Karthik Divi
·3 min read

Jupyter R Notebook Online IDE - Statistical Computing in the Cloud

onecompilerstudiojupyter-notebook-ronline-ide

R is still the go-to language for a lot of statistical work. But getting R, RStudio, and the right packages installed on a new machine is not trivial. Package compilation alone can take a while, especially on Linux. And if you are on a shared or locked-down computer, you might not be able to install R at all.

OneCompiler Studio gives you a Jupyter Notebook with an R kernel running in the cloud. No installation, no configuration.

What the workspace looks like

You get a Jupyter Notebook interface running an R kernel on a dedicated VM. The notebook works the same way you would expect: write R code in a cell, run it, see the output below. Tables print inline. Plots render inline. You can mix code cells with markdown cells to document your analysis.

Beyond the notebook, you also get:

  • A terminal with shell access for managing packages and files
  • A file tree for uploading datasets and organizing your work
  • 2 vCPUs and 2 GB memory (3.3 GB available) on your VM
  • Pre-installed R with common packages ready to use
  • The whole workspace launches in about a minute

Why Jupyter for R

RStudio is the most common R IDE, but Jupyter with an R kernel has its own strengths. The notebook format is great for exploratory analysis where you want to run code in pieces and keep the output visible. It is also the standard for sharing reproducible analyses. Someone can open your notebook and see your code, your results, and your explanations in one document.

If you already use Jupyter for Python work, using it for R means you stay in a familiar interface.

What you can do

  • Use tidyverse. dplyr, ggplot2, tidyr, readr. The packages that most R users reach for first. Install them from the terminal with install.packages() or directly in a cell.
  • Run statistical tests. t-tests, ANOVA, regression, chi-square. R's built-in stats functions are all there.
  • Build plots. ggplot2 charts render inline in the notebook. Base R plots work too.
  • Load your own data. Upload a CSV through the file tree, then read it with read_csv() or read.csv().
  • Install any CRAN package. The terminal gives you full access to install whatever you need.

When this is useful

A few situations where a cloud R environment saves time:

You are taking a statistics course and the instructor uses R. Instead of debugging an R installation on your laptop, you open a browser tab and start working.

You need to do some quick data analysis but you are on a machine without R. Maybe a work computer, maybe a Chromebook.

You are sharing an analysis with someone who does not have R installed. Send them the Studio link and they can see and run everything.

You are comparing approaches in R and Python. Open a Python Jupyter Studio in one tab and an R Jupyter Studio in another.

Try it

Open OneCompiler Studio for Jupyter R. You will have a Jupyter Notebook with an R kernel ready in about a minute.