How to Run R Code in Jupyter Notebook within VS Code: A Step-by-Step Guide

Created in November 14, 2024

2024

Introduction

Jupyter Notebook is an invaluable tool for data scientists, researchers, and developers, enabling them to create and share computational documents seamlessly. With the added power of an integrated development environment (IDE) like Visual Studio Code (VS Code), you can run Jupyter Notebook locally and access an array of helpful extensions that enhance your workflow.

However, despite Jupyter Notebook’s broad support for programming languages like Python, it doesn’t natively support R—something I quickly discovered while trying to use it for a blogging project. Determined to make it work, I embarked on a journey that involved scouring solutions online, seeking advice from the programming community, and leveraging the power of ChatGPT 4.0. Through a mix of trial, error, and persistence, I finally uncovered a method to run R code in Jupyter Notebook within VS Code.

In this blog post, I’m excited to share a step-by-step guide that will help others in the same situation. I’ll also walk you through the troubleshooting process that eventually led to my solution, so you can learn from my experience and avoid the roadblocks I encountered.

Step-by-Step Guide to Install R Kernel in Jupyter Notebook

1. Verify R Installation

Ensure R is installed by checking its version:

R --version

2. Verify Jupyter Notebook Installation

Make sure Python is installed and accessible in your PATH. Then, check for Jupyter:

python --version

jupyter --version

To confirm Jupyter Notebook is working, run:

jupyter notebook

This should open a Jupyter Notebook in your web browser. If there are issues, install Jupyter using:

pip install jupyter

3. Check if Jupyter Directory is in PATH

Ensure the Jupyter directory is included in your PATH:

echo $PATH

The PATH environment variable tells the system where to find executable files when you run a command.

4. Install IRkernel Package in R

Install the IRkernel package in R:

install.packages('IRkernel')

5. Register R Kernel with Jupyter

Run the following command in your R terminal (not the interactive R console):

IRkernel::installspec(user = FALSE)

Setting user = FALSE makes the kernel available for all users. Use user = TRUE if you prefer a user-specific installation.

Common Error and Solution

If you encounter this error:

Error in IRkernel::installspec(user = FALSE) :
jupyter-client has to be installed but “jupyter kernelspec --version” exited with code 127.
In addition: Warning message:
In system2("jupyter", c("kernelspec", "--version"), FALSE, FALSE) :
error in running command

Run the command in the R terminal instead of the R console to resolve it.

6. Create a New Jupyter Notebook in VS Code

Restart VS Code and create a new Jupyter Notebook. Select the R kernel to start coding in R within Jupyter Notebook.

Additional Tips

  • Install R Packages in Jupyter: You can install R packages directly in a Jupyter cell by using install.packages(“package_name”).
  • Switch Between Python and R Kernels: If you want to mix Python and R code in a Jupyter Notebook, you can use rpy2, a package that allows for inter-language compatibility. Alternatively, you can use Jupyter Lab with extensions that allow cell-specific kernel selection.

This setup allows you to use Jupyter Notebooks with R code, which can be particularly useful for data analysis, visualization, and sharing work with others who use Python or Jupyter Notebooks.