Shiny for R

Should your shiny app be an R package? | Martin Frigaard | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! This week’s guest was Martin Frigaard, Senior Shiny Application Developer! Some topics covered in this week’s Hangout were Shiny app development and package development, career transitions in data science, technical writing and journalism in data science, and maintainability and best practices for Shiny apps. One community member asked: “How does one become a Shiny developer?” Martin’s paraphrased answer: Start with R programming and spend time learning package development, which will make you a better Shiny app developer. Try to be bilingual between Shiny for R and Shiny for Python. Build pet projects with data you’re interested in to learn without pressure from stakeholders or deadlines. Once you have a decent understanding, you can advertise yourself as a freelance developer and start taking on projects. Resources mentioned in the video and chat: Martin Frigaard’s Book: Shiny App-Packages: Getting your app into an R package → https://mjfrigaard.github.io/shiny-app-pkgs/ Rhino Shiny App Framework → https://appsilon.github.io/rhino/ Shiny → https://shiny.posit.co/ Golem Shiny Framework “universe” → https://golemverse.org/ Golem Package → https://thinkr-open.github.io/golem/ RDM Weekly Newsletter by Crystal Lewis → https://rdmweekly.substack.com/ Intro to Golem from the book Engineering Production-Grade Shiny Apps → https://engineering-shiny.org/golem.html Shiny Basics Tutorial → https://shiny.posit.co/r/getstarted/shiny-basics/lesson1/ Mastering Shiny Book → https://mastering-shiny.org/ Analyzing Baseball Data with R → https://beanumber.github.io/abdwr3e/ Demystifying Shiny Modules by Deepsha Menghani → https://github.com/deepshamenghani/Demystifying_Shiny_modules Deepsha Mangani’s Cascadia Conf Talk on Modules → https://www.youtube.com/watch?v=ZEz_8tEuq4o Chores Package → https://simonpcouch.github.io/chores/ BTW Package → https://posit-dev.github.io/btw/ Storybench (Journalism and R) → https://www.storybench.org/ Scott Page’s Diversity Research → https://sites.lsa.umich.edu/scottepage/research-2/diversity-research/ Public Sector Shiny Gallery → https://pub.current.posit.team/public/Public_Sector_Shiny_Showcase/ Posit Blog: Why Your Data Science Team Might Need a Shiny Deployment Engineer → https://posit.co/blog/why-your-ds-team-might-need-a-shiny-deployment-engineer Posit Champions Business Case Video → https://posit.co/champions/business-case RV Package Docs → https://a2-ai.github.io/rv-docs/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 02:57 “How does one become a Shiny developer?” 04:09 “Can you talk about developing Shiny apps as packages?” 09:50 “What IS Shiny? It sounds like software engineering!” 16:53 “How do you get your team to switch to Shiny instead of Tableau or other point-and-click tools?” 22:20 “Can you explain Shiny modules like I’m five?” 30:20 “What else do you wish Shiny could do?” 35:16 “How do your journalism interests interact with your Shiny work?” 43:58 “What advice would you give analysts who want to improve their code quality as Shiny apps grow more complex?” 49:06 “Are there practices from IT org developers that Shiny app developers could learn from?”

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

Observability at scale: Monitoring Shiny Applications with OpenTelemetry Speaker(s): Barret Schloerke Abstract: Understanding what happens behind the scenes in production Shiny applications has always been challenging. When users experience slow response times or unexpected behavior, developers are left guessing where bottlenecks occur. This talk introduces OpenTelemetry integration for Shiny for R, a new approach to profiling code and understanding application behavior. Through a chat-enabled weather application, we’ll explore how complex user interactions trigger cascading events across multiple processes. You’ll learn how OpenTelemetry’s “high-quality, ubiquitous, and portable telemetry” can provide complete visibility into your Shiny application’s performance with minimal setup… just a few environment variables! After walking through the chat app, you’ll have a taste of how to implement comprehensive monitoring for your Shiny applications in production, enabling you to proactively identify and resolve unexpected performance issues. Materials - https://github.com/schloerke/presentation-2025-09-17-posit-conf-otel posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Barret Schloerke

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

Build Shiny apps with AI ✨

View the full video here: https://www.youtube.com/watch?v=fJNKdwdVQ8Q Try it out here, free: https://gallery.shinyapps.io/assistant/ Get started with Shiny for R and Python: https://shiny.posit.co #pythoncontent #positshorts

Shiny for R shinyapps Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Shiny Assistant: Prototype and build Shiny applications with the help of AI | Winston Chang | Posit

Have you ever had an idea for a great web application with Shiny but felt something holding you back from getting started? Maybe it’s that you don’t know where to start, or that you don’t know which packages use to build the app, or maybe it’s just that you can’t muster the energy to get started. Sometimes you just need a little help to get unstuck. We’re excited to announce a new addition to the Shiny ecosystem that can help: Shiny Assistant. Try it out here, free: https://gallery.shinyapps.io/assistant/ Get started with Shiny for R and Python: https://shiny.posit.co

Winston Chang

Shiny for R shinyapps Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

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

Shiny for Python Shiny for R shinylive Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

posit::conf(2023) Workshop: Getting Started with Shiny for R

Register now: http://pos.it/conf Instructor: Colin Rundel Workshop Duration: 1-Day Workshop This course is for you if you: • are comfortable with the basics of R, such as writing functions, indexing vectors and lists, debugging simple errors, and working with data structures like data frames • are interested in creating interactive web applications • have no or minimal experience with Shiny for R Shiny is an R package that makes it easy to build interactive web apps straight from R. This workshop will start at the beginning: designing and creating user interfaces, learning and mastering the reactive model that connects your R code to the interface, and deploying apps publicly and privately. We will wrap up with some intermediate-level tools: debugging and modularizing your apps and implementing dynamic user interfaces. In the end, you’ll be a confident Shiny user, able to design interactive apps to achieve your purpose and produce a polished and professional implementation. If you have a bit of experience, you’ll see things in a new way. If you don’t, we’ll get you started on the right footing

Shiny for R Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate