RStudio Team

devtools

Tools to make an R developer's life easier

dplyr

dplyr: A grammar of data manipulation

forcats

: tools for working with categorical variables (factors)

ggplot2

An implementation of the Grammar of Graphics in R

gt

Easily generate information-rich, publication-quality tables from R

haven

Read SPSS, Stata and SAS files from R

lubridate

Make working with dates in R just that little bit easier

plotnine

A Grammar of Graphics for Python

purrr

A functional programming toolkit for R

readr

Read flat files (csv, tsv, fwf) into R

readxl

Read excel files (.xls and .xlsx) into R

rmarkdown

Dynamic Documents for R

stringr

A fresh approach to string manipulation in R

tidyr

Tidy Messy Data

usethis

Set up commonly used components

pkgdown

Generate static html documentation for an R package

roxygen2

Generate R package documentation from inline R comments

ellmer

Call LLM APIs from R

tidyverse

Easily install and load packages from the tidyverse

tidyverse.org

Source of tidyverse.org

cli

Tools for making beautiful & useful command line interfaces

rvest

Simple web scraping for R

DBI

A database interface (DBI) definition for communication between R and RDBMSs

vroom

Fast reading of delimited files

magrittr

Improve the readability of R code with the pipe

vitals

Large language model evaluation for R

odbc

Connect to ODBC databases (using the DBI interface)

webinars

Code and slides for RStudio webinars

dbplyr

Database (DBI) backend for dplyr

duckplyr

A drop-in replacement for dplyr, powered by DuckDB for speed

gh

Minimalistic GitHub API client in R

leaflet

R Interface to Leaflet Maps

R6

Encapsulated object-oriented programming for R

renv

renv: Project environments for R

rlang

Low-level API for programming with R

rstudio-conf

Materials for rstudio::conf

testthat

An R to make testing

httr2

Make HTTP requests and process their responses. A modern reimagining of httr

code-review

rsconnect

Publish Shiny Applications, RMarkdown Documents, Jupyter Notebooks, Plumber APIs, and more

fs

Provide cross platform file operations based on libuv

log4r

A fast & lightweight approach to logging in R based on the widely-emulated Apache Log4j project

modeldb

Run models inside a database using R

httpuv

HTTP and WebSocket server package for R

profvis

Visualize R profiling data

bookdown

Authoring Books and Technical Documents with R Markdown

htmltools

Tools for HTML generation and output

tibble

A modern re-imagining of the data frame

shinyloadtest

Tools for load testing Shiny applications

connectapi

An R package for interacting with the Posit Connect server API

tidyselect

A backend for functions taking tidyverse selections

callr

Call R from R

chromote

Chrome Remote Interface for R

httr

httr: a friendly http package for R

hugodown

Make websites with hugo and RMarkdown

downlit

Syntax Highlighting and Automatic Linking

bigrquery

An interface to Google's BigQuery from R

waldo

Find differences between R objects

reprex

Render bits of R code for sharing, e.g., on GitHub or StackOverflow

education.rstudio.com

rticles

LaTeX Journal Article Templates for R Markdown

vctrs

Generic programming with typed R vectors

covr

Test coverage reports for R

lintr

Static Code Analysis for R

cheatsheets

Posit Cheat Sheets - Can also be found at https://posit.co/resources/cheatsheets/

actions

GitHub Actions for the R community

blob

A simple S3 class for representing BLOBs

broom

Convert statistical analysis objects from R into tidy format

commonmark

High Performance CommonMark and Github Markdown Rendering in R

conflicted

An alternative conflict resolution strategy for R

connections

https://rstudio.github.io/connections/

coro

Coroutines for R

cpp11

cpp11 helps you to interact with R objects using C++ code

desc

Manipulate DESCRIPTION files

design

Tidyverse design principles

diffviewer

HTML widget to visually compare files

dtplyr

Data table backend for dplyr

ellipsis

Tools for Working with ..

evaluate

A version of eval for R that returns more information about what happened

funs

Collection of low-level functions for working with vctrs

gargle

Infrastructure for calling Google APIs from R, including auth

generics

Common generic methods

glue

Glue strings to data in R. Small, fast, dependency free interpreted string literals

googledrive

Google Drive R API

gtable

The layout packages that powers ggplot2

hex-stickers

RStudio hex stickers

hms

A simple class for storing time-of-day values

isoband

isoband: An R package to generate contour lines and polygons

keyring

:closed_lock_with_key: Access the system credential store from R

lifecycle

Manage the life cycle of your exported functions and arguments

lobstr

Understanding complex R objects with tools similar to str()

memoise

Easy memoisation for R

mockery

A mocking library for R

modelr

Helper functions for modelling

multidplyr

A dplyr backend that partitions a data frame over multiple processes

nycflights13

An R data package containing all out-bound flights from NYC in 2013 + useful metdata

packrat

Packrat is a dependency management system for R

pak

A fresh approach to package installation

pillar

Format columns with colour

pins-r

Pin, discover, and share resources

pkgbuild

Find tools needed to build R packages

pkgdepends

R Package Dependency Resolution

pkgload

Simulate installing and loading a package

pool

Object Pooling in R

processx

Execute and Control Subprocesses from R

ragg

Graphic Devices Based on AGG

rappdirs

Find OS-specific directories to store data, caches, and logs. A port of python's AppDirs

rcmdcheck

Run R CMD check from R and collect the results

remotes

Install R packages from GitHub, GitLab, Bitbucket, git, svn repositories, URLs

revdepcheck

R package reverse dependency checking

rex

Friendly regular expressions for R

RMariaDB

An R interface to MariaDB

RMySQL

Legacy DBI interface for MySQL

RPostgres

A DBI-compliant interface to PostgreSQL

RSQLite

R interface for SQLite

rstudioapi

Safely access RStudio's API (when available)

sass

Sass compiler package for R

scales

Tools for ggplot2 scales

sessioninfo

Print Session Information

Shiny

Easy interactive web applications with R

shinytest

Automated testing for shiny apps

sloop

S language OOP ️

style

The tidyverse style guide for R code

svglite

A lightweight svg graphics device for R

systemfonts

System Native Font Handling in R

textrecipes

Extra recipes for Text Processing

tidy-dev-day

Tidyverse developer day

tidytemplate

A pkgdown template for core tidyverse packages

tidyups

tidyversedashboard

Tidyverse activity dashboard

urlchecker

Run CRAN URL checks from older versions of R

withr

Methods For Temporarily Modifying Global State

xml2

Bindings to libxml2

yaml

R package for converting objects to and from YAML

plumber

Turn your R code into a web API

positron

Positron, a next-generation data science IDE

Quarto

Open-source scientific and technical publishing system built on Pandoc

shinychat

Chat UI component for Shiny

rstudio

RStudio is an integrated development environment (IDE) for R

querychat

Chat with your data in R and Python

shinyapps

Deploy Shiny applications to ShinyApps

chatlas

Your friendly guide to building LLM chat apps in Python with less effort and more clarity

mirai

Minimalist Async Evaluation Framework for R

nanonext

R binding for NNG (Nanomsg Next Gen)

crosstalk

Inter-htmlwidget communication for R (with and without Shiny)

shiny-server

Host Shiny applications over the web

shinylive

Run Shiny on Python and R (compiled to wasm) in the browser

bslib

Tools for theming Shiny and R Markdown via Bootstrap 3, 4, or 5

shinytableau

Extend Tableau dashboards using Shiny

shiny-assistant

Chat assistant for Shiny

DT

R Interface to the jQuery Plug-in DataTables

googlesheets4

Google Spreadsheets R API (reboot of the googlesheets package)

shinydashboard

Shiny Dashboarding framework

rsconnect-python

The Posit Connect command-line interface

blastula

Easily send great-looking HTML email messages from R

fontawesome

Easily insert FontAwesome icons into R Markdown docs and Shiny apps

flexdashboard

Easy interactive dashboards for R

learnr

Interactive Tutorials with R Markdown

plumbertableau

R package for creating Plumber APIs that function as Tableau Analytics Extensions

pagedown

Paginate the HTML Output of R Markdown with CSS for Print

cranwhales

shinymeta

Record and expose Shiny app logic using metaprogramming

addinexamples

An R package showcasing how RStudio addins can be registered and used

ggbot2

gmailr

Access the Gmail RESTful API from R

later

Schedule an R function or formula to run after a specified period of time

leaflet.mapboxgl

Extends the R Leaflet package with a Mapbox GL JS plugin to allow easy drawing of vector tile layers

markdown

The first generation of Markdown rendering for R (born in 2012), based on commonmark. Note that this package is markdown, not *rmarkdown*

miniUI

promises

A promise library for R

py-shinywidgets

Render ipywidgets inside a PyShiny app

R-Websockets

HTML 5 Websockets implementation for R

rstudio-conf-2022-program

rstudio::conf(2022, "program")

Shiny for Python

Easy interactive web applications with Python

shiny-examples

shiny-incubator

Examples and ideas that don't belong in the core Shiny package and aren't officially supported

shiny-vscode

Shiny VS Code Extension

shinycoreci

Application-level automated tests for Shiny

shinycoreci-apps

SUPERSEDED - Apps for Shiny continuous integration testing

ShinyDeveloperConference

Materials collected from the First Shiny Developer Conference Palo Alto, CA January 30-31 2016

shinyvalidate

Input validation package for the Shiny web framework

websocket

WebSocket client for R

reticulate

R Interface to Python

revealjs

R Markdown Format for reveal.js Presentations

config

config package for R

tensorflow

TensorFlow for R

blogdown

Create Blogs and Websites with R Markdown

bookdown.org

Source documents to generate the bookdown.org website

tfdatasets

R interface to TensorFlow Datasets API

tinytex

A lightweight, cross-platform, portable, and easy-to-maintain LaTeX distribution based on TeX Live

ai-blog

Repository for the RStudio AI Blog (formerly: TensorFlow for R Blog)

ark

Ark, an R kernel

bookdown-demo

A minimal book example using bookdown

cloudml

R interface to Google Cloud Machine Learning Engine

distill

Distill for R Markdown

dygraphs

R interface to dygraphs

keras-customer-churn

Customer Churn Shiny Application

keras3

R Interface to Keras

r2d3

R Interface to D3 Visualizations

rmarkdown-website

rmarkdown-website-examples

Examples of R Markdown Websites

shinyapps-package-dependencies

Collection of bash scripts that install R package system dependencies

sparkapi

Sparklyr Extensions API

tfestimators

R interface to TensorFlow Estimators

tfruns

Track, Visualize, and Manage TensorFlow Training Runs

rainbowR 2026

The inaugural rainbowR virtual conference brought together LGBTQIA+ users of R, and their allies, to promote their work

Importing data with the tidyverse

Learn about readr, readxl, and haven

Hadley Wickham, Mine Çetinkaya-Rundel

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

Before Hadley Wickham became a pillar of modern data science, he was a spreadsheet-loving teenager making databases for his dad’s job. In this episode, he reflects on the early days of his involvement with R, the birth of tidyverse, and how real-world unpredictability — like a bear in a field — shapes data science. Data scientists around the world owe a lot to Hadley Wickham — but how did he go from wrangling databases as a teen to shaping the future of statistical computing? In this episode of The Test Set, Hadley takes us through the winding path of his early days with R, the birth of the tidyverse, and the quirks of open source development. We dig into the philosophies behind his tools, why team dynamics matter, and how large language models are sparking his curiosity all over again. What’s Inside: • Hadley’s first brush with R code … inside a Word doc • Consulting as a grad student — and learning what people really want from stats • How messy Excel sheets inspired the tidy data revolution • Writing R packages as a form of self-defense (and productivity) • The secret sauce of building the tidyverse team • On focus, burnout, and saying “no” to GitHub pull requests • Current obsession: using LLMs to make data science faster, easier, and more fun • How writing books is a form of tidying ideas, and how a Shiny textbook led to a custom bike

Hadley Wickham

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

From R+AI 2025, hosted by R Consortium Keynote LLMs are powerful, flexible, easy-to-use… and often wrong. This is a dangerous combination, especially for data analysis and scientific research, where correctness and reproducibility are core requirements. Fortunately, it turns out that by carefully applying LLMs to narrower use cases, we can turn them into surprisingly reliable assistants that accelerate and enhance, rather than undermine, scientific work. This is not just theory—I’ll showcase working examples of seamlessly integrating LLMs into analytic workflows, helping data scientists build interactive, intelligent applications without needing to be web developers. You’ll see firsthand how keeping LLMs focused lets us leverage their “intelligence” in a way that’s practical, rigorous, and reproducible. Bio Joe Cheng is the CTO and first employee at Posit, PBC (formerly known as RStudio), where he helped create the RStudio IDE, Shiny web framework, and Databot agent for exploratory data analysis. R Consortium Resources Main R Consortium Site: https://www.r-consortium.org/ R+AI website: https://rconsortium.github.io/RplusAI_website R Consortium webinars: https://r-consortium.org/webinars/webinars.html Blog: https://r-consortium.org/blog/ LinkedIn: https://www.linkedin.com/company/r-consortium/

Joe Cheng

Translating R for Data Science into Portuguese: A Community-Led Initiative (Beatriz Milz, UFABC)

Translating R for Data Science into Portuguese: A Community-Led Initiative Speaker(s): Beatriz Milz Abstract: How can open-source collaboration help make data science more accessible and expand Posit’s global impact? The book "R for Data Science" by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund is a key resource for learning R and the tidyverse. In a collaborative effort, volunteers from the R community translated the second edition into Brazilian Portuguese, making it freely available online. This talk explores the translation journey, the challenges of adapting technical content, and key lessons learned to support future translation teams. Materials - https://beamilz.com/talks/en/2025-posit-conf/ posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Hadley Wickham, Mine Çetinkaya-Rundel

Making the most of artificial and human intelligence for data science (Hadley Wickham, Joe Cheng)

Making the most of artificial and human intelligence for data science Speaker(s): Hadley Wickham; Joe Cheng Abstract: This presentation explores the complex and often contradictory nature of large language models (LLMs) in data science, acknowledging the simultaneous excitement and apprehension that we feel toward these technologies. We’ll provide a practical framework to help you understand the LLM ecosystem (from foundation models and hosting to SDKs and applications) that supports our current philosophy: augmenting, not replacing human intelligence. The talk demonstrates how Posit is addressing this space through two complementary approaches: building SDKs and tools that help you create your own LLM-powered solutions, and developing integrated LLM capabilities directly into data science workflows through tools like Positron assistant and databot. We’ll showcase practical, immediately useful applications while addressing current limitations, providing you with both the emotional preparation and technical foundation needed to effectively leverage LLMs in their data science practice today. posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Hadley Wickham, Joe Cheng

Keynote: I Wrote This Talk with an LLM - Hadley Wickham

In this keynote, I’ll explore the evolving relationship between data scientists, statisticians, and large language models through a unique experiment: this entire talk was created in collaboration with an LLM. From outline to slides, from code examples to key insights, I’ll share the practical realities of using AI as a thought partner in the R ecosystem. Drawing on my experience developing tidyverse packages and teaching data science, I’ll demonstrate how LLMs can augment (rather than replace) the R user’s workflow. We’ll examine specific examples where AI assistance shines—rapid prototyping, documentation generation, and creative ideation—alongside areas where human expertise remains irreplaceable. Most importantly, I’ll reflect on what this experiment reveals about the future of our community: How might AI change the way we teach R? What new skills should we prioritize? And how can we ensure that the tools we build remain accessible and empowering for all users? Join me for this meta-exploration of AI’s role in our work, with honest reflections on both the promise and limitations of these new collaborators in our statistical computing journey. This abstract was generated by Claude Sonnet 3.7 and lightly edited by me. I used the prompt: I am Hadley Wickham, chief scientist at RStudio/Posit and I’ve been invited to give a keynote on AI at the useR conference. Please write a talk abstract for a talk entitled ‘I wrote this talk with an LLM’ https://hadley.nz/

Hadley Wickham

I wrote this talk with an LLM - Hadley Wickham (useR! 2025 Keynote 1)

Presented by: Hadley Wickham (PositPBC) In this keynote, I’ll explore the evolving relationship between data scientists, statisticians, and large language models through a unique experiment: this entire talk was created in collaboration with an LLM. From outline to slides, from code examples to key insights, I’ll share the practical realities of using AI as a thought partner in the R ecosystem. Drawing on my experience developing tidyverse packages and teaching data science, I’ll demonstrate how LLMs can augment (rather than replace) the R user’s workflow. We’ll examine specific examples where AI assistance shines—rapid prototyping, documentation generation, and creative ideation—alongside areas where human expertise remains irreplaceable. Most importantly, I’ll reflect on what this experiment reveals about the future of our community: How might AI change the way we teach R? What new skills should we prioritize? And how can we ensure that the tools we build remain accessible and empowering for all users? Join me for this meta-exploration of AI’s role in our work, with honest reflections on both the promise and limitations of these new collaborators in our statistical computing journey. This abstract was generated by Claude Sonnet 3.7 and lightly edited by me. I used the prompt: I am Hadley Wickham, chief scientist at RStudio/Posit and I’ve been invited to give a keynote on AI at the useR conference. Please write a talk abstract for a talk entitled ‘I wrote this talk with an LLM’

Hadley Wickham

Post without categories

Charlotte Wickham, Hadley Wickham

The Test Set: A Posit Podcast Trailer

Introducing The Test Set–a Posit podcast for data science, coming July 1, 2025. For data science junkies, anomaly hunters, and those who play outside the confidence interval. Hosted by Michael Chow, Wes McKinney & Hadley Wickham Subscribe to receive updates: https://pos.it/thetestset

Hadley Wickham, Michael Chow

Demystifying LLMs with Ellmer

From R/Medicine 2025 Joe Cheng, CTO, Posit Joe Cheng is the CTO of Posit, PBC. He’s the original creator of the Shiny web framework and co-creator of ellmer. Today’s best LLMs are incredibly powerful–but you’re only scratching the surface of their capabilities if your use is limited to ChatGPT or Copilot. Accessing LLMs programmatically opens up a whole new world of possibilities, letting you integrate LLMs into your own apps, scripts, and workflows. In this workshop, we’ll cover: A practical introduction to LLM APIs -- Configuring R to access LLMs via the ellmer package -- Customizing LLM behavior using system prompts and tool calling -- Creating Shiny apps with integrated chatbots -- Using LLMs for natural language processing Attendees will leave the workshop armed with ready-to-run examples, and the confidence and inspiration to run their own experiments with LLMs. Attendees should be familiar with the basics of R and have a working R installation. Note that to avoid any potential firewall issues, it is recommended that participants use a personal computer for this workshop. Resources Presentation: https://jcheng5.github.io/rmedicine-2025 R/Medicine: https://rconsortium.github.io/RMedicine_website/ R Consortium: https://www.r-consortium.org/

Joe Cheng

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

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

Joe Cheng

LLMs for Data Science

[2025 - Day 1 - Data Science & Algos] Hadley Wickham shares insights from practical applications of LLMs in data science, exploring three key areas where these tools prove genuinely useful beyond the hype: writing code, writing prose, and rectangling non-rectangular data. For data scientists working with text, images, videos, or audio data, this talk offers valuable perspectives on leveraging LLMs effectively for real workflows and transforming fundamentally unstructured information. ABOUT THE SPEAKER: Hadley Wickham, Chief Scientist, Posit Sign up for our "No BS" Newsletter to get the latest technical data & AI content: https://datacouncil.ai/newsletter ABOUT DATA COUNCIL: Data Council brings together the brightest minds in data to share industry knowledge, technical architectures and best practices in building cutting edge data & AI systems and tools. FIND US: Twitter: https://twitter.com/datacouncilai LinkedIn: https://www.linkedin.com/company/datacouncil-ai/ Website: https://www.datacouncil.ai/

Hadley Wickham

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

When we think of LLMs (large language models), usually what comes to mind are general purpose chatbots like ChatGPT or code assistants like GitHub Copilot. But as useful as ChatGPT and Copilot are, LLMs have so much more to offer—if you know how to code. In this demo Joe Cheng will explain LLM APIs from zero, and have you building and deploying custom LLM-empowered data workflows and apps in no time. Posit PBC hosts these Workflow Demos the last Wednesday of every month. To join us for future events, you can register here: https://posit.co/events/ Slides: https://jcheng5.github.io/workflow-demo/ GitHub repo: https://github.com/jcheng5/workflow-demo Resources shared during the demo: Ellmer https://ellmer.tidyverse.org/ Chatlas https://posit-dev.github.io/chatlas/ Environment variable management: For R: https://docs.posit.co/ide/user/ide/guide/environments/r/managing-r.html#renviron For Python https://pypi.org/project/python-dotenv/ Shiny chatbot UI: For R, Shinychat https://posit-dev.github.io/shinychat/ For Python, ui.Chat https://shiny.posit.co/py/docs/genai-inspiration.html Deployment Cloud hosting https://connect.posit.cloud On-premises (Enterprise) https://posit.co/products/enterprise/connect/ On-premises (Open source) https://posit.co/products/open-source/shiny-server/ Querychat Demo: https://jcheng.shinyapps.io/sidebot/ Package: https://github.com/posit-dev/querychat/ If you have specific follow-up questions about our professional products, you can schedule time to chat with our team: pos.it/llm-demo

Joe Cheng

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

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

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

Summer is Coming: AI for R, Shiny, and Pharma - Joe Cheng Abstract: R users tend to be skeptical of modern AI models, given our weird insistence on answers being accurate, or at least supported by the data. But I believe the time has come—or maybe it’s a little late—for even the most AI-cynical among us to push past their discomfort and get informed about what these tools are truly capable of. And key to that is moving beyond using AI-enabled apps, and towards building our own scripts, packages, and apps that make judicious use of AI. In this talk, I’ll tell you why I believe AI has more to offer the R community than just wrong answers from chat windows or mediocre code suggestions in our IDEs. I’ll also introduce brand-new tools we’re developing at Posit that put powerful AI tools within reach of every R user. And finally, I’ll show how adding some AI could make your next Shiny app dramatically more useful for your users. Resources mentioned in the talk: - Slides: https://jcheng5.github.io/pharma-ai-2024 - {elmer} Call LLM APIs from R: https://elmer.tidyverse.org/ - {shinychat} Chat UI component for Shiny for R https://github.com/jcheng5/shinychat - R/Pharma GenAI Day Recordings: https://www.youtube.com/playlist?list=PLMtxz1fUYA5AYryl4t2mtqBngqWDrnMXJ Presented at the 2024 R/Pharma Conference

Joe Cheng

Joe Cheng - Shiny x AI

These days, you can’t turn around without encountering a large language model—they’re embedded in everything from Google search results to the lower-right corner of every Windows desktop. But… in your Shiny app? In this talk, we’ll discuss some ways the Shiny team is combining the magical chaos of LLMs with the structure and control of Shiny. You’ll learn how to use modern chat models to add features to your Shiny apps that will feel like science fiction to your users while minimizing the risks of hallucination, irreproducibility, and data exposure. Talk by Joe Cheng GitHub Repo: https://github.com/jcheng5/py-sidebot

Joe Cheng, Shiny Team

Updates from Posit, with Hadley Wickham, Charlotte Wickham, George Stagg, and James Blair

6:45 Hadley introduces the conference. 10:37 Hadley on Posit, PBC. Who are we, and what we do. 20:41 Charlotte Wickham on Quarto, an open-source scientific and technical publishing system. 31:05 George Stagg on webR, R for WebAssembly. Execute R code in your web browser. 43:34 James Blair with the latest on Posit's partnerships with Databricks and Snowflake. Please join us for our first Posit Conf 2024 keynote, where we’ll tell you about our mission, our products, and some of the exciting things we’ve been working on over the last year. Hadley Wickham, Chief Scientist, will talk briefly about Posit’s mission and products, before introducing the three speakers who will update you on some of the coolest projects we’ve worked on over the last year. James Blair, Senior Product Manager, will give you the latest on our partnerships with Databricks and Snowflake, and how we’re building seamless integrations that let you focus on data science instead of dealing with technical details. Charlotte Wickham, Developer Educator, will show you what’s new in Quarto, focusing on new ways to build beautiful PDFs with Typst. Finally, George Stagg, Senior Software Engineer, will tell you about the latest innovations in teaching using webR, a tool that lets you compile your R code into standalone HTML files. Talk by Hadley Wickham, James Blair, Charlotte Wickham, and George Stagg

Charlotte Wickham, George Stagg, Hadley Wickham

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

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

Joe Cheng, Julia Silge, Michael Chow

R-Ladies Rome (English) - R in Production - Hadley Wickham

In this inspiring talk, dive into the world of R in production with Hadley Wickham, Chief Scientist at Posit PBC (formerly RStudio). Explore the challenges and best practices for deploying R solutions in real-world production environments, from effective code structuring to ensuring scalability and reliability. Whether you're a seasoned data scientist or just beginning your journey with R, this event equips you with invaluable insights and actionable tips to drive impactful outcomes in your organization. Don't miss out on this engaging discussion! Material: - https://github.com/hadley/available-work - https://github.com/hadley/web-scraping 0:00 Welcome & R-Ladies Introduction by Dorota Rizik (R-Ladies NYC) 6:28 Introduction and Dr. Wickham's Talk 53:46 Q&A Have a look at our WebSite for more insights about our events: https://rladiesrome.org

Hadley Wickham

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

Tom Mock @ Posit PBC | Data Science Hangout

We were recently joined by Tom Mock, Product Manager at Posit PBC to chat about career growth, starting out in a sales role, TidyTuesday, and being so good they can’t ignore you. Speaker Bio: Tom Mock is a Product Manager at Posit, overseeing the Posit Workbench and RStudio team. He fell in love with R and data science through his graduate research, using R and RStudio to wrangle, analyze, model, and visualize my data. He became passionate about growing the R community, and founded #TidyTuesday to help newcomers and seasoned vets improve their Tidyverse skills. Links mentioned: TidyTuesday: https://github.com/rfordatascience/tidytuesday Table Contest: https://posit.co/blog/announcing-the-2024-table-contest/ Posit Conference: https://posit.co/conference/ Monthly Workflow Demos: https://www.addevent.com/event/Eg16505674 gt package: https://gt.rstudio.com/ So Good They Can't Ignore You book recommendation: https://www.goodreads.com/book/show/13525945-so-good-they-can-t-ignore-you Community Builder Quarto Site: https://pos.it/community-builder ________________________ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software To join future data science hangouts, add to your calendar here: https://pos.it/dsh We'd love to have you join us in the conversation live! Thanks for hanging out with us!

RStudio Team

Hadley Wickham - R in Production

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

Hadley Wickham

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

Hadley Wickham on R vs Python

Learn about tidyverse, ggplot2, and the secret to a tech company’s longevity as Hadley Wickham joins @JonKrohnLearns in this episode. He talks about Posit’s rebrand, why tidyverse needs to be in every data scientist’s toolkit, and why getting your hands dirty with open-source projects can be so lucrative for your career. Watch the full interview “779: The Tidyverse of Essential R Libraries and their Python Analogues — with Dr. Hadley Wickham” here: https://www.superdatascience.com/779

Hadley Wickham

779: The Tidyverse of Essential R Libraries and their Python Analogues — with Dr. Hadley Wickham

Tidyverse #RProgramming #RLibraries Tidyverse, ggplot2, and the secret to a tech company’s longevity: Hadley Wickham talks to @JonKrohnLearns about Posit’s rebrand, Tidyverse and why it needs to be in every data scientist’s toolkit, and why getting your hands dirty with open-source projects can be so lucrative for your career. This episode is brought to you by Intel and HPE Ezmeral Software (https://bit.ly/hpeintel). Interested in sponsoring a SuperDataScience Podcast episode? Visit https://passionfroot.me/superdatascience for sponsorship information. In this episode you will learn: • [00:00:00] Introduction • [00:02:55] All about the Tidyverse • [00:15:19] Hadley’s favorite R libraries • [00:28:39] The goal of Posit • [00:34:12] On bringing multiple programming languages together • [00:50:19] The principles for a long-lasting tech company • [00:53:34] How Hadley developed ggplot2 • [01:03:52] How to contribute to the open-source community Additional materials: https://www.superdatascience.com/779

Hadley Wickham

Running R-Shiny without a Server - posit::conf(2023)

Presented by Joe Cheng A year ago, Posit announced ShinyLive, a deployment mode of Shiny that lets you run interactive applications written in Python, without actually running a Python server at runtime. Instead, ShinyLive turns Shiny for Python apps into pure client-side apps, running on a pure client-side Python installation. Now, that same capability has come to Shiny for R, thanks to the webR project. In this talk, I'll show you how you can get started with ShinyLive for R, and why this is more interesting than just cheaper app hosting. I'll talk about some of the different use cases we had in mind for ShinyLive, and help you decide if ShinyLive makes sense for your app. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: I can't believe it's not magic: new tools for data science. Session Code: TALK-1151

Joe Cheng

It's a Great Time to be an R Package Developer! - posit::conf(2023)

Presented by Jenny Bryan and Hadley Wickham (Due to unforeseen circumstances, Hadley Wickham presented this talk "slide karaoke" style, from materials prepared by Jenny Bryan.) In R, the fundamental unit of shareable code is the package. As of March 2023, there were over 19,000 packages available on CRAN. Hadley Wickham and I recently updated the R Packages book for a second edition, which brought home just how much the package development landscape has changed in recent years (for the better!). In this talk, I highlight recent-ish developments that I think have a great payoff for package maintainers. I'll talk about the impact of new services like GitHub Actions, new tools like pkgdown, and emerging shared practices, such as principles that are helpful when testing a package. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Package development. Session Code: TALK-1132

Hadley Wickham, Jenny Bryan

J.J. Allaire - Keynote: Dashboards with Jupyter and Quarto | PyData NYC 2023

www.pydata.org https://drive.google.com/file/d/1O_ed6OKEXZBIzKn6yyF9W7f6NaNV-L3J/view?usp=drive_link Keynote by JJ Allaire J.J. is the Founder and CEO of Posit (which you might only know by its previous name, RStudio). J.J. is now leading the Quarto project, a Jupyter-based scientific and technical publishing system. In this talk, J.J. will introduce Quarto Dashboards, an easy way to create production quality dashboards from Jupyter Notebooks. J.J. will also more broadly discuss Posit's recent work in the open source PyData ecosystem along with plans for significantly expanding that work in the future. 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

JJ Allaire

"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

Keynote, JJ Allaire: Reproducible Manuscripts with Quarto

JJ. Allaire CEO at Posit, PBC JJ is software engineer and entrepreneur who builds tools that empower people with technology. JJ has conceived and designed several industry leading products by balancing market, customer, and technical considerations, and by maintaining intimate involvement in all aspects of software design and construction. He is currently the founder and CEO of statistical computing company RStudio (now, a part of Posit, https://posit.co/). https://github.com/jjallaire https://mobile.twitter.com/fly_upside_down

JJ Allaire

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

What does superseded mean? Package development lifecycle process and the meaning of superseded.

An important part of the process of package lifecycle and package development is not just adding new functions. It's is equally important to remove functions. Hadley Wickham shares about the package lifecycle process and what 'supersede' means for functions. See the full video about the purrr 1.0 release: https://youtu.be/EGAs7zuRutY More about the package lifecycle stages: https://lifecycle.r-lib.org/articles/stages.html Maintaining the house that tidyverse built: https://youtu.be/izFssYRsLZs

Hadley Wickham

What does deprecated mean? Package lifecycle and the process of deprecation.

An important part of the process of package lifecycle and package development is not just adding new functions. It's equally important to remove functions. Hadley Wickham shares about the package lifecycle process and what 'deprecation' means for functions. See the full video about the purrr 1.0 release: https://youtu.be/EGAs7zuRutY More about the package lifecycle stages: https://lifecycle.r-lib.org/articles/stages.html Maintaining the house that tidyverse built: https://youtu.be/izFssYRsLZs

Hadley Wickham

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

The Evolution of Shiny with Posit's CTO, Joe Cheng

Posit’s Joe Cheng talks about the evolution of Shiny and the future of Shiny for Python. Joe is one of the original architects of Shiny and leads Posit’s Open Source software development. Learn more about Shiny for Python at shiny.posit.co/py

Joe Cheng

Hadley Wickham | {purrr} 1.0: A complete and consistent set of tools for functions and vectors

{purrr} has reached the 1.0 milestone, with new features like progress bars, improvements to the map family, and tools for list flattening and simplification. 0:00 Introduction 0:11 What is purrr? 00:32 What is functional programming? 03:08 Announcing purrr 1.0 03:58 Progress bars 05:18 Better error messages 07:18 New map function: map_vec() 09:58 New list_* functions 12:04 Flattening and simplification 17:40 Breaking Changes 22:34 How the tidyverse handles deprecation 24:41 An overview of functional programming 26:22 Closing, resources to help with deprecation, how to submit issues See more in the {purrr} 1.0.0 release blog post! https://www.tidyverse.org/blog/2023/03/tidyverse-2-0-0/

Hadley Wickham

Embracing R and Python

Listen to Posit's Chief Scientist Hadley Wickham talk about the future of Posit. Visit www.posit.co to learn more

Hadley Wickham

Keynote: Hadley Wickham - Embracing multi-lingual data science | PyData Global 2022

www.pydata.org RStudio recently changed its name to Posit to reflect the fact that we're already a company that does more than just R. Come along to this talk to hear a few of the reasons that we love R, and to learn about some of the open source tools we're working on for python. 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

Hadley Wickham

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

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

JJ Allaire

Hadley Wickham: Thank you, from Open Source at Posit

The thing that’s made RStudio (now Posit) so amazing, is the community. We love and value our open source community, and we’re so appreciative of everyone who has helped make Posit what it is today. And our community, is… YOU. You live and work on every continent. You use R for school or work or fun; answering questions that are important to you. You create and share thousands of resources to help others. You contribute to code and documentation on hundreds of open source projects. You’re involved in the community, sharing with each other in organizations and user groups around the world. And, of course, you write millions of lines of code. We thank you, and wish you all the best in the coming year!

Hadley Wickham

Joe Cheng | The Past and Future of Shiny | RStudio (2022)

Shiny is a package that makes it easy to create interactive web apps using R. It's hard to believe, but it's been ten whole years since the launch of Shiny! I'll mark the occasion by sharing some stories about the creation of Shiny, the conditions that have made it a success over the years, and our plans for the near future. https://shiny.rstudio.com/ Session: Keynote

Joe Cheng

JJ Allaire & Hadley Wickham | RStudio: 2022 and Beyond

RStudio's CEO & founder JJ Allaire and Chief Scientist Hadley Wickham walk through RStudio's journey so far, the most difficult problem RStudio has solved, and why that problem matters. JJ discusses the company's virtuous cycle (and core operative principle): Commercial software enables investment into open source. The line between those two things will always remain where it is, and your intellectual property will always be yours. Can RStudio impact the practice of science more broadly and not just the practice of data science with R? JJ says "we can and we should." Hadley continues, saying one language is already not enough, which has caused RStudio to contemplate a new name... Session: Keynote

Hadley Wickham, JJ Allaire

Hadley Wickham | An introduction to R7 | RStudio (2022)

The R7 package is a new OOP system designed to be a successor to S3 and S4. It has been designed and implemented collaboratively by the RConsortium Object- Oriented Programming Working Group, which includes representatives from R-Core, BioConductor, RStudio/tidyverse, and the wider R community. In this talk, I'll introduce R7 to the wider world. Attendees will learn why we created R7 and how they can use it to create new classes and packages. I hope to inspire folks to download, try it out, and give us the feedback we need to make it better. Session: Just typing R code: advanced R programming

Hadley Wickham

Tom Mock | Quarto for the Curious | RStudio (2022)

Are you curious about Quarto? Maybe you saw it on Twitter or the RStudio::conf agenda. Perhaps this raised questions like: What exactly is Quarto? What about RMarkdown? (don’t worry it’s not going away!) What features does Quarto add? What should I do with my existing Rmd/ipynb files? This talk will answer all of those questions and more! I’ll present Quarto as a next-gen version of RMarkdown, compare the similarities, and then discuss the new features in Quarto for publishing documents, presentations, blog posts, lab notebooks and more! Lastly, I’ll cover what this means for our customers using RStudio Team, and the exciting new world for Python users. Talk materials are available at https://thomasmock.quarto.pub/quarto-curious/ Session: RMarkdown and Quarto

RStudio Team

Gagandeep Singh & Xu Fei | Yes, you can use Python with RStudio Team! | RStudio (2022)

Are you a bilingual data scientist who wants to use Python in RStudio products? Or do you have colleagues who use Python for data science? In this talk, we will present an end to end data science use case including automated jobs, model training and monitoring, APIs, and interactive dashboards, entirely built and deployed in Python with RStudio Pro products. Session: Some of my best friends use Python

RStudio Team

Devin Pastoor | Websites & Books & Blogs, oh my! Creating Rich Content with Quarto | RStudio (2022)

A number of packages have emerged in the R ecosystem to help create websites (hugodown), books (bookdown), and blogs (blogdown). In this talk, I'll show you how Quarto handles creating these types of content and more. Whether you're a grad student considering your first blog post about R, or a python expert about to write a technical book, I'll show you how Quarto enables you to focus on content while it takes care of the rest. In addition, I'll show the RStudio team is making it as easy as possible to publish and share content within your organization and to the whole community. Session: Quarto deep dive

RStudio Team

Shiny Programming Practices || Joe Cheng || Posit

Have you ever wanted to sit down and talk with Joe Cheng, the creator of Shiny and CTO of Posit (RStudio) and ask him how he approaches programming? Look no further - we've got that conversation for you right here! Shiny makes it easy to build interactive web apps straight from R or Python. You can host standalone apps on a webpage or embed them in Markdown-style documents or build dashboards. You can also extend your Shiny apps with CSS themes, htmlwidgets, and JavaScript actions. Learn more about Shiny: https://shiny.rstudio.com/ Check out Shiny for Python: https://shiny.rstudio.com/py/ Explore our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Joe Cheng (@jcheng) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Joe Cheng

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

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

Hadley Wickham, Jenny Bryan

Enabling Citizen Data Scientists at Dow Chemical with Posit Academy

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

RStudio Team

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

Alan Carlson | Robust, modular dashboards that minimize tech debt | RStudio

Robust, modular dashboards that minimize tech debt Presented by Alan Carlson, Snap Finance Abstract Dashboards can be complex but building them shouldn’t be! We’ve built a wrapper for developing production level dashboards that streamlines onboarding new developers and standardizes the initial infrastructure to mitigate tech debt. Now you and your team can spend more time developing insights and less time trying to spin up shiny code with {graveler}. Speaker Bio As the Tech Lead for the BI (Business Intelligence) team, Alan's primary focus at Snap is researching, creating, and maintaining methods that help the rest of Snap’s BI Team in their work. From dashboards to visualizations to R code in general, he has built multiple packages and bookdowns that make BI easier to train and to use within the RStudio environment. Helpful Links: Blog Post: https://www.rstudio.com/blog/make-robust-modular-dashboards-with-golem-and-graveler/ Graveler package: https://github.com/ghcarlalan/graveler Environment variables: https://docs.rstudio.com/connect/user/content-settings/#content-vars Git-backed publishing: https://docs.rstudio.com/connect/user/git-backed/ If you'd like to join events live: colorado.rstudio.com/rsc/community-events Question about style guides: Tidyverse Style Guide: https://style.tidyverse.org/ Efficient R Programming book that Colin Gillespie wrote: https://csgillespie.github.io/efficientR/ Questions about RStudio Team: ⬢ RStudio Connect: https://www.rstudio.com/products/connect/ ⬢ Chat with RStudio about RStudio Team: rstd.io/chat-with-rstudio

RStudio Team

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

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

RStudio Team

RStudio Team Deep Dive | In A Hosted Environment

You probably know that RStudio makes a free, open-source development environment for data scientists. It’s made with love and used by millions of people around the world. What you might not know is that we also make a professional platform, called RStudio Team. In this Live Session, Tom will walk you through our Rstudio Team Trial, where you can learn how to best test drive.... - Scaling your data science work - Seamlessly managing open-source data science environments - Automate repetitive tasks - Rapidly share key insights and data science products securely to your entire organization. - And, optionally integrate some of your favorite open-source packages into the trial experience Leading organizations like NASA, Janssen Pharmaceuticals, The World Health Organization, financial institutions, government agencies and insurance organizations around the globe use RStudio’s professional products to tackle world-changing problems and we’re inviting you to learn how. You’ll learn how RStudio Team gives professional data science teams superpowers, with all of the bells and whistles that enterprises need. If you don't have your own trial instance of Rstudio Team to follow along (not required), feel free to request yours here: https://www.rstudio.com/products/team/evaluation3/ Additional resources here: https://docs.google.com/document/d/1HGt7LSohhyxpCvETvVEFHugrdaSnTcZaXbI0jV5g9ok/edit?usp=sharing

RStudio Team

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

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

RStudio Team

Teaching and learning with RStudio Cloud | RStudio

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

RStudio Team

RStudio Cloud | Viewing Learning Work | Instructor View

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

RStudio Team

RStudio Cloud | Tuning Resources | Instructor View

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

RStudio Team

RStudio Cloud | Troubleshooting | Instructor View

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

RStudio Team

RStudio Cloud | Setting Up a Base Project | Instructor View

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

RStudio Team

RStudio Cloud | Roles | Instructor View

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

RStudio Team

RStudio Cloud | Reusing a Workspace | Instructor View

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

RStudio Team

RStudio Cloud | Project Types | Instructor View

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

RStudio Team

RStudio Cloud | Overview | Instructor View

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

RStudio Team

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

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

RStudio Team

RStudio Cloud | Invite Learners | Instructor View

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

RStudio Team

RStudio Cloud | Exporting Projects | Instructor View

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

RStudio Team

RStudio Cloud | Creating an Assignment | Instructor View

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

RStudio Team

RStudio Cloud | {rscloud} Package | Instructor View

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

RStudio Team

Posit Cloud | Creating a Shared Space | Instructor View

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

RStudio Team

Oliver Bridges - Smart DCC | Energy Meetup | RStudio

Navigating the Analytics Journey at Smart DCC Presentation by Oliver Bridges Abstract: This talk will highlight the journey we have been (and still are) on that took us from outsourcing our analytics, to running some basic SQL and using graphical-based visualization tools, to open-source RStudio, and eventually to RStudio Team. Our evolution has been shaped by the huge volumes of data and demands from our customers that really led us to RStudio. While none of us are defined as data scientists, rather data engineers, our whole team has been on this journey with us and we're continually learning as we go. From creating our own packages, APIs, pins, and Shiny dashboards we have now created workflows that allow us the deal with 10s of millions of records of aggregated data. As you start (or continue) on your own journey, I'd love to help share what we've learned along the way. Bio: Oliver Bridges is the Head of Data Science and Analytics at Smart DCC, designing and now managing a team of 15. Ollie has 15 years of experience in telecommunication and 10 years in smart metering. He has worked all around the world, including Cape Town, Copenhagen, Sydney, Amsterdam, Brussels, Dublin, and the UK. Ollie lives in Cornwall UK, and has worked remotely since the start of the pandemic, but before that worked in Manchester (about 300 miles from my house). In his spare time, he loves sport and coding! Smart DCC's mission is to help digitise Britain’s energy network, and make a critical contribution in the effort to achieve net-zero greenhouse gas emissions, and improve the nation’s connectivity

RStudio Team

Joe Cheng - Extending Tableau with R and Shiny

Cascadia R Conf 2021 Session Talks Joe Cheng Pronouns: he/him RStudio, PBC / Redmond, WA Session: Shiny and R Extending Tableau with R and Shiny Many organizations rely on Tableau to provide day-to-day insights from their data. Thanks to Tableau’s point-and-click interface and focused feature set, almost anyone can produce attractive and useful visualizations and dashboards. On the other hand, tasks that are routine to R users can sometimes be difficult or impossible to achieve with Tableau. So it’s common to use R to preprocess data that is then fed to Tableau, or use Tableau’s R integration features to fortify their data tables with columns calculated by R. But until now, there hasn’t been an obvious way to let Tableau take advantage of R’s powerful visualization and reporting capabilities. This talk will introduce {shinytableau}, an experimental new package that lets R users create reusable Tableau dashboard extensions, using the power of R and Shiny to generate visualizations that are not achievable with Tableau alone. Bio: Joe Cheng is RStudio's Chief Technology Officer, and the original creator of Shiny. He was the first employee at RStudio, joining founder J.J. Allaire in 2009 to help build the RStudio IDE. He continues to work on packages at the intersection of R and the web

Joe Cheng

Managing COVID vaccine distribution in West Virginia | RStudio

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

RStudio Team

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

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

RStudio Team

Hadley Wickham | testthat 3.0.0 | RStudio (2020)

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

Hadley Wickham

Alex Gold | Managing Packages for Open Source Data Science | RStudio

What You'll Learn: With over 15,000 R packages on CRAN, over 230,000 on PyPI, and more arriving every day, the task of managing a package environment for data science in R and Python can be daunting. In this webinar, you will learn about the most current strategies and tooling for creating and maintaining a reproducible package environment. Whether you’re an individual data scientist or the administrator of an entire RStudio Team cluster, you’ll better understand how you can enhance your ability to work easily and safely with open-source data science packages. About Alex: Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using R and RStudio products. Before coming to RStudio, Alex was a data scientist and worked on economic policy research, political campaigns, and federal consulting

RStudio Team

Maya Gans | Starting an R Book Club: Cooking Up Friendships in Isolation | RStudio

Amidst a global pandemic there’s been one consistency in my life: every Tuesday a group of friends meet to discuss Hadley Wickham’s Advanced R. I crowdsourced interest using the R4DS Slack and the results were magical: a group of incredibly curious and generous people motivated to learn and teach one another emerged. The meetings evolved from a group of strangers giving timid presentations to a safe space where we share and improve upon personal applications. The 1 club has grown to 3 regional cohorts, and became a model for discussing other books too. This talk will go over the structure of our meetings in hopes of empowering others to start their own book clubs, showcasing a different way people can create and engage in communities. About Maya: I am a mycologist turned data scientist. I love statistics, data visualization, and all things JavaScript. I am currently an intern at RStudio designing a visual block-based programming language. I create music-related infographics for JamBase.com. When I’m not coding, I’m climbing tall mountains

Hadley Wickham

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

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

RStudio Team

Hadley Wickham | Maintaining the house the tidyverse built | RStudio

Hadley will talk about how the tidyverse has evolved since its creation (just five years ago!). You'll learn about our greatest successes, learn from our biggest failures, and get some hints of what's coming down the pipeline for the future. About Hadley: Hadley Wickham is the Chief Scientist at RStudio, a member of the R Foundation, and Adjunct Professor at Stanford University and the University of Auckland. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. You may be familiar with his packages for data science (the tidyverse: including ggplot2, dplyr, tidyr, purrr, and readr) and principled software development (roxygen2, testthat, devtools, pkgdown). Much of the material for the course is drawn from two of his existing books, Advanced R and R Packages, but the course also includes a lot of new material that will eventually become a book called "Tidy tools"

Hadley Wickham