R

A fast JSON parser, generator and validator which converts JSON, NDJSON (Newline Delimited JSON) and GeoJSON (Geographic JSON) data to/from R objects. The standard R data types are supported (e.g. logical, numeric, integer) with configurable handling of NULL and NA values. Data frames, atomic vectors and lists are all supported as data containers translated to/from JSON. GeoJSON data is read in as simple features objects. This implementation wraps the yyjson C library which is available from .

The tidyprompt package allows users to prompt and empower their large language models (LLMs) in a tidy way. It provides a framework to construct LLM prompts using tidyverse-inspired piping syntax, with a library of pre-built prompt wrappers and the option to build custom ones. Additionally, it supports structured LLM output extraction and validation, with automatic feedback and retries if necessary. Moreover, it enables specific LLM reasoning modes, autonomous R function calling for LLMs, and compatibility with any LLM provider.

httpOnly Cookies are crucial for security, protecting against cross-site scripting attacks in R Shiny apps. Read more about them here.

Info: This is a draft for discussion purposes. It's not a polished PR and currently includes minimal error handling and documentation. It may be big enough to warrant a separate package, bu...

mlr3: Machine Learning in R - next generation. Contribute to mlr-org/mlr3 development by creating an account on GitHub.

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.

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()`.