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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

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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

Parallelize R code using user-defined functions in sparklyr

If you’re an Apache Spark user, you benefit from its speed and scalability for big data processing. However, you might still want to leverage R’s extensive ecosystem of packages and intuitive syntax. One effective way to do this is by writing user-defined functions (UDFs) with sparklyr. UDFs enable you to execute R functions within Spark, harnessing Spark’s processing power and combining the strengths of both tools. In this tutorial, you’ll learn how to: Open Posit Workbench as a Databricks user Start a Databricks cluster within Posit Workbench Connect to a cluster within Posit Workbench View Databricks data in RStudio Create a prediction function Create a user-defined function with sparklyr Read our most recent blog that covers parallelizing R code using user-defined functions (UDFs) in sparklyr: https://posit.co/blog/databricks-udfs/ Learn more about our Databricks partnership: https://posit.co/solutions/databricks/ Watch other tutorials on using Databricks and RStudio: https://youtube.com/playlist?list=PL9HYL-VRX0oR-3AgWbXtlfdr29626EjRJ&feature=shared

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

Reproducible data science with webR and Shinylive | George Stagg | Posit

A fundamental principle of the scientific method is peer review and independent verification of results. Good science depends on transparency and reproducibility. However, in a recent study a substantial 74% of research code failed to run without errors, often caused by diverse computing environments. This talk will discuss the principles of numerical reproducibility in research and show how software can be pinned to specific versions and self-contained as a universal binary package using WebAssembly. This ensures seamless reproducibility on any machine equipped with a modern web browser and, using tools such as Shinylive, could provide a new way for researchers to share results with the community. webR demo website: https://webr.r-wasm.org/v0.3.2/ Shinylive examples: https://shinylive.io/r/ https://shinylive.io/py/ Documentation: https://docs.r-wasm.org/webr/v0.3.2/ https://github.com/posit-dev/shinylive https://github.com/quarto-ext/shinylive

George Stagg

Quarto shinylive 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: Elevating R Markdown for Advanced Publishing | Christophe Dervieux

In the dynamic landscape of data analysis and scientific publishing, R Markdown has been pivotal for the R community, allowing users to seamlessly blend code, narrative and results in a cohesive narrative. Now, Quarto emerges as a powerful tool that builds on years of experience but also goes beyond R Markdown, providing more flexibility and power in scientific communication. This talk aims to present Quarto as the new alternative for scientific publishing. We will delve into how Quarto enhances the user experience for R enthusiasts, maintaining the syntax familiarity of R Markdown while introducing innovative and improved functionalities across multiple formats, similar to R Markdown ones. Why switch to Quarto from R Markdown? In which cases? How does Quarto integrate with existing workflows? Hopefully everyone will feel inspired to try out Quarto! https://quarto.org/docs/get-started/ Timestamps: 0:00 Introduction 0:41 Quarto is an open-source, scientific and technical publishing system 1:22 Computational documents and scientific markdown made easy for single source publishing 3:08 How to use Quarto 4:24 Quarto works with VS Code, Positron, Jupyter, & RStudio 5:22 Quarto’s multi-language workflow 7:21 Quarto syntax 8:40 Quarto formats (html, pdf, docx, typst, beamer, pptx, revealjs, etc.) 12:19 HTML Theming 14:10 Typst CSS for nice table output in PDF 16:24 Publishing (Quarto Pub, GitHub Pages, Posit Connect, Posit Cloud, Netlify, Confluence, Hugging Face, etc.) 17:36 Shortcodes 19:10 Quarto Extensions 19:49 Quarto Projects 22:53 Project configuration examples for a website and a book 23:42 Resources to get started!

Christophe Dervieux

Positron Quarto revealjs 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

Great Tables: Make beautiful, publication quality tables in Python | Rich Iannone & Michael Chow

Tables are undeniably useful for data work. We have many great DataFrame libraries available in Python, and they give us flexibility in terms of manipulating data at will, but what happens when presenting tables to others? It’s nice to display tables. Tables can efficiently carry information, just like plots do, and at times it is the better way of presenting data. Indeed, it is time to bridge the divide between raw DataFrame output and wondrously structured tables suitable for publication. Now, let us turn our attention to the state of ‘display tables’ in 2024. Let us go over what comprises key components for building effective information displays in tables. It may surprise one how new a well-crafted table can be hewn. We’ll take a look at the combinations of Python packages that fit together to make this important task possible, and marvel together at the tabular results they can provide! Learn more at https://posit-dev.github.io/great-tables/ Timestamps: 0:00 Intro: Meet Rich and Michael 0:41 What we mean by “publication ready tables” 1:29 Overview of what we’ll talk about in this video 1:44 Table Goals: Ways to make a table beautiful 4:41 Tables made from reproducible code! 5:11 The history of table generation to influence our API 6:15 Our modern take on a table display framework 6:35 The problem with Excel 7:38 Introducing Great Tables! 8:00 Key Ingredients of making a Great Table 8:24 Structure: Title, column spanners and nice column labels 8:52 Format: Compact dollar values and percentages 9:23 Styling: Fill color and bold text 10:09 Imports and Polars Selectors 11:08 Coding the structure 12:27 Coding the format 13:07 Coding the styling 15:03 Putting images and plots in your table cells 15:49 Advanced Design 16:07.fmt_nanoplot(): Small plots within table cells 19:08.data_color(): Heat maps in tales 20:51 Powerful and plentiful methods to format cell values 22:48 To sum up: TABLES RULE

Michael Chow, Rich Iannone

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

Your mission matters: A sustainable future with citizen data science at Dow

How do we keep food fresh? How do we use less water in our everyday lives? How do we make trash…valuable? How do we innovate to make the world a better place? The team at Dow Chemical is pushing the boundaries of material innovation for a more sustainable planet. To reach net zero carbon emissions by 2050, requires creativity and ingenuity from all different functions across the company. With Posit Academy and their citizen data science program, they have grown from a handful of researchers to over 300 people using Posit Workbench and deploying to Posit Connect

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

How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python)

This is part 5 of our multi-part series on creating professional dashboards with Shiny for Python. In this video, we dive into advanced styling techniques to enhance the visual appeal and professionalism of your Shiny dashboards. We’ll cover: Adding a logo and custom title Making use of custom HTML elements Using CSS to style Shiny components Customizing Altair charts for a polished look Advanced Plotly chart modifications Applying a consistent color theme and layout By the end of this video, you’ll have a styled dashboard, ready for a professional presentation. Access the GitHub repo with all parts of this project: https://github.com/KeithGalli/shiny-python-projects Shiny for Python Homepage: https://shiny.posit.co/py/ Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Video by @KeithGalli Video Timeline: 0:00 - Video Overview & Setup 1:50 - Modifying HTML and CSS in Shiny 7:44 - Adding a Logo Image 10:26 - Styling Labels and Containers (Aligning our Image w/ the Title — Custom Divs) 20:20 - Customizing Altair Charts (Gridlines, Font, Axis Labels, Etc.) 27:49 - Customizing Plotly Visualizations 37:16 - Customizing Seaborn & Folium Heatmaps 44:15 - Final Touches, Clean Up, Recap and Next Steps If you enjoyed this series, give it a thumbs up and subscribe to the channel to stay updated! All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

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

How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python)

This is part 4 of our multi-part series on creating professional dashboards with Shiny for Python. In this video, we’ll explore how to integrate popular Python visualization libraries like Matplotlib, Plotly, Seaborn, and Altair into your Shiny apps. This allows you to leverage your existing visualization skills and seamlessly include them in interactive dashboards. We’ll cover: Integrating Matplotlib and Seaborn for detailed visualizations Utilizing Altair and Plotly for dynamic charts Implementing Folium for interactive maps Customizing data tables with additional filters and selection modes By the end of this video, you’ll have a rich and diverse set of visualizations in your Shiny dashboard, setting the stage for the final styling touches in the next video. Shiny for Python Homepage: https://shiny.posit.co/py/ Access the GitHub repo with all parts of this project: https://github.com/KeithGalli/shiny-python-projects Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Video by @KeithGalli Video Timeline! 0:00 - Video Overview & Recap of Previous Video Dashboards 1:38 - Getting Setup with the Code (cloning branch from GitHub) 3:15 - Adding Matplotlib-based visualizations (render.plot Shiny for Python decorator) 10:15 - Create a Seaborn Heatmap Chart (Sales Volume by Hour of the Day) 14:59 - Creating Interactive Charts with Jupyter Widgets (Plotly, Altair, Bokeh, Pydeck, & More…) | render_widget decorator 20:14 - Implementing Folium for Location-Based Heatmaps (render.ui decorator) 25:32 - Enhancing DataFrames with Filters and Selection Modes (render.data_frame, render.DataGrid, render.DataTable, etc.) 28:49 - Additional Rendering Options, Final Touches and Next Steps Stay tuned for part 5, where we’ll focus on styling and finalizing our dashboard. If you enjoyed this video, give it a thumbs up and subscribe to the channel to stay updated! All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

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

How to make your Python Dashboard look Professional! (Layouts in Shiny)

This is part 3 of our multi-part series on creating professional dashboards with Shiny for Python. In this video, we focus on enhancing the visual appeal and structure of your Shiny dashboards using various layout components. We’ll cover: Implementing layout templates for quick setup Using cards and sidebars for a clean, organized look Customizing columns and grids for better data presentation Adding tab options for multiple views within a dashboard By the end of this video, you’ll have a more polished and professional-looking dashboard, setting the stage for advanced visualizations in upcoming videos. Shiny for Python Homepage: https://shiny.posit.co/py/ Access the GitHub repo with all parts of this project: https://github.com/KeithGalli/shiny-python-projects Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Video by @KeithGalli Video Timeline! 0:00 - Video Overview & Progress Thus Far 1:28 - Using Shiny Templates to Get Started Fast 3:21 - Using Layout Components to Customize our Apps (Cards, Sidebars, Tabs, etc.) 7:55 - Adding a Sidebar within a Card 12:38 - Adding a Card with Tabs to Display Various Visualizations 17:35 - Structuring Data in Columns / Grids (layout_columns() & layout_column_wrap()) 26:30 - Final Touches & Tips (Filling in Visualizations into our Tab Views) Stay tuned for part 4, where we’ll add more advanced visualizations, and part 5, where we’ll focus on customizing styles and adding final touches. If you enjoyed this video, give it a thumbs up and subscribe to the channel to stay updated! All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

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

When creating tutorials for beginners, keep this in mind

#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

How to make Interactive Python Dashboards! (Reactivity in Shiny)

This is a quick-start guide to Shiny for Python, part 2 of a multi-part series. Data scientists need to quickly build web applications to create and share interactive visualizations, giving others a way to interact with data and analytics. Shiny helps you do this. In this video, we’ll build off of the last tutorial where we learned the basics of building, sharing, and deploying a Shiny app in Python. This video specifically focuses on reactivity in Shiny. You can watch this video as a standalone, but it may be helpful to watch the previous video ( https://youtu.be/I2W7i7QyJPI). We’ll cover: ⬡ Creating toggle options for dynamic visualizations ⬡ Understanding Shiny’s reactivity model ⬡ Implementing various input selectors ⬡ Building reactive components and visualizations ⬡ Using reactive calculations and effects ⬡ Adding and formatting plots with Plotly ⬡ Documentation walkthrough to learn more about reactivity (reactivity.effect, reactivity.event, reactivity.isolate, reactivity.invalidate_later, etc…) Video Resources: Video #1: https://youtu.be/I2W7i7QyJPI?si=nx1dk5ovPc91pvlB Starter Code (from end of video #1): https://github.com/KeithGalli/shiny-python-projects/tree/video1 Final App: https://keithgalli.shinyapps.io/final-app/ Shiny Resources: Shiny for Python Homepage: https://shiny.posit.co/py/ Component Gallery: https://shiny.posit.co/py/components/ Express Documentation: https://shiny.posit.co/py/api/express/ Gordon Shotwell’s “How does Shiny Render Things?”: https://youtu.be/jvV4y2xogf8?si=8uGP8ZfboUj1QM4p Joe Cheng’s “Shiny Programming Practices”: https://youtu.be/B2JzHv4FOTU?si=t4Atii-RSc5ojgom Stay tuned for part 3, where we’ll explore how to make your dashboard look more professional (layouts in Shiny). Video by @KeithGalli Video Timeline! 0:00 - Intro & Overview 1:01 - Getting Started with Code 2:08 - Adding Shiny Components (Inputs, Outputs, & Display Messages) 3:21 - Creating an Additional Visualization (Sales Over Time by City) 7:55 - What are Reactive.Calcs and How Do We Use Them Properly? (DataFrame Best Practices) 10:27 - Creating an Additional Visualization (Sales Over Time by City) — Continued 14:30 - Filtering City Data with Select Inputs (UI.Input_Selectize) 21:15 - Rendering Shiny Inputs Within Text 22:15 - Quick Formatting Adjustments 22:54 - Understanding the Shiny Reactivity Model (How Does Shiny Render Things?) 24:23 - Adding a Checkbox Input to Change Out Bar Chart Marker Colors 28:00 - Deploying Our Updated App! 29:19 - Advanced Concepts in Shiny Reactivity (Reactive.Effect, Reactive.Event, Reactive.Isolate, Reactive.Invalidate_Later) & Other Resources All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

Joe Cheng

Shiny for Python 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

Being SO good they can’t ignore you

Tom recommended the book So Good They Can’t Ignore You in a recent Data Science Hangout. Libby also keeps this awesome list of Hangout book recommendations: https://datahumans.notion.site/2f4552fd18be4d439b6b6977077e6ca5?v=738d0654fd81490890b8f741a2ef0a3c #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

Communicating Data Science with Shiny! 🚀 Garrick Aden-Buie #datascience #datavisualization #shiny

Ever seen this diagram? It’s from R for Data Science, outlining the journey of tackling new data problems. 1️⃣ Start: Import and tidy your data. 2️⃣ Cycle: Transform, visualize, and model your data. 3️⃣ Communicate: Share your findings. But communication isn’t just the end—it’s an ongoing process! Shiny apps are your secret weapon for this. Scenario 1: Quick Shiny app for colleague feedback ️ refine your work. Scenario 2: Small app grows into a full-featured web application. From small, quick-use apps to robust final products, Shiny evolves with your project needs. Discover why Shiny is essential for every data scientist! Get Started: https://shiny.posit.co #positshorts

Garrick Aden-Buie

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

{shinylive}: Serverless Shiny Apps | Barret Schloerke | Posit

In the rapidly evolving landscape of web technologies, the integration of R (and Python) with modern web frameworks has become increasingly important for data scientists and developers. This presentation introduces {shinylive}, a new R package that exports Shiny applications to be run within statically hosted websites. We will explore the capabilities of {shinylive} through its use of the innovative R package {webR}, which allows for the execution of R code in the browser (via WebAssembly and service workers) without the need for a centralized server. The presentation will cover the technical foundation of {shinylive}, including its architecture and the integration process with Quarto documents. We will also discuss the practical aspects and drawbacks of exporting Shiny apps with {shinylive}, highlighting the ease of exporting apps to a folder for local use or hosting them on GitHub pages. {shinylive} bridges the gap between Shiny and static websites, making it a valuable resource for interactive data analysis and presentation. Link to app: https://schloerke.com/presentation-2024-04-18-appsilon-shinylive/ Link to script: https://github.com/posit-dev/r-shinylive/blob/main/examples/deploy-app.yaml Link to use_github_action(): https://github.com/posit-dev/r-shinylive#github-pages Shinylive website: https://posit-dev.github.io/r-shinylive/ {webr} docs: https://docs.r-wasm.org/webr/latest/

Barret Schloerke

Quarto r-shinylive Shiny shinylive 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

How to inspire people to make a change ✨

Key takeaways from our conversation on the path to effective data stewardship: Change management is hard. To inspire people to change, make it as easy as possible for them to see the path to get there by creating tools to make new tasks easy to complete. A problem not only has to exist, but that problem needs to be recognized to exist. In the data governance space, legal regulations can help push us forward because they create urgency. Data stewardship and data governance enable innovation. You see more of what’s possible when you know what data exists. Core systems form the foundation that enables cutting-edge work like AI development. People have good intentions and want to get their work done and impact the organization. Don’t create processes where the only way to get your job done is to break the process. In the act of governing, remember why you started pulling data together in the first place. Community engagement is incredibly powerful and hearing different perspectives is how we grow. We all face similar problems regardless of our role or industry. An internal community for cross-organizational collaboration is vital for knowing what other people are working on and collaborating. This not only helps you with your data, but also with your career. Implementing effective data stewardship is a journey with companies at all different stages. Those further along the journey may have teams and infrastructure dedicated to this, along with executive buy-in. Others are working to make it happen at the individual level – where it often starts with writing down the definitions of your data (who created it, when it was sourced, intended use, etc.) and recognizing that someone else will use your data output, potentially not in the way you initially intended. #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

Analyze and explore data stored in Snowflake using R

James Blair, Senior Product Manager, Cloud Integrations at Posit, will demonstrate using the R language to analyze and explore data stored in Snowflake. He will also show you how easy it is to set up an R environment inside Posit Workbench that runs as a native app on Snowpark Container Services. You also find out how using the dbplyr interface can be used to push computation data into Snowflake, giving you access to greater memory and compute power than in a standard R session. It’s easy to get started. In just a few minutes, you can work in your R session securely inside Snowflake using the RStudio Pro IDE in Posit Workbench. Posit also supports VS Code and Jupyter for data scientists who prefer to work in other languages like Python, so you can continue to use the tools you know and love. Learn more about the Snowflake and Posit integration: https://posit.co/solutions/snowflake/

dbplyr 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

How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny)

This is a quick-start guide to Shiny for Python. It’s part 1 of a multi-part series. Data scientists need to quickly build web applications to create and share interactive visualizations, giving others a way to interact with data and analytics. Shiny helps you do this. In this video, we’ll walk you through the basics of setting up Shiny for Python, creating your first app, and deploying it so others can use it. We’ll cover: Installing Shiny and necessary dependencies Writing and running your first Shiny app Basic UI and server structure Deploying your app online Helpful Links Shiny for Python Homepage: https://shiny.posit.co/py/ The link to the final app can be found here: https://keithgalli.shinyapps.io/final-app/ Follow along with the code examples provided in this repository: https://github.com/KeithGalli/shiny-python-projects Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Stay tuned for part 2, where we’ll dive deeper into advanced features and customization options. Video Timeline! 0:00 - Intro to Shiny & Video Overview 1:43 - Getting Started with the Shinylive Playground 2:44 - Building a custom visualization with Shinylive 5:18 - Easily sharing the code/application for a Shinylive app 6:12 - Building a Shiny Express App locally (VSCode) 9:40 - How to run app if you’re not using VSCode 10:17 - Further customization of our app (adding title, using CSV data, dynamic input) 17:15 - Deploying our Shiny app to the web 21:20 - Conclusion & what’s coming next in the series Video by @KeithGalli All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

Shiny for Python Shiny shinyapps shinylive 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

Lessons in career growth from gardening 🌱

#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

Joe Cheng | Managing long-running operations in Shiny | Posit

It’s been years since Shiny evolved to allow asynchronous operations within applications, improving scalability. The introduction of the {promises} package enabled concurrent processing between multiple Shiny sessions, a significant step forward in handling background tasks. However, this did not address the need for intra-session concurrency, where users expect to interact with the application while long-running calculations are executed in the background. Recently, we added a new ExtendedTask feature to Shiny to allow for such intra-session concurrency. This new feature provides a different approach for developers to incorporate asynchronous tasks, enabling smoother user interactions during intensive computations. Alongside ExtendedTask, this talk will also discuss newer methods for launching asynchronous tasks, besides the usual {future} package. The focus will be on the practical application and integration of these features into Shiny applications. Links mentioned in the video: ⬡ Shiny in Production: Principles, practices, and tools, https://youtu.be/Wy3TY0gOmJw?feature=shared Timestamps: 0:20 Make your slow code fast 1:43 Long-running operations are a problem 3:28 Inter-session concurrency and intra-session concurrency 4:24 Introducing ExtendedTask 5:17 Demo of a slow API using ExtendedTask 6:13 Slow code example (R) 7:16 Fix slow code with ExtendedTask (R) 8:55 Slow code example (Python) 7:16 Fix slow code with ExtendedTask (Python) 10:46 Links to get started 11:06 ExtendedTask backstory intro 11:28 ExtendedTask vs. Shiny Async 15:50 How reactive programming works in Shiny 21:31 How ExtendedTask works in the reactive process 25:38 What we’re still working on 26:35 {future} alternatives 31:47 Wrapping up

Joe Cheng

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

Future of Shiny to the FDA with WebR ✨

For more on How Pharma is Pioneering WebAssembly with webR & Shiny for FDA Clinical Trial Submissions: https://posit.co/blog/webr-fda-pilot/ #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

Shiny + Databricks 🫶

#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

AI in the 50s and 60s 👀

#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

How to overcome fear when applying to a new job

#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

Using your dataset in Shiny Templates | Carson Sievert | Posit

Watch the Shiny team’s Carson Sievert change the dataset in a Shiny Template. Find the right template for you at https://shiny.posit.co/py/templates/ 0:00 Intro with Carson Sievert 0:19 How to load the template code 1:14 Running your Shiny app in VS Code with a live reloading preview 1:29 How this template works 2:23 See the contents of your data in a data_frame 2:53 How this template imports data 3:33 A more optimized way to import a large amount of data 5:10 Changing the dataset 5:44 Troubleshooting the inevitable errors when changing the dataset

Carson Sievert, 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

Most important thing a data scientist can do 💰

Data Science Hangout with Greg Shick, Director of Analytics at Charles Schwab. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #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

Observing the creation of your data 🍕

Data Science Hangout with Zack Fragoso, Head of MLOps at Domino’s Pizza. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #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 Data Science Hangout

Starting a People Analytics team

Data Science Hangout with Elizabeth Esarove, Lead Advanced Analytics, People Analytics at AT&T to chat about using data science to provide supporting evidence for decisions regarding employee outcomes and business objectives. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #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

Follow these tips when asking for a raise 👀

Data Science Hangout with Elizabeth Esarove, Lead Advanced Analytics, People Analytics at AT&T to chat about using data science to provide supporting evidence for decisions regarding employee outcomes and business objectives. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #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

Introducing Shiny for Python Templates | Carson Sievert | Posit

Last month we introduced the Shiny for Python Components and Layouts galleries, which are a simple, visual overview of what Shiny can do, mixed with cheatsheet-like information. They are for new and seasoned users alike. We’re excited to announce a third section which brings those two things together into opinionated boilerplate code: Shiny Templates. This is just a start… We expect this gallery to grow as time goes on. These will allow you to hit the ground running, whether you need a quick simple app or a quick complicated one. Get started at https://shiny.posit.co/py/templates/ 0:00 Introduction to Shiny for Python Templates 0:38 How to work with a template locally in VS Code 2:24 Preview your app with the Shiny for Python VS Code Extension 2:45 A data_frame is a quick and easy way to see the contents of your data 4:39 See everything in your window with the “fillable=True” page_opts 5:16 Modifying inputs 7:40 Fix the logic in your @render.plot to show your modified inputs 9:00 Add a component to the sidebar 9:05 How to add input_dark_mode to your app 9:54 How to make your plot change to dark mode as well

Carson Sievert

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

70/20/10 model for upskilling at US FDA

Data Science Hangout with Kevin Snyder & Raju Rayavarapu @ U.S. FDA to chat about democratizing data science and utilizing principles of systems biology, pharmacology, toxicology, bioinformatics, and statistics to collaboratively develop open-source software solutions that support decision making and policy development. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #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

Evolve your perspective by leaning into these 2 things

Data Science Hangout with Brad Zielke, Sr. Director Data Sciences at Target on leaning into empathy and humility to surround yourself with diverse perspectives and evolve your perspective as a leader. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #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

This ONE THING is what hiring managers are looking for🤯

Data Science Hangout with Jamie Warner, Managing Director at Plymouth Rock Assurance. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #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

How to get your boss to pay attention to your work 👀

How do you capture your boss’ imagination and show the value of your data science project? Data Science Hangout with Michael Derstine, Vice President at Wells Fargo & Company. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #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

Deploying Databricks-backed content on Posit Connect

After you connect to Databricks using sparklyr and create your content, you’ll want to deploy and share your work. With the new pysparklyr::deploy_databricks() function, you can deploy your content to Posit Connect. Run the function and confirm to start deployment. The deploy_databricks() function will gather your document location, credentials, and URLs. It uses this information to deploy to Posit Connect. After it’s done, click the URL to see the deployed content on Posit Connect. Open the content from the publisher view. Click around and check out your work. In Posit Connect, you can edit sharing permissions, create a vanity URL, and more. Open the content URL to see how your work would look to stakeholders. Learn more: Databricks x Posit: https://posit.co/solutions/databricks/ Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ Posit Connect: https://posit.co/products/enterprise/connect/ sparklyr and Databricks Connect v2: https://spark.posit.co/deployment/databricks-connect.html Deploying to Posit Connect: https://spark.posit.co/deployment/databricks-posit-connect.html Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

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

Databricks Pro Driver in Posit Workbench

We’ve added a Databricks driver to our Professional Drivers. The RStudio Connections Pane allows users to connect to their Databricks clusters from the IDE. Select the Databricks driver from the list of available drivers. Select the Driver to establish the connection. The driver can be used with the new databricks() function from the odbc package to connect to Databricks clusters and SQL warehouses. Learn more: Databricks x Posit: https://posit.co/solutions/databricks/ Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ RStudio IDE and Posit Workbench 2023.12.0: What’s New: https://posit.co/blog/rstudio-2023-12-0-whats-new/ Posit Professional Drivers 2024.03.0: Support for Apple Silicon: https://posit.co/blog/pro-drivers-2024-03-0/ Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

odbc 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

Databricks Pane in Posit Workbench

We’ve introduced a Databricks Pane in RStudio Pro for discovering and managing Databricks clusters. Users can stay in RStudio Pro without navigating to the Databricks web interface to see their clusters. Click on the ‘Connect to’ icon to open up a new Databricks Connection pop-up. This forms the initial connection to Databricks. Once you click ‘ok’, you will be connected to Databricks. Learn more: Databricks x Posit: https://posit.co/solutions/databricks/ Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ RStudio IDE and Posit Workbench 2023.12.0: What’s New: https://posit.co/blog/rstudio-2023-12-0-whats-new/ Posit Professional Drivers 2024.03.0: Support for Apple Silicon: https://posit.co/blog/pro-drivers-2024-03-0/ Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

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

Databricks Authentication in Posit Workbench

Posit Workbench now has delegated Databricks credentials. Users can log into a Databricks Workspace when starting an RStudio or VS Code session. Authentication relies on OAuth-backed refresh tokens rather than Personal Access Tokens (PATs). This allows for more granular control over permissions, built-in expiration, and defined scopes. Once logged in, you can interact directly with your Databricks clusters in that Workspace in your preferred environment. Learn more: Databricks x Posit: https://posit.co/solutions/databricks/ Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ RStudio IDE and Posit Workbench 2023.12.0: What’s New: https://posit.co/blog/rstudio-2023-12-0-whats-new/ Posit Professional Drivers 2024.03.0: Support for Apple Silicon: https://posit.co/blog/pro-drivers-2024-03-0/ Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

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

Connecting RStudio and Databricks with sparklyr

You can connect RStudio and Databricks with the sparklyr package. First, load the necessary packages. Next, set up the connection with sparklyr::spark_connect(). See your data in the Connections pane. Check which databases you’re connected to. Navigate the data structure by expanding the levels. Navigate to the table you want to explore…and analyze your data in RStudio. Learn more: Databricks x Posit: https://posit.co/solutions/databricks/ Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ Posit Connect: https://posit.co/products/enterprise/connect/ sparklyr and Databricks Connect v2: https://spark.posit.co/deployment/databricks-connect.html Deploying to Posit Connect: https://spark.posit.co/deployment/databricks-posit-connect.html Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

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

Connecting RStudio and Databricks with ODBC

The odbc package, in conjunction with a driver, provides DBI support and an ODBC connection. With the new odbc::databricks_connect function, you can create an ODBC connection to determine and configure the necessary settings to access your Databricks account. Your Databricks HTTP path is the only argument you need to run databricks_connect(). Provide your HTTP path and you will be able to see your Databricks data in the RStudio Connections Pane. Then, you can analyze your data in RStudio. Learn more: Databricks x Posit: https://posit.co/solutions/databricks/ Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ RStudio IDE and Posit Workbench 2023.12.0: What’s New: https://posit.co/blog/rstudio-2023-12-0-whats-new/ Posit Professional Drivers 2024.03.0: Support for Apple Silicon: https://posit.co/blog/pro-drivers-2024-03-0/ Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

DBI odbc 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

GitHub Copilot on Posit Cloud

Speed up your coding projects in the RStudio IDE on Posit Cloud with GitHub Copilot, an AI coding assistant. Learn more in our blog post: https://posit.co/blog/github-copilot-on-posit-cloud/ Posit Cloud: https://posit.cloud/ GitHub Copilot: https://github.com/features/copilot RStudio User Guide: https://docs.posit.co/ide/user/ide/guide/tools/copilot.html

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

Make data science slides with code using Quarto

#positshorts

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

You Can Lead a Horse to Water…Changing the Data Science Culture for Veterinary Scientists

Presented by Jill MacKay A retrospective look at supporting data science skills in a research-focussed veterinary school This is a talk about environment management, but not in the way you’re thinking. In many industries, domain-specific experts need enough understanding of data science to support their work and communicate with data scientists, but often have insufficient training in these skills, and limited time with which to obtain data science skills and practice them. This is particularly challenging for those who are interdisciplinary and have limited control over their workload, such as medics and field scientists. In this talk, an educational scientist describes the previous 10 years of supporting veterinary scientists to adopt open science practices surrounding data science. What worked, what failed miserably, and reflections on why it can be so hard to get a horse to drink. Materials: https://github.com/jillymackay/positconf2023_vetdata https://jillymackay.com/post/positconf2023/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Teaching data science. Session Code: TALK-1095

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect 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 Quarto Posit Python Ml

Why You Should Stop Networking and Start Making Friends - posit::conf(2023)

Presented by Libby Heeren When we think about making connections, we think about networking. I’d like you to forget about networking and start thinking about making friends. I’ll share my perspective as a community builder and host of the Data Humans podcast on how I cultivated a community of practice for myself and how I became a force multiplier who increases engagement. You’ll learn how I made genuine human connections, the practical steps to making data friends, the power of vulnerability, and why we all benefit when we show up as our whole selves. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1167

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect 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 Quarto Posit Python Ml

Why You Should Add Logging To Your Code (and make it more helpful) - posit::conf(2023)

Presented by Daren Eiri Learn how the log4r package can help you better understand the errors your code may produce, and how to also get promptly alerted for severe errors by leveraging cloud monitoring solutions like Azure Monitor or AWS CloudWatch When an error happens in your API, Shiny App, or quarto document, it is not always clear what line of code you need to look at, and the error messages aren’t always helpful. By walking through a simple API example, I show how you can use logging packages like log4r to provide error messages that make sense to you. I also show how you can use cloud-based data collection platforms like Azure Monitor or AWS CloudWatch to set up alerts, so you can get notified by email or text message for those severe errors that you need to be immediately aware of. Gain more visibility into the health of your code by incorporating logging and pushing your logs to the cloud. Materials: https://dareneiri.github.io/positconf2023/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1164

log4r Quarto Shiny Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect 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 Quarto Posit Python Ml

What’s New in the Torch Ecosystem - posit::conf(2023)

Presented by Daniel Falbel torch is an R port of PyTorch, a scientific computing library that enables fast and easy creation and training of deep learning models. In this talk, you will learn about the latest features and developments in torch, such as luz, a higher-level interface that simplifies your model training code, and vetiver, a new integration that allows you to deploy your torch models with just a few lines of code. You will also see how torch works well with other R packages and tools to enhance your data science workflow. Whether you are new to torch or already an experienced user, this talk will show you how torch can help you tackle your data science challenges and inspire you to build your own models. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1163

Daniel Falbel

torch Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect 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 Quarto Posit Python Ml

What’s New in Quarto?* - posit::conf(2023)

Presented by Charlotte Wickham It’s been over a year since Quarto 1.0, an open-source scientific and technical publishing system, was announced at rstudio::conf(2022). In this talk, I’ll highlight some of the improvements to Quarto since then. You’ll learn about new formats, options, tools, and ways to supercharge your content. And, if you haven’t used Quarto yet, come to see some reasons to try it out. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Quarto (1). Session Code: TALK-1072

Charlotte Wickham

Quarto rstudio Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect 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 Quarto Posit Ml

What I Wish I Knew Before Becoming a Data Scientist - posit::conf(2023)

Presented by Kaitlin Bustos In this talk, I’m sharing my personal journey as a data scientist and the key lessons learned along the way. I’ll emphasize the importance of finding a positive community of like minded-allies, persevering through setbacks as success is not linear, and exploring by embracing the broad nature of the data science field. By sharing my experiences and acknowledging the challenges I’ve faced attendees will gain a fresh perspective on what it takes to succeed in a data science career and inspire them to pursue their passions in the field. Overall, this talk aims to provide a glimpse into the reality of a data science career. Attendees will take away a sense of motivation and empowerment to find their own unique path to success. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1169

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect 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 Quarto Posit Python Ml

What an Early 2000s Reality Show Taught Me about File Management - posit::conf(2023)

Presented by Reiko Okamoto Clutter, whether it’s physical or digital, destroys our ability to focus; home organization ideas can be extended to create an workspace where analysts feel inspired to work with data. Ideas from home organization shows are surprisingly applicable to file management. Using a room divider to establish dedicated zones for different activities in a studio apartment is analogous to creating self-contained projects in RStudio. Likewise, swapping mismatched hangers with matching ones to tidy a closet resembles the adoption of a file naming convention to make a directory easier to navigate. In this talk, I will share good practices in file management through the lens of home organization. We all know that clutter, whether it is in our physical space or on our machine, destroys our ability to focus. These practices will help R users of all levels create a serene, relaxing environment where they feel inspired to work with data. https://reikookamoto.github.io/; https://github.com/reikookamoto/posit-conf-2023-neat Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Getting %$!@ done: productive workflows for data science. Session Code: TALK-1090

rstudio Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect 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 Quarto Posit Python Ml

We Converted our Documentation to Quarto - posit::conf(2023)

Presented by Melissa Van Bussel Elevate your Quarto projects to new heights with these practical tips and tricks! “Wiki”, “User Guide”, “Handbook” – whatever you call yours, we converted ours to Quarto! A year ago, my team’s documentation, which had been created using Microsoft Word, was large and lacked version control. Scrolling through the document was slow, and, due to confidentiality reasons, only one person could edit it at a time, which was a significant challenge for our team of multiple developers. After realizing we needed a more flexible solution, we successfully converted our documentation to Quarto. In this talk, I’ll discuss our journey converting to Quarto, the challenges we faced along the way, and tips and tricks for anyone else who might be looking to adopt Quarto too. Slides: https://melissavanbussel.quarto.pub/posit-conf-2023; Code for slides: https://github.com/melissavanbussel/posit-conf-2023; My YouTube: https://www.youtube.com/c/ggnot2; My website: https://www.melissavanbussel.com/; My Twitter: https://twitter.com/melvanbussel; My LinkedIn: https://www.linkedin.com/in/melissavanbussel/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Quarto (2). Session Code: TALK-1140

Quarto Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect 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 Quarto Posit Python Ml

Visualizing Data Analysis Pipelines with Pandas Tutor and Tidy Data Tutor - posit::conf(2023)

Presented by Sean Kross The data frame is a fundamental data structure for data scientists using Python and R. Pandas and the tidyverse are designed to center building pipelines for the transformation of data frames. However, within these pipelines it is not always clear how each operation is changing the underlying data frame. To explain each step in a pipeline data science instructors resort to hand-drawing diagrams to illustrate the semantics of operations such as filtering, sorting, and grouping. In this talk, I will introduce Pandas Tutor and Tidy Data Tutor, step-by-step visual representation engines of data frame transformations. Both tools illustrate the row, column, and cell-wise relationships between an operation’s input and output data frames. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Teaching data science. Session Code: TALK-1096

tidyverse Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect 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 Quarto Posit Ml

Plumber

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

Parallelize R code using user-defined functions in sparklyr

Reproducible data science with webR and Shinylive | George Stagg | Posit

Quarto: Elevating R Markdown for Advanced Publishing | Christophe Dervieux

Great Tables: Make beautiful, publication quality tables in Python | Rich Iannone & Michael Chow

Your mission matters: A sustainable future with citizen data science at Dow

How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python)

How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python)

How to make your Python Dashboard look Professional! (Layouts in Shiny)

When creating tutorials for beginners, keep this in mind

How to make Interactive Python Dashboards! (Reactivity in Shiny)

Being SO good they can’t ignore you

Communicating Data Science with Shiny! 🚀 Garrick Aden-Buie #datascience #datavisualization #shiny

{shinylive}: Serverless Shiny Apps | Barret Schloerke | Posit

How to inspire people to make a change ✨

Analyze and explore data stored in Snowflake using R

How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny)

Lessons in career growth from gardening 🌱

Joe Cheng | Managing long-running operations in Shiny | Posit

Future of Shiny to the FDA with WebR ✨

Shiny + Databricks 🫶

AI in the 50s and 60s 👀

How to overcome fear when applying to a new job

Using your dataset in Shiny Templates | Carson Sievert | Posit

Most important thing a data scientist can do 💰

Observing the creation of your data 🍕

Starting a People Analytics team

Follow these tips when asking for a raise 👀

Introducing Shiny for Python Templates | Carson Sievert | Posit

70/20/10 model for upskilling at US FDA

Evolve your perspective by leaning into these 2 things

This ONE THING is what hiring managers are looking for🤯

How to get your boss to pay attention to your work 👀

Deploying Databricks-backed content on Posit Connect

Databricks Pro Driver in Posit Workbench

Databricks Pane in Posit Workbench

Databricks Authentication in Posit Workbench

Connecting RStudio and Databricks with sparklyr

Connecting RStudio and Databricks with ODBC

GitHub Copilot on Posit Cloud

Make data science slides with code using Quarto

You Can Lead a Horse to Water…Changing the Data Science Culture for Veterinary Scientists

Why You Should Stop Networking and Start Making Friends - posit::conf(2023)

Why You Should Add Logging To Your Code (and make it more helpful) - posit::conf(2023)

What’s New in the Torch Ecosystem - posit::conf(2023)

What’s New in Quarto?* - posit::conf(2023)

What I Wish I Knew Before Becoming a Data Scientist - posit::conf(2023)

What an Early 2000s Reality Show Taught Me about File Management - posit::conf(2023)

We Converted our Documentation to Quarto - posit::conf(2023)

Visualizing Data Analysis Pipelines with Pandas Tutor and Tidy Data Tutor - posit::conf(2023)

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