusive space in addition to these strides. In addition, there are unique challenges that underrepresented R users experience in their respective workspaces or academic environments. In late February of 2020, Danielle Smalls-Perkins and Dorris Scott created Mi-R (Minorities in R) as a result of their various experiences both in and outside the R community. The purpose of this talk is to reflect on the challenges, highlights, and future directions of the first six months since the creation of Mi-R. About Danielle: Danielle Smalls-Perkins co-founded MiR Community with the hope that the R community would continue to encourage the inclusion and recognition of contributions made from R users of diverse backgrounds. She loves to use R for understanding and storytelling. Danielle currently works as a Senior Strategist in Google's Trust and Safety Team. She advocates for model fairness, interpretability, and reducing harmful outcomes of algorithmic decision-making on vulnerable populations. About Dorris: Dorris Scott is the GIS Librarian and Social Science Data Curator at Washington University – St. Louis, where she provides consultation on projects that use geospatial data along with providing training in various GIS software, programming applications of geospatial data, and data management. She also serves as a liaison between Washington University Libraries and social science departments assisting faculty with their data needs such as data management and data curation. Dorris received her PhD in Geography from the University of Georgia, with a specialization in GIS applications for public health

Yanina Bellini Saibene | On programming, teaching, and building tutorials with learnr | RStudio

Full title: On programming, teaching, and building interactive tutorials with learnr Teaching R is part of my activities as a community organizer, an RStudio Certified Instructor, a conference chair, and a researcher. Since 2019, I use the learnr package to generate interactive tutorials to teach R synchronously and asynchronously. The addition of the Tutorials panel in RStudio IDE and the need for virtual classes made the use of this package even more interesting. In this talk, I will tell you how to generate interactive tutorials, how to add pedagogical tools to them, what other packages you can use with {learnr} and show multilingual examples. About Yanina: Yanina Bellini Saibene is a researcher at INTA (National Institute of Agricultural Technology) dedicated to applying data science to the agricultural sector and a professor in several regional specializations about Digital Agriculture and Data Analysis. Yanina is formally trained as a Licenciate in Information System with a Master degree in Data Mining and Knowledge Management. She is an active member of the R Community as an R-Ladies organizer and part of the R-Ladies Global Team. She also is a co-founder and co-chair of LatinR and part of the organizing team of useR!2020 and user!2021 (co-chair). She is also co-founder of MetaDocencia, a open, free, volunteer-lead, not-for-profit educational organization and part of the teams that translate educational and technical material to Spanish

Shelmith Kariuki | rKenyaCensus Package | RStudio

The rKenyaCensus package contains the results of the 2019 Kenya Population Census. The census exercise was carried out in August 2019, and the results were released in February 2020. Kenya leveraged on technology to capture data during cartographic mapping, enumeration and data transmission, making the 2019 Census the first paperless census to be conducted in Kenya. The data was published in four different pdf files (Volume 1 - Volume 4) which can be found in the Kenya National Bureau of statistics website. The data in its current form was open and accessible, but not usable and so there was need to convert it into a machine readable format. This data can be used by the government, non-governmental organizations and any other entities for data driven policy making and development. During the talk, I will explain the reasons behind development of the package, take you through the steps I took during the process and finally showcase analysis of certain aspects of the data. About Shelmith: Shelmith Kariuki is a Senior Data Analyst based in Nairobi, Kenya. She is an RStudio Certified Tidyverse trainer (https://education.rstudio.com/trainers/), currently working as a Data Analytics consultant with UN DESA. She previously worked as a Research Manager at Geopoll, and as a Data Analyst at Busara Center for Behavioral Economics. She also worked as an assistant lecturer in various Kenyan universities, teaching units in Statistics and Actuarial Science. She has extensive experience in data analysis using R. She co-organizes a community of R users in Nairobi (https://www.linkedin.com/feed/hashtag/nairobir/) and in Africa (https://twitter.com/AfricaRUsers). One of the missions of her community work is to make sure that there is an increased number of R adopters, in Africa. She is very passionate about training and using data analytics to drive development projects in Africa

Rika Gorn | From Zero to Hero: Best practices for setting up Rstudio Team in the Cloud | RStudio

Learn best practices for setting up the entire Rstudio team infrastructure - Server Pro, Connect, Package Manager from the perspective of a data scientist and for a data science audience - especially those who have never worked with servers, AWS, or bash. This talk will also be applicable to data scientists looking to start on an engineering project outside of Rstudio as well. I started out as a complete novice, & throughout my learning experience I noticed a distinct lack of resources for non-engineers. This talk will focus on best practices for AWS architecture and cloud formation, key security issues such as SSL and https, server configurations, deployment errors, and most importantly resources that are understandable for data scientists just getting into the data engineering or devops space. About Rika: Rika Gorn is the Manager of Business Intelligence at Spring Health - a mental healthcare tech start-up that provides comprehensive mental healthcare benefits. Previously, she worked on quality assurance for a mobile mental health team at Coordinated Behavioral Care, data analytics at Covenant House International, strategic management and evaluation at TCC Group, and program analysis at the Vera Institute of Justice. Rika received her Bachelors in Political Science from Hunter College and her Masters in Public Administration at the NYU Wagner School of Public Service. Rika is also a proud board member of R-Ladies NYC

RStudio Team

Mine Çetinkaya-Rundel | Feedback at scale | RStudio

As enrollments in statistics and data science courses grow and as these courses become more computational, educators are faced with an interesting challenge -- providing timely and meaningful feedback, particularly with online delivery of courses. The simplest solution is using assignments that are easier to auto-grade, e.g. multiple-choice questions, simplistic coding exercises, but it is impossible to assess mastery of the entire data science cycle using only these types of exercises. In this talk I will discuss writing effective learnr exercises, providing useful and motivating feedback with gradethis, distributing them at scale online and as an R package, and collecting student data for formative assessment with learnrhash. About Mine: Mine Çetinkaya-Rundel is Professional Educator and Data Scientist at RStudio as well as Senior Lecturer in the School of Mathematics at University of Edinburgh (on leave from Department of Statistical Science at Duke University). Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. Mine works on integrating computation into the undergraduate statistics curriculum, using reproducible research methodologies and analysis of real and complex datasets. She also organizes ASA DataFest and works on the OpenIntro project. She is also the creator and maintainer of datasciencebox.org and she teaches the popular Statistics with R MOOC on Coursera

Mine Çetinkaya-Rundel

Megan Beckett | Aesthetically automated figure production | RStudio

Automation, reproducibility, data driven. These are not normally concepts one would associate with the traditional publishing industry, where designers normally manually produce every artefact in proprietary software. And, when you have 1000s of figures to produce and update for a single textbook, this becomes an insurmountable task, meaning our textbooks quickly become outdated, especially in our rapidly advancing world. With R and the tidyverse in our back pocket, we rose to the challenge to revolutionize this workflow. I will explain how we collaborated with a publishing group to develop a system to aesthetically automate the production of figures for a textbook including translations into several languages. I think you’ll find this talk interesting as it shows how we applied tools that are familiar to us, but in an unconventional way to fundamentally transform a conventional process. About Megan: Megan Beckett is a Data Scientist at Exegetic Analytics, where she consults, develops and leads several analytical projects across a wide range of fields and industries. *"Scientifically creative; creatively scientific."* This aptly describes her philosophy and approach in her work and life. Megan helped co-found and organises the Cape Town R-Ladies chapter and is a co-organiser of the satRday events in South Africa. She loves to paint, with her most recent work exploring the [biodiversity of southern Africa](https://mappamundi.netlify.app/portfolio/), and running is her passion, whether on the road or the trail

Matt Thomas & Mike Page | How the Tidyverse helped the British Red Cross respond to COVID | RStudio

Full title: Cognitive speed: How the Tidyverse helped the British Red Cross respond quickly to COVID-19 We will discuss the importance of cognitive speed, defined here as the rate in which an idea can be translated into code, and why the Tidyverse excels in this domain. We will demonstrate this idea in relation to a suite of tools we were required to rapidly develop at the British Red Cross in order to respond effectively to the COVID-19 pandemic. To do this, we will exhibit how elements of the unifying design principles outlined in the ‘tidyverse design guide - Tidyverse team’ relate to the notion of cognitive speed, giving specific examples for various design considerations. We believe this talk will encourage reflection on better design practices for future R developers, using the design principles of the tidyverse as the guiding beacon. About Matt: Dr. Matt Thomas is Head of Strategic Insight and Foresight at the British Red Cross. Matt's team aims to help the Red Cross become more anticipatory and proactive by producing insights and tools including the Vulnerability Index (https://britishredcrosssociety.github.io/covid-19-vulnerability/) and Resilience Index (https://britishredcross.shinyapps.io/resilience-index/). He holds a PhD in Evolutionary Anthropology and, prior to joining the British Red Cross, was researching topics including reindeer herders in the Arctic, hunter-gatherers in the Philippines, and witches in China. Outside of work, Matt writes a column for an anthropology magazine (https://www.sapiens.org/column/machinations/) as well as fiction. About Mike: Mike Page is a data scientist on the Strategic Insight and Foresight team at the British Red Cross. Here, he helps to develop a suite of open source tools and dashboards including the Vulnerability Index (https://britishredcrosssociety.github.io/covid-19-vulnerability/) and Resilience Index (https://britishredcross.shinyapps.io/resilience-index/). Mike is also the author of several R packages including mortyr and newsrivr. In his spare time you can find him rock climbing around the Alps

Marcus Adams | Putting a GMP Shiny App into Production | RStudio

Full title: Not The App We Deserve. The App We Need: Putting a GMP Shiny App into Production In February 2020, the Digital Proactive Process Analytics (DPPA) group within Merck’s manufacturing division officially launched a Shiny app to automate the creation of Continuous Process Verification (CPV) reports into production. That’s right – the almighty, mysterious, coveted production. From a technical perspective, the app is nothing particularly special (except other than getting LaTeX successfully installed to support the use of R Markdown). Users enter a few parameters and out pops a PDF with a series statistical analyses of a product’s quality testing data. The R blogosphere is filled with examples of similar Shiny apps. What mattered was the app was in production, and furthermore it was approved for GMP use. This meant these reports could be submitted to the FDA and other regulatory agencies. This meant the data could be used to support product release decisions. This meant Merck’s engineers were about to save thousands of hours per year in compiling data, generating charts, and calculating summary statistics. This was the app manufacturing sites needed. Most of the work in getting this app into production was not implementing the top-level features. Sorry, no discussion of fancy statistical process control methods here. Instead this talk will discuss some of the many things the development team (none of which came from a software development background) needed to learn in order to create a robust, secure, and maintainable production application. About Marcus: Marcus Adams is an Associate Director, Engineering at the biopharmaceutical company Merck. He earned his BEng and MS in Chemical Engineering from the University of Delaware and Villanova University, respectively. His more than decade of experience at Merck spans the bio-pharmaceutical spectrum and includes experience in pre-clinical PK/PD modeling, product commercialization, in-line technology support, procurement, and vaccine distribution technology development. Currently, he works as a part of the Digital Proactive Process Analytics team, leveraging Merck’s Big Data Platform in the development of manufacturing information data models, report automation tools, and integrated-systems analysis applications. His professional interests include effective digital visualization, reproducible research/analysis, and convincing his coworkers of the diverse, flourishing world beyond Microsoft Excel

Kate Hertweck | R training and documentation for different levels of expertise | RStudio

Full title: Making the jump from learning to applying: R training and documentation for different levels of expertise How does someone make the leap from learning R to actively applying R in professional work? At what point (if ever!) do we get to call ourselves "experts in R? This talk explores what differentiates novice, practitioner, and expert R programmers, and how transitions between these stages occur. I'll discuss the type of support required for R users to move from one level of expertise to the next, and how different types of training and documentation can support R users at each level. Understanding variable levels of education among R practitioners supports our own professional work, from collaborative coding to package development, and helps build a bigger, more inclusive R community. About Kate: Kate Hertweck is the bioinformatics training manager at Fred Hutchinson Cancer Research Center, where they develop and teach courses on reproducible computational methods as a part of fredhutch.io. Kate's graduate training at University of Missouri in genomic evolution of plants was followed by a postdoctoral fellowship at the National Evolutionary Synthesis Center (NESCent) at Duke University, where they fell in love with R and began working exclusively in computational biology. Kate then spent four years as an assistant professor teaching bioinformatics, genomics, and plant taxonomy before transitioning to biomedical research training. Kate has been involved in The Carpentries, a non-profit organization that teaches reproducible computational methods, since 2014, serving as a leader in community governance since 2016. When not being an overenthusiastic instructor, Kate likes to spend their time doing fiber arts (knitting, crochet) and enjoying all things science fiction

JooYoung Seo | Accessible Data Science Beyond Visual Models | RStudio

Full title: Accessible Data Science Beyond Visual Models: Non-Visual Interactions with R and RStudio Packages Data science is full of vision-dominant practices, and most data scientists rely heavily on visual models. However, data science itself should require insight and computational thinking beyond what is just seen by eyes. JooYoung Seo, who is a blind data scientist and who was working for RStudio's accessibility projects over the summer 2020, will talk about his experience with some non-visual techniques to interact with data. If you would like to know more about various ways of making data science accessible via R, and new accessibility features introduced in RStudio IDE and Shiny, his demonstration without sight will be thought-provoking. About JooYoung: JooYoung Seo is a Ph.D candidate in the Learning, Design, and Technology program at the Pennsylvania State University, and internationally certified accessibility professional whose research and development focuses on accessible computing for all. As an RStudio's double-certified data science instructor (i.e., Tidyverse + Shiny), who is blind, he is committed to making data science ecosystem more accessible to people with and without dis/abilities using R. To this end, he has been actively contributing to R open-source projects including Shiny, RMarkdown, bookdown, and distill for accessibility, and interned on the RStudio IDE and Shiny team as an accessibility engineer in summer 2020

Shiny Team

Eric Cronstrom | How we made the switch: a case study on automating a complex report | RStudio

The Center for Charter Schools at Central Michigan University produces annual reports for about 60 schools. The reporting process used to be a cumbersome blend of many technologies. The Center used to use a blend of SQL, Excel, inDesign, and VBScript that would all culminate in a nice looking, branded report for each school. 2 years ago, staff turnover allowed the data team to rethink the process, having had experience in R from graduate work the team at the Center decided to go all in on R Studio and R Markdown for report production a mere 1 month before the reports were due to be published. This talk will be a case study of how we as an organization adopted RStudio tools to streamline a cumbersome process to fantastic results. About Eric: Eric is responsible for administering a wide range of day to day program functions associated with the performance and accountability of CMU partner schools. He ensures that the data the Center utilizes to evaluate school performance is accurate and stored within a sound data infrastructure. He also leverages his wide range of technical skills to lead the development and production of reports and respond to questions regarding school performance and demographic context. Prior to joining the Center, Eric was a database administrator for Central Michigan University Libraries. He has also served as a lecturer at Central Michigan University teaching courses in web design, database design and programming. He earned a master’s degree in information systems management and a bachelor’s degree in computer science from Central Michigan University. He is also pursuing an additional master’s degree in applied statistics at Central Michigan University

Ahmadou Dicko | Humanitarian Data Science with R | RStudio

Humanitarian actors are increasingly using data to drive their decisions. Since the Haiti 2010 earthquake, the volume of data collected and used by humanitarians has been growing exponentially and organizations are now relying on data specialists to turn all this data into life-saving data products. These data products are created by teams using proprietary point and click software. The process from the raw data to the final data product involves a lot of clicking, copying and pasting and is usually not reproducible. Another approach to humanitarian data science is possible using R. In this talk, I will show how to seamlessly develop reproducible, reusable humanitarian data products using the tidyverse, rmarkdown and some domain-focused R packages. About Ahmadou: Ahmadou Dicko is a statistics and data analysis officer at the United Nations High Commissioner for Refugees (UNHCR) where he uses statistics and data science to help safeguard the rights and well-being of refugees in West and Central Africa. He has an extensive experience in the use of statistics and data science in development and humanitarian projects. Ahmadou was the lead of the OCHA Center for Humanitarian Data team for West and Central Africa and has worked with several humanitarian and development organizations such as IFRC, FAO, IAEA, OCHA. Ahmadou is a RStudio trainer (https://education.rstudio.com/trainers/) and he is passionate about the R community. He is currently co-organizing the Dakar R User Group (https://www.meetup.com/DakaR-R-User-Group/) and co-leading the AfricaR initiative (https://africa-r.org/)

Mary Rudis | How R and Posit are revolutionizing Stats Education in Community Colleges | Posit

Talk from rstudio::conf(2019) There is no doubt that Posit has had an impact on how introductory statistics is taught in colleges today. When we consider the sheer dominance that giants like Texas Instruments, IBM, and Pearson Publishing have had in academic curriculum development, it’s no small wonder that tools like R and Python have been able to gain a foothold. Projects like DataCamp, ModernDive.com, “Introductory Statistics with Randomization and Simulation” courtesy of openintro.org, Wickham’s “R for Data Science” and Peng’s “R Programming for Data Science” are great resources for the student who has already some fundamental math or statistical background and has become comfortable around computing and applications-driven computational exercises. But many of us know that Data Science cannot simply be relegated to the privileged few that stumble into it by virtue of circumstance. My passion, and the purpose of my talk, is to provide educators with a digestible guidebook that would be appropriate for introduction to statistical concepts in high school, college, and under-resourced schools looking for ways to increase diversity in STEM. Organized in small, adaptable activities designed to be the amuse-esprit enticing both the timid and the skeptical to the proverbial banquet table that is Posit, this exploration into the world of statistics education should be of interest to a wide audience. My hope is to increase data literacy in real world context – with primary emphasis on descriptive statistics and distributions. About Mary Rudis: After graduating from Lehigh University in Bethlehem, PA with a degree in Mathematics, Mary began as a high school mathematics and computer science teacher, developing technology infrastructure for a small, private high school in Pennsylvania. Throughout her career, she brings innovative approaches and enjoys the role of trailblazer. Mary's most recent accomplishments as math department chair included developing mathematics curriculum and coordinating engineering, bioengineering and data science degrees at Great Bay Community College in Portsmouth, NH. Mary's primary interests are learning and instruction, developing data science curricula for two-year colleges and 4-year liberal arts colleges, and working with area high school students in STEM at the University of NH Tech Camp each summer

Heather & Jacqueline Nolis | Push straight to prod: API development with R and Tensorflow | RStudio

Talk from rstudio::conf(2019) When tasked with creating the first customer-facing machine learning model at T-Mobile, we were faced with a conundrum. We had been told time and time again to deploy machine learning models in production you had to use Python, but our very best data scientists were fluent in building neural networks in R with Keras and TensorFlow. Determined to avoid double work, we decided to use R in production for our machine learning models. After months of work, wrangling our containers to meet cloud security compliance, and conforming to DevOps standards, we succeeded in creating a containerized API solution using the keras and plumber R packages and Docker. Today R is actively powering tools that our customers directly interact with and we have open sourced our methods. In this talk, we'll walk through how to deploy R models as container-based APIs, the struggles and triumphs we've had using R in production, and how you can design your teams to optimize for this sort of innovation. About Heather Nolis: Heather Nolis is a founding member of the AI @ T-Mobile team, focusing the conversion of cutting-edge analyses to real-time, scalable data-driven products. She began her career in neuroscience but once realized how heavily that field relied on software built by other people, she pivoted - deciding to make software herself. You can find her @heatherklus on Twitter, where she speaks about diversity in technology, the ethical implications of data, and cats. About Jacqueline Nolis: Dr. Jacqueline Nolis is a co-founder of Nolis, LLC, a data science consulting firm. She has over a decade of experience using data to help companies including DSW, Union Bank, Microsoft, and Airbnb. She has a PhD from Arizona State University where her research focused on electric vehicle route optimization. For fun she likes to use machine learning for humor

Jake Thompson | Branding and Packaging Reports with R Markdown | RStudio (2020)

The creation of research reports and manuscripts is a critical aspect of the work conducted by organizations and individual researchers. Most often, this process involves copying and pasting output from many different analyses into a separate document. Especially in organizations that produce annual reports for repeated analyses, this process can also involve applying incremental updates to annual reports. It is important to ensure that all relevant tables, figures, and numbers within the text are updated appropriately. Done manually, these processes are often error prone and inefficient. R Markdown is ideally suited to support these tasks. With R Markdown, users are able to conduct analyses directly in the document or read in output from a separate analyses pipeline. Tables, figures, and in-line results can then be dynamically populated and automatically numbered to ensure that everything is correctly updated when new data is provided. Additionally, the appearance of documents rendered with R Markdown can be customized to meet specific branding and formatting requirements of organizations and journals. In this presentation, we will present one implementation of customized R Markdown reports used for Accessible Teaching, Learning, and Assessment Systems (ATLAS) at the University of Kansas. A publicly available R package, ratlas, provides both Microsoft Word and LaTeX templates for different types of projects at ATLAS with their own unique formatting requirements. We will discuss how to create brand-specific templates, as well as how to incorporate the templates into an R package that can be used to unify report creation across an organization. We will also describe other components of branding reports beyond R Markdown templates, including customized ggplot2 themes, which can also be wrapped into the R package. Finally, we will share lessons learned from incorporating the R package workflow into an existing reporting pipeline. https://rstudio.com/resources/rstudioconf-2020/branding-and-packaging-reports-with-r-markdown/

Alicia Schep | Auto-magic Package Development | RStudio (2020)

Vega-lite is a high-level grammar of interactive graphics implemented in Javascript; it renders interactive visualizations in the browser based on a JSON specification. In Python and Javascript, the Altair and vega-lite-api packages have demonstrated how the development of APIs to build Vega-Lite graphics can be partially automated based on the Vega-Lite JSON schema, which describes the required format for a Vega-Lite JSON specification. This talk will describe the development of the ‘vlbuildr’ package for building Vega-Lite specifications in R and the ‘vlmetabuildr’ package for building the ‘vlbuildr’ package. The ‘vlbuildr’ package seeks to provide a pipe-friendly, “R-like” functional interface for building up simple to complex specifications for Vega-Lite graphics, which can in turn be rendered as an HtmlWidget by the ‘vegawidget’ R package. Building such an API in a fully automated way from the Vega-Lite schema presents considerable challenges, so the approach taken here was to rely on partial automation. Human judgement dictates the basic contours of the API, such as what groups of functions to include and how various types of building blocks will go together. The part that is automated is filling in many details such as the different variants of a group of functions, the exact parameters needed for each function, and the documentation of those parameters -- the parts that would be extremely tedious to port over!

Ellis Hughes | Approaches to Assay Processing Package Validation | RStudio (2020)

In this talk I will discuss the steps that have been created for validating internally generated R packages at SCHARP (Statistical Center for HIV/AIDS Research and Prevention) and the lessons learned while creating packages as a team. Housed within Fred Hutch, SCHARP is an instrumental partner in the research and clinical trials surrounding HIV prevention and vaccine development. Part of SCHARP’s work involves analyzing experimental biomarkers and endpoints which change as the experimental question, analysis methods, antigens measured, and assays evolve. Maintaining a validated code base that is rigid in its output format, but flexible enough to cater a variety of inputs with minimal custom coding has proven to be important for reproducibility and scalability. SCHARP has developed several key steps in the creation, validation, and documentation of R packages that take advantage of R’s packaging functionality. First, the programming team works with leadership to define specifications and lay out a roadmap of the package at the functional level. Next, statistical programmers work together to develop the package, taking advantage of the rich R ecosystem of packages for development such as roxygen2, devtools, usethis, and testthat. Once the code has been developed, the package is validated to ensure it passes all specifications using a combination of testthat and rmarkdown. Finally, the package is made available for use across the team on live data. These procedures set up a framework for validating assay processing packages that furthers the ability of Fred Hutch to provide world-class support for our clinical trials

Dr. Travis Gerke | UnicoRns are real | RStudio (2020)

Common advice from experienced data scientists to job-seekers is to avoid job postings that describe a "data science unicorn": someone who has experience performing an unrealistically large array of technical and business-related job duties. Seeking a unicorn is viewed as a potential indicator that the company fails to understand their data science needs, and that new hires will not be poised for success due to lacking support and resources [Robinson & Nolis, 2019]. The R language, particularly when used with RStudio products, has evolved to enable production-level activities in the areas of data wrangling, reporting/dashboarding, database/software engineering, machine learning, and web application development. It is increasingly plausible that a data scientist will be able to efficiently perform a wide variety of job functions with experience only in a single language (R). Indeed, even entry level R users may tread into "unicorn" territory. Current standards for data scientist job descriptions and salaries do not accommodate this nuance, leaving both job-seekers and hiring managers unable to distinguish job requirements which should be read as warning signs from listings which are idyllic matches for the modern R unicorn. In this talk, we present data aggregated from several large compensation analytics companies which summarize current benchmarks for data science job descriptions and corresponding salary ranges. We then suggest job description language to target modern R users, considering both job duty compatibility and job post findability. These descriptions are presented with likely salary range pairings. Attention is given to deviations from traditional degree requirements, years of experience, and demands for multiple programming language literacy which may lack relevance for the R unicorn. Our overarching goal is to provide job description templates which encourage optimal matchmaking between R job seekers and organizations in need of their talents

Braulio Cuandon & Ana Alyeska Santos | Using R to Create Reproducible Engineering Test Reports

Talk from rstudio::conf(2020) Engineers at Biosense Webster, a Johnson and Johnson medical device company that specializes in diagnosing and treating cardiac arrhythmias, write multiple test reports to comply with FDA regulatory standards. These intricate reports require 36 hours of an engineer’s time on average, constraining the engineers from completing investigations and studies in a timely matter. Writing scripts in R that create reproducible reports can significantly reduce the time spent by an engineer creating these reports allowing them to do a much thorough investigation with a larger scope. Through Shiny, engineers could conveniently have their parameters and recorded data processed and stored in a database by accessing a web link and filling out the required information within a user-friendly interface. Upon the generation of the report, accurate and properly formatted test reports, compliant to both the company and FDA regulatory standards, are produced through Rmarkdown and knitr knitting all the outputs with complete data analysis tools such as normality plots and process capability measurements to a word document that follows company required headers, footers, and headings. The reproducible report creation shown in this report can be extended to other types of test reports and protocols. The pilot phase that has been conducted has shown that complete report production has been decreased from 36 hours to an hour

Aymen Waqar | Building an iPad dashboard using plumber & RStudio Connect in Pharma | RStudio (2020)

As companies are becoming aware of the need to embrace data-driven solutions, R has gained a huge momentum over recent years. Getting the insights to users has become a very important factor of Data Scientist work. While our world has advanced there is a need to build not only web applications, but also applications on mobile that are available offline. We would like to share with you how within months we have gone from nothing to a production-ready application that handles 500 concurrent users in healthcare. There are plenty of challenges to solve including restricted environments, internal processes and users availability. We will show you how to overcome them and iterate fast, navigating through complex infrastructure and integrating with proxy architecture to serve applications to end users in compliant manner. With RStudio Connect and Plumber you can deploy a scalable REST API that can feed insights to your users. This allows you to go one step further and implement native applications for tablets and smartphones. With the right tools, mindset and priorities you can achieve personal success by introducing a digital transformation within your organization, starting with something as small as converting a business critical Excel file that is slow, difficult to edit and maintain, to a robust application. Step by step your organization will evolve and become empowered by your insights uncovering even more untapped potential

Therese Anders | Peer review in data science courses | RStudio (2020)

Peer review enables instructors of large data science classes to provide substantive feedback to students beyond what is feasible with standard code review via automated grading and continuous integration. It facilitates peer learning, which is shown in literature to have positive learning outcomes, and can reduce the burden of grading by course staff. The ghclass package provides a suite of functions to manage courses via GitHub repositories. The package has recently been supplemented with the functionality to implement peer review. Developed during my 2019 summer internship with RStudio in collaboration with my mentor Mine Çetinkaya-Rundel, the peer review functions in ghclass interface with the GitHub API to create review repositories, move files between authors and reviewers, submit feedback, and collect grades. In this presentation, I will give a demonstration of the peer review functions in ghclass. A set of six functions allows instructors to 1) create a random review roster, 2) set up the review repository infrastructure within a GitHub organization, 3) move assignments from authors to reviewers, 4) collect grades, 5) return the feedback, and 6) obtain a rating of the review from the authors. I reflect on the pedagogy of implementing peer review in introductory data science classes and talk about lessons learned from a real-world test run of the package in the Fall semester 2019 at the University of Edinburgh, conducted by Mine Çetinkaya-Rundel. The presentation highlights ghclass as an R command-line based, open source, low profile, and powerful solution to enable peer review in classes ranging from a size of two to approximately 400 students. A 5 minute presentation in our Lightning Talks series

Mine Çetinkaya-Rundel

Colin Fay | Production-grade Shiny Apps with golem | RStudio (2020)

Shiny is an amazing tool when it comes to creating web applications with R. Almost anybody can get a small Shiny App in a matter of minutes, provided they have a basic knowledge of R. As of today, we can safely tell that it has become the de-facto tool for web application in the R world. Building a proof-of-concept application is easy, but things change when the application becomes larger and more complex, and especially when it comes to sending that app to production—until recently there hasn't been any real framework for building and deploying production-grade Shiny Apps. This is where 'golem' comes into play: offering Shiny developers an opinionated framework for creating production-ready Shiny Applications. With 'golem', Shiny developers now have a toolkit for making a stable, easy-to-maintain, and robust for production web application with R. 'golem' has been developed to abstract away the most common engineering tasks (for example, module creation, addition of external CSS or JavaScript file, ...), so you can focus on what matters: building the application. And once your application is ready to be deployed, 'golem' guides you through testing, and brings you tools for deploying to common platforms. In this talk, Colin and Vincent will present the 'golem' package, first talking about the "why 'golem'?", then presenting the general philosophy behind this framework, and help you get started building your first Shiny App with 'golem'

Katherine Simeon | Every voice matters: An analysis of @WeAreRLadies | RStudio (2020)

As a rotating curation, @WeAreRLadies is a twitter account that has a different curator (i.e., tweeter) each week with a mission to highlight female and minority genders and their work in R. So far, curators have tweeted from 18 different countries and represent a variety of domains and levels of R expertise, ranging from R novices to those developing their own packages. With 45 R-Ladies curators to date, the account has become a popular R-related twitter resource, gaining more than 13,000 followers in the past year and hundreds of interactions each week. This talk will present a text analysis and reflection on over a year of Twitter text data from @WeAreRLadies. As the founder and maintainer of this account, I witness firsthand the bidirectional relationship between one’s learning journey and their use of R. In this talk, I will attempt to quantify this through a text analysis that explores how one’s experiences learning and using R relates to how they talk (or tweet) about it. By analyzing tweet text as well as other metrics provided by twitter (e.g., number of likes, replies, and clicks), I will showcase different ways curators have engaged with the R Twitter community and explore how account engagement has changed as the number of curators and followers continue to grow. I will also discuss how curators’ different areas of expertise have resulted in tweets and discussions that both demonstrate the variety of tools available in R, and spotlight unifying ideas and best practices in R programming. Finally, I will reflect on lessons learned and future directions for @WeAreRLadies, as well as its contribution to the R-Ladies Global initiative. Overall, this talk will discuss how diverse perspectives of @WeAreRLadies curators have enriched the conversations in the R Twitter community by validating different learning journeys and by promoting and amplifying underrepresented voices. A 5-minute presentation in our Lightning Talks series

Davis Vaughn | Sliding Windows and Calendars | RStudio (2020)

A number of R packages exist to make computing moving averages on a single numeric series straightforward. But generally “real” life is much messier than that! Try computing a moving average over a twenty-day sliding window when you have a time series with missing data. Oh! By the way, you should also skip over weekends when looking back twenty days. And you know that random holiday that your company celebrates that no one else does? Skip over that too. These are hard but realistic problems, and until now there has been a lack of tools necessary to solve them. In this talk, I’ll present two packages designed to tackle these issues, slide and almanac. slide is a package designed to perform arbitrary sliding window calculations. The simplest example of this would be a moving average. What makes slide unique is its support for sliding relative to an index, such as a date vector, which allows you to correctly compute the boundaries of that twenty day window. almanac is package for creating custom business calendars, and then adjusting dates relative to them. Inspired by lubridate, almanac allows you to shift dates by a set number of “business” days while respecting the weekends and holidays defined by a user-specified calendar. For example, shifting a Friday forward by 1 business day would land on a Monday, unless that Monday happened to be a holiday, in which case the next business day would actually be Tuesday. Together, slide and almanac provide the tooling necessary to solve the problem mentioned earlier. Additionally, because slide works with any arbitrary function, we can use the same procedure to compute rolling regressions, cumulative sums, and any other sliding computation. A 5-minute presentation in our Lightning Talks series

Caroline Ledbetter | Rproject templates to automate and standardize your workflow | RStudio (2020)

Many teams and organizations have tasks and structures that are standard across projects. Lack of consistency and documentation can lead to lost productivity when team members join collaborations or previous work is consulted by your future self. Setting up folder structures can be particularly tedious. This talk will demonstrate using Rstudio project templates as part of an organizational package to automatically setup file structures, establish git repositories and add standardized readme files. It will also show how including report templates for Rmarkdown files can lead to more consistent and professional reports. Project info can be optionally stored so that project information can be easily added automatically to the top of reports and included in snippets for code file headers. Creating standard, easy to implement documentation and procedures can be particularly effective in encouraging skeptical collaborators to use git and Rmarkdown. Organizational packages can also be a great place to house functions that are specific and common to an organizations needs. The talk will showcase this functionality using the CIDAtools package that we developed. While the CIDAtools package was developed to address issues that sometimes arise from the less structured environment of academia, the tools presented can be equally useful in an industry setting. A 5-minute presentation in our Lightning Talks series

Amelia McNamara | Working with categorical data in R without losing your mind | RStudio (2019)

Categorical data, called “factor” data in R, presents unique challenges in data wrangling. R users often look down at tools like Excel for automatically coercing variables to incorrect datatypes, but factor data in R can produce very similar issues. The stringsAsFactors=HELLNO movement and standard tidyverse defaults have moved us away from the use of factors, but they are sometimes still necessary for analysis. This talk will outline common problems arising from categorical variable transformations in R, and show strategies to avoid them, using both base R and the tidyverse (particularly, dplyr and forcats functions). VIEW MATERIALS http://www.amelia.mn/WranglingCats.pdf (related paper from the DSS collection) http://bitly.com/WranglingCats https://peerj.com/collections/50-practicaldatascistats/ About the Author Amelia McNamara My work is focused on creating better tools for novices to use for data analysis. I have a theory about what the future of statistical programming should look like, and am working on next steps toward those tools. For more on that, see my dissertation. My research interests include statistics education, statistical computing, data visualization, and spatial statistics. At the moment, I am very interested in the effects of parameter choices on data analysis, particularly data visualizations. My collaborator Aran Lunzer and I have produced an interactive essay on histograms, and an initial foray into the effects of spatial aggregation. I talked more about spatial aggregation in my 2017 OpenVisConf talk, How Spatial Polygons Shape Our World

Emily Robinson | Building an AB testing analytics system with R and Shiny | RStudio (2019)

Online experimentation, or A/B Testing, is the gold standard for measuring the effectiveness of changes to a website. While A/B testing is used at tens of thousands of companies, it can seem difficult to parse without resorting to expensive end-to-end commercial options. Using DataCamp’s system as an example, I’ll illustrate how R is actually a great language for building powerful analytical and visualization A/B testing tools. We’ll first dive into our open-source funneljoin package, which allows you to quickly analyze sequential actions using different types of behavioral funnels. We'll then cover the importance of setting up health checks for every experiment. Finally, we'll see how Shiny dashboards can help people monitor and quickly analyze multiple A/B tests each week. VIEW MATERIALS http://bit.ly/rstudio19 About the Author Emily Robinson I work at DataCamp as a Data Scientist on the growth team. Previously, I was a Data Analyst at Etsy working with their search team to design, implement, and analyze experiments on the ranking algorithm, UI changes, and new features. In summer 2016, I completed Metis’s three-month, full-time Data Science Bootcamp, where I did several data science projects, ranging from using random forests to predict successful projects on DonorsChoose.org to building an application in R Shiny that helps data science freelancers find their best-fit jobs. Before Metis, I graduated from INSEAD with a Master’s degree in Management (specialization in Organizational Behavior). I also earned my bachelor’s degree from Rice University in Decision Sciences, an interdisciplinary major I designed that focused on understanding how people behave and make decisions

Hilary Parker | Cultivating creativity in data work | RStudio (2019)

Traditionally, statistical training has focused primarily on mathematical derivations, proofs of statistical tests, and the general correctness of what methods to use for certain applications. However, this is only one dimension of the practice of doing analysis. Other dimensions include the technical mastery of a language and tooling system, and most importantly the construction of a convincing narrative tailored to a specific audience, with the ultimate goal of them accepting the analysis. These "softer" aspects of analysis are difficult to teach, perhaps more so when the field is framed as mathematics and often housed in mathematics departments. In this talk, I discuss an alternative framework for viewing the field, borrowing upon the past work in other fields such as design. Looking forward, we as a field can borrow from these fields to cultivate and hone the creative lens so necessary to the success of applied work. VIEW MATERIALS https://www.slideshare.net/mobile/hilaryparker/rstudioconf2019l About the Author Hilary Parker I’m a Senior Data Analyst at Etsy, where I help product teams with data-driven development, via experimentation, opportunity sizing and impact analysis. I got my Ph.D. from the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, working with Jeff Leek. I studied genomics and built tools to help researchers use genomic technologies in personalized medicine applications. I graduated from Pomona College in 2008, where I double-majored in Mathematics and Molecular Biology. True to my liberal arts upbringing, I’m a passionate teacher. Most notably, I taught an introductory Biostatistics class at the American University of Armenia (and kept a pretty cool travel blog along the way)

Eric Nantz | Effective use of Shiny modules in application development | RStudio (2019)

As a Shiny application grows in scale, organizing code into reusable and streamlined components becomes vital to manage future enhancements and avoid unnecessary duplication. Shiny modules are customized R functions that are easily reused multiple times within an application by avoiding namespace collisions and assist with organizing the code base. Like R functions, modules can be simple utilities or elaborate pieces with multiple inputs and outputs. While the process of creating a module is uncomplicated, application developers can quickly encounter challenges including communication among modules, defining logical compositions, and avoiding hidden state modifications. In this talk, we will introduce practical principles and techniques developers can leverage to address these issues head-on such as documenting modules, passing parameters and return values effectively between modules, and how nesting modules enables dynamic user interfaces with minimal overhead. VIEW MATERIALS https://rpodcast.github.io/rsconf-2019 About the Author Eric Nantz I have a broad background in statistics, computer science, and system administration which gives me a unique set of skills for using state-of-the-art technology and techniques to accomplish important and innovative data analyses. In my professional role as a statistician, I support the design and analyses of clinical trials evaluating treatments for auto-immune disorders. I also perform statistical analyses of specialized biomarkers utilizing cutting-edge statistical software such as R and high-performance computing infrastructures. I am also the creator, producer, and host of the R-Podcast. The R-Podcast is dedicated to helping those who are new to statistical computing develop their skills and confidence in using the free and open-source statistical computing package called R to get their data analyses done

Hao Zhu | Empowering a data team with RStudio addins | RStudio (2019)

RStudio addins provide a mechanism to extend RStudio in various ways. Addins can interact with the RStudio IDE through RStudio API. It can also provide users a graphical interface with the power of Shiny. In practice, we found it very useful for enhancing or streamlining interaction with data and computing infrastructure. In this talk, we will demonstrate how our team develops and uses RStudio addins to empower our work. You will see some internal tools created to help us manage database connections, and an addin which helps us access external cloud computing resources. We will also show an example of using the addins in rcrossref and citr to download and manage citation and literature databases during rmarkdown document development. VIEW MATERIALS https://github.com/hebrewseniorlife/addin_demo About the Author Hao Zhu Hao is a data analyst and software developer working at the Hinda and Arthur Marcus Institute for Aging Research. He completed his training at Boston University School of Medicine in the program on Clinical Investigation. His interests include research reproducibility, data visualization and machine learning. At the Marcus Institute, he works with different teams on various topics, ranging from smartphone motion sensors to MRI images, and helps researchers understand their data by creating analytical reports and web applications. At the same time, Hao leads the development of R packages in the Biostatistics Core. He has contributed multiple R packages to the open source R community, such as kableExtra and memor. He also has a passion for teaching and has mentored several students at the Marcus Institute

Jesse Sadler | Learning and using the tidyverse for historical research | RStudio (2019)

My talk will discuss how R, the tidyverse, and the community around R helped me to learn to code and create my first R package. My positive experiences with the resources for learning R and the community itself led me to create a blog detailing my experiences with R as a way to pass along the knowledge that I gained. The next step was to develop my first package. The debkeepr package integrates non-decimal monetary systems of pounds, shillings, and pence into R, making it possible to accurately analyze and visualize historical account books. It is my hope that debkeepr can help bring to light crucial and interesting social interactions that are buried in economic manuscripts, making these stories accessible to a wider audience. VIEW MATERIALS https://github.com/jessesadler/rstudioconf-2019-slides About the Author Jesse Sadler I am an early modern historian interested in the social and familial basis of politics, religion, and trade. I received a Ph.D. in European History from UCLA in 2015 and have taught courses on cultural and intellectual history of early modern Europe and the Atlantic. My research investigates the familial basis of the early modern capitalism through archival research on two mercantile families from Antwerp at the end of the sixteenth and beginning of the seventeenth century. I am currently working on a manuscript that argues for the significance of sibling relationships and inheritance in the development of early modern trade. My manuscript places concepts such as patriarchy, emotion, exile, and friendship at the heart of the efficacy of long-distance trade networks and the growth of capitalism

Joe Rickert | R Consortium initiatives in medicine | Posit (2019)

R / Pharma and R / Medicine, two new R Consortium sponsored conferences that appeared in 2018, are both grass roots, community-level manifestations of fundamental trends driving the growth of R. R / Pharma organized as an R Consortium working group by R users at several prominent pharmaceutical companies demonstrated that R has reached sufficient critical mass in Pharma to be recognized as a technology important enough to explore inter-company cooperation, while the addition of Genetech/Roche to R Consortium shows the commitment of upper level management to migrate from SAS to R. - At the other, non-corporate end of the spectrum, the R / Medicine conference was well attended by practicing clinicians coming to R, and in some cases statistics and data analysis, for the first time in order to move forward with the practice of data driven medicine. - In this talk, I will present some highlights from these conferences, and other related R Consortium initiatives, describe some of the R-based projects that illustrate the kind of work being done, and indicate what to expect this year. About the Author Joseph Rickert Joseph is Posit's “Ambassador at Large” for all things R, editor of the R Views blog, and Posit's representative on the R Consortium’s board of directors. Joseph came to Posit via Revolution Analytics and Microsoft where he was a data scientist, blogger and community manager. Before joining Revolution Analytics Joseph worked as a statistician for a small healthcare economics consulting firm, and before that he held a variety of technical, marketing and sales positions while working for startups that spanned multiple industries including government contracting, local area networks, disk drives and test equipment. Joseph studied Classics and Mathematics as an undergraduate at Franklin & Marshall College and earned an M.A. in Humanities and an M.S. in Statistics from the California State University

Karl Broman | R qtl2 Rewrite of a very old R package | RStudio (2019)

For nearly 20 years, I've been developing, maintaining, and supporting an R package, R/qtl, for mapping quantitative trait loci (genetic loci that contribute to variation in quantitative traits, such as blood pressure) in experimental crosses (such as in mice). It's a rather large package, with 39k lines of R code, 24k lines of C code, and nearly 300 user-accessible functions. In the past several years, I've been working on rewriting the package, to better handle high-dimensional data and more complex experimental crosses. This has been a good opportunity to take advantage of many new tools, including Rcpp, Roxygen2, and testthat. I'll describe my efforts to avoid repeating the mistakes I made the first time around. VIEW MATERIALS https://bit.ly/rstudio2019 About the Author Karl Broman Karl Broman is Professor in the Department of Biostatistics & Medical Informatics at the University of Wisconsin–Madison; research in statistical genetics; developer of R/qtl (for R). Karl received a BS in mathematics in 1991, from the University of Wisconsin–Milwaukee, and a PhD in statistics in 1997, from the University of California, Berkeley; his PhD advisor was Terry Speed. He was a postdoctoral fellow with James Weber at the Marshfield Clinic Research Foundation, 1997–1999. He was a faculty member in the Department of Biostatistics at Johns Hopkins University, 1999–2007. In 2007, he moved to the University of Wisconsin–Madison, where he is now Professor. Karl is a Senior Editor for Genetics, Academic Editor for PeerJ, and a member of the BMC Biology Editorial Board. Karl is an applied statistician focusing on problems in genetics and genomics – particularly the analysis of meiotic recombination and the genetic dissection of complex traits in experimental organisms. The latter is often called “QTL mapping.” A QTL is a quantitative trait locus – a genetic locus that influences a quantitative trait. Recently he has been focusing on the development of interactive data visualizations for high-dimensional genetic data; see his R/qtlcharts package and his D3 examples

Edzer Pebesma | Spatial data science in the Tidyverse | RStudio (2019)

Package sf (simple feature) and ggplot2::geom_sf have caused a fast uptake of tidy spatial data analysis by data scientists. Important spatial data science challenges are not handled by them, including raster and vector data cubes (e.g. socio-economic time series, satellite imagery, weather forecast or climate predictions data), and out-of-memory datasets. Powerful methods to analyse such datasets have been developed in packages stars (spatiotemporal tidy arrays) and tidync (tidy analysis of NetCDF files). This talk discusses how the simple feature and tidy data frameworks are extended to handle these challenging data types, and shows how R can be used for out-of-memory spatial and spatiotemporal datasets using tidy concepts. VIEW MATERIALS https://edzer.github.io/rstudio_conf/2019/index.html About the Author Edzer Pebesma I lead the spatio-temporal modelling laboratory at the institute for geoinformatics. I hold a PhD in geosciences, and am interested in spatial statistics, environmental modelling, geoinformatics and GI Science, semantic technology for spatial analysis, optimizing environmental monitoring, but also in e-Science and reproducible research. I am an ordinary member of the R foundation. I am one of the authors of Applied Spatial Data Analysis with R (second edition), am Co-Editor-in-Chief for the Journal of Statistical Software, and associate editor for Spatial Statistics. I believe that research is useful in particular when it helps solving real-world problems

Nic Crane | The future's Shiny: Pioneering genomic medicine in R | Posit (2019)

Shiny’s expanding capabilities are rapidly transforming how it is used in an enterprise. This talk details the creation of a large-scale application, supporting hundreds of concurrent users, making use of the future and promises packages. The 100,000 genomes project is an ambitious exercise that follows on from the Human Genome Project - aiming to put the UK at the forefront of genomic medicine, with the NHS as the first health service in the world to offer precision medicine to patients with rare diseases and cancer. Data is at the heart of this project; not only the outputs of the genomic sequencing, but vast amounts of metadata used to track progress against the 100,000 genome target and the status and path of each case through the sample tracking pipeline. In order to make this data readily available to stakeholders, Shiny was used to create an application containing multiple interactive dashboards. A scaled-up version of the app is being rolled out in early 2019 to a much larger audience to support the National Genomics Informatics Service, with the challenge of creating a complex app capable of supporting so many users without grinding to a halt. In this talk, I will explain why Shiny was the obvious technology choice for this task, and discuss the design decisions which enabled this project's success. VIEW MATERIALS https://github.com/thisisnic/rstudio-conf-2019 About the Author Nic Crane Nic Crane is a Data Scientist at Elucidata, and has formerly worked for Mango Solutions and IBM. She is passionate about learning and teaching all things data science

David Robinson | The unreasonable effectiveness of public work | RStudio (2019)

In this talk, I'll lay out the reasons that blogging, open source contribution, and other forms of public work are a critical part of a data science career. For beginners, a blog is a great accompaniment to data science coursework and tutorials, since it gives you experience applying practical data science skills to real problems. For data scientists at any stage of their careers, open source development offers practice in collaboration, documentation, and interface design that complement other kinds of software development. And for data scientists more advanced in their careers, writing a book is a great way to crystallize your expertise and ensure others can build on it. All of these practices build skills in communication and collaboration that form an essential component of data science work. Each also lets you build a public portfolio of your skills, get feedback from your peers, and network with the larger data science community. VIEW MATERIALS https://bit.ly/drob-rstudio-2019 About the Author David Robinson David is the Chief Data Scientist at DataCamp, an education company for teaching data science through interactive online courses. His interests include statistics, data analysis, education, and programming in R. David is co-author with Julia Silge of the tidytext package and the O’Reilly book Text Mining with R. He also the author of the broom, gganimate, and fuzzyjoin packages, and of the e-book Introduction to Empirical Bayes. David previously worked as a data scientist at Stack Overflow, and received a PhD in Quantitative and Computational Biology from Princeton University

Julia Silge

Claus Wilke | Visualizing uncertainty with hypothetical outcomes plots | RStudio (2019)

Uncertainty is a key component of statistical inference. However, uncertainty is not easy to convey effectively in data visualizations. For example, viewers have a tendency to interpret visualizations of the most likely outcome as the only possible one. Viewers may also misjudge the likelihood of different possible outcomes or the extent to which moderately rare outcomes may deviate from the expectation. One way in which we can help the viewer grasp the amount of uncertainty present in a dataset is by showing a variety of different possible modeling outcomes at once. For example, in a linear regression, we could plot a number of different regression lines with slopes and intercepts drawn from the range of likely values, as determined by the variation in the data. Such visualizations are called Hypothetical Outcomes Plots (HOPs). HOPs can be made in static form, showing the various hypothetical outcomes all at once, or preferably in an animated form, where the display cycles between the different hypothetical outcomes. With recent progress in ggplot2-based animation, via gganimate, as well as packages such as tidybayes that make it easy to generate hypothetical outcomes, we can easily produce animated HOPs in a few lines of R code. This presentation will cover the key concepts, packages, and techniques to generate such visualizations. VIEW MATERIALS: https://docs.google.com/presentation/d/1zMuBSADaxdFnosOPWJNA10DaxGEheW6gDxqEPYAuado/edit?usp=sharing

Advanced R Markdown 2-day Workshop - rstudio::conf(2019L)

What is the 2-day Advanced R Markdown Workshop? That's a great question, I'm glad you asked. This is a two-day hands-on workshop based on the book "R Markdown: The Definitive Guide". This workshop is designed for those who want to take their R Markdown skills to the next level. We'll talk about many low-level details in the rmarkdown package and the whole R Markdown ecosystem. The two goals of this workshop are: 1) learn how to fully customize R Markdown output (HTML, LaTeX/PDF, Word, and PowerPoint); and 2) learn more about existing R Markdown extensions in the ecosystem, such as flexdashboard, bookdown, blogdown, pkgdown, xaringan, rticles, and learnr. We will also talk about how to use or develop new language engines (languages that are not R), how to develop HTML widgets, and integrate Shiny with R Markdown. You should take this workshop if you have experience programming in R and want to learn how to take advantage of the amazing breadth and depth of R Markdown. You'll get the most from it if you enjoy learning how R Markdown works under the hood (which will involve reading some source code), and are seriously interested in hacking (playing) with HTML, JavaScript, CSS, LaTeX, and command-line tools. We will give minimal tutorials on these languages and tools in the workshop, but it may be easier for you to keep pace with the instructor if you already know them before. This workshop is led by Yihui Xie, Software Engineer and Data Scientist at RStudio. Yihui is the main author of the open-source knitr package for reproducible research and dynamic report generation. He has also created and co-authored several other R packages, including rmarkdown, bookdown, blogdown, xaringan, tinytex, DT, shiny, and leaflet. He has authored and co-authored four books: Dynamic Documents with R and knitr, bookdown: Authoring Books and Technical Documents with R Markdown, blogdown: Creating Websites with R Markdown, and R Markdown: The Definitive Guide. Speakers: Yihui Xie and Alison Hill with guest lecturers Jiena Gu and Hao Zhu

Reproducible Examples with the reprex package

Reproducible Examples and the `reprex` package. https://speakerdeck.com/jennybc/reprex-reproducible-examples-with-r Jump to: 0:08 Intro 0:40 Basic usage of reprex 3:35 Motivation, why use reprex? "Help me help you" 4:08 Define `reprex`? Three commons ways to use the term. 1. noun, a reproducible example 2. the reprex package. a tool to build R `reprex`s 3. reprex::reprex(), a function in `reprex` to make a reprex. 5:26 When should you use a reprex? 6:14 reprex installation and setup. How do you actually get repex on your machine? 7:59 Advanced setup and discussion. 9:45 Please use advanced features responsibly. 11:02 Why does the reprex package exist? Anyone who has helped teach R or dealt with github issues, twitter, stack overflow & RStudio community questions knows that helping people diagnose their coding problems can be hard. This tool comes from hard-won experience. It’s aim to is help people ask well formed questions and increase the chances of getting well formed answers quickly. 12:52 philosophy behind reprex 1. code that I can run 2. code that I don't need to run 3. code that I can easily run 13:52 code that I can run. 17:25 Tips on writing good `reprex`s. Dos and don'ts. 18:52 How do I get my data into my reprex? Getting small data and CSV type data into your reprex is easy. “I have a big hairy data object and I can only show their problem by using it”, but that's not always the case. 21:02 code that I don't need to run reprex gives your reader the code and reveals the output being produced by that code. For experienced coders, that might be enough to help you. 22:44 code that I can easily run Don't copy and paste from the R console. This is usually annoying for your reader. Worse than console copy-pasta is the screenshot. (Many people think screenshots of code are downright offensive.) 25:03 reprex_clean If you copy someone else's reprex into your consolve, it may include their output, making your new reprex a untidy. Here are tips for taking someone else's reprex code and output, and create a clean reprex reply. 25:54 shock and awe More interesting features of the reprex package. - 26:29 What about figures and plots in your reprex? So happy you asked about that. reprex will automatically upload your images to imgur.com. - 28:23 Create a reprex by explicitly providing your code in the reprex call. - 29:00 when you need your reprex to work in the current working directory. - 30:45 Differently flavored markdown. Optimize your reprex markdown output for github, stack overflow, or the RStudio community. - 30:31 Make your reprex create an R script, with your reprex outputs as comments. This is handy for pasting into an email or slack-type-app. - 32:25 Rich text format, rtf output. (currently experimental feature as of this video) - 33:06 supress the reprex add at the bottom of your reprex - 33:19 Include session info. - 33:54 Auto styling of your code. Good if you're dealing with poorly formatting code. - 34:25 Change your comments string. - 34:32 Silence Tidyverse startup messages. - 35:00 Capture a reprex that sends messages to standard output and standard input (e.g. package installation compilation messages). 36:13 Set up personal defaults for your reprex usage. 36:54 reprex RStudio addins; render reprex and reprex selection. These accelerate your use of reprex. 39:01 The human side of reproducible examples. How to ask questions in ways that are most likely to get answered. Sorry for the tough love, but this is important. Why are you always asked to give a reprex? - Experts try to use reproducible examples to ensure their advice works. - Making a good reprex is hard. But, you are asking them to solve a problem for you, so meet them halfway. - Creating reprexes is good coding practice. - Making a good reprex is often a good way to debug your issue in the embarrassment-free privacy of your own home. - reprexes lead to discussions more likely to help people in the future. 44:34 Behind the scenes of reprex 44:44 Thanks for those that helped make reprex possible. Questions and Answers - 46:05 can reprex capture variables and objects in the current environment? (not yet, maybe in development) - 47:25 does reprex actually check that the code is self contained? (self contained) - 48:08 does readr::read_csv support the text argument? (yep, just read the help manual for readr)

Tidyverse Train-the-Trainer Certification Workshop - rstudio::conf(2019L)

What is the 2-day Tidyverse Train-the-Trainer Workshop? That's a great question, I'm glad you asked. Register at https://rstd.io/conf Learn more at https://rstd.io/conf-agenda ------------------------ Tidyverse Train-the-Trainer Certification Workshop - 2 Days - Day 1 of the course will be co-taught by Mine Cetinkaya-Rundel and Garrett Grolemund, RStudio Data Scientists and Professional Educators. - On Day 2, Mine will teach the Shiny track and Garrett will teach the Tidyverse track. This two-day workshop will equip you to teach R effectively. We will draw on RStudio's experience teaching R to recommend tips for designing, teaching, and supporting short R courses. On Day 1 of the course, you will learn practical activities that you can use immediately to improve your presentation style, learning outcomes, and student engagement. You will leave the class with a cognitive model of learning that you can use to develop your own effective workshops or courses within your organization. The course will also cover how to use RStudio Cloud and its curriculum of tutorials to jump-start your own lessons. On Day 2 of the course, participants will have the option to choose one of two tracks: Teaching the Tidyverse or Teaching Shiny. - Teaching the Tidyverse: Classroom examples will focus on how to teach students to do data analysis with the Tidyverse. We will use Master the Tidyverse, which is an award-winning two-day workshop developed by RStudio, as an example. Participants will receive the course materials for teaching Master the Tidyverse. You should take this workshop if you work for a training partner and want to qualify as an RStudio Certified Tidyverse Instructor or if you are an advocate for R in your organization. You should be proficient in the Tidyverse already and be prepared to submit examples of your work. Prior teaching experience is helpful, but not required. Please bring a laptop and a device that has video recording capabilities (such as a laptop or cell phone). Instructors: Garrett Grolemund, Mine Çetinkaya-Rundel

Mine Çetinkaya-Rundel

Shiny Train-the-Trainer Workshop - rstudio::conf(2019L)

What is the 2-day Shiny Train-the-Trainer Workshop? That's a great question, I'm glad you asked. Register at https://rstd.io/conf Learn more at https://rstd.io/conf-agenda -------------------- Shiny Train-the-Trainer Certification Workshop - 2 Day - Day 1 of the course will be co-taught by Mine Cetinkaya-Rundel and Garrett Grolemund, RStudio Data Scientists and Professional Educators. - On Day 2, Mine will teach the Shiny track and Garrett will teach the Tidyverse track. This two-day workshop will equip you to teach R effectively. We will draw on RStudio's experience teaching R to recommend tips for designing, teaching, and supporting short R courses. On Day 1 of the course, you will learn practical activities that you can use immediately to improve your presentation style, learning outcomes, and student engagement. You will leave the class with a cognitive model of learning that you can use to develop your own effective workshops or courses within your organization. The course will also cover how to use RStudio Cloud and its curriculum of tutorials to jump-start your own lessons. On Day 2 of the course, participants will have the option to choose one of two tracks: Teaching the Tidyverse or Teaching Shiny. - Teaching Shiny: Classroom examples will focus on teaching Shiny at the beginner and intermediate levels. The course materials will build on RStudio's Mastering Shiny workshop as well as the upcoming book from the author of the Shiny package, Joe Cheng, and they will cover the entire lifecycle of a Shiny app: build ️ improving ️ share. Participants will receive the course materials for teaching Mastering Shiny. You should take this workshop if you work as a training partner and want to qualify as an RStudio Certified Shiny Instructor or if you are an advocate for R in your organization. You should be proficient in Shiny already and be prepared to submit examples of your work. Prior teaching experience is helpful, but not required. Please bring a laptop and a device that has video recording capabilities (such as a laptop or cell phone). Instructors: Garrett Grolemund, Mine Çetinkaya-Rundel

Joe Cheng, Mine Çetinkaya-Rundel

Working with Two Datasets: Binds, Set Operations, and Joins -- Pt 4 Intro to Data Manipulation

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your R code clean and clear and reduce the cognitive load required for common but often complex data science tasks. `dplyr` docs: dplyr.tidyverse.org/reference/ - http://dplyr.tidyverse.org/reference/setops.html - http://dplyr.tidyverse.org/reference/join.html ---------------- Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw - /01:44 Intro and what’s covered Ground Rules: - /02:40 What’s a tibble - /04:50 Use View - /05:25 The Pipe operator: - /07:20 What do I mean by data wrangling? Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM - /00:48 Goal 1 Making your data suitable for R - /01:40 `tidyr` “Tidy” Data introduced and motivated - /08:10 `tidyr::gather` - /12:30 `tidyr::spread` - /15:23 `tidyr::unite` - /15:23 `tidyr::separate` Pt. 3: Data manipulation tools: `dplyr` https://youtu.be/Zc_ufg4uW4U - /00.40 setup - /02:00 `dplyr::select` - /03:40 `dplyr::filter` - /05:05 `dplyr::mutate` - /07:05 `dplyr::summarise` - /08:30 `dplyr::arrange` - /09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation) - /11:45 `dplyr::group_by` Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together - 00.42 `dplyr::bind_cols` - 01:27 `dplyr::bind_rows` - 01:42 Set operations `dplyr::union`, `dplyr::intersect`, `dplyr::set_diff` - 02:15 joining data - `dplyr::left_join`, `dplyr::inner_join`, - `dplyr::right_join`, `dplyr::full_join`, ______________________________________________________________ Cheatsheets: https://www.rstudio.com/resources/cheatsheets/ Documentation: `tidyr` docs: tidyr.tidyverse.org/reference/ - `tidyr` vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html `dplyr` docs: http://dplyr.tidyverse.org/reference/ - `dplyr` one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html - `dplyr` two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html ______________________________________________________________

What is data wrangling? Intro, Motivation, Outline, Setup -- Pt. 1 Data Wrangling Introduction

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. These videos introduce you to these tools. Keep your R code clean and clear and reduce the cognitive load required for common but often complex data science tasks. Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw - 01:44 Intro and what’s covered Ground Rules - 02:40 What’s a tibble - 04:50 Use View - 05:25 The Pipe operator: - 07:20 What do I mean by data wrangling? Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM - /00:48 Goal 1 Making your data suitable for R - /01:40 `tidyr` “Tidy” Data introduced and motivated - /08:15 `tidyr::gather` - /12:38 `tidyr::spread` - /15:30 `tidyr::unite` - /15:30 `tidyr::separate` Pt. 3: Data manipulation tools: `dplyr` https://youtu.be/Zc_ufg4uW4U - 00.40 setup - /02:00 `dplyr::select` - /03:40 `dplyr::filter` - /05:05 `dplyr::mutate` - /07:05 `dplyr::summarise` - /08:30 `dplyr::arrange` - /09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation) - /11:45 `dplyr::group_by` - /15:00 `dplyr::group_by` Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together - /00.42 `dplyr::bind_cols` - /01:27 `dplyr::bind_rows` - /01:42 Set operations `dplyr::union`, `dplyr::intersect`, `dplyr::set_diff` - /02:15 joining data `dplyr::left_join`, `dplyr::inner_join`, `dplyr::right_join`, `dplyr::full_join`, ______________________________________________________________ Cheatsheets: https://www.rstudio.com/resources/cheatsheets/ Documentation: `tidyr` docs: tidyr.tidyverse.org/reference/ - `tidyr` vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html `dplyr` docs: http://dplyr.tidyverse.org/reference/ - `dplyr` one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html - `dplyr` two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html ______________________________________________________________ New York Times “For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights”, By STEVE LOHRAUG. 17, 2014 https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html ______________________________________________________________

Tidy Data and tidyr -- Pt 2 Intro to Data Wrangling with R and the Tidyverse

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your code clean and clear and reduce the cognitive load required for common but often complex data science tasks. http://tidyr.tidyverse.org/reference/ - http://tidyr.tidyverse.org/reference/gather - http://tidyr.tidyverse.org/reference/spread - http://tidyr.tidyverse.org/reference/unite - http://tidyr.tidyverse.org/reference/separate ---------------- Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw - /01:44 Intro and what’s covered Ground Rules - /02:40 What’s a tibble - /04:50 Use View - /05:25 The Pipe operator: - /07:20 What do I mean by data wrangling? Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM - 00:48 Goal 1 Making your data suitable for R - 01:40 `tidyr` “Tidy” Data introduced and motivated - 08:10 `tidyr::gather` - 12:30 `tidyr::spread` - 15:23 `tidyr::unite` - 15:23 `tidyr::separate` Pt. 3: Data manipulation tools: `dplyr` https://youtu.be/Zc_ufg4uW4U - 00.40 setup - /02:00 `dplyr::select` - /03:40 `dplyr::filter` - /05:05 `dplyr::mutate` - /07:05 `dplyr::summarise` - /08:30 `dplyr::arrange` - /09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation) - /11:45 `dplyr::group_by` - /15:00 `dplyr::group_by` Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together - /00.42 `dplyr::bind_cols` - /01:27 `dplyr::bind_rows` - /01:42 Set operations `dplyr::union`, `dplyr::intersect`, `dplyr::set_diff` - /02:15 joining data `dplyr::left_join`, `dplyr::inner_join`, `dplyr::right_join`, `dplyr::full_join`, ______________________________________________________________ Cheatsheets: https://www.rstudio.com/resources/cheatsheets/ Documentation: `tidyr` docs: tidyr.tidyverse.org/reference/ - `tidyr` vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html `dplyr` docs: http://dplyr.tidyverse.org/reference/ - `dplyr` one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html - `dplyr` two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html ______________________________________________________________

Data Manipulation Tools: dplyr -- Pt 3 Intro to the Grammar of Data Manipulation with R

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your code clean and clear and reduce the cognitive load required for common but often complex data science tasks. `dplyr` docs: dplyr.tidyverse.org/reference/ - http://dplyr.tidyverse.org/reference/union.html - http://dplyr.tidyverse.org/reference/intersect.html - http://dplyr.tidyverse.org/reference/set_diff.htm ---------------- Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw - /01:44 Intro and what’s covered Ground Rules - /02:40 What’s a tibble - /04:50 Use View - /05:25 The Pipe operator: - /07:20 What do I mean by data wrangling? Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM - /00:48 Goal 1 Making your data suitable for R - /01:40 `tidyr` “Tidy” Data introduced and motivated - /08:10 `tidyr::gather` - /12:30 `tidyr::spread` - /15:23 `tidyr::unite` - /15:23 `tidyr::separate` Pt. 3: Data manipulation tools: `dplyr` https://youtu.be/Zc_ufg4uW4U - 00.40 setup - 02:00 `dplyr::select` - 03:40 `dplyr::filter` - 05:05 `dplyr::mutate` - 07:05 `dplyr::summarise` - 08:30 `dplyr::arrange` - 09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation) - 11:45 `dplyr::group_by` Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together - /00.42 `dplyr::bind_cols` - /01:27 `dplyr::bind_rows` - /01:42 Set operations `dplyr::union`, `dplyr::intersect`, `dplyr::set_diff` - /02:15 joining data `dplyr::left_join`, `dplyr::inner_join`, `dplyr::right_join`, `dplyr::full_join`, ______________________________________________________________ Cheatsheets: https://www.rstudio.com/resources/cheatsheets/ Documentation: `tidyr` docs: tidyr.tidyverse.org/reference/ - `tidyr` vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html `dplyr` docs: http://dplyr.tidyverse.org/reference/ - `dplyr` one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html - `dplyr` two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html ______________________________________________________________