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Securely store an API key on Connect Cloud and deploy an LLM-powered Shiny for R application ⚡️ Secret variable management on Connect Cloud helps R users build production applications in the cloud. This short demo explores a Shiny for R web application that enables users to generate synthetic datasets on the fly with an OpenAI API key. In just minutes, you can turn code from a public GitHub repository into a live application on Connect Cloud. → Create a free account: https://lnkd.in/gi6Ws2jh → How-to guide: https://lnkd.in/gBH9JrUm

Deploying Shiny for Python apps built with Tapyr to Posit Connect 🦏 Tapyr is a framework by Appsilon that allows you to quickly and easily create and deploy enterprise-ready Shiny for Python dashboards. It brings a standard app structure and built-in testing framework to Shiny for Python. Posit Connect is Posit's secure environment for sharing, managing, and scaling data science content. In our latest blog post, Fabian Hee demonstrates how to publish Shiny for Python apps built with Tapyr to Posit Connect for sharing with your stakeholders! Check it out here: https://lnkd.in/gC5gipyN

We are thrilled to announce Shiny Assistant! 🎉 Have you ever had an idea for a great web application with Shiny but felt something holding you back from getting started? Shiny Assistant is a new AI-powered tool that can help with that. You can ask it questions about Shiny, or to create a Shiny application from scratch, or ask it to make changes to an existing application. Welcome your knowledgeable colleague who is always ready to help out with your Shiny projects in both R and Python. Learn more in the blog post: https://lnkd.in/gEKeMfxM Try it out today! https://lnkd.in/g5pdeR4f

Improve Shiny app interactivity and performance with ggiraph! Shiny is a fantastic tool for interactive dashboards, but developers need to keep user experience top-of-mind. Loading all our data at once can slow down performance, which can quickly lose user engagement. This is where dynamic database querying can assist. By fetching data only when needed, we can have both efficient loading times and real-time interactivity. Explore how to add drill-down views with ggiraph and database querying in the recent blog post by Isabella V., Sr. Product Marketing Manager at Posit. This post covers how to: ✔️ Connect to a database ✔️ Build a Shiny app with a country filter ✔️ Add a visualization to your Shiny app ✔️ Use ggiraph to add plot interactivity ✔️ Use showModal() to add modal windows ✔️ Create plot drill-downs within modal windows Explore more→ https://lnkd.in/d4_Krj3V

Managing user access to data can be complex—but it doesn’t have to be! With dynamic user identity in Posit Connect, you can tailor access to sensitive data based on each user’s role and credentials, creating a personalized experience without manual adjustments. Whether your team is accessing Snowflake or Databricks, dynamic user identity ensures data is securely filtered, so each user sees exactly what they need—nothing more, nothing less. Curious to see it in action? Read the guide on using Posit Connect to implement dynamic user identity for data governance written by Zack Verham, Principal Software Engineer at Posit → https://lnkd.in/gmt6_FWF

Need to test your Shiny for Python apps and don't know where to start? Karan Gathani shines a light on testing Shiny for Python apps in these two YouTube videos: • Creating tests for Shiny for Python apps: Use the `shiny add test` CLI command to automatically generate a test file at the specified location https://lnkd.in/g2K7HQUw • Using controllers to write robust Shiny for Python app tests: Controllers are now available in Shiny for Python v1.0. They provide a structured and consistent way for users to interact with and test UI components in Shiny apps https://lnkd.in/gJdduCDS Improve your Shiny for Python skills and add some tests today!

Deploy an LLM-powered Shiny for Python application to Connect Cloud in minutes! Connect Cloud lets you quickly deploy data applications and documents from public GitHub repositories for Python and R projects. In a short demo, Alex Chisholm, Product Manager of Hosted Products at Posit, showcases secret variable management on Connect Cloud to help deploy an LLM-powered Shiny for Python application. ⚡️Check out the demo → https://lnkd.in/g_KaTVdj

We are thrilled to announce RStudio IDE and Posit Workbench 2024.09.0, code-named “Cranberry Hibiscus”! • Bundled Quarto v1.5, which brings new support for Table CSS translation into Typst-based PDFs, improved website development, and many other updates. • We have managed credential support for AWS, Azure, Databricks, and Snowflake. • The Data Viewer now displays a summary of the dimensions of list-columns that contain data frames. • The “Save as Image” dialog now includes a checkbox that controls whether plots are scaled according to the current display’s DPI. Learn about these and other additional features in our blog post: https://lnkd.in/g_Crfyed

Best practices for setting up database connections in your R or Python workflows with Databricks, Snowflake, and DuckDB! In a recent blog post, Sara Altman, Data Science Educator at Posit covers key topics, including: ✅ How to connect to DuckDB, Databricks, and Snowflake using R and Python 📊 Examples of querying data directly from these databases ️ ⚡️ Tips for optimizing data processing and analysis workflows By the end, you’ll have a solid understanding of how to seamlessly integrate database access into your existing projects and improve overall performance. Check it out → https://lnkd.in/dFM9punk

Reproducible data science with webR and Shinylive 💡 webR is a version of the R interpreter built for WebAssembly, a portable binary code format. With webR, you can run live and interactive R code in most modern web browsers. Shinylive uses this technology to allow you to run Shiny applications without the need for a computational server. Together, these tools can help you overcome common reproducibility barriers, such as language and package management or system and library management, so that somebody else can easily rerun and verify the results of your computational procedures. George Stagg shares insights on YouTube: https://lnkd.in/gwViwGhW