`renv` in conda

Get your reproducibility here!

Reproducibility is nice. It’s nice to be able to rerun a project from a couple of years ago and get the same results, reports and figures. It’s even nicer if you can hand your project over to someone else, and they can generate the same results.

But it takes a lot of work.

What could change in the intervening couple-of-years, or on transfer from one computer to another, that would alter the results generated in a project?

  • the raw data could get moved, relabelled, appended, or lost
  • the source code could change,
  • the code might not even be written with reproducibility in mind (eg, not setting random seeds; not using versioned data-releases)
  • literally any part of the software could be updated, deprecated or otherwise changed

Environment management tools can help with the latter issue (as can containers, of which I have less experience). Ideally, they allow you to store the details of any packages / libraries that are used when running a given project in the source-code for that project; then when you need to rerun the project, you can use those exact same packages.

For the past few years, I have used conda environments to handle all my research projects (which were mostly bioinformatics / genomics).

The great thing about conda is it’s language independence: you can specify project-dependencies from several worlds, be they python, R, command-line tools. It also simplifies installing packages, like numpy, that would otherwise involve installing a lot of low-level libraries. And it’s a well established project. There’s no point placing your reproducibility requirements in the hands of a tool that might not exist in two years time.

Speaking from my (R-biased) viewpoint, conda has posed some problems as well:

  • There have been several times when I could no longer build a conda-environment from a previously valid specification. This is typically due to broken packages being removed from anaconda.org (the database that conda uses when installing packages).

  • Installing packages is a non-native experience. R user’s are used to using install.packages("somePackage") to install packages from CRAN (this already handles the installation dependencies of ‘somePackage’). But to add this to a conda environment you would use conda install r-somepackage at the command line.

  • Installing non-conda packages

    • Installing a non-conda-package (eg, using remotes::install_github) can be done. But, if that package depends on anything else that isn’t yet in your conda env, try and make sure the other packages are installed using conda install.
    • If the required package is available through CRAN, it’s better to make a conda skeleton for that package and install that through conda from your personal anaconda account (details here).
    • Where I developed local packages for use within an analysis project, they were installed into the conda env using R CMD INSTALL -l <conda-path> <my-package.tar> as part of a project-setup script.
  • Multiple channels. For each package you might want to install, there are multiple versions available, but also there are multiple ’channel’s from which the package could be obtained. For example, most bioinformatics-specific packages are found in the ‘bioconda’ channel. For more general R packages, there is the ‘r’ channel, and also the ‘conda-forge’ channel. I always install things from conda-forge whenever there is a choice (it plays nicer with bioconda, and, though this isn’t a problem anymore, won’t compel you to use MRO)

  • Solving environments can take ages. This has been a major pain over the years. Before it can install a package, conda has to ensure that a version of the package (and any dependencies) can be found that will install alongside your current environment (or find a way to modify your current environment so that the package can be installed). Since there’s lots of channels and lots of versions of each package this can take a long time. And, if your environment gets too big (a mistake I’ve made many times), it may be impossible to find an installation recipe that is compatible with your environment.

  • Dev environment == Analysis environment. This is more of an R issue than a conda issue (since I don’t think ‘renv’ solves it either). It would be nice to be able to have two related environments for a given project: one within which the analyses run, and an expanded version of the analysis-env within which to write & test code. Each extra package you add to a working environment increases the chances that environment may break in the future. Though you might want rstudio, devtools, testthat et al in the environment where you write code, you shouldn’t need to include those in the environment where your analyses run.

  • IDE support. There are two ways to use rstudio alongside conda environments. You either install rstudio system-wide, or you install it as part of a conda environment. If you do the latter, you will be restricted to using an older version of R and Rstudio (v1.1.456 as of Feb 2021). So, please, don’t! You only need a handful of R packages installed for rstudio to work (minimally), but if you install rstudio via conda it will add dozens of dependencies to your environment. If on linux, you’re better to activate an environment that contains r-base and r-rstudioapi (and a few other libraries) and install rstudio from the command line. Then, start rstudio from the command line and it will work using whichever R is present in the activated environment.

Ultimately, try to avoid environment bloat and you’ll be happy.


{renv} (and it’s predecessor {packrat}) is an R-specific environment management tool. To use it in a new project you run the following:


That will start a blank environment (only containing the base R packages, like stats and graphics) based upon the R release that you are using. Then any time you need to install an R package for use in your project, call install.packages() or install_github(). The packages will be installed into (or linked from) a project-specific package directory. Then you can call


and it will store the details of all the packages that your project is using, in such a way that you could load up that project on another computer and easily generate the same environment (to do this call renv::restore()).

For this blog, I have - a minimal conda environment that contains r-base, r-renv and r-rstudioapi; - rstudio installed system-wide; - and an renv-defined environment that manages all the R packages used while writing (blogdown etc).

And, oh joy, I can install packages in seconds again!

Probabilistic modelling resources Shiny Resources