Using the RStudio server
- Click on Interactive Apps in the top navigation menu
- Click on RStudio
Launching an RStudio server
- Select your partition from the dropdown
- Select the name of your DCC group. If you are a member of multiple groups, make sure the group you select has access to the partition you select.
- Input the number of hours you would like the server to remain active (please try to remain small, as it will continue running even if you are not using it)
- Input the desired amount of nodes, memory, CPUs, and GPUs (try to start small with only a few gigabytes of memory and cores). The higher the requirements you select, the longer your wait times.
- Optionally, enter a Datacommons mount path and/or any additional Slurm parameters
- Use the "Select Path" button to select the base of installed R packages
- Press the blue "Launch" button on the bottom of the page
Connecting to RStudio
- After pressing the blue "launch" button, your job will be queued to start an RStudio server. You should see this automatically
- Wait a few seconds to a few minutes for the RStudio server to finish launching. The status will automatically change from "Starting" to "Running" when the server is ready
- Press the blue "Connect to RStudio Server" button when the server is running to access your RStudio server
Using RStudio Server
- In the top left of the interface, click on "File" > "New File" > "R Script" to create a new R Script
- To save this file, use Ctrl+S (Command+S on macOS) and choose a file path
- Alternatively, upload your existing .r files using the "Upload" button toward the top of the pane in the bottom left corner
- When you are ready, you can run your R Script by pressing the "Run" button toward the top right of the top left pane
Packages available in RStudio
Several pre-built options are available through RStudio server to support a variety of package installations. Once you select your option, you may also install additional packages.
|JAGS is Just Another Gibbs Sampler. It is a program for the statistical analysis of Bayesian hierarchical models by Markov Chain Monte Carlo.
|JAGS Singularity Recipe
|RStan and supporting/common packages
|RSTAN Singularity Recipe
|Bioconductor is version 3.14; R version 4.1.0
|This image is based on the Microsoft Docker Bioconductor image, more about Bioconductor
|Bioconductor Singularity Recipe
Installing your own packages