R

The 4th Shiny Contest is now in full swing! This community event celebrates learning, sharing, and recognizing outstanding work within the Shiny developer community.

Resources for demoing local and Launcher jobs with RStudio - sol-eng/background-jobs

Cloud Run Websocket support now allows you to deploy a R Shiny Server - a dashboard hosting tool to host R Shiny dashhboards - as a serverless app to GCP Cloud Run

Showcase your R shiny application and grow your user base. Real time usage stats and reviews for all your apps. Contribute today.

Although the concealment of Javascript is by design and makes Shiny in the first instance easy to use, in the long run when you want to build serious and more visual appealing apps, you most likely need to utilize javascript to make most of the web framework

Tidy your JSON data in R with tidyjson.

Helpers to work with JSON in R.

The R graph gallery displays hundreds of charts made with R, always providing the reproducible code.

The ultimate tool for finding the perfect color palette for data visualization with R and paletteer. Explore over 2000 palettes, see them in action on various charts, simulate color blindness, and export ready-to-use R code snippets.

Allows one to fully create an R package in a single .Rmd file. Includes functionality and .Rmd templates for a literate programming approach to R package development.

The R task Force - R experts for all your needs

This article describes best practice approaches for developing shiny dashboards. The creation of the dashboard in package form, as well as the use of unit tests should enable the development of robust solutions and guarantee high quality.

Integrating CSS/SASS, HTML, and JavaScript/TypeScript into R Shiny applications can significantly enhance the user interface and interactivity of your apps....

An R package to quickly obtain clean and tidy college football play by play data - sportsdataverse/cfbfastR

library(shiny) is better with library(bslib), so let’s make it official and update our shinyapp snippet together.

Design tokens are the single source of truth to name and store design decisions.

Build CLI applications in R.

Create an interactive table of Shiny inputs by providing data and a table definition.

How to fix error & warning messages in R - List of most common errors & warnings - Tutorials & examples on how to fix in RStudio

httr2

Shiny is a package that makes it easy to create interactive web apps using R and Python.

a small data filter module for shiny.

Rectangling is the art and craft of taking a deeply nested list (often sourced from wild caught JSON or XML) and taming it into a tidy data set of rows and columns. This vignette introduces you to the main rectangling tools provided by tidyr: `unnest_longer()`, `unnest_wider()`, and `hoist()`.

Working with modern APIs you will often have to wrangle with data in JSON format. This article presents some tools and recipes for working with JSON data with R in the tidyverse. We’ll use purrr::map functions to extract and transform our JSON data. And we’ll provide intuitive examples of the cross-overs and differences between purrr ... Read more

R - JSON Files - JSON file stores data as text in human-readable format. Json stands for JavaScript Object Notation. R can read JSON files using the rjson package.

this is exactly the data i have when i load data from mongolite no i need to remove the list with string concatenation