RStats

Astral (creators of uv) joins OpenAI

Wondering why Astral joined OpenAI? We recently hosted Charlie Marsh from Astral on The Test Set, where he discussed how AI agents were already reshaping Astral’s roadmap. Let’s hear where his head was at before the news broke. #python #pythonprogramming #ai #llm #astral #uv #openai #chatgpt

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

Seriously, have you tried this AI stuff? I’ve never written code like this in my life.

Unearned confidence #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts

Positron rstudio 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

AI-Powered Data Engineering Workflows: Positron for Databricks Users

Modern data engineering demands more. Move beyond the limitations of hosted notebooks and embrace the power of an IDE for modern data engineering. We’ll show you how Positron combines the best of local development with the scale of Databricks. In this session led by James Blair at Posit, we’ll cover: AI-Driven Development: Moving from concept to code faster with built-in AI. Seamless Exploration: Navigating data effortlessly with the Catalog Explorer. Production-Grade Pipelines: Building, testing, and deploying robust workflows. Helpful Links: Download Positron: https://positron.posit.co/ Databricks Asset Bundles: http://docs.databricks.com/dev-tools/bundles GitHub Repo for Demo: https://github.com/blairj09/databricks-data-engineering Lakeflow Declarative Pipelines: http://docs.databricks.com/aws/en/ldp Talk to us about integrating Posit & Databricks: https://posit.co/schedule-a-call/

Positron 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

querychat in R: Query Your Data with Natural Language | Shiny + LLMs | Veerle van Leemput

Learn how to use querychat, an R package that lets users query data using natural language instead of predefined filters. Perfect for data scientists who want to build flexible, interactive Shiny dashboards without anticipating every possible user question. In this tutorial, you’ll discover: How querychat translates natural language to SQL using LLMs Setting up querychat in seconds with your data Building custom Shiny interfaces with reactive data frames Why it’s reliable, transparent, and safe for production use Enterprise deployment with Azure, AWS Bedrock, or Google Vertex AI Key Features: Drop-in Shiny component Works with any DBI-compatible database Read-only by design for data safety Full SQL transparency for reproducibility Supports Claude, GPT, Gemini via ellmer Resources: querychat Documentation: [https://posit-dev.github.io/querychat/py/index.html] Blog Post: “Where Questions Become Queries” [https://shiny.posit.co/blog/posts/querychat-python-r/ Github repo: [https://github.com/posit-dev/querychat/tree/main/pkg-r] Perfect for R users working with Shiny, data exploration, and interactive analytics. Timestamps 00:00 - Introduction: The Dashboard Problem 00:50 - Why Dashboards Can’t Answer Every Question 01:47 - What is querychat? 02:46 - Getting Started (Installation & Setup) 03:08 - API Key Setup (Claude, GPT, Gemini) 03:20 - First querychat App in 5 Lines of Code 04:04 - Live Demo: Querying Data with Natural Language 05:01 - How querychat Works Under the Hood 06:44 - Four Key Benefits (Reliability, Transparency, Reproducibility, Safety) 07:37 - Tool Calling & DuckDB Integration 08:46 - Building Custom Shiny Interfaces 09:01 - Customizing querychat Behavior (Greetings, Data Descriptions, Instructions) 11:00 - Creating Custom Dashboards with Reactive Data Frames 12:00 - Complete App Structure Walkthrough 13:10 - Safety & Security Deep Dive 14:00 - Enterprise Deployment (Azure, AWS, Google Vertex AI) 15:12 - Resources & Documentation

DBI ellmer querychat Shiny 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

querychat in Python: Query Your Data with Natural Language | Shiny + LLMs | Veerle van Leemput

Learn how to use QueryChat to add natural language querying to your Python data applications. This tutorial shows you how to let users ask questions about data without building complex filters or dashboards. In this video, data scientist Veerle Eeftink demonstrates how to integrate QueryChat with Shiny for Python to create interactive data exploration tools powered by LLMs (Claude, GPT-4, Gemini). What You’ll Learn: Set up QueryChat in minutes with any data source Understand how QueryChat translates natural language to SQL Build custom dashboards with reactive data frames Configure LLM behavior with greetings, data descriptions, and instructions Ensure safety and read-only access for production environments Key Features: Works with Claude, GPT-4, Gemini, and other LLMs Transparent SQL generation (no black box) Integrates with Shiny, Streamlit, and other frameworks Uses DuckDB or bring your own SQL database Read-only operations for data safety Enterprise-ready with private LLM support Resources: QueryChat Documentation: [https://posit-dev.github.io/querychat/py/index.html] Blog Post: “Where Questions Become Queries” [https://shiny.posit.co/blog/posts/querychat-python-r/] SheScores Demo App [https://github.com/hypebright/shescores-dashboard/tree/main/Python] #Python #DataScience #LLM #AI #Shiny #QueryChat #NaturalLanguage #SQL #DataVisualization Timestamps 00:00 - Introduction: The dashboard problem 01:28 - What is QueryChat? 02:36 - Demo: Three things you’ll learn today 03:00 - Setup: LLM API keys and installation 04:04 - Quick start: First QueryChat app in 5 lines 04:27 - Live demo: Natural language queries 06:10 - How QueryChat works under the hood 07:19 - Four benefits: Reliability, transparency, reproducibility, safety 08:17 - Tool calling explained 08:54 - Schema information and DuckDB integration 09:29 - Building custom interfaces 09:45 - Customizing behavior: Greetings and data descriptions 11:16 - Adding custom instructions to the LLM 11:35 - Building a Shiny dashboard with QueryChat 13:09 - Code walkthrough: Sidebar chat and reactive data frames 14:14 - Complete dashboard demo 14:52 - Safety and security deep dive 16:22 - Resources and links

querychat Shiny for Python Shiny 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

Take Positron to Work with Positron Pro

While you’ve downloaded Positron on your desktop and are loving it, you may still have a few questions about using it at work: What happens when my analysis requires more memory than my laptop has? How can I bring Positron into my company’s secure environment? How can I access data in Databricks or Snowflake from Positron? Nick Rohrbaugh, Senior Product Marketing Manager at Posit shares how Positron Pro, available exclusively within Posit Workbench, transforms from a powerful desktop editor into a fully governed, enterprise-ready IDE. In this webinar, you will learn how: Data teams get immediate access to scalable, server-side compute and secure, one-click data authorization. IT leaders can centralize, secure, and manage Positron alongside RStudio, Jupyter, and VS Code, all from a single platform. Helpful links: Positron: https://posit.co/products/ide/positron/ Posit Workbench: https://posit.co/products/enterprise/workbench/

Positron rstudio 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

Keeping LLMs in Their Lane: Focused AI for Data Science and Research

From R+AI 2025, hosted by R Consortium Keynote LLMs are powerful, flexible, easy-to-use… and often wrong. This is a dangerous combination, especially for data analysis and scientific research, where correctness and reproducibility are core requirements. Fortunately, it turns out that by carefully applying LLMs to narrower use cases, we can turn them into surprisingly reliable assistants that accelerate and enhance, rather than undermine, scientific work. This is not just theory—I’ll showcase working examples of seamlessly integrating LLMs into analytic workflows, helping data scientists build interactive, intelligent applications without needing to be web developers. You’ll see firsthand how keeping LLMs focused lets us leverage their “intelligence” in a way that’s practical, rigorous, and reproducible. Bio Joe Cheng is the CTO and first employee at Posit, PBC (formerly known as RStudio), where he helped create the RStudio IDE, Shiny web framework, and Databot agent for exploratory data analysis. R Consortium Resources Main R Consortium Site: https://www.r-consortium.org/ R+AI website: https://rconsortium.github.io/RplusAI_website R Consortium webinars: https://r-consortium.org/webinars/webinars.html Blog: https://r-consortium.org/blog/ LinkedIn: https://www.linkedin.com/company/r-consortium/

Joe Cheng

How to use pointblank to understand, validate, and document your data

R/Medicine Webinar This workshop focused on the data quality and data documentation workflows that the pointblank package makes possible. The speaker Richard Iannone used functions that allowed us to: quickly understand a new dataset validate tabular data using rules that are based on our understanding of the data fully document a table by describing its variables and other important details The pointblank package was created to scale from small validation problems (“Let’s make certain this table fits my expectations before moving on”) to very large (“Let’s validate these 35 database tables every day and ensure data quality is maintained”) and Ionnone delved into all sorts of data quality scenarios so the viewer will be comfortable using this package in their organization. Data documentation is seemingly and unfortunately less common in organizations (maybe even less than the practice of data validation). We’ll learn all about how this doesn’t have to be a tedious chore. The pointblank package allows you to create informative and beautiful data documentation that will help others understand what’s in all those tables that are so vital to an organization. Speaker Richard Iannone, Software Engineer, Posit, PBC Rich is a software engineer at Posit that enjoys creating useful R and Python packages. He trained and worked as an atmospheric scientist and discovered working with R to be a breath of fresh air compared to the Excel-based analysis workflows common in that field. Since joining Posit he has been focused on developing packages that help organizations with data management and data visualization/publishing. When not working on R and Python packages, Rich also enjoys other things like playing and listening to music, watching movies, and getting outside

AI-Powered Data Science in Positron

We’re so excited to introduce Positron, a free, next-generation data science IDE that makes it easy to work in both R and Python. Positron builds on our years of experience developing RStudio and is a fork of VS Code, designed specifically for data work. This means you get a modern coding interface with features tailored for data science, like a built-in data explorer, AI assistance, interpreter management, and more. This is our 2nd event in our Positron series and focused on AI-Powered Data Science. Here at Posit, we strive to create products where AI works with you, not against you. In efforts to continue this mission, we are excited to introduce agentic AI capabilities in Positron, our new, free code editor for R and Python, that are designed from the ground up to follow these principles. Positron’s AI capabilities automate the tedious parts of the data science workflow, but always keep you, the expert, in control. 00:00 Introduction 00:32 What is Positron? (The Next-Gen Data Science IDE) 03:43 Introducing Positron Assistant 05:10 Bring your own LLM 05:47 Your environment as context 07:19 Inline code suggestions 07:58 Introducing Databot 08:22 The WEAR loop 10:15 Demo time 10:56 Opening Positron Pro in Posit Team 14:29 Opening a new folder in Positron 15:22 Cloning a repo from GitHub 17:24 Positron icons 19:15 Positron search bar and command palette 20:18 Changing interpreters and opening a Quarto document 21:30 Running code and populating the Variables Pane 22:41 Using the Data Explorer 25:28 Creating a plot and debugging with Positron Assistant 30:46 Editing code using inline code suggestions 34:42 Sharing a Quarto document 36:12 Opening Databot 37:02 Exploring data with Databot 43:37 Creating a report using Databot findings 45:12 Wrap up Additional resources: GitHub Repo for this Example: https://github.com/posit-dev/positron-ai-workshop Getting Started with Positron: Quick Tour: https://posit.co/resources/videos/get-started-with-positron-a-quick-tour-and-community-qa/ Introducing Databot (Blog Post): https://posit.co/blog/introducing-databot/ Posit AI Newsletter: https://posit.co/blog/2025-09-26-ai-newsletter/ Positron Assistant: https://positron.posit.co/assistant.html

Positron Quarto rstudio 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

Accelerating Study Insights with Shiny and Posit | Regeneron x Atorus

In this session, Atorus and Regeneron, and Posit showcase their innovative partnership. Topics covered: -Building a Unified Analytical Environment: Learn how to reduce friction by integrating fragmented SAS and R workflows. -Case Study: A Config-Driven App: See a live demo of how a simple configuration file can build a fully functional application for data review and safety monitoring. -Operational Shifts: Understand how internal teams are empowered to independently manage and extend their own applications in a regulated environment. This isn’t just about technology—it’s about the strategic shift required to simplify development and accelerate insights within a regulated environment. Learn more about Posit’s work in the pharma space: https://posit.co/use-cases/pharma/?utm_source=youtube&utm_medium=social&utm_term=pharma&utm_content=atorus-regeneron

config Shiny 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 Programming Education RStats Open Source OSS Regeneron Atorus R Shiny Positron R Data Science Pharma

Top AI Powered Coding Assistants

which have you tried? #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts

Positron rstudio 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

What can you do with Positron Assistant?

Positron Assistant is an AI client integrated within Positron, designed to enhance data science workflows. Positron Assistant brings LLM integration for both chat and inline code completions.This tool is built to assist with a range of tasks, including generating and refactoring code, answering questions, providing debugging support, and offering insightful suggestions for next steps in your projects. It can interact with the Variables pane, Plots pane, and the Console in your IDE. Capabilities include: • Editor inline chat (chat with Positron Assistant in your script to augment your code) • Contextual code completion (add instructional comments to your scripts to complete code) • Agent-assisted workflows (allow Positron Assistant to execute tasks autonomously) Learn more about Positron Assistant: https://positron.posit.co/assistant.html Sign up for our Workflow Demo on October 29, where we will demonstrate Positron Assistant: https://posit.co/workflow-demo/ai-powered-data-science-in-positron/

Positron 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

Deploy a Streamlit web app with me

deploy all the things! #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyternotebook #positshorts

Positron rstudio 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

Theming Made Easy: Introducing brand.yml (Garrick Aden-Buie, Posit) | posit::conf(2025)

Theming Made Easy: Introducing brand.yml Speaker(s): Garrick Aden-Buie Abstract: brand.yml is an exciting new project from Posit that radically simplifies theming. Every data science tool supports some form of theme and appearance customization, but each app framework, output format, or visualization tool requires its own special syntax for theming. The goal of brand.yml is to create a portable and unified interface for brand-related theming that can be used anywhere that data science artifacts are produced. As a collaboration between the Shiny and Quarto teams, brand.yml provides a single interface to setting baseline themes in reports and apps across the R and Python ecosystems. In this talk, I’ll introduce brand.yml and showcase the many ways that brand.yml can bring consistent styles to your data science outputs. Materials - https://github.com/gadenbuie/brand-yml-posit-conf-2025 Slides - https://pkg.garrickadenbuie.com/brand-yml-posit-conf-2025 posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Garrick Aden-Buie

The Psychology of Technologists (Cat Hicks, Catharsis Consulting) | posit::conf(2025)

The Psychology of Technologists Speaker(s): Cat Hicks Abstract: Technology teams are struggling to be heard. Technologists are grappling with high turnover, high burnout, and low resilience in the face of rapidly-accelerating technological change. Even though effective technical teams are an essential part of our world continuing to work, many organizations struggle to understand their technical teams, stuck in old stereotypes that treat technical people like isolated machines. To build a thriving future, we need a new way of working together and understanding our own minds as we build technology. In my empirical research across thousands of developers, technical managers and their teams, we’ve explored what a psychologically-rich environment looks like for modern technologists and found important signals that can guide teams toward greater resilience, innovation, and thriving. In this keynote we’ll unlock access to the psychological sciences and evidence that you can use to make your every day more human. Links: Dr. Cat Hicks - https://www.drcathicks.com/ Catharsis Consulting - https://www.catharsisinsight.com/ Center for Open Science (COS) - https://www.cos.io/ Fight for the Human Newsletter - https://www.fightforthehuman.com/ posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Making the most of artificial and human intelligence for data science (Hadley Wickham, Joe Cheng)

Making the most of artificial and human intelligence for data science Speaker(s): Hadley Wickham; Joe Cheng Abstract: This presentation explores the complex and often contradictory nature of large language models (LLMs) in data science, acknowledging the simultaneous excitement and apprehension that we feel toward these technologies. We’ll provide a practical framework to help you understand the LLM ecosystem (from foundation models and hosting to SDKs and applications) that supports our current philosophy: augmenting, not replacing human intelligence. The talk demonstrates how Posit is addressing this space through two complementary approaches: building SDKs and tools that help you create your own LLM-powered solutions, and developing integrated LLM capabilities directly into data science workflows through tools like Positron assistant and databot. We’ll showcase practical, immediately useful applications while addressing current limitations, providing you with both the emotional preparation and technical foundation needed to effectively leverage LLMs in their data science practice today. posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Hadley Wickham, Joe Cheng

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RStats

Astral (creators of uv) joins OpenAI

Seriously, have you tried this AI stuff? I’ve never written code like this in my life.

AI-Powered Data Engineering Workflows: Positron for Databricks Users

querychat in R: Query Your Data with Natural Language | Shiny + LLMs | Veerle van Leemput

querychat in Python: Query Your Data with Natural Language | Shiny + LLMs | Veerle van Leemput

Take Positron to Work with Positron Pro

Keeping LLMs in Their Lane: Focused AI for Data Science and Research

How to use pointblank to understand, validate, and document your data

Who are ’the ggplot2 extenders’ and how to become one (Gina Reynolds) | posit::conf(2025)

From messy to meaningful data: LLM-powered classification in R (Dylan Pieper) | posit::conf(2025)

Deploy with Confidence: Strategies to Maximize Success (on Posit Connect) (Ryszard Szymański)

Building Web Apps and APIs in R with Ambiorix (Kennedy Mwavu & John Coene) | posit::conf(2025)

AI-Powered Data Science in Positron

Accelerating Study Insights with Shiny and Posit | Regeneron x Atorus

Top AI Powered Coding Assistants

What can you do with Positron Assistant?

Deploy a Streamlit web app with me

Zero Tolerance for Dirty Data: A Pointblank Prescription for Data Hygiene (Arnav Patel, Synovus)

When R Met Python: A Meet Cute on Posit Connect (Blake Abbenante, Suffolk Construction)

What R We Counting? (Ben Arancibia, GSK) | posit::conf(2025)

Using paired programming to have fun & sell your solutions (Kris Fabick & Kristin Carr)

Uncharted Nuuk: Data Exploration in Search of the Unexpected (Emil Malta, Statistics Greenland)

Trustworthy Data Visualization (Kieran Healy, Duke University) | posit::conf(2025)

Translating R for Data Science into Portuguese: A Community-Led Initiative (Beatriz Milz, UFABC)

Theming Made Easy: Introducing brand.yml (Garrick Aden-Buie, Posit) | posit::conf(2025)

The Psychology of Technologists (Cat Hicks, Catharsis Consulting) | posit::conf(2025)

The Power of Snowflake and Posit Workbench (Jonathan Regenstein, Snowflake) | posit::conf(2025)

Talk data to me: How to present youR data to any audience (Freda Warner, CIHR) | posit::conf(2025)

See if talking to the doctor is right for you (Max Hockstein, Georgetown) | posit::conf(2025)

Same Data, Different Tools: Visualizing with R and Python (Olivia Hebner, Summit)

Running a polyglot data science community (Melissa Van Bussel, StatCan) | posit::conf(2025)

rOpenSci Champions: Building Communities of Open-Source Leaders (Noam Ross, rOpenSci)

Ripples of Change with R- It’s more than just coding (Yvonne Kienast, CIHI) | posit::conf(2025)

R-multiverse: a new way to publish R packages (Will Landau, Eli Lilly) | posit::conf(2025)

R & Python playing nice, in Production (Claudia Penaloza, Continental Tires) | posit::conf(2025)

Quarto for Business Collaboration & Technical Documentation in Word docx format (Bill Pikounis, J&J)

Putting an {ellmer} AI in production with the blessing of IT (Andrie de Vries) | posit::conf(2025)

Purrrfectly parallel, purrrfectly distributed (Charlie Gao, Posit) | posit::conf(2025)

Precision Medicine for All: Using Tidymodels to Validate PRS in Brazil (Flávia Rius) | posit::conf

Practical {renv} (Shannon Pileggi, The PCCTC) | posit::conf(2025)

Positron: The First Five Minutes (Isabella Velásquez, Posit) | posit::conf(2025)

Positron AI Session (George Stagg, Winston Chang, Tomasz Kalinowski, Carson Sievert) | posit::conf

Posit and the modern data stack: Using Posit, Snowflake, and Databricks (James Blair)

Polyglot Data Science: Why and How to Combine R and Python (Jeroen Janssens) | posit::conf(2025)

Plotgardener Genomic Data Visualization Made Easy (Rishabh Sharma Vemuri & Abiye Berhanu)

Multiple Console Sessions in Positron (Dhruvi Sompura, Posit) | posit::conf(2025)

Making the most of artificial and human intelligence for data science (Hadley Wickham, Joe Cheng)

Making Ecological Modelling Accesible with EcoCommons (Jenna Wraith, QCIF) | posit::conf(2025)

Make Big Geospatial Data Accessible with Arrow (Cari Gostic, Sonoma Technology) | posit::conf(2025)

maidr: Empowering Accessible, Multimodal Data Visualizations (JooYoung Seo) | posit::conf(2025)

It’s all fun and games til your analysis code is finished (Alex Rossell Hayes) | posit::conf(2025)

Integrating AI, Data Science Platforms, and Psychological Safety (Chris Engelhardt, Gen Re)

How I got unstuck with Python (Julia Silge, Posit) | posit::conf(2025)

History repeats itself: What the Du Bois Challenge taught me about reproducing design (Simi Ndaba)

Hacking Productivity with LLMs (Nic Crane, NC Data Labs) | posit::conf(2025)

From SDKs to Agents: Building with R and Python on Databricks (Rafi Kurlansik & Zac Davies)

From Data to Dollars: Improving Medical Billing Accuracy Using NLP (Julianne Gent, Emory Healthcare)

Failure (and Mistakes) (Laura Gast, USO) | posit::conf(2025)

Extending the horizons of R with Rust (Andrés Quintero, ixpantia) | posit::conf(2025)

Elevating Public Health Decision-Making with R Packages (Kylie Ainslie, RIVM) | posit::conf(2025)

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