R

An attempt to get an R runtime and function working on AWS Lambda using a container. - r-on-lambda/Dockerfile at main · mdneuzerling/r-on-lambda

A collection of awesome rShiny packages, tools, addons and examples

A (somewhat) currated list of resources for R Shiny - GitHub - dpastoor/awesome-shiny: A (somewhat) currated list of resources for R Shiny

Minimal Docker images for R. Contribute to r-hub/r-minimal development by creating an account on GitHub.

R configurations for Docker. Contribute to rocker-org/rocker development by creating an account on GitHub.

R configurations for Docker. Contribute to rocker-org/rocker development by creating an account on GitHub.

Introduction My Use Case Workflow Building the Docker image Uploading the docker image to Docker Hub Setting up Travis to use the Docker image References Introduction The below summarize the workflow I’ve converged on, after reading through various tutorials on Docker, examples, etc. If you’re here, I presume you have some interest in R package development and/or using Docker, which is a tool for containerizing an environment for running software.

Slim.AI Container of the Week Series and Examples. Contribute to slimdevops/slim-containers development by creating an account on GitHub.

This is a guest post by Colin Gillespie from Jumping Rivers answering your questions from their most recent series of webinars, Practical Advice for Putting R in Production.

by Hong Ooi This article is a lightly-edited version of the "Microsoft365R and Shiny" vignette in the latest Microsoft365R release. We describe how to incorporate Microsoft365R and interactive authentication with Azure Active Directory (AAD) into a Shiny web app. There are a few steps involved: Register your app with AAD Use the app ID to authenticate and get an OAuth token Pass the token to the Microsoft365R functions App registration The default Microsoft365R app registration only works when the package is used on a local machine; it does not support running in a remote server. Because of this, when you...

You mastered R, Shiny, Python, and Dash. Your app is production-ready. Now learn about hosting options and pick one that best suits your needs!

Use Docker Compose to simplify Shiny app deployment and reach dev/prod parity. Part 1 looks at local development.

I modified two Hugo themes to be able to share R code using blogdown. hugo-future-imperfect and hugo-statnmap-theme are multilingual themes allowing for code folding, syntax highlighting, list of related articles, citation card, SEO graph or contact form. All options listed below are detailed in the Readme page of themes on Github. There are also listed with their configuration parameters in the config file of the “exampleSite” directory in the repository.

Table of conversion of dates and time formats in R

Tips to helps debug R packages errors on local installation of different OS

Code folding is an interesting feature in Rmarkdown documents. Find out how to realise it in bookdown documents and blogdown websites. Code folding in bookdown and blogdown Option code_folding: true, like in classical rmarkdown documents, is not working in bookdown or blogdown but it is possible to enable it with some tricks. All files presented here, the javascript and Rmd files necessary for bookdown and the html files necessary for blogdown, to enable code folding are available on my github blog tips repository.

Build better Shiny apps with Appsilon's proven methods for effective user tests. Follow along to discover how you

Docker Compose Example with Shiny Apps. Contribute to analythium/docker-compose-shiny-example development by creating an account on GitHub.

A comprehensive guide to setup an R development environment for Ubuntu running in the Windows Subsystem for Linux (WSL)

Using serverless containers to deploy scalable R functions.

This post provides a guide to effectively and securely scale your ShinyProxy deployment of shiny apps in production with Docker Swarm and Traefik

This document is full of supplemental resources and content from the Shiny in Production Workshop delievered at rstudio::conf 2019.

This guide aims to introduce the reader to the main elements of the R ecosystem.

Collection of notifications, confirm dialogs and alerts for 'Shiny' applications - dreamRs/shinypop

If you’ve used the Plumber package to make R models or other code accessible to others via an API, sooner or later you will need to decide how to handle and report errors. By default, Plumber will catch R-level errors (like calls to stop()) and report them to users of your API as a JSON-encoded error message with HTTP status code 500 – also known as Internal Server Error. This might look something like the following from the command line: $ curl -v localhost:8000/ > GET /status HTTP/1.1 > Host: localhost:8000 > User-Agent: curl/7.64.0 > Accept: */* > < HTTP/1.1 500 Internal Server Error < Date: Sun, 24 Mar 2019 22:56:27 GMT < Content-Type: application/json < Date: Sun, 24 Mar 2019 10:56:27 PM GMT < Connection: close < Content-Length: 97 < * Closing connection 0 {"error":["500 - Internal server error"],"message":["Error: Missing required 'id' parameter.\n"]} There are two problems with this approach: first, it gives you almost zero control over how errors are reported to real users, and second, it’s badly behaved at the protocol level – HTTP status codes provide for much more granular and semantically meaningful error reporting. In my view, the key to overcoming these problems is treating errors as more than simply a message and adding additional context when they are emitted. This is sometimes called structured error handling, and although it has not been used much historically in R, this may be changing. As you’ll see, we can take advantage of R’s powerful condition system to implement rich error handling and reporting for Plumber APIs with relative ease.

Simple functions to lookup items in key-value pairs. See Mehta (2021) .