Posit PBC

We are excited for ShinyConf 2025! There are dozens of talks to look forward to, but let's highlight one talk by Daniel Chen: "Creating and Sharing Scalable Applications with Shiny" Daniel - a developer evangelist at Posit – will discuss the challenges of building large-scale Shiny apps and how to use Shiny modules to create reusable components and refactor complex code, emphasizing best practices for both Shiny for R and Python. Abstract: Shiny provides a framework for building interactive web applications, enabling seamless integration with data science libraries. It offers a reactive programming model, allowing users to create dynamic dashboards and applications with minimal code. However, when applications get larger, you may end up repeating UI elements or have complex reactive logic. To make the codebase sustainable, we can turn to shiny modules. Shiny modules allow us to create functions that we can package up that still retain the reactive context. This allows us to refactor our shiny code as if it were any other piece of data analysis code. This talk will give a practical example of refactoring code into a shiny module that you can then turn into a separate package. We will use Shiny for Python as the working example in the talk, but the concepts will apply to Shiny for R We hope to see you there! Attend ShinyConf 2025, Apr 9-11, a global virtual conference focused on Shiny and data-driven applications. Learn from experts about the latest advancements in Shiny, including AI integration, scalable application development, and easy theming. Discover more about the workshops and free talks, and register at https://lnkd.in/gqk7XTMK.

We are excited for ShinyConf 2025! There are dozens of talks to look forward to, but let's highlight one talk by Garrick Aden-Buie: "Theming Made Easy: Introducing brand.yml for Shiny" Garrick – a software engineer on Posit's Shiny team – will introduce brand.yml, a simple YAML file that streamlines styling for Shiny (in R or Python) and beyond, ensuring a consistent and professional look across applications, reports, and presentations. Abstract: In the world of Shiny app development, effective communication is paramount. For your insights to truly resonate and drive decision-making, your Shiny apps need to be more than just functional – they need to project a professional image that aligns with your organization's brand. Achieving this consistent and professional look can be surprisingly challenging. That’s why we’re excited to introduce brand.yml, a simple YAML file that streamlines styling for Shiny — in R or Python – and beyond. In this talk, we’ll discover just how easy it is to set up brand.yml for your Shiny for R apps. brand.yml consolidates key visual elements – colors, fonts, and logos – into a single, easily managed file, helping ensure that even your smallest Shiny apps feel authentically on-brand. We’ll also demonstrate how that same file can be used not only in Shiny for R and Python but also to theme your reports, slides, dashboards, and apps in Quarto or R Markdown and more. You spend countless hours making sure that your Shiny apps bring insights to life through engaging interactivity and effective communication. With brand.yml, you can create Shiny apps that inspire confidence with a consistent and polished visual identity. We hope to see you there! Attend ShinyConf 2025, Apr 9-11, a global virtual conference focused on Shiny and data-driven applications. Learn from experts about the latest advancements in Shiny, including AI integration, scalable application development, and easy theming. Discover more about the workshops and free talks, and register at https://lnkd.in/gqk7XTMK.

We're pleased to announce that you can now host and share your Gradio applications with the release of Posit Connect 2024.12.0! Gradio is an open-source Python package with a thriving user community because it allows you to quickly build a demo or web application for your machine learning model, API, or any arbitrary Python function. Gradio support in Posit Connect makes it easier for data scientists using popular Python and R frameworks to build and share interactive applications with others across their organization. Learn more about deploying and sharing Gradio apps in our blog post: https://lnkd.in/gHxPeUAy

This week's PydyTuesday project invites you to catch 'em all... the insights, that is⚡️ Dive into a comprehensive Pokemon dataset featuring stats on 949 Pokemon across multiple generations. 👀 The visualizations attached to this post showcase amazing community submissions from previous weeks! Your creation could be featured next - show us what you can do with this week's Pokemon data! Explore questions like: ⬢ Which Pokemon types are the heaviest on average? Does that relate to their speed, attack, or defense? ⬢ How have Pokemon designs evolved across generations? Can you use their colors or the URLs of their images in a plot? ⬢ What patterns emerge when comparing height, weight, and battle performance? Here’s your toolkit: Positron → Write your code Quarto → Tell your story Connect Cloud → Share your work 🔍 Get the data: https://lnkd.in/gguaRVdR 🐍 Learn more about Posit's PydyTuesday project: https://lnkd.in/g_VHKkNf 🎥 Posit PydyTuesday Tutorials: https://lnkd.in/gXCnXKbX Post your submissions to LinkedIn or Bluesky with the hashtags #TidyTuesday and #PydyTuesday for a chance for your work to be featured!

Generating realistic datasets is useful in various settings, from education to app testing. Sara Altman's new blog post explores how you can leverage Large Language Models (LLMs) directly within R to generate custom, realistic datasets using the ellmer package. This blog post provides a step-by-step guide to creating a command-line tool in R that simulates data and embedding the data generation functionality in a Shiny app, highlighting key tasks like: • Starting a chat with the `chat_*()` functions, essentially a record of the conversation between the user and the chatbot. • Specifying a system prompt to instruct the LLM to generate tabular data based on the criteria given by the user. • Using tool calling to ensure that the LLM generates correctly formatted data and can automatically retry if an error occurs. • Adding these functionality to a Shiny app. Read the full post here: https://lnkd.in/gJT-ita4 And try out the app in the Posit AI Portfolio! https://lnkd.in/gFjHrV5z

We are excited to share that bslib v0.9.0 is on CRAN, bringing brand.yml support to Shiny for R and R Markdown! brand.yml simplifies brand management by consolidating your visual identity—colors, typography, and styling—into a single, easy-to-maintain YAML file. Last year, we launched brand.yml with initial support in Quarto and Shiny for Python, and we’re happy to bring it to R as well. This one file can be used to maintain consistent branding across your Shiny apps, Quarto projects, and now any R-based project that uses bslib for theming, including shiny, R Markdown, pkgdown, flexdashboard, and more! Learn how to get started in the blog post: https://lnkd.in/gHAqkayW

During a recent coffee chat with Wes McKinney and Hadley Wickham, a participant asked a great question: "How can Posit Workbench or Connect help us integrate Python scripts and R models into a cohesive and efficient workflow?"   Teams working with both R and Python often run into collaboration roadblocks: different workflows and development environments, trouble sharing models and data, difficulty getting results to stakeholders, and headaches integrating models into other systems. This blog post helps unpack further how Posit’s professional products, Workbench and Connect, along with our open-source ecosystem, help create a smoother, more collaborative data science experience. https://lnkd.in/g5NFEPQu

Want a Python challenge? This week, we’re upping our Python skills by decoding Amazon's Corporate DNA! When annual reports meet natural language processing, data science gets interesting. This week's PydyTuesday challenge transforms Amazon's historical PDFs into a linguistic dataset for advanced text analysis. Parse through years of corporate communication to reveal:  ⬢ What years does the data set span? ⬢ Does the overall sentiment score for each year reflect the economic climate of the time?  ⬢ Once you remove "stop words," what are the 15 most frequently used words for each year?  ⬢ Has the frequency of words like "AI" and phrases like "artificial intelligence" increased over the years? 🛠️ Your Data Science Toolkit:  Positron → Write your code  Quarto → Tell your story  Great Tables → Make beautiful tables Connect Cloud → Share your work 🗂️ Get the data: https://lnkd.in/guCAhV4g  🐍 Learn more about Posit's PydyTuesday project: https://lnkd.in/g_VHKkNf  🎥 Posit PydyTuesday Tutorials: https://lnkd.in/gXCnXKbX Post your submissions to LinkedIn or Bluesky with the hashtags #TidyTuesday and #PydyTuesday for a chance for your work to be featured! Learn about Shiny Assistant below: https://lnkd.in/gPkqVreg

Going to be in Boston for BioIT World next week? Imagine this: A stunning waterfront restaurant in Boston, incredible food, free-flowing drinks, and the most interesting people in life sciences all in one room. No PowerPoints. No name tags. Just real connections over an amazing meal. 🍽️ Where: The Nautilus Boston (Pier 4) 📅 When: Wednesday, April 2nd, 7-10 PM 🛎️ RSVP Here: https://lnkd.in/gAh5B63N Bring your appetite. Bring your stories. Leave the work stress at the door. Who's in?

Discover how the mall package simplifies LLM integration in R! We're excited to share Camila Lívio, Ph.D. Lívio's guest post, in which she demonstrates mall’s practical application in analyzing COP climate reports. Camila walks you through how she uses mall to efficiently summarize dense PDF reports, extract key entities, and visualize the frequency of relevant terms, all within her R environment. With mall's text summarization and extraction features, Camila can uncover crucial trends and insights related to 'energy transition' across years of climate negotiations. Learn how to efficiently summarize and extract crucial information from text-heavy datasets and see Mall's potential for your own research or analysis projects. Read the post here! https://lnkd.in/gbt8d6vq #RStats