.devcontainer/README.md

R Projects Development Setup

This repo contains the various configurations, container settings, and other setup files that I will use in my new development setup for R projects on linux. Typically I would install R directly in my system along with the various IDEs and other tools that integrate with R. Now that I have systems with larger amounts of memory and faster CPUs, I am now experimenting with a development environment based on container technology, in particular Docker contaners. I have the following goals for my new development setup:

🎥 Check out this previous Shiny Developer Series livestream to see my detailed walkthrough!

Pre-requisites

Visual Studio Code Setup

The .devcontainer directory in this repository contains the custom configuration files and build instructions for creating the development container. The Dockerfile and devcontainer.json were adapted from the VSCode dev containers GitHub repository here. Highlights of the customizations I made:

With everything set up, I was able to mostly follow the official VS Code documenation page on developing inside a container to get the development container launched. I am still learning the ropes so to speak with day-to-day usage of VS-Code with R coding. Here are some additional observations that I will update as I continue my journey:

RStudio Setup

In recent years, the open-source version of RStudio Server has been succesfully integrated into Docker containers in multiple projects including the excellent Rocker Project led by Carl Boettiger, Dirk Eddelbuettel, and Noam Ross. Recently, they created version-stable R containers based on R version 4.0.0 or later at the rocker-org/rocker-versioned2 GitHub repository based on the recent Ubuntu 20.04 LTS release, as well as integrated the new RStudio Package Manager (RSPM) which hosts compiled binaries of R packages for installation on Linux. I decided to create a custom container for RStudio server following principles in their new infrastructure with a custom Dockerfile inspired by their Dockerfile_rstudio_4.0.2, and wrapping the execution within Docker Compose. Highlights of the customizations I implemented:

Configuring a central package cache

I have set up a local directory on my system that is mounted as a volume in each container to hold the R package cache. As long as your logged-in user has read and write priveleges to this directory on your host system, there should be no issues with each container reading and writing to the cache dir. In the containers, the directory is located at /renv/cache while on my host system it is located at /opt/local/renv/cache, but this could be any directory on your host system. In the Docker configuration files for each container, I set up the following environment variables:

RENV_PATHS_CACHE_HOST=/opt/local/renv/cache
RENV_PATHS_CACHE_CONTAINER=/renv/cache
RENV_PATHS_CACHE=/renv/cache

Using Renv with VS-Code

One (intentional) effect of using renv is that out of the box the project's package library will not link to packages in the default library that are not included in the base installation of R. The interactions between an R session and VS-Code are largely driven by the {launguageserver} package available on CRAN. My solution to ensure any project with renv enabled can use all of the same integrations with VS-Code is to create a custom .Rprofile that contains certain triggers to bootstrap the installation of languageserver and perform necessary environment configurations if the session detects that the TERM_PROGRAM environment variable is set to vscode. The file .devcontainer/library-scripts/.Rprofile-vscode is automatically copied to the container's renv cache directory, and can be manually copied to overwrite the current project's .Rprofile file. Much of this solution was adapted from ideas discussed in issue vscode-R/259. The contents are below:

# setup if using with vscode and R plugin
if (Sys.getenv("TERM_PROGRAM") == "vscode") {
    source(file.path(Sys.getenv(if (.Platform$OS.type == "windows") "USERPROFILE" else "HOME"), ".vscode-R", "init.R"))
}
source("renv/activate.R")

if (Sys.getenv("TERM_PROGRAM") == "vscode") {
    # obtain list of packages in renv library currently
    project <- renv:::renv_project_resolve(NULL)
    lib_packages <- names(unclass(renv:::renv_diagnostics_packages_library(project))$Packages)

    # detect whether key packages are already installed
    # was: !require("languageserver")
    if (!"languageserver" %in% lib_packages) {
        message("installing languageserver package")
        renv::install("languageserver")
    }

    if (!"httpgd" %in% lib_packages) {
        message("installing httpgd package")
        renv::install("nx10/httpgd")
    }

    if (!"vscDebugger %in% lib_packages) {
        message("installation vscDebugger package")
        renv::install("ManuelHentschel/vscDebugger@v0.4.3")
    }

    # use the new httpgd plotting device
    options(vsc.plot = FALSE)
    options(device = function(...) {
      httpgd::httpgd()
      .vsc.browser(httpgd::httpgdURL(), viewer = "Beside")
    })
}

Next Steps

I would still consider this journey a work in progress, so here are some additonal tasks I hope to complete. Contributions and feedback are more than welcome!



rpodcast/shinylearning documentation built on April 4, 2022, 10:44 p.m.