Shiny Team

Shiny at posit::conf(2025)

Find Shiny talks and workshops at posit::conf(2025) in Atlanta

Shiny Team

Posit at SciPy 2025

Catch up on the Shiny team’s talks and tutorials from SciPy 2025

Shiny Team

Shiny 1.4 brings bookmarking and Generative AI docs

Shiny v1.4 is now available on PyPI! This release includes bookmarking, new Generative AI documentation, and more

Shiny Team

Creating Responsive Layouts in Shiny with layout_columns()

Learn how to use Shiny’s layout_columns() to build adaptive web apps that look great on any screen size

Shiny Team

Level Up Your Shiny Forms: Accordions + Dynamic Goodness

Mastering Shiny Forms: Dynamic Updates with Accordion Panels

Shiny Team

Greg Swinehart - We CAN have nice Shiny apps: What’s new in Shiny’s UI & UX

The Shiny team has made big steps to help apps’ UI and UX scale more thoughtfully and elegantly. We’ve built a cohesive theme that refreshes all of our colors and components. Our layouts will better serve your apps as they grow. And we’ve created Shiny Templates, which are opinionated boilerplate code to help users create small, simple apps or large, complicated, multi-page dashboards that just look right.

Talk by Greg Swinehart

Greg Swinehart, Shiny Team

Shiny for Python 1.2.0

Shiny’s @render.data_frame has a new integration with narwhals, gaining support for multiple data frame types!

Shiny Team

Shiny 1.2 Release: Wrap your data frame with Narwhals! 🎉 | Barret Schloerke | Posit

In this video, Barret Schloerke from the Shiny team walks us through the exciting features of Shiny 1.2. One of the major updates includes the integration of Narwhals, allowing effortless computation on data frames without needing detailed knowledge of the data structure. Barret also demonstrates a simple Shiny app displaying S&P 500 data using PyArrow, Polars, and Narwhals.

Learn how these updates simplify your data workflows, with minimal code needed to run complex data frame operations.

Useful Links:

• Shiny Component Gallery: [https://shiny.posit.co/py/components/] • Learn more about Narwhals: [https://narwhals-dev.github.io/narwhals/]

Timestamps: 0:00 - Introduction and Shiny 1.2 Release Overview 0:12 - Narwhals Integration and Data Computation 0:31 - PyArrow Application Code Breakdown 1:20 - Using S&P 500 Data with Pandas, Polars, PyArrow, and Narwhals

#Shiny #DataScience #narwhals #ShinyApp #Polars #Python

Barret Schloerke, Shiny Team

Testing Shiny Apps in Python

The shiny add test command simplifies the process of creating test files for Shiny apps, allowing developers to easily generate and run robust tests using built-in UI testing controllers for various components

Shiny Team

Announcing Shiny for Python 1.0

Shiny for Python’s level of quality, runtime stability, API and feature set make it ready for a big announcement

Shiny Team

Editable data tables in Shiny for Python 0.9.0

Editable data frames, empowered renderers, browser error console, and more in Shiny for Python 0.9.0

Shiny Team

Introducing Shiny for Python Templates

Get started quickly with Shiny for Python’s new app templates

Shiny Team

Introducing: Shiny Express

A brand new way to write Shiny apps in Python

Shiny Team

Shiny for Python 0.6.1

Templates, an improved debugger, and a sneak peek at Shiny Express

Shiny Team

Shiny for Python 0.6.0

Fewer decorators, new page functions and, many new components

Shiny Team

Keynote, Winston Chang: Lessons and opportunities with Shiny for Python

About the keynote: For the past 10 years, the R community has been able to use Shiny to bring interactive data analyses to the web. Last summer, we announced that Shiny would also be available to for Python. In this talk, I’ll discuss what we’ve learned along the way, as well as the opportunities that Shiny for Python opens up. We are at the beginning of an exciting new era for Shiny, and I hope that community of old and new Shiny users make the most of it!

Speaker’s bio: Winston Chang is a software engineer at Posit, PBC, who has served in various roles on the Shiny team, including team lead. He has contributed to many widely-used packages in the R ecosystem, including Shiny, devtools, and ggplot2, and is the creator of several packages, including shinydashboard, R6, and profvis. Recently he has been busy working on Shiny and Shinylive for Python. Winston has a Ph.D. in psychology from Northwestern University and is the author of the R Graphics Cookbook, published by O’Reilly Media

Shiny Team, Winston Chang

Carson Sievert: Towards the next generation of Shiny UI

About the talk: Shiny, the R package for creating interactive web graphics, recently celebrated its 10th birthday. Since then, Shiny has grown tremendously in many areas (e.g., performance, functionality, extensions, etc); however, a “hello world” Shiny app still looks like it did 10 years ago. This is mostly because Shiny goes to great lengths to ensure backwards compatibility; and as a result, default Shiny UI will likely continue to be based on Bootstrap 3 (a CSS styling framework released in 2010). However, thanks to the new bslib R package, it is now easy to opt-into a modern Bootstrap 5 foundation that “just works” with Shiny, R Markdown, flexdashboard, pkgdown, bookdown, and more. In addition to upgrading Shiny’s Bootstrap dependency, bslib also makes it much easier to do custom theming, leverage modern layout techniques, and create custom components (all from R without any CSS/HTML/JS required). At this point, bslib is still maturing, and does not yet provide what we’d consider a “complete UI toolkit”, but it should eventually replace and/or improve upon all of Shiny UI. In this talk, I’ll highlight bslib features that we’re most excited about (e.g., expandable cards, accordions, (sidebar) layouts, input controls, etc.), discuss some best design practices for improving user experience with these tools, and present some real world examples of these tools in action.

Speakers’ bio: Carson is a software engineer on the Shiny team at Posit. He joined Posit in 2018, and in recent years, has focused primarily on Shiny for Python and improving Shiny UI. The Shiny UI work has manifested in the creation and development of many R packages such as bslib, thematic, htmltools, htmlwidgets, sass, shiny, rmarkdown, flexdashboard, and more. Carson also has a PhD in statistics, is a recipient of the ASA’s Chambers Statistical Software Award, has maintained the R package plotly since 2015, authored the book “Interactive data visualization with R, plotly, and shiny”, and ran a successful freelance consulting service for numerous years

Carson Sievert, Shiny Team

Barret Schloerke: Lessons Learned Testing 2500+ Shiny Apps Every Day

About the Talk: The Shiny team tests 2500+ different combinations of Shiny Applications, R versions, and operating systems to verify no feature regressions occur within the bleeding edge of the Shiny-verse. Over the past few years, we have learned a few lessons to keep our tests robust and honest. There is a non-zero chance that a test failure is not your fault. When working with CI systems, external influences such as installation failures or slower computing power will prevent tests from executing properly. A picture is worth a thousand tests but visual testing is known to have a non-zero false-positive rate. To address this, we have utilized threshold based image comparisons for flaky tests. We have found that it is best to test the minimal amount of content where possible. Changes in App package dependencies can produce unintended changes in expected outputs. These hard learned lessons have reduced unreliable test results allowing the Shiny team to quickly respond to feature regressions.

Speaker’s bio: Dr. Barret Schloerke is a Software Engineer at Posit. He currently develops and maintains many R packages in the Shiny ecosystem at RStudio including shiny, reactlog, plumber, learnr, leaflet, and shinyloadtest. Dr. Schloerke received his PhD in Statistics from Purdue University under the direction of Dr. Ryan Hafen and Dr. William Cleveland, specializing in Large Data Visualization

Barret Schloerke, Shiny Team

JooYoung Seo | Accessible Data Science Beyond Visual Models | RStudio

Full title: Accessible Data Science Beyond Visual Models: Non-Visual Interactions with R and RStudio Packages

Data science is full of vision-dominant practices, and most data scientists rely heavily on visual models.

However, data science itself should require insight and computational thinking beyond what is just seen by eyes.

JooYoung Seo, who is a blind data scientist and who was working for RStudio’s accessibility projects over the summer 2020, will talk about his experience with some non-visual techniques to interact with data.

If you would like to know more about various ways of making data science accessible via R, and new accessibility features introduced in RStudio IDE and Shiny, his demonstration without sight will be thought-provoking.

About JooYoung: JooYoung Seo is a Ph.D candidate in the Learning, Design, and Technology program at the Pennsylvania State University, and internationally certified accessibility professional whose research and development focuses on accessible computing for all. As an RStudio’s double-certified data science instructor (i.e., Tidyverse + Shiny), who is blind, he is committed to making data science ecosystem more accessible to people with and without dis/abilities using R. To this end, he has been actively contributing to R open-source projects including Shiny, RMarkdown, bookdown, and distill for accessibility, and interned on the RStudio IDE and Shiny team as an accessibility engineer in summer 2020

Shiny Team

Alan Dipert | Integrating React.js and Shiny | RStudio (2019)

React.js is a thriving JavaScript library that eases encapsulating and sharing sophisticated component libraries. The React.js ecosystem is filled with components for doing everything from color selection (react-color) to animation (react-spring). While it’s always been technically possible to integrate React.js components with Shiny applications, it hasn’t always been particularly obvious how. To make it easier, we augmented the excellent reactR package with functions specifically designed to make it easier to create new htmlwidgets, inputs, and outputs based on React.js components. In this talk, I will further motivate and demonstrate these new tools and do my best to empower the audience to try them out.

About the Author Alan Dipert Alan is a software engineer on the Shiny team at RStudio. In the past, he’s helped build web applications, reporting pipelines, and many things between. When he’s not working, Alan likes to spend his time reading or being with his family

Shiny Team