JupyterLab ou RStudio : une bonne alternative sous Linux ?

Q: Does JupyterLab support R package installation?

Yes, use `install.packages()` or `devtools::install_github()` inside a notebook cell, just like in RStudio.

Q: Can I debug R code in JupyterLab?

Debugging is possible via extensions like `jupyterlab-debugger` and the IRkernel’s `debug()` function, but it is less integrated than RStudio’s built‑in debugger.

JupyterLab, alternative Linux à RStudio

Mis à jour le 3 novembre 2026

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JupyterLab can replace RStudio for users who prefer a notebook‑centric workflow and want to mix R with other languages. It provides a code editor, an R console via the IRkernel, plotting, package management, and debugging through extensions. It is best for data scientists who already use Jupyter notebooks and need a single, web‑based interface. Traditional RStudio users who rely on a dedicated R IDE feel‑good workflow may find JupyterLab less polished for R‑specific tasks.

Couverture fonctionnelle70%

Licence: BSD-3-Clause
Prix: Gratuit
Dernière version: 4.6.5
Note: —

Installer JupyterLab

Ce qui fonctionne pareil

Both offer code editing with syntax highlighting for R
Both provide an integrated R console to run scripts
Both support plotting and display of visual output
Both include package management tools
Both allow project‑style organization of files

Ce qui change

JupyterLab is web‑based; RStudio runs as a native desktop app
RStudio’s debugging is built‑in; JupyterLab relies on extensions for debugging R code
RStudio has a dedicated R console; JupyterLab uses a notebook cell console that mixes code and output
JupyterLab’s file browser is part of the web UI, not a separate pane
RStudio offers a more polished R‑specific UI; JupyterLab is more generic
Missing: RStudio’s integrated profiler and session snapshot features

Ouvrir vos fichiers RStudio

JupyterLab can open R scripts (.R) and R Markdown (.Rmd) files directly, but it does not natively read RStudio’s .Rproj project files or .RData workspace snapshots. Exporting from JupyterLab to an RStudio project requires saving the script and manually creating a new .Rproj file if needed.

Passer de RStudio à JupyterLab, étape par étape

Install the IRkernel package in R and run IRkernel::installspec() to register the R kernel with JupyterLab.
Launch JupyterLab from the terminal or launcher and open a new notebook selecting the R kernel.
Copy RStudio scripts into the JupyterLab file browser or use the “Upload” button.
Run cells to execute code; use the built‑in plot area for visual output.
Install JupyterLab extensions such as jupyterlab-git or jupyterlab-variableInspector for added functionality.
Save notebooks as .ipynb or export to .Rmd if you need RStudio compatibility.

Fonctionnalités clés de JupyterLab

Interface web accessible via tout navigateur moderne
Prend en charge plusieurs kernels (Python, R, Julia, etc.)
Terminal intégré et navigateur de fichiers
Extensible via plugins (ex. Git, LaTeX, visualiseurs de données)
Rendu riche des sorties (graphiques, tableaux, widgets)
Raccourcis clavier pour la navigation des notebooks
Complétion de code intégrée et linting

Installer JupyterLab sous Linux

Debiansudo apt install jupyterlab

Fedorasudo dnf install jupyterlab

Arch / Manjarosudo pacman -S jupyterlab

Questions fréquentes

Does JupyterLab support R package installation?

Yes, use install.packages() or devtools::install_github() inside a notebook cell, just like in RStudio.

Can I debug R code in JupyterLab?

Debugging is possible via extensions like jupyterlab-debugger and the IRkernel’s debug() function, but it is less integrated than RStudio’s built‑in debugger.

Will my RStudio plots appear correctly in JupyterLab?

Plots generated with base R or ggplot2 render inline in JupyterLab’s output area, so you’ll see them just as in RStudio, though the layout differs.

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