The shinyapps R package is deprecated and no longer maintained. It has been fully replaced by the rsconnect package for deploying applications to shinyapps.io.
This package is no longer receiving updates or support. Users should migrate to rsconnect for all deployment needs. The repository may be removed in the future, so any workflows or documentation referencing this package should be updated to use rsconnect instead.
Contributors#
Resources featuring shinyapps#
How to deploy Shiny apps in 2026 | Alex Chisholm | Data Science Lab
The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you!
The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow.
On this call, Libby Heeren is joined by Posit product manager Alex Chisholm as he walks through the evolution of shiny app deployment over the years, how to deploy shiny apps in the modern era, and peeks into Posit’s roadmap for future development. Do you call it “deployment” or “publishing” when it comes to Shiny apps? 🤔
This is a super friendly and conversational space, and being there live in the Discord chat can’t be beat!! We hope you get to join us sometime soon.
Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Daniel Chen, Alex Chisholm
Alex Chisholm’s LinkedIn: http://www.linkedin.com/in/chisholm1
Resources from the hosts and chat:
Posit Connect Cloud for deploying Shiny apps in the modern era: https://connect.posit.cloud/ Install Positron: https://positron.posit.co/ Simon Couch’s blog post on local LLMs not being good enough yet: https://www.simonpcouch.com/blog/2025-12-04-local-agents/ Blue-Green Shiny App Deployments using Posit Connect posit::conf(2025) talk by Ryszard Szymański: https://youtu.be/QEEGLWj0nas Digital Ocean: https://www.digitalocean.com/ Ollama local LLM: https://ollama.com/ py-sidebot app template: https://shiny.posit.co/py/templates/sidebot/ querychat app template: https://shiny.posit.co/py/templates/querychat/ Dan Chen mentioned Render in the chat as an alternative to Digital Ocean: https://render.com/ Alex Chisholm’s AB testing GitHub repo example: https://github.com/alex-chisholm/shiny-r-abtesting Edward in the chat shared a GitHub repo for using GitHub actions to execute remote SSH commands: https://github.com/appleboy/ssh-action Abu in the chat shared blue-green vs. canary deployments: https://octopus.com/devops/software-deployments/blue-green-vs-canary-deployments/ Frank in the chat mentioned Simon’s blog on using local LLMs with the chores package: https://www.simonpcouch.com/blog/2025-12-10-chores-0-3-0/
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Thanks for learning with us!
Timestamps 00:00 Introduction 03:03 Meaningful applications and value creation 05:31 The evolution of Shinyapps.io and Posit Connect 08:12 DigitalOcean and Droplets 09:36 DigitalOcean vs. commercial cloud providers, 11:48 Comparisons: DigitalOcean, Azure, and AWS 14:47 Replicating local environments with Docker 16:51 The open-source Shiny Server 18:20 Use case: University of Illinois CITL 20:02 Key considerations for deployment decisions 21:53 GitHub Actions and version control 23:31 Addressing single points of failure and maintainability 24:38 Posit Connect Cloud features and portfolio 26:01 Beyond Shiny: Quarto, Streamlit, and Dash 27:07 Handling secrets and database credentials 28:56 Custom vanity links vs. UUIDs 30:04 Blue-Green deployment strategies 31:55 “Is it easy to set up a developer workflow?” 34:46 Guardrails for AI powered apps and token usage 37:32 Small language models and Ollama 38:29 Sidebot AI demo and LLM integration 39:41 Understanding manifest.json and dependencies 45:00 Automatic publish on GitHub push 46:51 The future of Shinyapps.io and migration 48:33 “Did you just build a custom agent for that specific dashboard?” 51:43 Publishing from RStudio IDE to Connect Cloud 54:16 Preview: Inspecting website APIs for data harvesting
Exploring Positron settings | Isabel Zimmerman & Davis Vaughan | Data Science Lab
The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you!
The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow.
On this call, Libby Heeren is joined by Posit engineers Isabel Zimmerman and Davis Vaughan as they share some of their favorite settings in Positron, a super customizable data science IDE. Come laugh with us as we can’t seem to figure out that VSCode calls rainbow parentheses “bracket pair colorization”
Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Daniel Chen, Isabel Zimmerman, Davis Vaughan
Resources from the hosts and chat: Install Positron: https://positron.posit.co/ Positron docs on keyboard shortcuts: https://positron.posit.co/keyboard-shortcuts.html Nathan Jeffery’s “click to open a .RDS file” keybinding: https://nathan-jeffery.netlify.app/blog/2025-08-26-read-rds-positron/ Positron R pipe setting (paste in browser and it’ll open in Positron): positron://settings/positron.r.pipe One of Dan Chen’s faves, the native tab feature in VSCode + Positron: https://lucasprag.com/posts/underrated-vscode-feature-native-tabs/ The list of RStudio keybindings that you get when you turn on RStudio keybindings in Positron: https://positron.posit.co/migrate-rstudio-keybindings.html Indent rainbow extension: https://open-vsx.org/extension/oderwat/indent-rainbow Rainbow brackets setting (paste in browser and it’ll open in Positron): positron://settings/editor.bracketPairColorization.enabled Setting hierarchy (User vs Workspace settings) in Positron: https://code.visualstudio.com/docs/configure/settings#_settings-precedence Rainbow CSV extension (not by Posit): https://marketplace.visualstudio.com/items?itemName=mechatroner.rainbow-csv Positron +1ePositron, an extension pack for dev and data science, by Garrick Aden-Buie: https://open-vsx.org/extension/grrrck/positron-plus-1-e Publishing from VS Code or Positron: https://docs.posit.co/connect/user/publishing-positron-vscode/ Posit Connect Cloud plans: https://connect.posit.cloud/plans Enter Folder extension that Libby mentions: https://open-vsx.org/extension/xiangda/enter-folder Catppuccin themes (shared by Rory Lawless, and now some of Libby’s favorites!): https://open-vsx.org/extension/Catppuccin/catppuccin-vsc
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Thanks for learning with us!
Timestamps 00:00 Introduction 00:42 Guest Introductions: Isabel and Davis 02:41 Positron Settings overview 04:11 How to enable “Format on Save” 04:34 “How do I open settings in JSON or UI?” 05:10 Auto Save on focus change 08:26 Enabling RStudio key bindings 09:28 “Why doesn’t the cursor move with code edits?” 12:18 User vs. Workspace settings 14:34 Creating and using Profiles 16:13 “Can I use the magrittr pipe with Control+Shift+M?” 17:23 Searching and managing keyboard shortcuts 19:42 Creating custom code snippets 21:31 The Indent Rainbow extension 24:04 Enabling rainbow parenthesis/brackets 25:08 Managing Python and R interpreters 26:32 Rearranging and hiding UI panes 28:04 Rainbow CSV and favorite extensions 29:26 Using the Enter Folder extension 31:05 Understanding the setting hierarchy 32:48 Adding symbols to Quick Open search 36:00 “Is there a way to shift focus using keyboard shortcuts?” 38:04 Modifying keybindings JSON for specific languages 39:20 “How do you find trustworthy extensions?” 43:11 “How can I publish to shinyapps.io from Positron?” 44:03 Deploying with Posit Publisher and Connect Cloud 48:32 Customizing themes with RainGlow extension 50:36 “Is there an Import Data Set wizard in Positron?” 53:01 Conclusion and community resources
People over computers: engineering leadership | Elliot Murphy | Data Science Hangout
ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you!
We were recently joined by Elliot Murphy, a VP of Engineering at Posit, to chat about engineering leadership philosophy, balancing maintenance versus innovation and risk, non-traditional career paths and the value of lifelong learning, and data science tool breakdowns including Posit Cloud, Posit Connect Cloud, and shinyapps.io.
In this Hangout, we explore some philosophical approaches to engineering and management, where Elliot stresses that focusing on people is more important than computers, and human relationships tend to outlast relationships with a given company or employer. Elliot discusses his role as primarily listening and synthesizing the brilliance of others, creating a supportive environment, and making space for people to take on risk and propose new ideas. He also highlighted the value of clear communication when addressing high-pressure environments (like when you’re sunsetting a software product).
Resources mentioned in the video and zoom chat: Cognition in the Wild (Book discussed regarding joint cognition) → https://mitpress.mit.edu/9780262581462/cognition-in-the-wild/ datapasta (R package) → https://github.com/MilesMcBain/datapasta AI Capabilities in Positron Workflow Demo → https://pos.it/workflow-demos
If you didn’t join live, one great discussion you missed from the zoom chat was about the surprising value and broad resources offered by local libraries, including free access to services like LinkedIn Learning, paid periodicals like the NY Times, and streaming platforms like Kanopy. Attendees praised libraries as the “best resource we hardly hear about” and key providers of freedom of information. Do you love your local library?!
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Thanks for hanging out with us!
Timestamps: 00:00 Introduction 02:04 “Can you tell us a little bit about your journey to where you are?” 04:10 “What does a VP of engineering do? And what types of things are important to you from a philosophical standpoint for engineering and management?” 06:21 “What about your job lets you focus on the people?” 06:33 “Is there also a pressure to innovate?” 09:28 “How do you argue to leadership at a strategic level that that maintenance work is important to take up people’s time?” 11:48 “How do you balance engineers and data scientists wanting to incorporate best practices with fast prototyping?” 16:46 “What distinguishes science from engineering in data and also more broadly?” 18:28 “Any resources or any books that helped you bridge the gap between a non traditional background and your current position?” 23:00 “What is the secret to creating a good environment for the team?” 25:23 “Are there basics that you feel analysts or data scientists should be aware of in terms of engineering that would help their work?” 28:47 “How do you make decisions about when to either sunset a project or change directions?” 31:20 “What can those doing maintenance work do to add value to the organization to be safe from being displaced by automation?” 35:02 “What do you think many data scientists or analysts are missing about engineering infrastructure today that could simplify their day to day workflows?” 35:52 “How can a professional transition from a business intelligence role to a data scientist position?” 43:47 “What is your mantra or best practice to crack these professional certification exams?” 45:22 “Tell us the difference between Posit Cloud, Posit Connect Cloud, Posit Connect, and shinyapps.io.” 49:03 “How can a team avoid the trap of over-engineering?”
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
Create a PydyTuesday Shiny app with Shiny Assistant
Watch Sara Altman quickly prototype a Shiny app for PydyTuesday with Shiny Assistant, an AI-powered tool designed to help you with Shiny. Shiny Assistant can create Shiny apps from scratch, debug or assist with existing apps, and answer general questions about Shiny.
Shiny Assistant: https://gallery.shinyapps.io/assistant/
Learn more:
- Shiny for Python: https://shiny.posit.co/py/
- The potential for AI-powered Shiny app prototyping with Shiny Assistant: https://posit.co/blog/ai-powered-shiny-app-prototyping/
- TidyTuesday project: https://github.com/rfordatascience/tidytuesday
- Posit PydyTuesday GitHub repo: https://github.com/posit-dev/python-tidytuesday
- Other videos in this PydyTuesday playlist: https://youtube.com/playlist?list=PL9HYL-VRX0oSDQjicFMLIIdcLv5NuvDp9&si=i09_CuKmjiV86D-9
#pythoncontent
Shiny Assistant - Prototype Shiny for Python Apps with AI
Ryan Johnson walks through how to use Shiny Assistant, an AI-powered tool for Shiny for Python app development. Learn how to quickly create and interact with a Shiny app based on your own description. Shiny Assistant is a powerful tool for rapid prototyping and iteration. You can also ask it questions about how to use Shiny.
Shiny Assistant: https://gallery.shinyapps.io/assistant/
Learn more:
- Shiny for Python: https://shiny.posit.co/py/
- The potential for AI-powered Shiny app prototyping with Shiny Assistant: https://posit.co/blog/ai-powered-shiny-app-prototyping/
- TidyTuesday project: https://github.com/rfordatascience/tidytuesday
- Posit PydyTuesday GitHub repo: https://github.com/posit-dev/python-tidytuesday
- Other videos in this PydyTuesday playlist: https://www.youtube.com/playlist?list=PL9HYL-VRX0oSDQjicFMLIIdcLv5NuvDp9
#pythoncontent
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?
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The changing landscape of data science | Kanchana Padmanabhan | Data Science Hangout
To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you!
We were recently joined by Kanchana Padmanabhan, Director of Data and AI at Homebase, to chat about data science team structures, the role of math in understanding LLMs, building effective hackathons, and communicating model insights to stakeholders.
In this Hangout, we explore the importance of understanding the probabilistic nature of LLMs and how that understanding should influence how data scientists approach their work. We also discussed how to structure a hackathon to encourage learning, centering on the customer problem, and collaboration between technical teams and business stakeholders.
Resources mentioned in the video and zoom chat: List of R Conferences for 2025 → https://rworks.dev/posts/r-conferences-2025/ Posit Conference Call for Talks → https://posit.co/blog/speak-at-posit-conf-2025/ Julia Silge’s workflow demo on model cards → https://www.linkedin.com/posts/posit-software_join-us-for-a-live-workflow-demo-on-creating-activity-7287998741557522432-jseQ?utm_source=share&utm_medium=member_desktop Shiny Assistant Gallery → https://gallery.shinyapps.io/assistant Data Science Hangout Playlist → https://www.youtube.com/playlist?list=PL9HYL-VRX0oTu3bUoyYknD-vpR7Uq6bsR Add Posit Team End-to-End Workflows to calendar → https://evt.to/aoimiohuw Making of a Manager Book → https://www.amazon.com/Making-Manager-What-Everyone-Looks/dp/0735219567
If you didn’t join live, one great discussion you missed from the zoom chat was about how to gain domain knowledge for a new industry, where attendees shared their experiences and advice. Let us know below if you’d like to hear more about this topic!
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Alex Chisholm - Deploying data applications and documents to the cloud
Creating engaging data content has never been easier, yet easily sharing remains a challenge. And that’s the point, right? You cleaned the data, wrangled it, and summarized everything for others to benefit. But where do you put that final result? If you’re still using R Markdown, perhaps it’s rpubs.com. If you’ve adopted Quarto, it could be quartopub.com. Have a Jupyter notebook? Well, that’s a different service. And this is just for docs. Want to deploy a streamlit app? Head to streamlit.io. Shiny? Log into shinyapps.io. Dash? You could use ploomber.io, if you have a docker file - and know what that is. This session summarizes the landscape for online data sharing and describes a new tool that Posit is working on to simplify your process.
Talk by Alex Chisholm
Slides: https://docs.google.com/presentation/d/1zulnuaT2Dm_vM0l9Gd3vS26KWJuAf0gJ1pcFKjTUNbI/edit?usp=sharing
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 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
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
How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny)
This is a quick-start guide to Shiny for Python. It’s part 1 of a multi-part series. Data scientists need to quickly build web applications to create and share interactive visualizations, giving others a way to interact with data and analytics. Shiny helps you do this.
In this video, we’ll walk you through the basics of setting up Shiny for Python, creating your first app, and deploying it so others can use it. We’ll cover:
- Installing Shiny and necessary dependencies
- Writing and running your first Shiny app
- Basic UI and server structure
- Deploying your app online
- Helpful Links
Shiny for Python Homepage: https://shiny.posit.co/py/
The link to the final app can be found here: https://keithgalli.shinyapps.io/final-app/
Follow along with the code examples provided in this repository: https://github.com/KeithGalli/shiny-python-projects
Check out the complete documentation here: https://shiny.posit.co/py/api/express/
Stay tuned for part 2, where we’ll dive deeper into advanced features and customization options.
Video Timeline! 0:00 - Intro to Shiny & Video Overview 1:43 - Getting Started with the Shinylive Playground 2:44 - Building a custom visualization with Shinylive 5:18 - Easily sharing the code/application for a Shinylive app 6:12 - Building a Shiny Express App locally (VSCode) 9:40 - How to run app if you’re not using VSCode 10:17 - Further customization of our app (adding title, using CSV data, dynamic input) 17:15 - Deploying our Shiny app to the web 21:20 - Conclusion & what’s coming next in the series
Video by @KeithGalli
All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw
Towards the Next Generation of Shiny UI
Presented by Carson Sievert
Create awesome looking and feature rich Shiny dashboards using the bslib R package.
Shiny recently celebrated its 10th birthday, and since its birth, has grown tremendously in many areas; however, a hello world Shiny app still looks roughly like it did 10 years ago. The bslib R package helps solve this problem making very easy to apply modern and customizable styling your Shiny apps, R Markdown / Quarto documents, and more. In addition, bslib also provides dashboard-focused UI components like expandable cards, value boxes, sidebar layouts, and more to help you create delightful Shiny dashboards.
Materials:
Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.#
Talk Track: Shiny user interfaces. Session Code: TALK-1124
![[79] Create a Python Web App Using Shiny (Gordon Shotwell)](/resources/videos/2023-05-09_79-create-a-python-web-app-using-shiny-gordon-shotwell/thumbnail_hu_2630e0f875c36c4d.webp)
![[79] Create a Python Web App Using Shiny (Gordon Shotwell)](/resources/videos/2023-05-09_79-create-a-python-web-app-using-shiny-gordon-shotwell/thumbnail_hu_8c6d0e42f5563750.webp)
![[79] Create a Python Web App Using Shiny (Gordon Shotwell)](/resources/videos/2023-05-09_79-create-a-python-web-app-using-shiny-gordon-shotwell/thumbnail_hu_c27721823620966c.webp)
[79] Create a Python Web App Using Shiny (Gordon Shotwell)
Join our Meetup group for more events! https://www.meetup.com/data-umbrella
Resources#
- website for presentation: https://shiny.rstudio.com/py/
- https://shiny.rstudio.com/py/docs/reactive-mutable.html
- https://www.shinyapps.io/
- https://huggingface.co/new-space
- https://shiny.rstudio.com/py/docs/shinylive.html
- https://shiny.rstudio.com/py/api/reactive.poll.html#shiny.reactive.poll
About the Event#
Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. If you want to develop a python web application you usually need to choose between simple, limited frameworks like Streamlit and more extensible frameworks like Dash. This can cause a lot of problems if you get started with a simple framework but then discover that you need to refactor your application to accommodate the next user request. Shiny for Python differs from other frameworks because it has tremendous range. You can build a small application in a few minutes with the confidence that the framework can handle much more complex problems. In this workshop we will go through the core limitations of Streamlit, and build a Shiny app which avoids those limitations.
Timestamps#
00:00 Welcome
00:23 Reshama introduces Data Umbrella
03:45 Reshama introduces Gordon Shotwell
04:21 Gordon Shotwell begins
04:29 The motivation to develop Shiny for Python
06:05 The main strength of both the R and Python library
06:56 What Gordon Shotwell will build during his presentation
07:25 Shiny documentation website
08:01 QuickStart for R users showing differences between the R and Python libraries
08:44 All the function reference in Shiny
09:08 Demo starts
09:50 Virtual environment
10:36 How to start shiny app in the terminal
11:15 Install shiny extension in VS Code which makes it easier to preview the web app
11:36 How the output function works on the preview app to execute
12:22 Penguin dataset description for the demo
12:45 Modules/submodules shiny app is built on
13:04 How to add a sidebar layout (sidebar, panel sidebar and panel main)
13:43 How to read in the data and the output functions
14:31 How to define some server logic
14:59 The conventional shiny rule
16:30 Use of slide input
17:50 Where the reactive magic comes in
19:30 Important note on what can really slow down your shiny app
20:14 Importance of Python data copy method when using external dataset
21:01 Important note to avoid dependency inside the render function
21:30 Q&A
29:35 Adding a plot to the output: The UI sides
30:12 Adding a plot to the output: The render sides
32:16 The core principle of reactivity in which you do not want to repeat yourself
33:26 Reactivate calculation concept which allows you to store intermediate values in one place
37:24 Q&A
38:53 Reactive calculations and rendering functions
39:30 Side-effects or user effect. Another class of interactions
41:18 How to tell reactive effect what it should respond to or what events to watch before executing
41:53 How to update the data filter in the side-effect function
42:22 The second important pattern for shiny
43:00 One of the important things to pay attention to once you start learning/using shiny
44:45 Series of Q&A until the end of the video. Some response includes live demo
01:01:03 Gordon Shotwell ends his presentation
01:01:17 Reshama closes the session
About the Speaker#
Gordon Shotwell is a Software Engineer at Posit. He’s been using Shiny to solve business problems for the past ten years.
- LinkedIn: https://www.linkedin.com/in/gshotwell/
Key Links#
- Transcript: https://github.com/data-umbrella/event-transcripts/blob/main/2023/78-gordon-shiny.md
- Meetup Event: https://www.meetup.com/data-umbrella/events/292848290/
- Video: https://youtu.be/pXidQWYY14w
#python #deployment
posit::conf(2023) Workshop: Shiny in Production: Tools and Techniques
Register now: http://pos.it/conf Instructors: Eric Nantz and Mike Thomas Workshop Duration: 1-Day Workshop
This course is for you if you: • had a Shiny application work just fine on your machine, but encounters critical issues after deployment • are eager to prospectively apply techniques before deployment to plan for the unexpected • want to know the benefits and trade-offs between various ways of hosting Shiny applications
Shiny brings tremendous possibilities to share innovative data science workflows with others inside an intuitive web interface. Many in the Shiny community have shared effective development techniques for building a robust application. Even with the best intentions during application development, a myriad of issues can arise once it leaves the confines of your machine. In this one-day workshop, you will implement core techniques to account for common scenarios that arise once your application is used in production, such as accounting for thousands of simultaneous users, how effective profiling can address performance bottlenecks, and ensuring your application is doing as little as possible to ensure a smooth and responsive experience.
This course assumes intermediate knowledge of building Shiny applications in R and prior experience deploying an application to a platform such as the shinyapps.io service or products like Posit Connect
Open Source Environmental Monitoring with Shiny! | Wayne Jones, Shell
What are the critical factors for successful uptake of an application?
On Tuesday, October 18th at 12 ET, we were joined by Wayne Jones, Principal Data Scientist at Shell to learn about the Shiny application that has become a globally adopted industry standard tool.
GWSDAT (www.gwsdat.net ) is an open source, user-friendly, Shiny application for the visualisation and interpretation of groundwater monitoring data. In this meetup, Wayne will tell the story behind GWSDAT since its first release 8 years ago and will explain the critical factors for successful uptake in the environmental monitoring industry.
Resources shared: ⬢ gwsdat.net ⬢ Github: https://github.com/WayneGitShell/GWSDAT/tree/master/inst/extdata ⬢ GWSDAT LinkedIn Group: https://www.linkedin.com/groups/8715423/ ⬢ Shinyapps.io version of the app: https://stats-glasgow.shinyapps.io/GWSDAT/ ⬢ RStudio Connect: https://www.rstudio.com/products/connect/ ⬢ CRAN Task View for Reproducible Research: https://cran.r-project.org/web/views/ReproducibleResearch.html
Data Science Hangout | Michael Chow, Posit | Exploring Team Structure w/ Data Scientists & Engineers
We were joined by Michael Chow, Data Scientist and Software Engineer at RStudio. Michael also previously led a team at the California Integrated Travel Project.
On this week’s hangout there were a lot of thoughts shared on structuring a data science team from both Michael and the broader group:
⬢ Jacqueline Nolis also shared thoughts on this on a data science hangout that there were virtues to different ones, but ended up sold on the decentralized model where data scientists are embedded in teams: https://youtu.be/CcPE29bYGVo?t=325
⬢ Michael agreed that data scientists and analysts should be sitting with the teams that they’re pushing out reports for. Otherwise, I would be trying to send people into those teams to figure out their priorities.
⬢ A data scientist should work with a Project Manager or whoever’s leading the team to push up metrics but also help change the roadmap.
⬢ It leaves a tricky question of where data engineers should be and how they should interact with the team. Today data engineers are often doing more tooling empowerment, so it can be okay to have them a bit more centralized and connect to the data scientists to enforce best practices or enable new pieces for them.
⬢ I think a nice model is for data scientists/analysts to live in the teams and data engineers to be like spokes of a wheel where then the data scientists connect with them and work closely to enforce better best practice and enable new important things.
⬢ Tatsu shared that in thinking of the structure, it’s also important to find your translators and to use the power of feedback. Reach out to those people to start to put that feedback into action.
⬢ George shared that insurance companies have come from a really traditional landscape where they have lots of actuaries working on lots of excel spreadsheets and there can be a lack of knowledge sharing and tool sharing. This is where the data science element comes in. To me, within the organization, you need to have this team which is a mini-spoke if you will, because they are central to the actuarial team. If they are too far removed and they’re back with the IT team, you end up with the old problems because they may not get the business concept communicated back. It’s all about getting enough skills, so they can get stuff done, especially proof of concepts. Maybe after that you can take a step back and then start to look at the centralized model again.
⬢ A central team can help converge to what they see as best practice, but if you’re pushing out something new, exploring a new line of work or area it can be important to set the data engineer there to actually do whatever they need to. Make sure that the converging doesn’t stifle creativity or prevent a team from doing the right thing.
⬢ Manny jumped in to share the perspective from data science being with IT as well, data science is a new field for their company (in real estate) and there’s an identity of where does data science fall. The IT team is fantastic and they’re very structured. Data science is so fluid and creative and non structured at the moment, so you kind of have to look at where it actually should fall.
- please note that some of the points above are summarized and not 100% actual quotes.
Resources shared:
⬢ Tatsu shared in the chat, a few projects that Michael is working on: vetiver: https://vetiver.tidymodels.org/articles/vetiver.html , siuba: https://github.com/machow/siuba ⬢ Libby shared a helpful tip on creating a 2 minutes YouTube video with a cover letter, to get the attention of a hiring manager ⬢ Javier shared an example Shiny app used in an interview: https://javierorraca.shinyapps.io/Bloomreach_Shiny_App/ ⬢ Michael mentioned David Robinson’s screencasts: https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ ⬢ Michael mentioned an article on “What data scientists really do according to 35 data scientists”: https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists ⬢ Rachael shared a blog post link where Jacqueline Nolis talked about team structure as well: https://www.rstudio.com/blog/building-effective-data-science-team-answering-your-questions/#Structure
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Data Science Hangout | Katie Schafer, Beam Dental | Building a Data Science Portfolio
We were recently joined by Katie Schafer, VP of Advanced Analytics at Beam Dental, to discuss the most important things going on with data science leadership.
Great tips & resources shared for this question: What can I do to be able to present my skills and build a portfolio if I’m not able to share my work?
Many people expressed that they are in this position.
Katie: Is it possible to simulate something akin to some of the data you would use? For project portfolios, submitting the actual work is not something that I’ve seen often.
In interviews, you need to be able to discuss the work. Describing the projects is really tough because you want to say something meaningful but you don’t want to be too specific. Having a role play conversation with yourself on questions you’re getting & practicing answers that are more generalized can be helpful. Take one specific problem, translate that to an abstract problem that you tackled with statistics and programming.
Javier: I’ve been in a similar boat. There’s a package called synthpop: https://lnkd.in/g_paFGNB , which helps take actual data points and simulates them to make a completely synthetic data set.
When I was interviewing for my job now I built a shiny app that I deployed to a free shinyapps.io. I styled the whole app to be consistent with their brand. I don’t attribute that alone to getting the job, but the interviewers were super impressed. The ability to change out the theme is so easy, so for anyone who’s trying to get a job this could be an easy way for you to have something that you can change whenever you interview with a new company.
Rick: If you’ve ever worked with a designer - they have a template and they’re not just creating everything from scratch. If you have a nicely organized project as a data scientist, you’re showing your skills as a software engineer.
Zac: I do a lot of personal projects because I don’t use R at work. For these projects I focus the topic on what skills I want to show off. I’m looking to start my career, so I write a blog & include reasons why I did something. I think this makes it a lot easier to share what I’ve done https://lnkd.in/gzhwuCqw
Libby: For me with portfolio work, it’s been difficult because sometimes my contributions are one algorithm inside a larger shared project, so I stick to descriptions, talking about what I did and why I did it, what the result was.
Larry: I describe the topic and present the deck. Sometimes I just make fake data, but talk about the steps I took.
Katie: Some of my richest connections have also come from speaking. R-Ladies has been a great starting point for that. A lot of meetups have gone virtual so you can now join a chapter even if you don’t have one in your city.
Jiwan: Maybe start with a 5 min lightning talk with a topic you’re excited about!
Other resources shared during the chat: Prabha: Technical debt in ML systems - https://proceedings.neurips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf Rachael: R community explorer app: https://benubah.github.io/r-community-explorer/rugs.html Javier: GitHub for the {bslib} app, feel free to clone as you see fit: https://github.com/JavOrraca/bslib_demo_shiny
Jobs shared during the chat: Niklas: Several data science roles at my company (sustainability SaaS). 2 data engineers, 1 data analyst, 1 data scientist - https://higg.com/careers/ Jill: We are hiring for a Data Analyst role here at Viemed. We are a healthcare company and we LOVEEE DATA. https://viemed.apscareerportal.com/jobs/1338665/apps/new?embed=1"
Nick Strayer || Part IV: Styling a Shiny Wordle App with CSS || RStudio
00:00 Introduction 00:44 Switching from verbatimTextOutput to uiOutput 01:42 Switching from renderText to HTML DOM elements 03:17 In-line styling with divs 07:30 Converting individual letters from block elements to adjacent grids with CSS grid 08:56 Adding CSS at the head of the UI variable in Shiny with tags$head (and wrapping with HTML!) 10:36 CSS targeting of the background color 12:24 Link: Complete Guide to CSS Grid 14:05 Moving text position within each individual div using CSS classes 16:48 Creating a gap between grid elements 17:13 Rounding border edges for letter grids 19:00 Formatting letter grid background color to indicate result “correctness” 21:30 Increasing font size 23:37 Updating the legend to use color, not text indicators 26:40 Adjusting padding to improve app aesthetic 28:08 Formatting the app UI with justified centering 31:56 Adjusting the text input and Go button 34:07 Why Flexbox is the right tool for this task 35:09 Exploring Flexbox Dev Tools in Chrome 39:14 Adjusting the colors of letter grids using Inspect Element 40:40 Making text bold with font-weight 41:04 Hint on how to approach formatting the keyboard
In final installment of this four-part series, RStudio’s Nick Strayer walks through using CSS to stylize our Shiny Wordle app.
Code + word list: https://github.com/wch/shiny-wordle Check out the full Shiny app here: https://winston.shinyapps.io/wordle/ You can learn more about Shiny here: https://shiny.rstudio.com/
Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/
Content: Nick Strayer (@NicholasStrayer) Animation, design, and editing: Jesse Mostipak (@kierisi) Music: Lakal by Blue Dot Sessions
Wordle: https://www.powerlanguage.co.uk/wordle/
Sjoerd Wierenga & Job Spijker | Public Health | Shiny in Production | Posit
R in Public Sector: Organizational & Technical Aspects of Shiny in Production with the Dutch National Institute for Public Health and the Environment.
00:00 - Introductions 2:47 - Organizational aspects of Shiny in production 32:52 - Technical aspects of Shiny in production 52:33 - Ask us everything / Open Discussion
Questions: 29:00 - When you first introduced Shiny, what other tools were you comparing it to? How did you explain the difference to your leaders? 30:00 - What were the most important aspects of your prototype app to create buy-in? 52:33 - As Clusterbuster began to be used by more people, did you face any performance issues? How did you adjust your app to deal with more concurrent users? 56:10 - Can you say anything about the update frequency of the data? 57:15 - Which model was used to define the clusters? 58:23 - Did you ever consider not using a database? 1:01:50 - What’s the communication with the data engineering team? 1:03:51 - How often do you collect feedback from users and update your app? 1:05:10 - Was your data loaded into Docker in a form of some aggregates? How did you create them? 1:06:26 - What is the main advantage of keeping it all in R with Shiny? Did you feel at any point you were sacrificing simplicity? 1:08:14 - Did you use any specific methods to increase the performance of your app? Did you scope your data, or load it all in the global file? 1:12:03 - How did you make sure regions and users felt comfortable using your app? 1:13:25 - What types of businesses are hotbeds for covid clusters? Has this info informed policy changes? 1:14:50 - How did the data quality issues improve over the rollout? 1:16:47 - Did you use CI/CD? 1:17:38 - Did you have any functionality within your apps to send individual-level data to municipalities? 1:19:47 - For huge amounts of data, have you tested out different file types to store your data set within your containers? 1:20:54 - For people just starting to use Shiny, what is one piece of advice you would give them?
Proof on Concept with fictitious data: https://rivm.shinyapps.io/clusterbuster/ Blog post from the team as well! https://www.rstudio.com/blog/how-the-clusterbuster-shiny-app-helps-battle-covid-19-in-the-netherlands/ Code-first blog post mentioned: https://www.rstudio.com/blog/code-first-data-science-for-the-enterprise2/
How the “Clusterbuster” app provides actionable information to 300 health professionals Presented by: Sjoerd Wierenga
In this talk we want to give an overview of what it took to create the Clusterbuster from an organizational perspective. We will go into detail on how we got from an abstract question to an application that is user-friendly, safe, and valuable. Furthermore, we will offer a glimpse of what is yet to come, and where we see possibilities to turbocharge a more data-driven public policy approach.
How to build a production shiny app within the context of public health governance. Presented by: Job Spijker
This presentation goes into the more technical details about the production environment of the Clusterbuster application. We will show how we deployed the application, how we ensured security and mitigated the risks in case of a security breach, and how we organized our code for maintainability and refactoring.
Presenter Biographies:
Sjoerd Wieringa: As the son of two healthcare professionals, with a background in Public Administration, and a passion for technology, it is no surprise that Sjoerd Wierenga now works at the National Institute for Public Health and the Environment leading a team of highly skilled Data Scientists that created an application to support the battle against COVID-19. After having worked as a healthcare manager for several years, he decided he wanted to learn how to program. Which he has been doing now since 2016 in different capacities.
Job Spijker: Job Spijker is a senior research and data scientist at the Dutch National Institute of Public Health and the Environment. He has a PhD in Earth Sciences with a focus on computational and statistical methods of spatial data. He is currently involved in projects about how the institute’s environmental and health data can be leveraged to create insightful actionable information to assist policy makers at local, regional, and national level
Winston Chang || Part III: Adding a Keyboard to a Wordle Shiny App || RStudio
00:00 Introduction 00:25 Setting up a keyboard 00:54 Using an HTML p tag to print out letter indicators 01:56 Back to our keyboard! 03:44 Setting up a search and replace 06:32 Removing letters using regular expressions 08:43 Making guesses a reactiveVal() 11:00 Avoiding an infinite loop with reactiveVal()
In Part III of this four-part series, Winston walks through how to build a keyboard in a Shiny Wordle app.
Code + word list: https://github.com/wch/shiny-wordle Check out the full Shiny app here: https://winston.shinyapps.io/wordle/ You can learn more about Shiny here: https://shiny.rstudio.com/
Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/
Content: Developer (@winston_chang) Animation, design, and editing: Jesse Mostipak (@kierisi)
Wordle: https://www.powerlanguage.co.uk/wordle/
Winston Chang || Part II: Handling Duplicate Letters in a Shiny Wordle App || RStudio
00:00 Introduction 00:52 Setting up the problem with duplicate letters 02:08 Coding the first pass for exact matches in the correct position 06:29 Re-evaluating how to approach the problem 12:28 Removing only one instance of a letter 13:56 Testing our code 14:54 Setting up the second pass 19:08 Scoping with a double arrow 19:52 Debugging with a browser() statement 21:28 Checking our code
In Part II of this four-part series, Winston walks through how to handle duplicate letters when building your Shiny Wordle app.
Code + word list: https://github.com/wch/shiny-wordle Check out the full Shiny app here: https://winston.shinyapps.io/wordle/ You can learn more about Shiny here: https://shiny.rstudio.com/
Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/
Content: Developer (@winston_chang) Animation, design, and editing: Jesse Mostipak (@kierisi)
Wordle: https://www.powerlanguage.co.uk/wordle/
Winston Chang || Part I: Build a Basic Wordle App with Shiny || RStudio
00:00 Introduction 00:12 What is Wordle? 00:36 The Wordle app we’ll build by the end of this four-part series 01:08 How to approach the problem 01:38 Word list (link to file below) 01:52 UI function with fluidPage() 02:24 Print out what player guesses using verbatimTextOutput() 03:36 Run app in Viewer Panel 04:04 Adding an action button with actionButton() 04:29 Using bindEvent() with actionButton() 06:02 Limiting guesses to words with five characters 07:40 Using req() and cancelOutput() 08:54 Incorporating the word list 10:13 Matching player guess to word list 11:06 Matching player guess to target word 13:50 Writing a function to match guess to target word with feedback 18:15 Checking word length between guess and target 23:02 Why we’re using intermediary functions 28:51 Printing formatted letter information
In Part I of this four-part series, Winston walks through how to build a basic Wordle app using Shiny!
Code + word list: https://github.com/wch/shiny-wordle Check out the full Shiny app here: https://winston.shinyapps.io/wordle/ You can learn more about Shiny here: https://shiny.rstudio.com/
Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/
Content: Developer (@winston_chang) Animation, design, and editing: Jesse Mostipak (@kierisi)
Wordle: https://www.powerlanguage.co.uk/wordle/
Matt Thomas & Mike Page | How the Tidyverse helped the British Red Cross respond to COVID | RStudio
Full title: Cognitive speed: How the Tidyverse helped the British Red Cross respond quickly to COVID-19
We will discuss the importance of cognitive speed, defined here as the rate in which an idea can be translated into code, and why the Tidyverse excels in this domain. We will demonstrate this idea in relation to a suite of tools we were required to rapidly develop at the British Red Cross in order to respond effectively to the COVID-19 pandemic. To do this, we will exhibit how elements of the unifying design principles outlined in the ‘tidyverse design guide - Tidyverse team’ relate to the notion of cognitive speed, giving specific examples for various design considerations. We believe this talk will encourage reflection on better design practices for future R developers, using the design principles of the tidyverse as the guiding beacon.
About Matt: Dr. Matt Thomas is Head of Strategic Insight and Foresight at the British Red Cross. Matt’s team aims to help the Red Cross become more anticipatory and proactive by producing insights and tools including the Vulnerability Index (https://britishredcrosssociety.github.io/covid-19-vulnerability/ ) and Resilience Index (https://britishredcross.shinyapps.io/resilience-index/) . He holds a PhD in Evolutionary Anthropology and, prior to joining the British Red Cross, was researching topics including reindeer herders in the Arctic, hunter-gatherers in the Philippines, and witches in China. Outside of work, Matt writes a column for an anthropology magazine (https://www.sapiens.org/column/machinations/ ) as well as fiction.
About Mike: Mike Page is a data scientist on the Strategic Insight and Foresight team at the British Red Cross. Here, he helps to develop a suite of open source tools and dashboards including the Vulnerability Index (https://britishredcrosssociety.github.io/covid-19-vulnerability/ ) and Resilience Index (https://britishredcross.shinyapps.io/resilience-index/) . Mike is also the author of several R packages including mortyr and newsrivr. In his spare time you can find him rock climbing around the Alps
Shiny and R to Build Dynamic Dashboards
In a static report, you answer known questions. With a dynamic report, you give the reader the tools to answer their own questions. Unleash the full flexibility of analytic app development with Shiny.
In this talk, Winston Chang will discuss how to use R and Shiny to quickly create data dashboards. You’ll also get a glimpse of some new features in Shiny for presenting and interacting with data. He will also demonstrate how you can easily deploy apps to the web via RStudio’s hosting service shinyapps.io.
Related blog post: https://blog.rstudio.com/2017/05/18/shinydashboard-0-6-0/