Docker

 

These exercises follow the material in the Docker section of Reproducible R.

1. Run R using a Docker container from Docker Hub

• Pull the Rocker R image for version 4.2.3 from Docker Hub
• Confirm that this image is available on your computer
• Launch a container from this image
  • HINT: is the -i and -t flags in the docker run command to run R interactively in container
• execute R code from within the container

2. Customize the Rocker R Docker image

• Make a Dockerfile that contains R version 4.2.3, the CRAN packages dplyr and ggplot2, the Bioconductor packages DESeq2 and rtracklayer, and the conda package samtools.

  • HINT: Make sure you install BiocManager from CRAN so that you can install Bioconductor packages and also install Herper so that you can install samtools

• Build an image using this Dockerfile

• Launch a container with this image with a volume from your local computer mounted

• Confirm that the container is running

• Confirm that you can see the files in the mounted folder

  • HINT: The R Rocker image does not set the working directory. You need to mount the local directory somewhere within the docker image and then navigate to that location from R to see those files. I recommend mounting your local directory to ‘/home/local_data’ within that container’s file system (this folder will automatically be made in container)
  • HINT #2: you can also use the ‘docker exec’ command from a separate terminal tab to run a command within a container. You can type ‘docker exec IMAGEID ls’ to list the file system.

• make a plot from the ‘mtcars’ data set shows the relationship between weight (wt) and miles per gallon (mpg) that comes with R and save to your computer

  • HINT: this table is available as a variable called ‘mtcars’

• confirm that samtools is installed

• push this image to Docker Hub