Unit D: R Packages

Unit D: R Packages#

Learning Goals

After this lesson, you should be able to:

  • Install packages for R with Pixi

  • Explain the advantages and disadvantages of using Pixi with R

  • Describe alternatives to Pixi for managing R packages and the R environment

This unit explains how to use Pixi to manage packages in R projects, the current state of Pixi’s support for R and its limitations, and alternatives to Pixi for R.

See also

As a complement to this unit, see DataLab’s R Basics workshop reader.

R Packages#

Pixi (and Conda) were developed with Python in mind. The developers have since expanded the scope to include other research computing software, such as R. Support for R is still a work in progress, but Pixi is already usable for most R projects.

See also

To learn more about the status of R support in Pixi, see this GitHub issue.

To install R, use pixi add to add the r package. This will also install all of R’s dependencies.

Instead of using R’s install.packages function, you can use pixi add to install individual R packages. This has the benefit of ensuring that the packages are recorded in your project’s pixi.toml. Pixi will also automatically install system-level dependencies of the package (packages for working with other software, databases, and geospatial data often have these). R package names begin with the prefix r-, so for example, the Tidyverse package is r-tidyverse and the ggplot2 package is r-ggplot2.

Conda-forge provides many of the packages on the Comprehensive R Archive Network (CRAN). These are maintained semi-manually by volunteers, so a few packages are missing, and versions sometimes lag behind CRAN. Conda-forge is also missing many packages that aren’t on CRAN but are part of the wider R Universe.

If you need to install an R package that isn’t available on conda-forge, you can:

  • Install the package with R’s install.packages function (or similar). The package will not be recorded in pixi.toml. If the package has any system-level dependencies, make sure to install them first with pixi add.

  • Contribute the package to conda-forge yourself. The conda-forge administrators provide instructions for contributing a package. You don’t need to be the developer of the package to do this, although it’s polite to notify them.

  • Use an R environment management package (see Alternatives to Pixi) instead of or together with Pixi.

Important

R uses three environment variables (see Environment Variables) to determine where to search for installed packages: R_LIBS, R_LIBS_USER, and R_LIBS_SITE. When we run R in a Pixi environment, we want R to find only the packages that are part of that environment. To achieve this, we need to set the three R_LIBS variables.

Pixi will automatically set any environment variables defined in the [activation.env] table of pixi.toml when the environment is started. So to set the R_LIBS variables, add this to pixi.toml:

[activation.env]
# Make sure R only searches for packages within the Pixi environment.
PIXI_R_LIBS = "$CONDA_PREFIX/lib/R/library"
R_LIBS = "$PIXI_R_LIBS"
R_LIBS_USER = "$PIXI_R_LIBS"
R_LIBS_SITE = "$PIXI_R_LIBS"

The CONDA_PREFIX variable is automatically set by Pixi when the environment is started. It refers to the directory where the environment’s packages are installed. The PIXI_R_LIBS variable is just a convenience for setting the other three—it isn’t used by Pixi or R.

If you want to use Quarto in a Pixi environment, it’s also necessary to set the QUARTO_R environment variable to ensure Quarto finds R in the environment rather than some other copy of R on your computer. To do this, append this line to the [activation.env] table:

QUARTO_R = "$CONDA_PREFIX/bin/R"

Alternatives to Pixi#

If Pixi’s current R support doesn’t meet the needs of your project, there are some alternatives to consider. As of writing, there are two R packages specifically designed for managing the R package environment:

  • The renv package provides reproducible environments for R projects. You can use its snapshot function to record the R packages your project uses to a file, then use its restore function later to (re)install those packages. The package also has several other features, such as Python support. The package only tracks and installs R packages, not system-level dependencies, so it can’t reproduce many kinds of environments.

  • The rix package provides reproducible environments for R projects via Nix. Nix is an system-level environment manager for Linux and macOS, which uses a domain-specific programming language to describe fully reproducible environments. The rix package generates Nix code automatically, so that you can benefit from Nix without learning the language. This approach is excellent for making projects reproducible, but it’s also complicated and requires you to install Nix separately.

Tip

If renv sounds like a good fit for your project, but you also need to work with several different versions of R, take a look at rig, a standalone tool for installing multiple versions of R side-by-side.

Tip

Several DataLab staff tried using renv for real projects for about a year, but found it to be less convenient and reliable than tools like Pixi (and Conda).

We have not tried to use rix for real projects, since installing Nix is a non-starter for many of our collaborators.

So we recommend using Pixi, but you should choose whatever works best for you.

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