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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

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

10 tips for data science beginners

Do you have any to add? #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

Let’s try Positron Assistant for the first time together

lil ai buddy #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #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

Data Exploration 101

Don’t skimp #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

Positron 101: Make your DS life easier

Personally, I think the Plots pane is #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #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

Set up your first coding project in less than 10 minutes

What are you waiting for #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #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

Get starting with data - load data into your IDE

Finding the data is the hardest part, prove me wrong #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

Deploying Scikit-learn models for in-database scoring with Snowflake and Posit Team

Modern data science workflows often face a critical challenge: how to efficiently deploy machine learning models where the data lives. Traditional approaches require moving large datasets out of the database for scoring, creating bottlenecks, security concerns, and unnecessary data movement costs. In this demo, Nick Pelikan at Posit highlights how orbital bridges the gap between Python’s rich ML ecosystem and database-native execution by converting scikit-learn pipelines to optimized SQL. This enables data scientists to: Use familiar Python tools for model development Automatically translate complex feature engineering to SQL Deploy models without rewriting code in different languages Maintain model accuracy through faithful SQL translations While this example focuses on Python, Nick also gave this demo using Orbital for R users: https://youtu.be/pnEjYNgOG9c?feature=shared Helpful resources: Follow-along blog post: https://posit.co/blog/snowflake-orbital-scikit-learn-models/ Supported models for Orbital: https://orbital.tidymodels.org/articles/supported-models.html Orbital (Python) GitHub Repo: https://github.com/posit-dev/orbital Orbital (R) GitHub Repo: https://github.com/tidymodels/orbital Databricks and Orbital for R and Python model deployment: https://posit.co/blog/databricks-orbital-r-python-model-deployment/ Emily Riederer’s XGBoost example with Orbital: https://www.emilyriederer.com/post/orbital-xgb/ Sara Altman’s blog post on Shiny + Databases: https://posit.co/blog/shiny-with-databases/ Emil’s introduction of orbital: https://emilhvitfeldt.com/talk/2024-08-13-orbital-positconf/ Nick’s Blog on Running Tidymodel Prediction Workflows with Orbital: https://posit.co/blog/running-tidymodel-prediction-workflows-inside-databases/ If you’d like to learn more about using Posit Team, you can always schedule time to chat with Posit here: https://posit.co/schedule-a-call/

Shiny tidymodels 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 is an IDE & which one should you use as a beginner

Did you have a first? #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #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

Next-gen data science: a conversation with the Ravit Show and Eric Pité

Eric Pité, SVP of Product & Strategy, sat down with the Ravit Show at Databricks Data + AI Summit to have a conversation about the evolution of open source data science, the Posit and Databricks integration, and the place of AI in modern data science. Some highlights from the conversation are below: – The evolution from RStudio to Posit It’s more than just a name change — it’s a signal of how seriously they’re embracing Python while staying true to their R roots and open-source mission. – Open source meets enterprise Eric shared how Posit is navigating the balance between community contribution and commercial sustainability — and why that mission still matters in an AI-first world. – Positron + Databricks Their strategic partnership is one to watch. Positron is making it easier to do high-quality data science (in R and Python) inside the Databricks ecosystem, with an emphasis on collaboration, reproducibility, and performance. – AI’s place in modern data science Eric and Ravit chatted about how Posit sees the role of AI in helping data teams be more productive, without losing rigor or transparency. – The power of storytelling with code One of our favorite parts is the emphasis on communication. Building models is not enough—data scientists need to share insights clearly, and Posit is building for that. Learn more about the Databricks and Posit partnership by exploring the partnership page and resources: https://posit.co/use-cases/databricks/

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

Monthly Workflow Demos now on Zoom Sessions | Recordings still shared below

Please note while we previously hosted the Posit Workflow Demos here on YouTube, we have moved over to Zoom Sessions for a seamless event experience and more interactive Q&A. You can view upcoming workflow demos and register to join us here: https://events.zoom.us/ev/Ajss5j9VeRMe0zw-AtFKf7AUAsthzYhaYjYPeEIu1uYAQe1K0ud1~Agb_hSt1UGxd9fUyIDC32e6jAdEtbl7G_AaLZUYhRvyZR5cGpY5Kp_A0-w

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

Welcome to PydyTuesday! | How to Level Up your Python Skills

In this video, you’ll learn where to find the PydyTuesday github landing page, where data lives on the tidytuesday repo, and how to get started to try stuff out and build new things! We hope you join us in participating in PydyTuesday! Don’t forget to use the hashtags #TidyTuesday and #PydyTuesday wherever you like to hangout online - Bluesky, Mastodon, LinkedIn, etc. - have fun out there! We can’t wait to see the predictive models, visualizations, dashboards, and data apps that you create Resources and Repos to star: TidyTuesday GitHub Repo: https://github.com/rfordatascience/ti … Posit PydyTuesday GitHub Repo: https://github.com/posit-dev/python-t … TidyTuesday hashtag search on Bluesky: https://bsky.app/search?q=tidytuesday Other videos in this PydyTuesday playlist: • PydyTuesday | Python How-to Videos #pythoncontent

RStudio Data Science Machine Learning Python Stats Data Visualization Data Viz Ggplot Technology Coding Connect Shiny RMarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Github Data Wrangling Tidy Data Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Open Source OSS Reticulate Pydytuesday Python Projects Python Projects for Beginners Python Tutorial Positron Positron Ide

Let’s set up your Python environment with only 2 steps | PydyTuesday Python Projects

In this video, let’s get your project set up. Learn how to install Positron, create a new Python project, and set up a virtual environment. We hope you join us in participating in PydyTuesday! Don’t forget to use the hashtags #TidyTuesday and #PydyTuesday wherever you like to hangout online - Bluesky, Mastodon, LinkedIn, etc. - have fun out there! We can’t wait to see the predictive models, visualizations, dashboards, and data apps that you create Resources and Repos to star: TidyTuesday GitHub Repo: https://github.com/rfordatascience/ti … Posit PydyTuesday GitHub Repo: https://github.com/posit-dev/python-t … TidyTuesday hashtag search on Bluesky: https://bsky.app/search?q=tidytuesday Other videos in this PydyTuesday playlist: • PydyTuesday | Python How-to Videos #pythoncontent

Positron RStudio Data Science Machine Learning Python Stats 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 Blogdown Gt Tidymodels Statistics Debugging Programming Education Open Source Reticulate Python Tutorial Positron Ide Python for Beginners Pydytuesday

Let’s install your first Python library in 2 minutes

Here is how to get set up to install your Python library in Positron. Learn how to use the terminal to install your first Python library, in this case pydytuesday. Also, learn the basics for managing packages using “pip.” We hope you join us in participating in PydyTuesday! Don’t forget to use the hashtags #TidyTuesday and #PydyTuesday wherever you like to hangout online - Bluesky, Mastodon, LinkedIn, etc. - have fun out there! We can’t wait to see the predictive models, visualizations, dashboards, and data apps that you create Resources and Repos to star: TidyTuesday GitHub Repo: https://github.com/rfordatascience/ti … Posit PydyTuesday GitHub Repo: https://github.com/posit-dev/python-t … TidyTuesday hashtag search on Bluesky: https://bsky.app/search?q=tidytuesday Other videos in this PydyTuesday playlist: • PydyTuesday | Python How-to Videos #pythoncontent

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 Forcats Ggplot2 Purrr Github Data Wrangling Tidy Data Odbc Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Reticulate Python for Beginners Install Python Libraries Python Tutorial Positron Ide Positron Data Science

Great Tables 3: Data Color and Polishing

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: ‘How could I do that?’ Other videos in this series: Great Tables 1: Structure, Format, and Style: https://youtu.be/QM7DbsY-nc4 Great Tables 2: Introducing Units Notation: https://youtu.be/SN0_vIL1Rhk About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He’s been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

Great Tables 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

Great Tables 2: Introducing Units Notation

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: ‘How could I do that?’ Other videos in this series: Great Tables 1: Structure, Format, and Style: https://youtu.be/QM7DbsY-nc4 Great Tables 3: Data Color and Polishing https://youtu.be/Huteb5OmcrA About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He’s been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

Great Tables 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

Great Tables 1: Structure, Format, and Style

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: ‘How could I do that?’ Other videos in this series: Great Tables 2: Introducing Units Notation: https://youtu.be/SN0_vIL1Rhk Great Tables 3: Data Color and Polishing https://youtu.be/Huteb5OmcrA About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He’s been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

Great Tables 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

Johnson & Johnson’s Open Source Journey with R in Clinical Trials

Learn how Johnson & Johnson embraces R and open-source tooling in clinical trials. Abstract: Embracing open source has been a transformative journey for J&J, fueled by a commitment to collaboration, transparency, and innovation. Our R journey began with a mindset shift—transitioning from decades-old solutions to actively contributing to and harnessing the power of open-source communities. We navigated the challenges of cultural adaptation while cultivating a programmer-friendly environment that promotes sharing and continuous learning. We enhanced our technical capabilities by open-sourcing key projects focused on R and strengthened our ecosystem and industry partnerships. This webinar reflects on our experiences, lessons learned, and the strategic impact of embracing R. Speakers: Sumesh Kalappurakal, Tadeusz Lewandowski, Nicholas Masel, and Mark Bynens. Learn more about Posit’s work in Pharma: https://posit.co/use-cases/pharma/

RStudio Data Science Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny RMarkdown Package Manager CRAN Interoperability Serious Data Science Readr Tidyr Github Data Wrangling Tidy Data Odbc Gt Tidymodels Statistics Debugging Programming Education RStats Open Source OSS Reticulate Pharma Clinical Trials Clinical Trials Statistical Programming Pharmaverse Johnson & Johnson Posit Posit Data Science Pharma Data Science

Build a Python table in under 1 minute using Great Tables

Create wonderful-looking tables directly from Pandas or Polars DataFrames with the Great Tables package. Typically, Great Tables is used in a notebook environment or within a Quarto document and rendered in HTML or as an image file. Ready to build some awesome tables? Install from PyPI with: $ pip install great_tables. GitHub Repo: https://github.com/posit-dev/great-tables Design Philosophy of Great Tables: https://posit-dev.github.io/great-tables/blog/design-philosophy/

Great Tables Quarto 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 Tidyr Purrr Github Data Wrangling Tidy Data Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source OSS Reticulate Great Tables Polars Dataframe Polars Dataframes

TidyTuesday + Posit | PydyTuesday | Weekly Community Python Data Project

Posit software engineer Isabel Zimmerman discusses the TidyTuesday project and the Posit PydyTuesday Initiative. Learn how to participate in weekly TidyTuesday projects, watch Isabel explore Central Park squirrel data, and discover how to deploy your work to Posit Connect Cloud. Find her code here: https://github.com/isabelizimm/pydy-tuesday Check out these repositories to join the TidyTuesday and the Posit PydyTuesday Initiative: TidyTuesday repo with datasets: https://github.com/rfordatascience/tidytuesday Posit PydyTuesday repo: https://github.com/posit-dev/python-tidytuesday Learn more about Quarto and Connect Cloud: Quarto website: https://quarto.org/ Posit Connect Cloud: https://connect.posit.cloud/ Other videos in this Posit PydyTuesday playlist: https://www.youtube.com/playlist?list=PL9HYL-VRX0oSDQjicFMLIIdcLv5NuvDp9 #pythoncontent

Isabel Zimmerman

python-tidytuesday Quarto 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 Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source OSS Tidy Tuesday Pythoncontent

Create Quarto dashboards with Python

Learn how to create Quarto dashboards with Python! Senior Product Marketing Manager Isabella Velásquez walks through building a Quarto dashboard from scratch, using water insecurity data from the TidyTuesday project. You’ll learn how to structure a Quarto document, preview dashboards, and customize layouts and themes. See Isabella’s code here: https://github.com/ivelasq/water-insecurity-dashboard And the dashboard here: https://ivelasq-water-insecurity-dashboard.share.connect.posit.cloud/ Resources: Quarto dashboards guide: https://quarto.org/docs/dashboards/ Posit Connect Cloud: connect.posit.cloud Hello, Dashboards!: https://www.youtube.com/watch?v=HW7QbqI4fH0&t=590s 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

Isabella Velásquez

python-tidytuesday Quarto 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 Open Source OSS Reticulate Tidy Tuesday Pythoncontent

Free AI packages for R and Python #pythoncontent

Some free R and Python packages that can help when working with LLMs #datascience #artificialintelligence #machinelearning #openai #pythonprogramming #pythonprojects #dataanalysis #dataanalytics #positshorts

RStudio Data Science Machine Learning Python Stats 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 Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Lazy Evaluation Statistics Debugging Programming Education RStats Open Source OSS Reticulate Artificial Intelligence Python Packages Free Python Tools

Transform Boring Forms into Eye-Catching UI: Shiny Party Tricks That Will Make You Look Like a Pro 🎉

Learn how to elevate your Shiny forms from basic to professional with dynamic accordions. This tutorial demonstrates how to implement intelligent form organization and visual feedback using Shiny’s accordion components. We cover both basic accordion implementation and advanced dynamic features including status indicators, automatic title updates, and section validation. Perfect for developers looking to enhance their Shiny applications with modern, user-friendly form interfaces. Key features covered: Basic form to accordion conversion Dynamic panel updates Status indicator implementation Visual feedback systems Section validation techniques Reference Documentation: For accordion implementation, see: https://shiny.posit.co/r/reference/shiny/latest/accordion For dynamic updates, see: https://shiny.posit.co/r/reference/shiny/latest/updateAccordion For input validation, see: https://shiny.posit.co/r/reference/shiny/latest/validate For modular development, see: https://shiny.posit.co/r/articles/build/modules/ Timestamps: 0:00 - Introduction 0:23 - Basic form structure overview 0:50 - Introduction to accordion components 1:11 - Basic accordion implementation 1:35 - Dynamic accordion enhancement 2:00 - Status indicators and visual feedback 2:29 - Updating accordion panels 2:55 - Documentation resources 3:18 - Conclusion

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

Stop Your Shiny Apps from Breaking on Mobile! Master Responsive Design in Minutes 📱💻

A comprehensive guide to implementing responsive design in Shiny applications using UI layout columns. This tutorial covers how to create adaptive layouts that automatically adjust based on screen size, from desktop to mobile devices. Learn how to leverage Shiny’s bootstrap-based grid system to create professional, responsive applications that look great on any device. Key concepts covered: • Default Shiny responsive behavior • UI layout column implementation • Screen size breakpoints • Bootstrap grid system • Responsive card layouts • Mobile-first design principles Reference Documentation: UI Layout Columns: https://shiny.posit.co/r/reference/shiny/latest/layout_column Layout Guide: https://shiny.posit.co/r/articles/build/layout-guide/ Bootstrap Integration: https://shiny.posit.co/r/articles/build/bootstrap/ Responsive Design: https://shiny.posit.co/r/articles/build/responsive/ Timestamps: 0:00 - Introduction 0:23 - Default Shiny layout behavior 0:38 - Understanding screen responsiveness 1:05 - Introduction to UI layout columns 1:38 - Screen size breakpoints explained 2:02 - Desktop layout demonstration 2:30 - Mobile layout adaptation 2:44 - Bootstrap grid system overview 3:21 - Documentation resources 3:45 - Conclusion

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

Shiny Forms with Accordions 🎉

Learn how to elevate your Shiny forms from basic to professional with dynamic accordions. This tutorial demonstrates how to implement intelligent form organization and visual feedback using Shiny’s accordion components. #positshorts

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

Who are the data heroes of…data heroes? 💫

#positshorts

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

Create slideshows with Markdown & Python Code! (Quarto Tutorial)

Learn how to transform Markdown and Python code into interactive slide presentations using Quarto and reveal.js. This tutorial covers essential features like code execution, data visualization, LaTeX equations, custom styling, and slide transitions. Starting with basic slides, we’ll explore advanced functionality including background customization, animations, chalkboard annotations, and brand theming. Perfect for data scientists, educators, and developers looking to create engaging technical presentations. Github repo: https://github.com/KeithGalli/quarto-projects Quarto slideshow demo page: https://quarto.org/docs/presentations/revealjs/ Quarto crash course video: https://youtu.be/_VKxTPWDhA4?si=VZxkW3kgtx1W_AIW Blog article on _brand.yml: https://posit.co/blog/unified-branding-across-posit-tools-with-brand-yml/ Video by @KeithGalli Video timeline! 0:00 - Video Overview 0:39 - Getting Started with the Code (link in description) 2:22 - Quarto Slideshow Basics 6:15 - Generating Powerpoint (pptx) Presentation Outputs 7:14 - RevealJS HTML Output Options & Features (Code Animation, Line Highlighting) 11:00 - Data Visualization in Quarto (Matplotlib, Plotly, Seaborn) 14:40 - Displaying Dataframes on Slides 18:15 - Presentation Features (Incrementally Revealing Items, Image Positioning, Slide Transitions, etc.) 25:02 - Keyboard Shortcuts Functionality (Zoom in, Chalkboard, Save Presentation as PDF, etc.) 29:14 - Styling Presentations with a _brand.yml File (New in Quarto 1.6) 34:39 - Jupyter Notebook to RevealJS Slideshow #Python #Quarto #DataVisualization #pythoncontent

Pharma Open-Source Packages and the R Validation Hub | A conversation with Aaron Clark

Posit’s Director of Life Sciences, Phil Bowsher, sat down with Aaron Clark, Senior Principal Clinical Scientist at Arcus Biosciences, to discuss various topics within the open-source clinical reporting space including Aaron’s career in pharma, the R Validation Hub, the open-source package riskmetric, and more. More about Posit’s work in the pharma space: https://posit.co/use-cases/pharma/

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 Readr Stringr Tidyr Github Data Wrangling Tidy Data Odbc Blogdown Gt Tidymodels Statistics Programming Education RStats Open Source OSS R Validation Hub Clinical Reporting Drug Development Open Source Pharma Pharma SAS Pharma Data Science

Open Source in Clinical Reporting | A Conversation with Ben Arancibia at GSK

Posit’s Director of Life Sciences, Phil Bowsher, sat down with GSK’s Director of Data Science, Ben Arancibia, to discuss various topics within the open-source clinical reporting space. More about Posit’s work in the pharma space: https://posit.co/use-cases/pharma/ Watch GSK’s latest web event with Posit: https://www.youtube.com/watch?v=xDrt6txplek

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 Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source OSS GSK Pharma SAS Pharma Clinical Reporting Statistial Programming

Real-World Python Dashboard Project w/ Quarto! (Airbnb Review Analysis)

In this video we use real-world Airbnb data to put together a dashboard with Quarto & Python. We start with the basics of layout and simple components and then progress to more advanced topics such as integrating Shiny within your Quarto doc. Github Repo: https://github.com/KeithGalli/quarto-projects Check out the recent Quarto Crash Course Video! https://youtu.be/_VKxTPWDhA4?si=sf7cbvSJGj6gnlCl Check out Mine’s Quarto Dashboards with R Code Videos! https://youtu.be/HW7QbqI4fH0?si=oyJhLaJ0n3Vg4v7Y Learn more about Shiny for Dashboards! https://youtu.be/I2W7i7QyJPI?si=Osqk5odwSOZ0ub01 Bootstrap Icons: https://icons.getbootstrap.com/ Video Timeline! 0:00 - Video Overview 0:41 - About our Data & Get Started with the Code 1:38 - Basic Quarto Dashboard Layout Information 5:08 - Adding Value Boxes to our Dashboard 10:56 - Prepping our Airbnb Data for our Dashboard 17:00 - Adding Matplotlib Data Visualizations to our Dashboard (line graph, bar chart) 26:26 - Add a Map of Airbnb Listings using Latitude & Longitude (Folium Library) 33:18 - Adding Additional Dashboard Pages with Interactive Tables (itables library) 42:23 - When to use Quarto versus Shiny for Dashboards? 44:00 - Using Shiny within a Quarto Project 49:14 - Hosting your Quarto Documents with Posit Connect Cloud #python #quarto #dashboards #pythoncontent

Fastest way to Convert Jupyter Notebooks into Analytics Reports! (using Quarto)

Transform messy Jupyter notebooks into polished HTML reports with Quarto. Learn how to create professional, client-ready reports that effectively communicate data science insights to stakeholders. Using a telecom customer churn analysis example, we’ll cover: Converting notebooks to formatted HTML reports Customizing layouts and interactive elements Adding business context to technical analysis Publishing and sharing reports effectively Perfect for data scientists and analysts who need to present technical work to business audiences. Link to Code: https://github.com/KeithGalli/telecom-churn-analysis Quarto Crash Course Video: https://youtu.be/_VKxTPWDhA4?si=kcbQ8M9p6HH5QE2w Share your work with Posit Connect Cloud: https://pos.it/keith_qc Video by @KeithGalli Video timeline! 0:00 - Video Overview & Accessing Code/Data 1:46 - Rendering Jupyter Notebook as HTML Output 3:30 - Making quick improvements to our HTML Report (adjusting YAML parameters, hiding code/output cells) 8:15 - Understanding the Business Context & Insights from our Analysis and adding written details to report. 14:30 - Improving page formatting (margins, body size, etc) 17:30 - Adding business recommendations for our telecom client 21:00 - Further aesthetic improvements (larger font-size, organizing info into columns, using a qmd file, etc.) 30:17 - Publishing our HTML report using Posit Connect Cloud #python #jupyter #quarto

Quarto Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro RMarkdown CRAN Interoperability Serious Data Science Dplyr Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Statistics Debugging Programming Education RStats Open Source OSS Quarto

Generate 100s of custom reports in minutes with Python & Quarto! (Parameterized report automation)

A practical guide to generating hundreds of customized reports using Quarto and Python. Learn how to leverage Quarto’s parameter system to create PDFs and HTML reports at scale. Using a movie dataset example, we’ll cover: How to automate report generation with Python and Quarto Create dynamic templates with data visualization and formatting Adapt parameters to work with both Python and R code Build scalable reporting workflows for any dataset This tutorial demonstrates how to transform what could be hours of manual reporting work into an efficient automated process. Quarto Crash Course Video: https://youtu.be/_VKxTPWDhA4?si=Q09V5xXlyo1YVQqs Generating Analytics Reports (typst) Video: https://youtu.be/Q3phTByW138?si=FIMiAWAHP6HhUnhG Link to Code (starting): https://github.com/KeithGalli/quarto-crash-course Link to Code (finished): https://github.com/KeithGalli/quarto-crash-course/tree/parameterized-reports Information on the dataset can be found here: https://github.com/KeithGalli/quarto-crash-course/tree/master?tab=readme-ov-file#resources Video timeline! 0:00 - Video Overview 0:58 - Walking through some starter code (linked in description) 3:20 - Passing parameters with Python into Quarto Markdown Files 4:27 - Passing parameters using R 5:18 - Using a Python script to render many reports with variable parameters. 6:36 - Dynamically generating markdown sections with Python (output: asis - execution option) 11:56 - Adding images, table of contents, & pagebreaks to our reports 15:00 - Adding graphs to our reports 17:54 - Tying everything together 21:47 - Adding fenced divs & styling/formatting dynamically with code

Quarto 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

Quarto Websites 4: Add lists of content with listings | Charlotte Wickham | Posit

Adding a listing page to your website is a great way to showcase your projects, talks, publications or blog posts. In this video you’ll learn how to create a listing page in Quarto and see two ways to populate it with content: Quarto documents, or a yaml file. In this video: 0:50 Use a listing to add a blog 3:36 Listing options 5:47 Why use a listing? 7:22 Use a YAML file to populate a project portfolio 9:50 Customize the display of a listing 12:10 Advanced customization of listings 13:42 Remove pages Links: Listings: https://quarto.org/docs/websites/website-listings.html Andrew Heiss’ teaching listing: https://www.andrewheiss.com/teaching/ Code: Starter source code: https://github.com/cwickham/quarto-website-video/tree/v0.3 Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.4 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) Are comfortable editing plain text documents (e.g.qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) Want to walk away with your own personal website Taught by: Charlotte Wickham ( https://www.cwick.co.nz/ ) Emil Hvitfeldt ( https://emilhvitfeldt.com/ ) Videos in this series: Build your homepage [https://youtu.be/l7r24gTEkEY] Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

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

Quarto Websites 3: Customize appearance with CSS/SCSS | Emil Hvitfeldt | Posit

You now have a set of content you are happy with on your website, but how do you customize the look and feel of your site beyond options set in YAML? In this video, you’ll start by learning the basics of CSS and SCSS and how to make good design choices. Then, you’ll see how to apply these choices to your Quarto website. In this video: 0:14 What is HTML? 3:23 CSS Selectors 8:05 CSS Attributes 8:25 Layout attributes 10:24 Reducing repetition with SASS/SCSS? 15:26 Consistent design 16:36 Choosing colors 17:50 Choosing fonts 19:28 Maintaining accessibility 22:13 Apply SCSS to your website 24:16 Change the appearance of headings 25:28 Change the appearance of navigation bar 30:30 Use google fonts Links: Color contrast checker: https://colourcontrast.cc/ Google fonts: https://fonts.google.com/ Code: Starter source code: https://github.com/cwickham/quarto-website-video/tree/v0.2 Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.3 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) Are comfortable editing plain text documents (e.g.qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) Want to walk away with your own personal website Taught by: Charlotte Wickham ( https://www.cwick.co.nz/ ) Emil Hvitfeldt ( https://emilhvitfeldt.com/ ) Videos in this series: Build your homepage [https://youtu.be/l7r24gTEkEY] Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

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

Quarto Websites 2: Add pages and navigation | Charlotte Wickham | Posit

Now you’ve got a homepage, you’ll likely want to add some other pages. In this video, learn how to add pages to your website, and help people find them, by adding them to your website navigation. In this video: 1:00 Add a page to your website 2:54 Your file structure determines your URL structure 5:49 Add a link to your page in navigation 7:50 Customize navigation item text and icon 9:12 Control where items appear in the navigation bar 10:16 Navigation bar options 11:11 Switch to side navigation 12:22 Other types of navigation 16:30 Wrap Up Links: List of icons you can use in navigation items: https://icons.getbootstrap.com/ Top navigation bar options: https://quarto.org/docs/websites/website-navigation.html#top-navigation Quarto website navigation: https://quarto.org/docs/websites/website-navigation.html Code: Starter source code: https://github.com/cwickham/quarto-website-video/tree/v0.1 Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.2 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) Are comfortable editing plain text documents (e.g.qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) Want to walk away with your own personal website Taught by: Charlotte Wickham ( https://www.cwick.co.nz/ ) Emil Hvitfeldt ( https://emilhvitfeldt.com/ ) Videos in this series: Build your homepage [https://youtu.be/l7r24gTEkEY] Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

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

Quarto Websites 1: Build your homepage | Charlotte Wickham & Emil Hvitfeldt | Posit

In this video, you’ll get a running start by using a template we’ve designed to be functional and attractive, and that will serve as a foundation for the rest of the video series. You’ll customize the content of your homepage, and how it looks, and along the way learn about the two key files in a Quarto website index.qmd and _quarto.yml. Finally, you’ll learn one way to publish your website so other people can see it. In this video: 0:21 Use a template to get started 2:33 Preview the template homepage, index.qmd 4:12 Customize the content of your homepage 5:45 “About” pages 7:22 Customize the image on your homepage 9:24 Website configuration, _quarto.yml 10:40 Customize colors with YAML 13:45 Customize fonts with YAML 17:00 Publish your site Links: About pages: https://quarto.org/docs/websites/website-about.html Appearance options you can set in YAML: https://quarto.org/docs/output-formats/html-themes.html#basic-options Code: Starter source code: https://github.com/EmilHvitfeldt/website-template Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.1 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) Are comfortable editing plain text documents (e.g.qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) Want to walk away with your own personal website Taught by: Charlotte Wickham ( https://www.cwick.co.nz/ ) Emil Hvitfeldt ( https://emilhvitfeldt.com/ ) Videos in this series: Build your homepage [https://youtu.be/l7r24gTEkEY] Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

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

pandas vs Polars DataFrames? 👨🏻‍🍳 #python

Here’s Marco Gorelli’s take on why lazy execution matters So with pandas, everything happens eagerly. You give the library some instructions, and it evaluates your instructions the moment you give them. With Polars, there’s an option to give it some instructions, it can wait a bit, and then do them all together in the most optimal way that it can detect. If I was to tell you to cook me a recipe ‍ …and I gave you the steps one at a time …and for each step, you have to go out and buy the ingredients that you need for that step ^ This is going to be a pretty inefficient way of making a cake But if I can give you the recipe beforehand: …you can buy all the ingredients together …you can maybe do 2 steps at the same time ^ This is going to be a much nicer cooking experience #positshorts

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

Quarto Crash Course | Create Professional Reports, Dashboards & Websites w/ Markdown & Python Code!

Welcome to this comprehensive Quarto crash course using Python! Whether you’re a complete beginner or an experienced user, this tutorial covers the topics you need to know about the Quarto publishing system. We’ll explore: Basic setup and installation Creating HTML reports, PDFs, and interactive dashboards from your Python script or Jupyter notebook. Building presentations with Revealjs Customizing outputs with CSS and layouts (fenced divs, classes, and more) Working with parameters for dynamic reports Publishing to Posit Connect Cloud Creating complete websites Automated report generation Perfect for data scientists, analysts, and developers looking to create beautiful, reproducible reports from their code. We’ll use Python throughout the tutorial with real-world examples using movie analytics data. Let’s dive in! Video by @KeithGalli Github repo: https://github.com/KeithGalli/quarto-crash-course Deploy with Posit Connect Cloud! https://pos.it/keith_qc #python #quarto #posit — Resources Mentioned — Slideshow example: https://quarto.org/docs/presentations/revealjs/demo/#/title-slide Slideshow example (source code): https://github.com/quarto-dev/quarto-web/blob/main/docs/presentations/revealjs/demo/index.qmd Example HTML report: https://019302a7-e9e3-3454-3575-23148999a7f7.share.connect.posit.cloud/ Quarto Gallery: https://quarto.org/docs/gallery/ Bootstrap Icons: https://icons.getbootstrap.com/ Video timeline! 0:00 - About the Crash Course 0:50 - Quarto Overview 2:12 - Installation & Setup 6:22 - Markdown Basics 8:46 - Quarto Markdown Features 19:37 - Quarto Styling & Formatting (fenced divs, CSS classes, etc.) 34:53 - Parameters & CLI Options 40:46 - HTML & Publishing 49:46 - Static Docs (PDFs, Docx) 54:53 - Dashboards 1:06:50 - Slideshows (Revealjs) 1:16:11 - Websites 1:19:52 - Automated Report Generation (Parameterized Reports)

cli Quarto revealjs 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

Quarto Dashboards 3: Theming and Styling | Mine Çetinkaya-Rundel | Posit

Theming and styling Quarto dashboards built with R and/or Python. Before watching this video, you might want to watch Parts 1 & 2. This video takes you through 0:00 - Theming (including Bootswatch themes, light/dark mode, customizing themes with SCSS) 3:55 - Styling 4:55 - Live coding demo Slides can be found at https://mine.quarto.pub/quarto-dashboards/3-theming-styling and the starter documents for the accompanying exercises at https://github.com/mine-cetinkaya-rundel/olympicdash. Materials for all parts of the videos can be accessed at https://mine.quarto.pub/quarto-dashboards. You already analyze and summarize your data in computational notebooks with R and/or Python. What’s next? You can share your insights or allow others to make their own conclusions in eye-catching dashboards and straight-forward to author, design, and deploy Quarto Dashboards, regardless of the language of your data processing, visualization, analysis, etc. With Quarto Dashboards, you can create elegant and production-ready dashboards using a variety of components, including static graphics (ggplot2, Matplotlib, Seaborn, etc.), interactive widgets (Plotly, Leaflet, Jupyter Widgets, htmlwidgets, etc.), tabular data, value boxes, text annotations, and more. Additionally, with intelligent resizing of components, your Quarto Dashboards look great on devices of all sizes. And importantly, you can author Quarto Dashboards without leaving the comfort of your “home” – in plain text markdown with any text editor (VS Code, RStudio, Neovim, etc.) or any notebook editor (JupyterLab, etc.). This workshop will walk you through building an increasingly complex dashboard using various layout options and deploy them as static web pages (with no special server required) as well as with a Shiny Server on the backend for enhanced interactivity. This course is for you if you: do data analysis in computational notebooks share your results with your audience in static or interactive dashboards want to improve the design, user interface, and experience of your dashboards

Mine Çetinkaya-Rundel

ggplot2 leaflet Quarto rstudio 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

Quarto Dashboards 2: Components | Mine Çetinkaya-Rundel | Posit

Building dashboards in R and/or Python with Quarto, one component at a time. Before watching this video, you might want to watch Part 1. This video takes you through 0:00 - An overview of dashboard components 0:11 - Navigation bar and pages 4:55 - Sidebars, rows, columns, and tabsets 11:07 - Cards 22:40 - Live coding demo Slides can be found at https://mine.quarto.pub/quarto-dashboards/2-dashboard-components and the starter documents for the accompanying exercises at https://github.com/mine-cetinkaya-rundel/olympicdash. Materials for all parts of the videos can be accessed at https://mine.quarto.pub/quarto-dashboards

Mine Çetinkaya-Rundel

Quarto 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

Quarto Dashboards 1: Hello, Dashboards! | Mine Çetinkaya-Rundel | Posit

You already analyze and summarize your data in computational notebooks with R and/or Python. What’s next? You can share your insights or allow others to make their own conclusions in eye-catching dashboards and straight-forward to author, design, and deploy Quarto Dashboards, regardless of the language of your data processing, visualization, analysis, etc. With Quarto Dashboards, you can create elegant and production-ready dashboards using a variety of components, including static graphics (ggplot2, Matplotlib, Seaborn, etc.), interactive widgets (Plotly, Leaflet, Jupyter Widgets, htmlwidgets, etc.), tabular data, value boxes, text annotations, and more. Additionally, with intelligent resizing of components, your Quarto Dashboards look great on devices of all sizes. And importantly, you can author Quarto Dashboards without leaving the comfort of your “home” – in plain text markdown with any text editor (VS Code, RStudio, Neovim, etc.) or any notebook editor (JupyterLab, etc.). This video takes you through 0:00 - Overview of building dashboards with Quarto 0:15 - Dashboard basics 7:40 - First dashboard in R 10:30 - First dashboard in Python 11:43 - Live coding demo Slides can be found at https://mine.quarto.pub/quarto-dashboards/1-hello-dashboards/#/title-slide and the starter documents for the accompanying exercises at https://github.com/mine-cetinkaya-rundel/olympicdash. Materials for all parts of the videos can be accessed at https://mine.quarto.pub/quarto-dashboards

Mine Çetinkaya-Rundel

ggplot2 leaflet 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

Updates from Posit, with Hadley Wickham, Charlotte Wickham, George Stagg, and James Blair

6:45 Hadley introduces the conference. 10:37 Hadley on Posit, PBC. Who are we, and what we do. 20:41 Charlotte Wickham on Quarto, an open-source scientific and technical publishing system. 31:05 George Stagg on webR, R for WebAssembly. Execute R code in your web browser. 43:34 James Blair with the latest on Posit’s partnerships with Databricks and Snowflake. Please join us for our first Posit Conf 2024 keynote, where we’ll tell you about our mission, our products, and some of the exciting things we’ve been working on over the last year. Hadley Wickham, Chief Scientist, will talk briefly about Posit’s mission and products, before introducing the three speakers who will update you on some of the coolest projects we’ve worked on over the last year. James Blair, Senior Product Manager, will give you the latest on our partnerships with Databricks and Snowflake, and how we’re building seamless integrations that let you focus on data science instead of dealing with technical details. Charlotte Wickham, Developer Educator, will show you what’s new in Quarto, focusing on new ways to build beautiful PDFs with Typst. Finally, George Stagg, Senior Software Engineer, will tell you about the latest innovations in teaching using webR, a tool that lets you compile your R code into standalone HTML files. Talk by Hadley Wickham, James Blair, Charlotte Wickham, and George Stagg

Charlotte Wickham, George Stagg, Hadley Wickham

Quarto 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

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

In this video, Barret Schloerke from the Shiny team walks us through the exciting features of Shiny 1.2. One of the major updates includes the integration of Narwhals, allowing effortless computation on data frames without needing detailed knowledge of the data structure. Barret also demonstrates a simple Shiny app displaying S&P 500 data using PyArrow, Polars, and Narwhals. Learn how these updates simplify your data workflows, with minimal code needed to run complex data frame operations. Useful Links: • Shiny Component Gallery: [https://shiny.posit.co/py/components/] • Learn more about Narwhals: [https://narwhals-dev.github.io/narwhals/] Timestamps: 0:00 - Introduction and Shiny 1.2 Release Overview 0:12 - Narwhals Integration and Data Computation 0:31 - PyArrow Application Code Breakdown 1:20 - Using S&P 500 Data with Pandas, Polars, PyArrow, and Narwhals #Shiny #DataScience #narwhals #ShinyApp #Polars #Python

Barret Schloerke, Shiny Team

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

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 shinyapps 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

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

Winston Chang

Shiny shinyapps 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 AI skill should we learn? 🌱

#positshorts

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 an LLM-powered Shiny for Python app to Connect Cloud in minutes!

Connect Cloud lets you quickly deploy data applications and documents from public GitHub repositories for Python and R projects. This short demo showcases secret variable management on Connect Cloud to help deploy an LLM-powered Shiny for Python application. → Signup for a free Connect Cloud account → https://connect.posit.cloud/ → View the how-to guide → https://docs.posit.co/connect-cloud/how-to/python/llm-shiny-python.html

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

Using controllers to write robust Shiny for Python app tests | Karan Gathani | Posit

With the Shiny for Python v1.0 release, controllers were exposed to provide a structured and consistent way for users to interact with and test UI components in Shiny applications. They offer an abstraction layer that encapsulates the complexity of interacting with specific UI elements, making it easier for developers to write robust tests and automate interactions with their Shiny apps. Reference documentation - https://shiny.posit.co/py/api/testing/ https://shiny.posit.co/py/docs/playwright-testing.html

Karan Gathani

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

Creating tests for Shiny for Python apps | Karan Gathani | Posit

With the Shiny for Python v1.0 release, Shiny provides a simple way to create a test file for your Shiny app. The shiny add test command is a helpful CLI tool for Shiny app developers. It simplifies the process of creating test files for their applications. When you run this command, it prompts you to input two pieces of information: the path to your Shiny app file and the desired location for the new test file. Once you provide these details, the command automatically generates a test file at the specified location. This new file includes a pre-made test template, giving you a solid starting point for writing your app’s tests. Reference documentation - https://shiny.posit.co/py/docs/unit-testing.html https://shiny.posit.co/py/docs/playwright-testing.html

Karan Gathani

cli 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

GenAI and Pharma: Learning as we go | Episode 1: Copilot

In this episode, Phil and Cole discuss using Copilot in clinical trial submissions. Due to an industry-wide shift in pharma, statistical programmers are transitioning to open-source tooling in drug development. This skills gap that occurs when moving to open source is one area that can be aided with generative AI tools. In this episode, Cole and Phil show some of the features and functionality of a popular tool in the data science space, Copilot. More about open source in Pharma: https://posit.co/solutions/pharma/

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

shiny	Search all fields
title:shiny	Title only
date:>2025-01-01	After a date
(a OR b) AND c	Group with parens

RMarkdown

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

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

10 tips for data science beginners

Let’s try Positron Assistant for the first time together

Data Exploration 101

Positron 101: Make your DS life easier

Set up your first coding project in less than 10 minutes

Get starting with data - load data into your IDE

Deploying Scikit-learn models for in-database scoring with Snowflake and Posit Team

What is an IDE & which one should you use as a beginner

Next-gen data science: a conversation with the Ravit Show and Eric Pité

Monthly Workflow Demos now on Zoom Sessions | Recordings still shared below

Welcome to PydyTuesday! | How to Level Up your Python Skills

Let’s set up your Python environment with only 2 steps | PydyTuesday Python Projects

Let’s install your first Python library in 2 minutes

Great Tables 3: Data Color and Polishing

Great Tables 2: Introducing Units Notation

Great Tables 1: Structure, Format, and Style

Johnson & Johnson’s Open Source Journey with R in Clinical Trials

Build a Python table in under 1 minute using Great Tables

TidyTuesday + Posit | PydyTuesday | Weekly Community Python Data Project

Create Quarto dashboards with Python

Free AI packages for R and Python #pythoncontent

Transform Boring Forms into Eye-Catching UI: Shiny Party Tricks That Will Make You Look Like a Pro 🎉

Stop Your Shiny Apps from Breaking on Mobile! Master Responsive Design in Minutes 📱💻

Shiny Forms with Accordions 🎉

Who are the data heroes of…data heroes? 💫

Create slideshows with Markdown & Python Code! (Quarto Tutorial)

Pharma Open-Source Packages and the R Validation Hub | A conversation with Aaron Clark

Open Source in Clinical Reporting | A Conversation with Ben Arancibia at GSK

Real-World Python Dashboard Project w/ Quarto! (Airbnb Review Analysis)

Fastest way to Convert Jupyter Notebooks into Analytics Reports! (using Quarto)

Generate 100s of custom reports in minutes with Python & Quarto! (Parameterized report automation)

Quarto Websites 4: Add lists of content with listings | Charlotte Wickham | Posit

Quarto Websites 3: Customize appearance with CSS/SCSS | Emil Hvitfeldt | Posit

Quarto Websites 2: Add pages and navigation | Charlotte Wickham | Posit

Quarto Websites 1: Build your homepage | Charlotte Wickham & Emil Hvitfeldt | Posit

pandas vs Polars DataFrames? 👨🏻‍🍳 #python

Quarto Crash Course | Create Professional Reports, Dashboards & Websites w/ Markdown & Python Code!

Quarto Dashboards 3: Theming and Styling | Mine Çetinkaya-Rundel | Posit

Quarto Dashboards 2: Components | Mine Çetinkaya-Rundel | Posit

Quarto Dashboards 1: Hello, Dashboards! | Mine Çetinkaya-Rundel | Posit

Updates from Posit, with Hadley Wickham, Charlotte Wickham, George Stagg, and James Blair

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

Build Shiny apps with AI ✨

Shiny Assistant: Prototype and build Shiny applications with the help of AI | Winston Chang | Posit

What AI skill should we learn? 🌱

Deploy an LLM-powered Shiny for Python app to Connect Cloud in minutes!

Using controllers to write robust Shiny for Python app tests | Karan Gathani | Posit

Creating tests for Shiny for Python apps | Karan Gathani | Posit

GenAI and Pharma: Learning as we go | Episode 1: Copilot

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