Ggplot2

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

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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

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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

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When creating tutorials for beginners, keep this in mind

#positshorts

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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

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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

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{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

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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

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Lessons in career growth from gardening 🌱

#positshorts

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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

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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

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Shiny + Databricks 🫶

#positshorts

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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

Hadley Wickham on R vs Python

Learn about tidyverse, ggplot2, and the secret to a tech company’s longevity as Hadley Wickham joins @JonKrohnLearns in this episode. He talks about Posit’s rebrand, why tidyverse needs to be in every data scientist’s toolkit, and why getting your hands dirty with open-source projects can be so lucrative for your career. Watch the full interview “779: The Tidyverse of Essential R Libraries and their Python Analogues — with Dr. Hadley Wickham” here: https://www.superdatascience.com/779

Hadley Wickham

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

779: The Tidyverse of Essential R Libraries and their Python Analogues — with Dr. Hadley Wickham

#Tidyverse #RProgramming #RLibraries Tidyverse, ggplot2, and the secret to a tech company’s longevity: Hadley Wickham talks to @JonKrohnLearns about Posit’s rebrand, Tidyverse and why it needs to be in every data scientist’s toolkit, and why getting your hands dirty with open-source projects can be so lucrative for your career. This episode is brought to you by Intel and HPE Ezmeral Software ( https://bit.ly/hpeintel). Interested in sponsoring a SuperDataScience Podcast episode? Visit https://passionfroot.me/superdatascience for sponsorship information. In this episode you will learn: • [00:00:00] Introduction • [00:02:55] All about the Tidyverse • [00:15:19] Hadley’s favorite R libraries • [00:28:39] The goal of Posit • [00:34:12] On bringing multiple programming languages together • [00:50:19] The principles for a long-lasting tech company • [00:53:34] How Hadley developed ggplot2 • [01:03:52] How to contribute to the open-source community Additional materials: https://www.superdatascience.com/779

Hadley Wickham

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

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

Validating and Testing R Dataframes with Pandera via reticulate - R-Python Interoperability

Presented by Niels Bantilan Original Full Title: Validating and Testing R Dataframes with Pandera via reticulate: A Case Study in R-Python Interoperability Data science and machine learning practitioners work with data every day to analyze and model them for insights and predictions. A major component of any project is data quality, which is a process of cleaning, and protecting against flaws in data that may invalidate the analysis or model. Pandera is an open source data testing toolkit for dataframes in the Python ecosystem: but can it validate R dataframes? This talk is composed of three parts: first I’ll describe what data testing is and motivate why you need it. Then, I’ll introduce the iterative process of creating and refining dataframe schemas in Pandera. Finally, I’ll demonstrate how to use it in R with the reticulate package using a simple modeling exercise as an example. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: R or Python? Why not both!. Session Code: TALK-1123

reticulate 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

Using R, Python, and Cloud Infrastructure to Battle Aquatic Invasive Species - posit::conf(2023)

Presented by Uli Muellner and Nicholas Snellgrove Invasive species are a huge threat to lake ecosystems in Minnesota. With over 10,000 water bodies across the state, having up-to-date data and decision support is critical. Researchers at the University of Minnesota have created four complex R and Python models to support lake managers, all pulled together and presented with the most recent infestation data available. Come along with us to see how we connected these models in the AIS Explorer, a decision support application built in Shiny to help prioritize risks and placing watercraft inspectors, using tools like OCPU and cloud toolings like Lambda, EventBridge and AWS S3. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: R or Python? Why not both!. Session Code: TALK-1118

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

Using R with Databricks Connect - posit::conf(2023)

Presented by Edgar Ruiz Spark Connect, and Databricks Connect, enable the ability to interact with Spark stand-alone clusters remotely. This improves our ability to perform Data Science at-scale. We will share the work in sparklyr, and other products, that will make it easier for R users to take advantage this new framework. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Tidy up your models. Session Code: TALK-1084

Edgar Ruiz

Databricks Data Science Rstudio 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

Using R to develop production modeling workflows at Mayo Clinic - posit::conf(2023)

Presented by Brendan Broderick Developing workflows that help train models and also help deploy them can be a difficult task. In this talk I will share some tools and workflow tips that I use to build production model pipelines using R. I will use a project of predicting patients who need specialized respiratory care after leaving the ICU as an example. I will show how to use the targets package to create a reproducible and easy to manage modeling and prediction pipeline, how to use the renv package to ensure a consistent environment for development and deployment, and how to use plumber, vetiver, and shiny applications to make the model accessible to care providers. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Leave it to the robots: automating your work. Session Code: TALK-1149

plumber renv Shiny 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

Using Data to Protect Traditional Lifeways - posit::conf(2023)

Presented by Angie Reed The spirit of Penobscot Nation’s work to protect the health of their relative, the Penobscot River, is embodied in the Penobscot water song which says ““Water, we love you, thank you so much water, we respect you.”” Because the Penobscot River is not a natural resource - she is a relative, family - this song describes the foundation of our efforts to protect her health and well-being. The identity of Penobscot people cannot be disconnected from the river, and protecting this traditional lifeway is at the heart of our work. For over a decade we have used R to manage, transform, analyze, and visualize data, and the free, open-source Posit products help us leave a legacy of good data management and the ability to share results with Penobscot Nation citizens. You will learn more about how our use of R has helped us achieve more stringent protections for the Penobscot River and how we engage young people in every step of this work. We are also part of a larger network of tribal environmental professionals, working together to learn R and share data and insights. We will give you information about how you can volunteer to help expand the network of folks providing technical assistance on any R and RStudio related topics. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: End-to-end data science with real-world impact. Session Code: TALK-1144

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

USGS R Package Development: 10-year Reflections - posit::conf(2023)

Presented by Laura DeCicco Ten years ago, the first set of git commits was submitted to a new R software package repository “dataRetrieval” with the goal to provide an easy way for R users to retrieve U.S Geological Survey (USGS) water data. At that time, the perception within the USGS was the use of R was exclusive to an elite group of “very serious scientists.” Fast forward, we now find many newer USGS hires having a solid grasp of the language from the start along with the use of R in a wide variety of applications. In this talk, I’ll discuss my experiences maintaining the dataRetrieval package, how it’s shaped my career, impacted USGS R usage, and why data providers should consider sponsoring their own R packages wrapping their data API services. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1171

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

Unlock the Power of DataViz Animation and Interactivity in Quarto - posit::conf(2023)

Presented by Deepsha Menghani Plot animated and interactive visualizations with Plotly and Crosstalk in Quarto using R. In thi sintro to Plotly & Crosstalk in R: Using code examples, learn to integrate dashboard elements into Quarto with animated plots, interactive widgets (checkboxes), and linked plots via brushing. This talk showcases how to use packages, such as Plotly and Crosstalk, to create interactive data visualizations and add dashboard-like elements to Quarto. Using a fun dataset available through the “Richmondway” package, we examine the number of times Roy Kent uses salty language throughout all seasons of ““Ted Lasso.”” We illustrate this using animated plots, interactive selection widgets such as checkboxes, and by linking two plots with brushing capabilities. Materials: Slides: https://deepshamenghani.github.io/posit_plotly_crosstalk/#/title-slide Code repo: https://github.com/deepshamenghani/posit_plotly_crosstalk Richmondway data package: https://github.com/deepshamenghani/richmondway In-Depth Guide to Creating and Publishing an R Data Package (Richmondway) Using Devtools: https://medium.com/p/245b0fd4c359 Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Quarto (2). Session Code: TALK-1143

crosstalk devtools Quarto 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

Towards the Next Generation of Shiny UI

Presented by Carson Sievert Create awesome looking and feature rich Shiny dashboards using the bslib R package. Shiny recently celebrated its 10th birthday, and since its birth, has grown tremendously in many areas; however, a hello world Shiny app still looks roughly like it did 10 years ago. The bslib R package helps solve this problem making very easy to apply modern and customizable styling your Shiny apps, R Markdown / Quarto documents, and more. In addition, bslib also provides dashboard-focused UI components like expandable cards, value boxes, sidebar layouts, and more to help you create delightful Shiny dashboards. Materials: https://rstudio.github.io/bslib/ https://bslib.shinyapps.io/flights/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Shiny user interfaces. Session Code: TALK-1124

Carson Sievert

bslib Quarto rstudio Shiny shinyapps Python Shiny Databricks Data Science Rstudio Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect 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

tidymodels: Adventures in Rewriting a Modeling Pipeline - posit::conf(2023)

Presented by Ryan Timpe An overview of the benefits unlocked on our data science team by adopting tidymodels. Data science sure has changed over the past few years! Everyone’s talking about production. RStudio is now Posit. Models are now tidy. This talk is about embracing that change and updating existing models using the tidymodels framework. I recently completed this change, letting go of our in-production code and revisioning it with tidymodels. My team ended up with a faster, more scalable pipeline that enabled us to better automate our workflow and increase our scale while improving our stakeholders’ experiences. I’ll share tips and tricks for adopting the tidymodels framework in existing products, best practices for learning and upskilling teams, and advice for using tidymodel packages to build more accessible data science tools. Materials: https://www.ryantimpe.com/files/tidymodels_adventures_positconf2023.pdf Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Tidy up your models. Session Code: TALK-1082

rstudio tidymodels AIMLmodelling Automation Humans 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

The Road to Easier Shiny App Deployments - posit::conf(2023)

Presented by Liam Kalita We’re often helping developers to assess, fix and improve their Shiny apps, and often the first thing we do is see if we can deploy the app. If you can’t deploy your Shiny app, it’s a waste of time. If you can deploy it successfully, then at the very least it runs, so we’ve got something to work with. There are a bunch of reasons why apps fail to deploy. They can be easy to fix, like Hardcoded secrets, fonts, or missing libraries. Or they can be intractable and super frustrating to deal with, like manifest mismatches, resource starvation, and missing libraries. At the end of this talk, I want you to know how to identify, investigate and proactively prevent Shiny app deployment failures from happening. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: The future is Shiny. Session Code: TALK-1089

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

The Power of Prototyping in R Shiny: Saving Millions by Building the Right Tool - posit::conf(2023)

Presented by Maria Grycuk The development of software can be costly and time-consuming. If the end users are not involved in the process from the start the tool we built may not meet their needs. In this presentation, I will discuss how prototyping in Shiny can help you build the right tool and save you from spending millions of dollars on a tool no one will use. I will explore the advantages of using Shiny for prototyping, particularly its ability to rapidly build interactive applications. I will also discuss how to design effective prototypes, including techniques for gathering user feedback and using that feedback to refine your tool. I will emphasize the importance of presenting real-life data, particularly when building data-driven tools. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Shiny user interfaces. Session Code: TALK-1125

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

The People of Posit: Bringing Personality to R Packages - posit::conf(2023)

Presented by JP Flores and Sarah Parker The R programming language offers the versatility to perform statistical analyses, create publication-ready plots, and render high-quality reports and presentations. Despite having this environment of indispensable tools, it can be daunting for a beginner-level programmer to get started. Luckily, the Posit community is one of a kind and values inclusivity, collaboration, and empathy. By putting a face to the R packages we use on a daily basis, we hope to make every programmer feel included and capable. We want to inspire attendees to create their own projects or packages, connect with others inside and outside of their field of expertise, and challenge themselves to learn something new, knowing the community is right there to support them. Materials: http://www.sarmapar.com/people_of_posit/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1165

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

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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)

Validating and Testing R Dataframes with Pandera via reticulate - R-Python Interoperability

Using R, Python, and Cloud Infrastructure to Battle Aquatic Invasive Species - posit::conf(2023)

Using R with Databricks Connect - posit::conf(2023)

Using R to develop production modeling workflows at Mayo Clinic - posit::conf(2023)

Using Data to Protect Traditional Lifeways - posit::conf(2023)

USGS R Package Development: 10-year Reflections - posit::conf(2023)

Unlock the Power of DataViz Animation and Interactivity in Quarto - posit::conf(2023)

Towards the Next Generation of Shiny UI

tidymodels: Adventures in Rewriting a Modeling Pipeline - posit::conf(2023)

The Road to Easier Shiny App Deployments - posit::conf(2023)

The Power of Prototyping in R Shiny: Saving Millions by Building the Right Tool - posit::conf(2023)

The People of Posit: Bringing Personality to R Packages - posit::conf(2023)

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