R Dev Container

The aim of the R Dev Container is to provide a standard environment for the exploration and development of the R source code. A prototype was developed under Google Summer of Code 2023 and development is continuing under Google Summer of Code 2024.

Getting started

Run in a web browser with GitHub Codespace

Click on the button below followed by "Create Codespace" to create and enter your own web based containerised develoment environment.

The Starting Codespace section of the R Dev Container Docs will walk you through the process. Then you can work through the tutorials in the docs to lean how to use the codespace for R development.

More details about GitHub codespaces can be found in the GitHub Codespaces Docs. GitHub currently offers up to 60 hours for free on dual core codespaces.

The Starting Gitpod section of the R Dev Container Docs will walk you through the process. Then you can work through the tutorials in the docs to lean how to use the codespace for R development.

More details about Gitpod Workspaces can be found in the Gitpod Workspace Docs. Gitpod currently offers Up to 896 cores, 12,000 GB RAM & GPUs in free tier.

Run on your local computer

The R Dev Container can also be run locally on your computer. Running the environment requires you to have Docker, VSCode, and the VSCode Dev Containers extension installed. Upon opening a copy of the r-dev-env repository in VSCode you will be prompted to start the dev container.

The Local setup section of the R Dev Container Docs will walk you through the process. Then you can work through the following the tutorials in the docs to learn how to use the codespace for R development.

More information about Dev Containers can be found in the Microsoft Documentation.

Project Background

The standard development process used by members of the R Core Team involves an iterative process of making changes to the source code, re-building R and exploring/testing the changes made. This process presents a big hurdle for new contributors to R, as setting up their system to build R can be non-trivial, requiring a lot of trouble-shooting, especially on Windows and macOS. On Linux and macOS, it is not straight-forward to work with different versions of R side-by-side and care needs to be taken not to corrupt or over-write the user's existing version of R/R packages.

For these reasons, we propose to create containerized development environments for R. Our focus will be on creating a complete environment, including an IDE, that will provide a familiar space for R developers to work on contributions to base R.

Contributors

GSoC contributor: Atharva Shirdhankar

Mentors: Heather Turner, James Tripp, Iain Emsley