R

Virtual workshop on June 20 as part of e-Rum 2020 to learn how to build your own interactive visualisation packages.

Tools and guides for putting R in production

This is a style guide to help authors write chapters for Loss Data Analytics

evaluator

Web framework for R inspired by express.js

I recently built a small Shiny app where I had to search for how to do a lot of things. Here are 6 things I learned doing that. Maybe a Shiny beginner will find something useful in here.

The goal of this initiative is to make cloud computing with R easier, by providing robust tools for working with cloud computing platforms.

Choose your own adeventure and get in the 'usethis' pull request flow with this flow chart.

Download free Shiny dashboard templates from Appsilon. Select a ready-to-go template and customize it to add your unique style.

Tools for building and working with docker containers in R - GitHub - thebioengineer/dockyard: Tools for building and working with docker containers in R

Determine system requirements from R packages using the RStudio Package Manager. This is a weekend project, not a real package, so please think twice before using it for anything serious. - getsys...

Locking down R package dependencies and versions is a solved problem, thanks to the easy-to-use renv package. System dependencies — those Linux packages that need to be installed to make certain R packages work — are a bit harder to manage. Option 1: Hard-coding The easiest option is to hard-code the system dependencies. I did this recently when I was creating a Dockerfile for a very simple Plumber API: RUN apt-get update -qq && apt-get -y --no-install-recommends install \ make \ libsodium-dev \ libicu-dev \ libcurl4-openssl-dev \ libssl-dev My Dockerfile used only three R packages and so its system dependencies were not complicated.

AWS has announced support for container images for their serverless computing platform Lambda. AWS doesn’t provide an R runtime for Lambda, and this was the excuse I needed to finally try to make one. An R runtime means that I can take advantage of AWS Lambda to put my R functions in the cloud. I don’t have to worry about provisioning servers or spinning up containers — the function itself is the star.

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.