This is Jupyter with the R kernel, set up so statisticians and data analysts can work in R the way they already do in notebooks. The R kernel comes installed, so you can open the notebook and run a cell without touching IRkernel setup or a local R install.
The Jupyter R kernel (IRkernel) is installed. Open main.ipynb, select the R kernel and run a cell.
The full cloud editor with the built-in notebook UI, an integrated terminal and extensions, all in your browser.
Clone, commit and push your notebooks from the terminal, or connect a GitHub repo whenever you want.
Boots in about a minute on a 2 vCPU machine with the R kernel already in place.
Jupyter is an open-source notebook environment for interactive computing, and this version runs R instead of Python. A notebook mixes runnable code cells with text and charts, so you can work through an analysis step by step while keeping your reasoning next to each result. That suits R's strengths in statistics and data visualization well.
The R kernel here is IRkernel, the standard bridge that lets Jupyter talk to R. It comes installed, so you get the familiar cell-by-cell workflow in R with nothing to configure before you start.
The starter is a single notebook, main.ipynb, with one R cell that prints "Hello, World!". It exists to show the run flow, so you can clear it and start your own analysis.
Running on the VS Code notebook editor, you get cell output inline, including data frames and plots, plus an integrated terminal for installing R packages or managing files.
Open any .ipynb file, click Select Kernel, choose Jupyter Kernel and pick R. Then run a single cell with Run Cell or the whole notebook with Run All. Output appears directly under each cell.
To add a package such as ggplot2 or dplyr, run install.packages() in a cell or use the terminal, then load it with library() in your next cell. The notebook interface opens in a browser preview, so it all stays in the tab.
Statistical analysis, exploratory data work, plots with ggplot2, or teaching material where the R code and the explanation sit together. It fits anything that benefits from an iterative, cell-at-a-time approach in R.
R. The workspace ships with the Jupyter R kernel (IRkernel) installed. If you'd rather work in Python, there's a separate Jupyter template for that.
Open a .ipynb file, choose Select Kernel, pick Jupyter Kernel and then R. Use Run Cell for a single cell or Run All to run the whole notebook. Output appears under each cell.
Yes. Run install.packages() inside a cell or from the terminal, then load the package with library(). CRAN packages work as usual.
Your files live in the workspace and you can commit them with Git. Push to a GitHub repo from the terminal to keep a permanent copy.
No. R and the IRkernel are already in the container, so you skip the local setup and start running cells right away.
This template runs on a paid plan. Templates that are free are marked as such on the Studio page. You can upgrade from the pricing page to launch a Jupyter R workspace.
