Brian J. Knaus

Brian J. Knaus’s blog about genomics and biology.

Running R in Docker

Docker is a ‘container,’ or a mini-operating system, you can run within your existing operating system. This is nice because you can build an environment where you can build and test code. And if it breaks, you can kill it and start a new container and start again. In the context of R development there are at least two good reasons someone would want to run R in Docker. First, the CRAN Repository Policy (Version $Revision: 3679 $) asserts that submitted packages should be tested with the current version of R-devel. You may not want to install this in your usual environment. Installing it into a container allows you to isolate this installation from the rest of your work environment. Second, if you are using compiled code you may need to use a tool such as the AddressSanitizer. This requires you to compile a version of R using certain flags that are helpful for identifying memory leaks but have a performance cost that make them undesireable for a production system. Again, creating a container for a version of R built with these flags can isolate it from your production environment. Here we explore how to run R within Docker.

Installing Docker

In Debian-like Linux (including Ubuntu) you can install Docker using apt.

sudo apt install

Docker basics

When you have Docker installed you can test and modify its system status in a manner similar to other daemons.

service docker status
service docker start
service docker stop

With the Docker system running you can query its status.

sudo docker version
sudo docker run hello-world

Help can be found by adding ‘help’ to a command.

sudo docker --help
sudo docker run --help

Docker images are sets of layers we download. When we ‘run’ the image we create a container. We can run several containers from one image. We can query the images we have available as well as the running container.

sudo docker images
sudo docker ps

Note that when we quit that no changes were made to the image.

Docker images

In theory, we could create our own environment. However, others have already done a lot of the hard work for us by creating Docker images. We can search for Docker images using the seaerch command.

sudo docker search rocker

When we find an image we’re interested in we can run it as follows.

sudo docker run --name=my-r-base --rm -ti rocker/r-base /bin/bash

This command included a few options. The run command runs the image. If it is not found locally, or needs to be updated, it is done automatically for you. The –name= option allows you to assign a name to the container. If you don’t assign one it will be done automatically for you. The –rm option automatically removes the container for you when you exit. The -ti options allocates a pseudo-TTY and provides an interactive session. The name of the image we want to run as a container is rocker/r-base and once its started we’ll want to execute /bin/bash (our shell).

When we’ve finished we can quit as follows.


We typically want to share some data between our host and our container. Using the -v option we can mount a host directory. Here we mount the git repository for my R package as a data volume in the container.

sudo docker run --name=my-r-base --rm -ti rocker/r-base /bin/bash -v ~/gits/vcfR:/RSource/vcfR

When we are done, we can exit as demonstrated above.

If you decide you would like to remove an image it can be done using rmi.

sudo docker rmi rocker/r-base

More help on Docker can be found at these links.

Find and run the whalesay image


The Rocker project includes containers for running R. Some of this is documented here: Docker for R. There is also a Rocker Github site for the images and documentation is on the Rocker wiki.

Rocker is hosted at GitHub. If we haven’t used their images before we’ll want to clone one to a local directory. I keep all my git repositories in a directory calls ‘gits’ in my home directory. We can clone our repository there.

cd gits
git clone

When we use this image we’ll want to start by making sure we have the most current version. This is accomplished by ’pull’ing the repository.

docker pull rocker/r-devel-san

We can now start the image.

sudo docker run --name=r-devel-san -v ~/gits/vcfR:/RSource/vcfR --rm -ti r-devel-san /bin/bash

The rocker containers seem to run Debian. You can validate this by querying for release information.

cat /etc/*-release

Update: rocker/r-devel-san appears to now be on the docker repository. This means it can be run without pulling it. Instead we can simply call it at the terminal (as above and repeated here).

sudo docker run --name=r-devel-san -v ~/gits/vcfR:/RSource/vcfR --rm -ti r-devel-san /bin/bash


We’ve now illustrated how to install and use Docker to create ‘containers’ or isolated environments within a host operating system. We’ve also learned about the R specific Docker based project Rocker. With these tools in hand we should now be able to create environments for testing R packages in environments that we do not wish to install into our production environment.


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