Shiny

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?"

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

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

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

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?"

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?"

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/

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?"

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/

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/

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

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

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

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!

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/

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

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!

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 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

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

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

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

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

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 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

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

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

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

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

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 :)

"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

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

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

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

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

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!)

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

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

[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

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!)

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!)

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 | 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!)

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

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!)

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 | 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!)

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

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

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

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

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 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

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

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

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 | 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

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 | 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 | 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

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

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

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"

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

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

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

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

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

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

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/

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

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

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

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)

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

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

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

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'

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

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

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

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

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

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

Events featuring Shiny#

R/Pharma 2025

Shiny in Production 2025

Resources featuring Shiny#

Charlie Gao: Advances in the Shiny Ecosystem

Shiny for Python

Shiny for R

Strategic Budget Optimization through Marketing Mix Modeling (MMM)

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

Turbocharge your Shiny Apps with remote submission to HPC

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

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

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

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

Daniel Chen - LLMs, Chatbots, and Dashboards_ Visualize Your Data with Natural Language - PyData

End-to-End Data Science Workflow with the Posit Team Snowflake Native App

Hadley Wickham: Spreadsheets, bikes, and the accidental empire of R packages

James Blair: Part 1 — Portfolios, practice, and staying curious

Keeping LLMs in Their Lane: Focused AI for Data Science and Research

Create and Deploy a Python Shiny Express Application to Connect Cloud

AskRADS: An AI Recommendation Agent for Maximizing ROI of Data Science Collaborations (Regis James)

Building a Real-Time COVID-19 Surveillance System with R (Hugo Fitipaldi, Lund University)

Data 911: how Posit can support decision-makers in times of environmental crisis (Marcus Beck)

Design of Everyday Shiny Apps (Casey Aguilar-Gervase & Maya Gans, Atorus) | posit::conf(2025)

Disposable Shiny Apps (James Wade, Dow) | posit::conf(2025)

Engineer Insightful Shiny Apps with Behavioral Science (Jeremy Winget, CrossStack) | posit::conf

Get Your Ducks in a Row with Databases (Melissa Albino Hegeman, NYS OGS) | posit::conf(2025)

Keeping Data Alive: Persistent Storage Options for Dynamic Cloud Applications (Alex Chisholm, Posit)

Lift Off! Building REST APIs that Fly (Joe Kirincic, RESTORE-Skills) | posit::conf(2025)

maidr: Empowering Accessible, Multimodal Data Visualizations (JooYoung Seo) | posit::conf(2025)

Modular, layout-as-code approach for customizable Shiny dashboards (Kim Schouten, De NZa)

Observability at scale (Barret Schloerke, Posit) | posit::conf(2025)

Old Apps, New Tricks: How AI can write Automated Tests for your Shiny Apps (Karan Gathani, Posit)

Semantic Search for the Rest of Us with DuckDB (Marcos Huerta, Carmax) | posit::conf(2025)

shinyGovStyle- accessible gov design in RShiny (Cameron Race & Sarah Wong-Brown) | posit::conf(2025)

Shinystate (Eric Nantz, Eli Lilly) | posit::conf(2025)

surveydown (John Paul Helveston, GWU) | posit::conf(2025)

Talk data to me: How to present youR data to any audience (Freda Warner, CIHR) | posit::conf(2025)

Theming Made Easy: Introducing brand.yml (Garrick Aden-Buie, Posit) | posit::conf(2025)

Web applications with Shiny and React and AI (Winston Chang, Posit) | posit::conf(2025)

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

Accelerating Study Insights with Shiny and Posit | Regeneron x Atorus

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

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

Building the Future of Data Apps: LLMs Meet Shiny

Positron Assistant for Developing Shiny Apps - Tom Mock

Daniel Chen - LLMs, Chatbots, and Dashboards: Visualize Your Data with Natural Language

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

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

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

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

Daniel Chen - Shiny for Python: Building Production-Ready Dashboards in Python | SciPy 2025

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

AI and Shiny | Keynote ShinyConf 2025

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

Demystifying LLMs with Ellmer

Harnessing LLMs for Data Analysis | Led by Joe Cheng, CTO at Posit

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

Create a PydyTuesday Shiny app with Shiny Assistant

Standardizing a safety model with tidymodels, Posit Team & Databricks at Suffolk Construction

Shiny Assistant - Prototype Shiny for Python Apps with AI

{shinylive}: Serverless Shiny applications workshop

R-Ladies Rome (English) - Interactive R, Python, and Shiny in the Browser with Quarto and Shinylive

Deploy your work to Posit Connect Cloud

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

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

Shiny Forms with Accordions 🎉

Stop Your Shiny Apps from Breaking on Mobile! Master Responsive Design in Minutes 📱💻

Transform Boring Forms into Eye-Catching UI: Shiny Party Tricks That Will Make You Look Like a Pro 🎉

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

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

Real-World Python Dashboard Project w/ Quarto! (Airbnb Review Analysis)

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

Workflow Demo Live Q&A - December 18th!

Connecting Shiny Apps to Databases with Posit Team

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

Joe Cheng - Summer is Coming: AI for R, Shiny, and Pharma

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

{mirai} and {crew}: next-generation async to supercharge {promises}, Plumber, Shiny, and {targets}