RStudio

We can provide an R environment with RStudio for courses that require it. If this has been set up for your course, you will see an app with a name like "RStudio - Course Code" in your list of interactive apps in your Open OnDemand dashboard.

When launching an RStudio session, you can set a number of CPU cores available to the session, as well as the duration of the session. Using a smaller number of CPUs can help your session start faster, since it will be easier to fit it into running nodes. Session durations cannot be extended, so if you're going to be working for a while, it may be better to set a long session duration, and cancel the session from your "My Interactive Sessions" page if you finish early.

Using R in batch compute: If you have occasional needs to run R code in batch compute sessions, using slurm commands like sbatch, you can use the underlying apptainer container for the RStudio app to provide a directly analogous environment. If you're going to be using R batch compute regularly, or if you have questions about using R in batch compute, reach out to atg@fas.harvard.edu

Managing Packages

You should be able to install packages for yourself as normal with an install.packages() command in your R terminal. The default behavior for R is to install these packages in a folder in your home directory, which R may need to create for you.

However, instructors may wish to install packages for students in a course, so each student doesn't need to install required packages themself. To facilitate this, we set up an R package install location in the course shared folder for courses using RStudio. The default installation location for new packages is still in a user's home directory, so instructors who want to install packages for their students should do the following:

Find the shared package install location with .libPaths(). Your output will be different, but look for a location containing "courseSharedFolders". Note the number to the left of that location.

> .libPaths()
[1] "/shared/home/uuu000/R/x86_64-pc-linux-gnu-library/4.5"                           
[2] "/usr/local/lib/R/site-library"                                                   
[3] "/usr/local/lib/R/library"                                                        
[4] "/shared/courseSharedFolders/000000outer/000000/R/x86_64-pc-linux-gnu-library/4.5"

Install package(s) to the shared location. You can use the .libPaths() function and the index number you noted to install to the shared location without going to the trouble of copying and pasting it. In the example above, one could install ggplot2 to the shared location like so:
```
install.packages('ggplot2', lib=.libPaths()[4])
```

If you encounter problems with this process, please reach out to us at atg@fas.harvard.edu.