Simon Emms

Software Engineer, Technical Leader, Solutions Designer

Dockerising an R App


R is a statistical programming language with a huge repository of tools to crunch numbers, manipulate data and do all manner of data science tasks. I've worked with a couple of teams with data scientists who use it and love it. Which is great, except for one problem.

R is a pain in the arse to Dockerise.

One irritating, but not insurmountable problem is the lack of official templates for R. Personally, I tend to stick to the official templates built by Docker which I then extend. That way you know that they're safe to use, are built to proper standards and are kept (reasonably) up-to-date. Fortunately, there is the Rocker organisation that maintains a series of images that you can use.

However, the biggest pain point by far is dependency management and the final size of the images. By design, when using RStudio developers will typically install dependencies at runtime. Here, that's fine because it's a development environment. When you're containerising your R app, this is not acceptable as containers should be immutable, pre-compiled and fast-loading. Some of these dependencies take many MINUTES to download, compile and install.

Largest mass in the universe

And R dependencies are big. Really big. If you thought node_modules was big, R is something else. I recently developed a fairly simple R app for the British Red Cross and the final image size was over 2GB (yes, that's GIGABYTES). Rocker don't provide an Alpine image which doesn't help, but I don't think that's a big problem due to the size of the dependencies and even R itself. Rocker's r-base image comes out at over 800MB. This is built on debian which is 118MB - using Alpine would only reduce that by 100MB which, seeing as the R base is over 700MB, hardly seems worth the effort.

There are other issues with R dependencies. With NodeJS you have your package.json, with Python you have your requirements.txt. R doesn't really have any matching concept (although there are some workarounds) so you have to maintain your dependencies in both your Dockerfile and where you call it in your R app.

Finally, any dependencies that you need in your OS are not installed. This is fairly standard, but the default behaviour of the installer is to exit without an error which is incredibly frustrating.

So, how do you do it then

The key to installing the dependencies is the install2.r application which is bundled with all Rocker images. This installs dependencies from the CRAN installation repository. There is also a corresponding installGithub.r binary which installs dependencies from GitHub.

In your *.R files, you will use the library() function to call your dependencies at the top of the script. Basically, every time you use it, you need to update your Dockerfile with each dependency. Yes, it's a pain to do it each time, but that's what you have to do.



As mentioned above, this doesn't error by default. It'll print the errors in the logs (along with lots of other things) so you'll never know if the build has failed. Therefore, you need to use the --error flag with this.

There is also a --skipinstalled flag which stops reinstalling any dependency that's already present in the system.


Again, make sure you pass the --error flag otherwise any errors won't break the build.

Typically, you wouldn't need to use this. I only had to use this for the Red Cross because of an bug with the latest version of Tidyverse when using Ubuntu 18.04 (which is the basis of the R image). I would only suggest using this if you need to install a specific version of a dependency, because I can't work out how to do that with install2.r.

One final note, this requires remotes to be installed. So you will need to run install2.r --error remotes before installing anything with installGithub.r.

Full Dockerfile example

This is an example using Shiny server.

FROM rocker/shiny:3.6.3
COPY . ./src/shiny-server

# Install any OS dependencies - this is just an example and not required for these dependencies
RUN apt-get update \
    && apt-get install -y libudunits2-dev

# List of dependencies - ensure corresponds with `library()` calls in *.R files
RUN install2.r --error \
    --skipinstalled \
    readr \

USER shiny

Once you're here, the usual Docker command of docker build -t r-app . will build this Dockerfile into your image.


Photo by Pietro Jeng

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