Reproducible research environments with Docker

Academic research takes place in computational environments of continually increasing complexity. This creates ever-higher barriers not only to reproducing or extending the results of other researchers, but also barriers to collaboration and training of new researchers.

Wouldn’t it be nice if we could all work in equivalent computing environments, such that whatever worked for me on my computer would work for you on yours? Wouldn’t it be nice if we could just clone and copy our entire software environment when we needed to move our computations over to a more powerful cloud or cluster computer? Or perhaps roll back our environment to the state when everything was working yesterday?

Reproducibility and R

The situation for users for R is much better than most, since CRAN not only handles most dependencies but provides binaries for most major operating systems. Though subsequent changes in packages can break this chain and cause scripts that once worked to no longer due so, emerging solutions like packrat try to tackle this problem.

Nevertheless, R is not immune from these issues. Many packages make use of code libraries from other languages, from pandoc‘s system for rendering documents to interfaces with SQL databases, to manipulation of other formats like XML, to highly optimized linear algebra libraries. While an experienced user following careful documentation can usually install the appropriate libraries to create a functionally equivalent system, this raises substantial barriers for collaborators, students, and others. A reproducibility study at NESCENT recently found that even computationally savvy experts (though not necessarily specific to the computer language or domain in question) working with code provided by authors who were explicitly committed to reproducibility (including me), could not even reconstruct the necessary computational environment. This inability to install all the correct components for software to run in the same way as it did for the original researchers is commonly known as ’dependency hell’ and has often been shown as the primary cause that computational components of research could not be replicated, (not least because it is one of the very first steps required before replication can be attempted).

Just about anyone who has taught a course involving running software in class will be familiar with the challenges and lost instruction time from installing such dependencies on student’s machines. Even when all dependencies are installed successfully, there is a risk that these changes will break some of the user’s existing code by altering it’s environment.

Jumping in

  • Install Docker for your operating system using boot2docker. (Note the Linux instructions do not require boot2docker. I recommend the 1-line curl-script install method for Ubuntu). Now you can launch boot2docker to open a terminal window from where we will run Docker. NOTE: RStudio example requires Docker version >= 1.2.

There’s several different ways we can interact with the container. The simplest approach is just run an R terminal on the container:

sudo docker run --rm -it cboettig/rstudio /usr/bin/R

This should open up the R command line in the current window.

  • Download and run an RStudio server instance:
sudo docker run -d -p 8787:8787 cboettig/rstudio

(These commands will be slow on the first run since the image must be downloaded. Afterwards they should be pretty quick.)

  • You can now reach your RStudio server at https://<system_ip_address>:8787. For Windows/Mac users, run boot2docker ip to get the value of system_ip_address. (This should be but may vary as it is set dynamically).

For Linux users, you can just use https://localhost:8787. For cloud instances, check your server’s public IP address, and append the port (:8787)..

  • Login using the default rstudio:rstudio for user:pw, or configure particular users (see below).


  • Replace cboettig/rstudio with cboettig/ropensci to run a richer (but larger) development environment. See below for details.

  • Set user name and password using environmental variables, e.g.

docker run -d -p 8787:8787 -e USER=<username> -e PASSWORD=<password> -e cboettig/rstudio
  • Link the container to a local folder (directory) using the -v option:
docker run --rm -it -v $(pwd):/home/rstudio/$(basename "$PWD") cboettig/rstudio /usr/bin/R

Note that in this example we have launched an interactive R terminal, rather than an RStudio server. This linking would work just as well with RStudio-server. However, because we have linked the working directory to the container, we are now free to use all our favorite tools from our native operating system to edit and manage our files, rather than being confined to RStudio.

Note that the volumes link (-v) is just taking the path to the directory on the host followed by the path where it should appear inside the container. We could specify these manually, but $(pwd) is just a convenient way to get the full path of the current working directory, and $basename "$PWD" a way to get just the name of that directory. So if working in foo, the directory /path/to/foo would be found at /home/rstudio/foo on the container. Here we’re using the default user, rstudio, but you would want to change that if specifying a different user name as shown in the previous example.

Linking files on a Mac or PC requires an extra step. This links us to the boot2docker volume, but we still must link boot2docker virtual machine to the host OS. It seems like boot2docker may still be working on this issue. Meanwhile, it is straight-forward to do this linking by using vagrant to launch boot2docker. I discuss this below.

  • Download and run an RStudio server instance: sudo docker run -d -p 8787:8787 cboettig/rstudio

  • You can now reach your RStudio server at https://<system_ip_address>:8787. For Windows/Mac users, run boot2docker ip to get the value of system_ip_address. (This should be but may vary as it is set dynamically). For linux users, you can just use localhost. For cloud instances, check your server’s public IP address.

  • login using the default rstudio:rstudio for user:pw, or configure particular users (see below).

RStudio on Digital Ocean

Docker also makes it very fast and easy to deploy an RStudio instance in the cloud. More importantly, it lets you deploy an instance that already has your preferred computational environment already completely installed and configured through your docker container. Running Docker on a cloud machine can be useful for many purposes, such as:

  • Giving your collaborator access to your computational environment
  • Scaling up a computation to a larger machine
  • running RStudio from your ipad

Because we use RStudio for this, we have both a completely secured connection and a familiar environment in which to interact with R. RStudio in the browser looks and feels identical to the desktop edition. Getting started with RStudio in the cloud is particularly easy using Docker and the DigitalOcean cloud provider. We can run RStudio in the DigitalOcean cloud for less than 1 cent an hour ($5 / month if run continuously). Creating an account and launching your first DigitalOcean droplet is dead easy. Head over to because they do a much better job of walking you through the few simple steps than I would. Create the cheapest droplet type to get started and ssh into your image once it is up and running, using the ip-address for your droplet and the password sent to you by email or an ssh key.

ssh root@ip-address

Enable swapping if testing on the smallest image

The smallest Digital Ocean servers have only 512 MB of memory and no swap enabled. Adding swap lets the machine cache things it doesn’t need in active memory, which can be important for running things like install.packages. On larger droplets this probably is not so much of an issue. Here, we enable 1GB of swap. SSH into your cloud server (or use the shell DigitalOcean provides in the browser), and run:

sudo fallocate -l 1G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile

Launch RStudio

We’re now ready to run RStudio. If you didn’t select the docker application while creating your image, you can install it now:

curl -sSL | sudo sh

We’re now ready to launch RStudio using docker, just as we did locally above.

docker run -e USER=your_username -e=PASSWORD a_secure_pw \
  -d -p 8787:8787 --name rstudio cboettig/rstudio

Note that we pass a custom username and secure password in using the -e arguments since our RStudio instance will be publically reachable by it’s ip. We’ve also given the container a name rstudio so we can easily refer to it later. We now go to https://<your-droplet-ip-address>:8787 and login with these custom credentials and we’re ready to compute.

Saving your work

The best way to save your work is to commit your entire docker image. This will save all installed packages, active RStudio sessions, files, and everything else, even if we destroy the droplet later. This gives us the option of running our environment on a larger DigitalOcean image when the need arises, or running it locally using the boot2docker approach outlined above.

From an ssh terminal into your droplet where you first ran docker, this command will save your container as an image called user/rstudio (use any name combination you like).

docker commit rstudio user/rstudio

If you create an account on we can upload this as a public or private image that we can access from anywhere for later use:

docker push user/rstudio

Be sure to use this name to run your container in the future to pick up where you left off. If you’d rather not use the Docker Hub to host your image, you can download the image file instead:

docker save -o rstudio.tar rstudio

Then from your local machine, scp the tar file to download it:

scp root@<ip-address>:~/rstudio.tar .

You can now destroy your droplet to avoid future charges when not in use. To deploy this image later, copy it over to the new machine and load in docker:

scp rstudio.tar root@<new-ip-address>:~
docker load rstudio.tar

and then call docker run as before to get up and running.

Misc: Remote Linux clusters without root

You can run the docker images on a remote linux cluster where you don’t have root access, even if it doesn’t have a web-accessible API (such as a university server). Ask your friendly system administrator to install vagrant and virtualbox. Then we can use a lightweight virtualbox in which we can run Docker (this is just what boot2docker does in windows and mac, in fact, we’ll use an image based on boot2docker since it has just what we need, and at just 24 MB is way smaller than a standard ubuntu virtualbox image).

A Vagrantfile for getting up and running with this image is found in the vagrant directory. This handles exporting ports to the host machine, and sharing files with the host machine (which can also be tricky for boot2docker users on Mac/Windows, so this might be a work-around for them). Run this image with:

vagrant up

You may want to first adjust the Vagrantfile to allocate more or less memory and CPU nodes to the virtual machine. Vagrant’s default is 512 MB and 1 CPU, though the file currently puts this at 2 CPUS and 1024 MB RAM.

You can now connect to the RStudio server using some ssh port forwarding:

ssh -o "ExitOnForwardFailure yes" -f -N -L 8787:localhost:8888 <your.server>

The first number (8787) is the port we want things to appear on localhost, e.g. we can now connect by visiting https://localhost:8787. The second (8888) is the port we configured for the host machine with Vagrant.

Note that sometimes you need to tunnel through a head node to the compute node where you want R to be running, replacing <your.server> with:

ssh <> nc -q0 <> 22