How to Run R in Google Colab: Native Kernel, Bioconductor & GPU Guide (2026)

1. Introduction

Google Colab provides free access to high-performance cloud computing environments, including multi-core CPUs, high-memory instances, and NVIDIA GPUs (T4, A100, L4). While traditionally recognized as a Python machine learning environment, Google Colab natively supports R.

For bioinformaticians, computational biologists, and statisticians, this unlocks free cloud-hosted computing for compute-heavy R workflows—such as single-cell RNA-seq clustering (Seurat, SingleCellExperiment), differential expression (DESeq2), and deep learning (torch for R)—without taxing local hardware.

This definitive 2026 guide covers:

• The two methods for initializing native R notebooks in Google Colab
• Installing complex Bioconductor packages non-interactively
• Setting up persistent package directories in Google Drive to eliminate re-installation delays
• Configuring cloud GPUs for R deep learning workflows

2. Two Ways to Start an R Notebook in Colab

Method 1: Instant 1-Click Direct URL (Recommended)

The fastest way to launch an R notebook is using Google’s direct initialization parameter:

https://colab.research.google.com/#create=true&language=r

Clicking this link automatically allocates an ephemeral container with an active native R kernel, bypassing Python entirely.

Method 2: Switching the Runtime from the UI

If you have an existing Colab notebook, switch the computational engine directly:

1. In the top toolbar, click Runtime → Change runtime type.
2. Under the Runtime type dropdown, select R (instead of Python 3).
3. Under Hardware accelerator, select None (CPU) or T4 GPU.
4. Click Save. The notebook will restart with an active R environment.

3. Video Walkthrough

Watch this step-by-step visual demonstration on configuring Google Colab for R and Bioconductor:

4. Checking Environment & System Resources

Once your R notebook is active, execute this diagnostic script in your first cell to confirm your R version, allocated RAM, and CPU cores:

# ==============================================================================
# Colab Hardware & Session Diagnostics in R
# ==============================================================================
cat("=== R Version & Platform ===\n")
cat(R.version.string, "\n\n")

# Detect CPU cores allocated by Google Cloud
num_cores <- parallel::detectCores()
cat(paste("Available CPU Cores:", num_cores, "\n"))

# Inspect total system memory (RAM)
mem_info <- system("grep MemTotal /proc/meminfo", intern = TRUE)
cat(paste("System Memory:", mem_info, "\n"))

# Verify active working directory
cat(paste("Working Directory:", getwd(), "\n"))

5. Installing Bioconductor Packages Non-Interactively

In an interactive desktop RStudio session, install.packages() and BiocManager::install() frequently prompt the user with questions like:

• “Do you want to install from sources the package which needs compilation? (Yes/no/cancel)”
• “Update all/some/none? [a/s/n]”

In Google Colab, interactive prompts cause cells to freeze indefinitely. You must always enforce non-interactive flags:

# ==============================================================================
# Non-Interactive Bioconductor Package Installation
# ==============================================================================
# Step 1: Install BiocManager from official CRAN cloud mirror
if (!requireNamespace("BiocManager", quietly = TRUE)) {
  install.packages("BiocManager", repos = "https://cloud.r-project.org", quiet = TRUE)
}

# Step 2: Install Bioconductor packages with 'ask = FALSE' and 'update = FALSE'
bioc_packages <- c("DESeq2", "ComplexHeatmap", "SummarizedExperiment")

BiocManager::install(
  bioc_packages,
  ask = FALSE,
  update = FALSE,
  quiet = FALSE
)

# Step 3: Verify packages load cleanly
library(DESeq2)
library(ComplexHeatmap)
cat("Bioconductor packages loaded successfully!\n")

6. Pro-Tip: Persisting R Packages Across Colab Sessions

Colab instances are ephemeral: once disconnected, Google reclaims the container and erases all installed software. Compiling large C++ packages (like DESeq2, Rcpp, or Seurat) repeatedly wastes 15–30 minutes every session.

Solution: Mount Google Drive and Redirect .libPaths()

You can mount your Google Drive and designate a persistent folder for your R libraries:

# ==============================================================================
# Persistent R Package Setup via Google Drive
# ==============================================================================

# Step 1: Mount Google Drive into the Linux filesystem via Python bridge
system("python3 -c 'from google.colab import drive; drive.mount(\"/content/drive\")'")

# Step 2: Create a dedicated directory in your Drive for R libraries
drive_lib_dir <- "/content/drive/MyDrive/R_colab_libs"
if (!dir.exists(drive_lib_dir)) {
  dir.create(drive_lib_dir, recursive = TRUE)
}

# Step 3: Prepend Drive directory to R's library search path
.libPaths(c(drive_lib_dir, .libPaths()))
cat("Current Library Paths:\n")
print(.libPaths())

# Step 4: Now install packages directly into your Google Drive path!
# Once installed here, they persist forever across all future Colab sessions.
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2", lib = drive_lib_dir, repos = "https://cloud.r-project.org")
}

library(ggplot2, lib.loc = drive_lib_dir)
cat("Loaded ggplot2 from persistent Google Drive storage.\n")

7. Enabling GPU Acceleration for R in Colab

To train deep learning models or accelerate matrix decompositions with an NVIDIA GPU in R:

1. Go to Runtime → Change runtime type → Select T4 GPU → Click Save.
2. Run the following cell to verify CUDA detection and initialize torch in R:

# ==============================================================================
# GPU Acceleration Verification in R
# ==============================================================================

# 1. Inspect NVIDIA driver and GPU allocation via system CLI
system("nvidia-smi")

# 2. Install torch for R (compiled against CUDA)
if (!requireNamespace("torch", quietly = TRUE)) {
  install.packages("torch", repos = "https://cloud.r-project.org")
}

library(torch)

# Check if CUDA is accessible from within R
cuda_status <- cuda_is_available()
cat(paste("CUDA GPU Available in R:", cuda_status, "\n"))

if (cuda_status) {
  cat(paste("GPU Device Name:", cuda_get_device_name(0), "\n"))

  # Allocate a tensor directly onto the GPU
  x <- torch_randn(c(1000, 1000), device = torch_device("cuda"))
  cat("Successfully allocated 1000x1000 matrix on NVIDIA GPU memory!\n")
}

8. Best Practices for Long-Running Genomic Workflows

• Prevent Disconnection Timeouts: Avoid closing the active browser tab while long Markov chain Monte Carlo (MCMC) runs or alignments are processing.
• Checkpoint Intermediate Objects: Always save heavy data structures as .rds files directly into mounted Google Drive (saveRDS(se_object, file = "/content/drive/MyDrive/project/se.rds")).
• High-RAM Instances: If analyzing single-cell datasets exceeding 50,000 cells, select Colab Pro High-RAM to receive 51 GB of system memory, preventing out-of-memory (OOM) kernel crashes.

9. Frequently Asked Questions (FAQ)

How do I switch the Google Colab runtime to native R?

Navigate to Runtime → Change runtime type in the Colab navigation bar, select R from the Runtime type dropdown, and click Save. Alternatively, navigate directly to https://colab.research.google.com/#create=true&language=r to spawn an instant native R notebook.

Can I install and run Bioconductor packages in Google Colab?

Yes. First install BiocManager using install.packages("BiocManager", repos="https://cloud.r-project.org"). Then install any Bioconductor package non-interactively by specifying BiocManager::install("DESeq2", ask = FALSE, update = FALSE).

How can I persist Bioconductor packages and data across Colab sessions?

Mount Google Drive inside your session and set your library path using .libPaths("/content/drive/MyDrive/R_colab_libs"). By directing package installations to this folder, all compiled binaries and dependencies remain permanently preserved in your Drive, eliminating repeated compilation when instances reset.