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.
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
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
*.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
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
One final note, this requires
remotes to be installed. So you will need to run
install2.r --error remotes before installing anything with
Full Dockerfile example
This is an example using Shiny server.
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 \
Once you're here, the usual Docker command of
docker build -t r-app . will build
Dockerfile into your image.
Photo by Pietro Jeng