Quick Start: Running R in Open OnDemand

This page takes a brand-new user from a fresh account to running R in the RStudio IDE on a compute node — all in the browser, with no SSH or Slurm scripts. It is deliberately short and opinionated. Once a step makes sense, follow the linked reference pages for the full detail.

Before you begin

You need two things first:

  1. A CRCD account and Resource Allocation — see Step 1: Getting an Account.
  2. An active PittNet VPN (GlobalProtect) connection if you're off campus — the portal is reachable only from within PittNet.

Your username is your Pitt username in all lowercase. The full portal walkthrough, including logging in and managing files, is on Open OnDemand.

1. Open the RStudio app

Sign in to https://ondemand.htc.crc.pitt.edu with your Pitt credentials, then launch RStudio Server from the RStudio Server 2026 tile on the Dashboard, or from Interactive Apps → RStudio Server. It runs the RStudio IDE on a compute node, so you can do compute-intensive R work.

2. Request resources and launch

On the launch form, set the job parameters and click Launch. The fields that matter for a first run:

  • R version — the R module to load, e.g. 4.6.0.
  • Number of cores — 1–128 (roughly 8 GB of memory per core); leave at 1 unless your code is genuinely parallel.
  • Number of hours — the wall-time limit; the session ends when the time is up.
  • Memory (GB) — an optional explicit memory request.
  • Account — leave blank to use your default Resource Allocation.

Your session is queued while it waits for resources, then starts. When its card in My Interactive Sessions shows Running, click Connect to RStudio Server to open the IDE in a new tab.

Right-size your request

An interactive session holds a compute node and charges your allocation for the whole wall-time, used or not. Requesting more cores also means a longer wait in the queue — a few cores is plenty unless your code is parallel or memory-hungry. Remember to delete the session when you're done (Step 5).

Working with GPUs?

For GPU-accelerated R, launch RStudio Server on gpu instead (Interactive Apps → RStudio Server on gpu, under the Deep Learning group). Its form adds a GPU type (e.g. l40s), a node constraint (e.g. amd,40g), and Number of gpu cards (1–4). See OnDemand Interactive Apps.

3. Get your R packages

The R modules already bundle many CRAN and Bioconductor packages, so a lot of common libraries load with library(...) right away — nothing to set up.

Open a terminal — Clusters → HTC Shell Access in OnDemand — load the same R module that you specified in the form (use module spider r to show available versions), start R, and install what you need:

install.packages("pkg")                     # from CRAN
BiocManager::install("Bioconductor_pkg")    # from Bioconductor

.libPaths() shows where R searches for and installs packages; your personal library lives under your home directory.

4. Run your code

In RStudio, use the Console or open a new R Script and run a few lines to confirm you're on a compute node with the R you expect:

sessionInfo()                 # R version and loaded packages
Sys.info()[["nodename"]]      # the machine you're running on

The node name should be a compute node (e.g. htc-n…), not a login node — your code is running on the resources you requested.

Your session is a Slurm job

RStudio runs on a dedicated compute node with exactly the resources you asked for — a normal Slurm job, not something in your browser. From RStudio's Terminal tab, squeue -M all -u $USER lists it with its job ID, node, and partition. That's also why it waits in the queue, counts against your allocation, and must be ended with Delete.

If RStudio misbehaves

The interface is single-threaded, so a long-running command can make another action (like saving) time out with Status code 502 — usually nothing crashed; wait for the command to finish and retry. Status code 503 means you've hit your time limit; start a new session. RStudio also stores its state under ~/.local/share/rstudio (75 GB home quota); if it fills up or you switch R versions, remove that folder (rm -rf ~/.local/share/rstudio) and it regenerates.

5. End your session

Quitting RStudio does not free the node

Choosing File → Quit Session (or the red icon in RStudio) only closes RStudio — it does not end your interactive session, and you keep consuming your allocation. To release the node, return to My Interactive Sessions and click the red Delete button.

You're up and running

You have launched an RStudio session, set up your R packages, and run code on a compute node. Sensible next stops:

Stuck?

Open a help ticket using the link on our service catalog.