Tutorial 4

Virtual environments and docker containers. We’ll practice creating virtual environments again (just like last tutorial), so we can compare it with creating docker containers.

R Environments

  1. Create a new RStudio Project
  2. Note the currently installed packages
  3. Install the renv library if you do not have it installed
  4. Initialize the current project as an renv project
  5. Note the new packages in the environment.
  6. Install an R package
  7. Snapsnot the package
  8. Note how the renv.lock file does not change
  9. Create and save an R script that uses the package you just installed
  10. Re-snapshot the project
  11. Note how the renv.lock file updates now
  12. Close the current RStudio project
  13. Note how you have your old packages back
  14. Re-open the RStudio project you just created and see how t5he packages change.

Optional: Python environment

For people and teams working on a python project and want to practice conda environments.

Note

conda environments are not going to be on the exam. This exercise is solely to give you instructions to practice creating python environments.

  1. Check that you are currently in the base conda environment:
conda info
  1. Note the packages in the current conda environment and their versions
conda list
  1. Create a new conda environment (we are going to name it myenv) with python 3.12
conda create -n myenv -c conda-forge python=3.12
  1. Activate this environment
  2. Run python and compare your python versions and see if you are able to import pandas
  3. conda install another package into the environment, pandas and import it from the new environment.

Run a docker container

Let’s get some practice with running docker containers

Docker Desktop needs to be runnign in the background.

Your first container

We did this in the Computer Setup process, but let’s run the hello-world container with docker run

$ docker run hello-world

Hello from Docker!
This message shows that your installation appears to be working correctly.

To generate this message, Docker took the following steps:
 1. The Docker client contacted the Docker daemon.
 2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
    (amd64)
 3. The Docker daemon created a new container from that image which runs the
    executable that produces the output you are currently reading.
 4. The Docker daemon streamed that output to the Docker client, which sent it
    to your terminal.

To try something more ambitious, you can run an Ubuntu container with:
 $ docker run -it ubuntu bash

Share images, automate workflows, and more with a free Docker ID:
 https://hub.docker.com/

For more examples and ideas, visit:
 https://docs.docker.com/get-started/

$

This command runs a container that executes a script that prints out the hello docker text. When this script runs, it exits and the container exits to give you your prompt back

An R Container

  1. Compare the version of R you have right now on your computer. You can open up and launch R or run R --version to get your current R version. 2.Run the R 4.3.2 container:
docker run --rm -it rocker/r-ver:4.3.2
  1. Compare the version from the version you have on your computer
  2. Try to load a package you have installed on your computer in the container (it should fail)
  3. Exit out of this docker container

An RStudio Container

Note

If you are on an ARM based M-chip mac, you need to pass in the --platform=linux/amd64 flag to the docker run command. https://ubc-dsci.github.io/reproducible-and-trustworthy-workflows-for-data-science/lectures/110-containerization-2.html#specifying-the-image-architectureplatform

  1. Run the RStudio container and connect it to your local computer’s port 8787
docker run --rm -e PASSWORD="pizza" -p 8787:8787 rocker/rstudio:4.4.2

  1. Explain in your own words what each of the flags are doing in the docker run command. You can use the official docker run documentation: https://docs.docker.com/reference/cli/docker/container/run/

  2. Open and log into the RStudio container and try to run some R code in a script

  3. Save this script

  4. Close the container and run it again

  5. Notice how all your work inside the container is lost

An RStudio Tidyverse Container

  1. Do the same process as before, but this time use the rocker/tidyverse:4.4.2 container.
  2. Without turning off this container, open a new terminal and run a separate rocker/rstudio:4.4.2 container
  • You will need to change the local port mapping
  1. Make sure you are able to log into and run both RStudio sessions in the container
  • Note how only one of the RStudio sessions has tidyverse installed and the other doesn’t