Nick Strayer

ShinyUiEditor: From Alpha to Powerful Shiny App Development Tool - posit::conf(2023)

Presented by Nick Strayer Since its alpha debut at last year's conference, the ShinyUiEditor has experienced continuous development, evolving into a powerful tool for crafting Shiny app UIs. Some key enhancements include the integration of new bslib components and the editor's ability to create or navigate to existing server bindings for inputs and outputs. In addition to new features, the editor is now available as a VSCode extension enabling it to integrate smoothly into more developers' workflows. This talk will showcase how these new capabilities empower users to efficiently create visually appealing and production-ready applications with ease. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Shiny user interfaces. Session Code: TALK-1126

Nick Strayer

bslib Shiny shinyuieditor 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

Shiny UI Editor Project Walkthrough || Nick Strayer || RStudio

The Shiny UI Editor is a dynamic drag-and drop interface to help you design beautiful Shiny apps. The Shiny UI Editor is a visual tool for building the UI portion of a Shiny application that generates clean and human-readable code. The goal of the Shiny UI Editor is to allow people to build the broad-level UI for their Shiny app without writing code. The editor is intended for those who may not be comfortable with the HTML-style code of Shiny’s UI functions or who simply don’t want to fiddle with sizes to get things laid out correctly. Learn more about the Shiny UI Editor here: https://rstudio.github.io/shinyuieditor/ And read up on GridLayout here: https://rstudio.github.io/gridlayout Content: Nick Strayer (@NicholasStrayer) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Nick Strayer

gridlayout rstudio Shiny shinyuieditor RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny RMarkdown Package Manager CRAN Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging RStats Open Source OSS Reticulate Drag and Drop Shiny Ui Shiny Ui Editor Nick Strayer Tony Pelleriti Posit

Shiny UI Editor Feature Tour || Nick Strayer || Posit (RStudio)

The Shiny UI Editor is a dynamic drag-and drop interface to help you design beautiful Shiny apps. The Shiny UI Editor is a visual tool for building the UI portion of a Shiny application that generates clean and human-readable code. The goal of the Shiny UI Editor is to allow people to build the broad-level UI for their Shiny app without writing code. The editor is intended for those who may not be comfortable with the HTML-style code of Shiny’s UI functions or who simply don’t want to fiddle with sizes to get things laid out correctly. Learn more about the Shiny UI Editor here: https://rstudio.github.io/shinyuieditor/ And read up on GridLayout here: https://rstudio.github.io/gridlayout Content: Nick Strayer (@NicholasStrayer) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Nick Strayer

gridlayout rstudio Shiny shinyuieditor RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny RMarkdown Package Manager CRAN Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging RStats Open Source OSS Reticulate Shiny Ui Shiny Ui Editor Drag and Drop Nick Strayer Tony Pelleriti Posit

Hey Shiny Team, what are some of your biggest learnings from 2022? || Shiny Developers || RStudio

BIG THINGS happened on the Shiny team in 2022! Our team built out a new Shiny UI Editor, Shiny for Python, and Shiny for Python in the browser using WebAssembly. So we asked some of our Developers what their biggest learnings have been from building these products! Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Content: Winston Chang (@winston_chang), Carson Sievert (@cpsievert), Nick Strayer (), Michael Chow (@chowthedog) Producer: Jesse Mostipak (@kierisi) Video editing + motion design: Tony Pelleriti (@TonyPelleriti)

Carson Sievert, Michael Chow, Nick Strayer, Shiny Team, Winston Chang

rstudio Shiny for Python Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny RMarkdown CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Tidymodels Statistics Debugging RStats Open Source OSS Winston Chang Nick Strayer Carson Sievert Michael Chow Shiny for Python Shiny Ui Editor Posit Drag and Drop Pyshiny Tony Pelleriti

Nick Strayer || Part IV: Styling a Shiny Wordle App with CSS || RStudio

00:00 Introduction 00:44 Switching from verbatimTextOutput to uiOutput 01:42 Switching from renderText to HTML DOM elements 03:17 In-line styling with divs 07:30 Converting individual letters from block elements to adjacent grids with CSS grid 08:56 Adding CSS at the head of the UI variable in Shiny with tags$head (and wrapping with HTML!) 10:36 CSS targeting of the background color 12:24 Link: Complete Guide to CSS Grid 14:05 Moving text position within each individual div using CSS classes 16:48 Creating a gap between grid elements 17:13 Rounding border edges for letter grids 19:00 Formatting letter grid background color to indicate result "correctness" 21:30 Increasing font size 23:37 Updating the legend to use color, not text indicators 26:40 Adjusting padding to improve app aesthetic 28:08 Formatting the app UI with justified centering 31:56 Adjusting the text input and Go button 34:07 Why Flexbox is the right tool for this task 35:09 Exploring Flexbox Dev Tools in Chrome 39:14 Adjusting the colors of letter grids using Inspect Element 40:40 Making text bold with font-weight 41:04 Hint on how to approach formatting the keyboard In final installment of this four-part series, RStudio's Nick Strayer walks through using CSS to stylize our Shiny Wordle app. Code + word list: https://github.com/wch/shiny-wordle Check out the full Shiny app here: https://winston.shinyapps.io/wordle/ You can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Nick Strayer (@NicholasStrayer) Animation, design, and editing: Jesse Mostipak (@kierisi) Music: Lakal by Blue Dot Sessions Wordle: https://www.powerlanguage.co.uk/wordle/

Nick Strayer

rstudio 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 Css Wordle Nick Strayer

Nick Strayer | Stochastic Block Models with R: Statistically rigorous clustering | RStudio (2020)

Nick Strayer | January 31, 2020 Often a machine learning research project starts with brainstorming, continues to one-off scripts while an idea forms, and finally, a package is written to disseminate the product. In this talk, I will share my experience rethinking this process by spreading the package writing across the whole process. While there are cognitive overheads involved with setting up a package framework, I will argue that these overheads can serve as a scaffolding for not only good code but robust research practices. The result of this experiment is the SBMR package: a native R package written to fit and investigate the results of Bipartite Stochastic Block Models that forms the backbone of my PhD dissertation. By going over the ups and downs of this process, I hope to leave the audience with inspiration for moving the package writing process closer to the start of their projects and melding research and code more closely to improve both

Nick Strayer