RStudio in Docker
Docker is a container platform that helps replicate setups for testing and production from any system. It gives you the ability to set up a mini-operating system to meet specific criteria.
When developing some of my applications I wanted the ability to test these applications on different versions of R. But setting this up on Linux proved to be more difficult than on my Windows machine. Docker makes it significantly easier.
You do not need a Linux system to run Docker; Windows and macOS are supported as well as several other platforms. Once you get into the docker application, the language is the same.
Brief Overview of Docker
Docker sets up images with rules defined from a Dockerfile. The Dockerfile is basically a set of instructions to install and set up a mini-operating system.
FROM ubuntu:latest # Update apt-get sources RUN apt-get update && apt-get install -y r-base
When you build the image off of that Dockerfile you get the latest version of Ubuntu and the latest version of R for that setup.
(As of this writing, that latest version of R is still 3.2.3.)
Once the image is built you run it from within a container. Inside that container you can execute R and begin working with R from within the terminal.
You can use it for things other than R as well: python, MySQL, MongoDB, etc. If you can access it from a terminal or online through a port, you can use Docker.
When you exit a container, your data still remains. If you’re familiar with Amazon Web Service’s EC2’s, think of a container as an instance. The image is how you initially set up the instance. But once you run that instance, anything you do from within stays inside that instance. Only when you’ve deleted the instance do you have to start over again from your original image.
So, this gives significant flexibility to play around from a base setup and manipulate different settings for different purposes.
If you have not yet installed Docker, do so now if you want to play along.
I wanted a RStudio server setup with R 3.2.3. Unfortunately, I didn’t have much luck setting this up myself. Online searches didn’t seem to help me make progress.
We need RStudio Server to access RStudio through a web browser. You cannot install RStudio and run it as an application as if you were still on your local computer.
Thankfully, a group of people have made most of this much easier.
rocker is a set of images for various R and RStudio setups. When we run the base image we get the latest version of RStudio (1.0.143) and R (3.4.0). However, we can also use R versions 3.3.1 to 3.3.3. Originally I wanted 3.2.3 but 3.3.1 will be fine.
From this point forward I will be referring to two different repositories: Docker Hub and GitHub. Docker Hub is where we obtain base images that future images will be built from. You can also use Docker Hub just as you would GitHub to serve as a repository to your own images.
GitHub is where the code is stored to build the images.
Pull rocker/rstudio from Docker Hub
Before we can start building images we have to load the rocker/rstudio Docker Hub repository.
sudo docker pull rocker/rstudio
This process may take several minutes as it downloads various images that are needed to continue. When done we can view the images downloaded with
sudo docker images
You should see output similar to this:
REPOSITORY TAG IMAGE ID CREATED SIZE rocker/rstudio latest 7a807646f0be 13 days ago 993 MB
Pull rocker from GitHub
Clone the rocker GitHub repo into your projects directory. When finished, fire up a terminal or console and go to the cloned directory.
Your directory contents should be similar to this:
total 76 drwxrwxr-x 9 timtrice timtrice 4096 May 17 07:08 . drwxrwxr-x 49 timtrice timtrice 4096 May 17 07:08 .. -rw-rw-r-- 1 timtrice timtrice 206 May 17 07:08 circle.yml -rw-rw-r-- 1 timtrice timtrice 1209 May 17 07:08 CONTRIBUTING.md drwxrwxr-x 2 timtrice timtrice 4096 May 17 07:08 doc drwxrwxr-x 5 timtrice timtrice 4096 May 17 08:07 .git drwxrwxr-x 2 timtrice timtrice 4096 May 17 07:08 icon -rw-rw-r-- 1 timtrice timtrice 18092 May 17 07:08 LICENSE drwxrwxr-x 7 timtrice timtrice 4096 May 17 07:08 r-apt drwxrwxr-x 2 timtrice timtrice 4096 May 17 07:08 r-base drwxrwxr-x 2 timtrice timtrice 4096 May 17 07:08 r-devel -rw-rw-r-- 1 timtrice timtrice 4916 May 17 07:08 README.md drwxrwxr-x 8 timtrice timtrice 4096 May 17 08:50 rstudio -rw-rw-r-- 1 timtrice timtrice 549 May 17 07:08 .travis.yml
Once we’ve downloaded the images we can start off right away with R from within the terminal. Run the following code:
sudo docker run --rm -ti rocker/r-base
-rm flag tells docker to clean up the container after we exit. In other words, it will no longer exist once we exit the container.
-ti flag tells STDIN to stay open so that we can work within R.
When you run the code for the first time Docker will download associated images needed to run r-base. You will then be greeted with the standard R terminal:
R version 3.4.0 (2017-04-21) -- "You Stupid Darkness" Copyright (C) 2017 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) ...
Exit R with the
q() function. Once back at the command line when you run the docker images command again you’ll see the new image,
rocker/r-base has been added.
sudo docker images
REPOSITORY TAG IMAGE ID CREATED SIZE rocker/r-base latest 6999257c71ed 8 days ago 636 MB rocker/rstudio latest 7a807646f0be 13 days ago 993 MB
Build RStudio Image
To run RStudio we need to build another image based off the RStudio Docker file. This file is located in the rstudio directory.
sudo docker build -t rstudio-3.4.0 rstudio
-t flag gives the image a tag name. I use
rstudio-3.4.0 so I know this image is for RStudio using R version 3.4.0.
rstudio at the end is the directory location of the Dockerfile.
Building this image only takes a second or two.
sudo docker images
REPOSITORY TAG IMAGE ID CREATED SIZE rocker/r-base latest 6999257c71ed 8 days ago 636 MB rocker/rstudio-stable latest 7a807646f0be 13 days ago 993 MB rocker/rstudio latest 7a807646f0be 13 days ago 993 MB rstudio-3.4.0 latest 7a807646f0be 13 days ago 993 MB
Run RStudio with R 3.4.0
Now we’re set up for fun. We want to start up our
rstudio-3.4.0 image to a container. From our command line, run
sudo docker run -d -p 8787:8787 rstudio-3.4.0
In our terminal, a hash will appear and then our command line will pop back up. This is fine.
In a web browser, point to localhost:8787. You will log into RStudio with the same username and password: rstudio.
You will then be greeted by that big beautiful 4-panel suite.
At this point of time you are in a container built off the rstudio-3.4.0 image. Again, in AWS terms, think of this as a running instance. Whatever you do in this container remains so as long as the container is active.
If you only want to practice but want the container removed when you’re finished, use the
-rm flag in your run call.
When you’re finished working, you can’t just log out of RStudio and expect the container to shut down. It’s still running in the background. We can view this when we view our list of containers:
sudo docker ps -a
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 77010cde66fa rstudio-3.4.0 "/init" 13 minutes ago Up 13 minutes 3838/tcp, 0.0.0.0:8787->8787/tcp angry_volhard
STATUS says “Up 13 minutes”.
To shut down the container, we use the
stop command along with
sudo docker stop 77010cde66fa
The container will close down.
sudo docker ps -a
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 77010cde66fa rstudio-3.4.0 "/init" 14 minutes ago Exited (0) 3 seconds ago angry_volhard
When we want to start it back up again use the
sudo docker start 77010cde66fa
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 77010cde66fa rstudio-3.4.0 "/init" 22 minutes ago Up 9 seconds 3838/tcp, 0.0.0.0:8787->8787/tcp angry_volhard
Go back to localhost:8787, log into RStudio and you will find it just as you left it.
If you wish to delete the container, use the
sudo docker rm 77010cde66fa
The container will be removed and you can start over again from your image.
Build and Run Rstudio with R 3.3.1
Now, for my purposes I don’t want R 3.4.0; I already have that on my local machine. I want to test some packages with R 3.3.1.
What we need to do in this case is build a new image using R 3.3.1. RStudio will keep the same version.
In our rocker directory there is a path to a Dockerfile for R 3.3.1 in rstudio/3.3.1. I modify the tag of the image to
rstudio-3.3.1 and change the directory path to build the new image.
sudo docker build -t rstudio-3.3.1 rstudio/3.3.1
Again, we will need to download and pull some new images doing this for the first time. This should only take a couple of minutes tops.
When finished, we can run a new container:
sudo docker run -d -p 8787:8787 rstudio-3.3.1
When we log into localhost:8787 we see we are using R 3.3.1:
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair" Copyright (C) 2016 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) ...
Commit Your Changes
When you get a container set up just the way you like it, you can commit that container to a new image.
First, make sure you have stopped the container (or
exit if running from a terminal).
You need a Docker Hub account to use their online repository. The Free Plan gives you unlimited public repositories and one private repository.
Log in from your terminal or console,
sudo docker login
Get the CONTAINER ID you want to commit
sudo docker ps -a
7826d7faad16 rstudio-3.3.1 "/init" 20 minutes ago Exited (0) 4 seconds ago kickass_swartz 77010cde66fa rstudio-3.4.0 "/init" 50 minutes ago Exited (0) 23 minutes ago angry_volhard
In my case, I want to save 7826d7faad16.
Then commit the changes:
sudo docker commit -m "Verified setup" -a "Tim Trice" 7826d7faad16 <REPOSITORY>/<NEW IMAGE NAME>
Use your login name as REPOSITORY and whatever value you want for NEW IMAGE NAME.
Notice also the slash in between, just like referencing a GitHub repo. So my command would be
sudo docker commit -m "Verified setup" -a "Tim Trice" 7826d7faad16 timtrice/rstudio-3.3.1
When we list the images we see our new image has been saved:
sudo docker images
REPOSITORY TAG IMAGE ID CREATED SIZE timtrice/rstudio-3.3.1 latest cf53aaad96d7 2 minutes ago 991 MB ...
Then push the image to your Docker Hub repository.
# Replace timtrice/rstudio-3.3.1 with your image REPOSITORY sudo docker push timtrice/rstudio-3.3.1
Now, in Docker Hub you will find your image has been added. Click on the link to the image and add in some extra details so you document well what your image is and what it does.
Setting up Docker with R and RStudio is great for testing and debugging. The last thing I would recommend is that, before you start cloning your repos or starting new projects, install and use Packrat. Packrat isolates your packages to your project effectively making them invisible to other projects. This adds an additional layer to customize your setup.
You could, of course, keep this information in different containers. For example, say you save a container using dplyr version 0.5.0 and another container using a developer version. But I don’t think this is feasible. Packrat will document all of your package changes and you can easily reset to an earlier snapshot if things break. So set up Packrat for your projects before you start going crazy with the