RStudio vs JupyterLab

For Data Science

Dhananjay Deshpande | dcd2139 | Data Science Institute | Columbia University

Oct 28, 2019

Many Data Science Tools Exist

  • Jupyter Notebook
  • JupyterLab
  • Matlab
  • Microsoft Excel
  • RStudio
  • Tableau
  • ...

Tools

What Tools should I choose as a Data Scientist?

Hard question. So many options !!

Lets compare a couple of popular Integrated Development Environments

Tools of Interest
RStudio

An integrated development environment for R, with a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management.

https://rstudio.com
JupyterLab

An integrated development environment that enables you to work with documents and activities such as Jupyter notebooks, text editors, terminals, and custom components in a flexible, integrated, and extensible manner.

Enhanced version of Jupyter Notebook.

https://jupyterlab.readthedocs.io
Flavors
RStudio

RStudio Desktop: program is run locally as a regular desktop application.

RStudio Server: allows accessing RStudio using a web browser while it is running on a remote Linux server.

reference
Project Jupyter

Jupyter Notebook: an open-source web application for data science.

JupyterLab: An interactive development environment that includes support for Jupyter Notebook.

reference
History
RStudio

Initial version released: 28 Feb 2011

License: Open Source (AGPL v3)

Original Developer: R Studio, Inc

reference
JupyterLab

Beta Version released: Feb 20, 2018 (Jupyter Notebook: 2015, IPython: 2001)

License: Open Source (MIT)

Original Developer: Project Jupyter

reference
Industry Adoption
RStudio

companies

https://rstudio.com
Project Jupyter

companies

reference
Project Jupyter

companies

reference
Language Support
RStudio

Best for R.

knitr can execute code in many languages besides R. Some of the available language engines include:

  • Python
  • SQL
  • Bash
  • Rcpp
  • Stan
  • Javascript
  • CSS

reference
JupyterLab

Best for Python.

The Jupyter system supports over 100 programming languages (called “kernels” in the Jupyter ecosystem) including

  • Python
  • Java
  • R
  • Julia
  • Matlab
  • Octave
    • reference
Community
RStudio https://github.com/rstudio/rstudio
JupyterLab https://github.com/jupyterlab/jupyterlab
Installation
RStudio

To Install RStudio on Mac

  • Go to www.rstudio.com and click on the "Download RStudio" button.
  • Click on "Download RStudio Desktop."
  • Click on the version recommended for your system, or the latest Mac version, save the .dmg file on your computer, double-click it to open, and then drag and drop it to your applications folder.

JupyterLab 


With conda
$ conda install -c conda-forge jupyterlab

With pip
$ pip install jupyterlab

Run JupyterLab
$ jupyter lab

Demo

RStudio

Demo

JupyterLab

Demo
Development
Feature RStudio JupyterLab
Seperation between text and code No Yes
Markdown, Latex, Image, Visualization support Yes Yes
Syntax Highlighting Supported Supported (Large Number of Languages)
Function help Yes Possible with Extensions
Keyboard bindings for vim, emacs Yes Yes
Feature RStudio JupyterLab
Debugging Yes No (Supported in Classic Notebook)
Documentation and Help Available Available
Support for Extensions Yes Yes
Themes Yes Yes
Data Cleaning
Feature RStudio JupyterLab
File Explorer Yes Yes
File formats supported csv, xls, xlsx, sav, dta, por, sas, stata csv, json
Pagination for Data Frames Yes No
Package support for data cleaning Yes Yes
Analysis and Exploration
Feature RStudio JupyterLab
Notebook support Yes Yes
Descriptive Analysis Support Yes Yes
Spatial Analysis Support Yes Yes
Temporal Analysis Support Yes Yes
Variable Comparative Analysis Support Yes Yes
Feature RStudio JupyterLab
Interactive Visualization support Yes Yes
Modeling and Prediction
Feature RStudio JupyterLab
Integration with Tensorflow Yes Yes
Integration with PyTorch Yes Yes
Integration with Stan Yes Yes
Integration with Slurm Yes Yes
Presentation
Feature RStudio JupyterLab
Export formats html, pdf, md, word (with knitr) html, pdf, md, json
D3 Integration Yes Yes
Presentation mode support Yes Yes
Website, blog creation Yes Yes
Publish A Book Yes Yes
Collaboration
Feature RStudio JupyterLab
Integration with Git Yes Yes
Google Drive Integration Yes Yes
Dropbox Integration Yes Yes
Collaboration on the cloud Yes Yes
Summary and Recommendations

As of Oct 28, 2019

  • RStudio is a more mature product than JupyterLab
  • RStudio has multiple features out of the box
  • JupyterLab adds a lot more capabilities to Jupyter Notebook
  • JupyterLab could eventually be the only tool needed for Data Science
  • Recommend using RStudio for R and JupyterLab for Python (for now)

Lets Discuss
Thank you