Why to learn R/Python?

Python or R is an open-source programming language. Python is one of the most widely used general programming language and R is one of the most widely used programming language for research and data analysis.

Code sharing, customizing, reusability of codes, and literally endless possibility of expansion of data science ideas are some of the benefits of command-driven programs over menu-driven programs.

R seems to know anything that a data analyst needs to analyze. The code is highly customizable. One problem can be solved in different ways. Output formatting can be styled using tidyverse and kableExtra. However, I think R has attracted many data analyst for its amazing publication ready data-based figures usually done with ggplot2.

Installation of R and integration with Jupyter Notebook in Visual Studio Code

  • Install R Download R

  • Install Python Download Python

  • Install pip (package installer for python) using cmd (command prompt): python -m pip install --upgrade pip

  • For python3, update pip if needed: python3 -m pip install --upgrade pip

  • Install Jupyter using cmd: python -m pip install jupyter

  • You can also install Jupyter Lab using cmd: python -m pip install jupyterlab

  • You can launch Jupyter Notebook or Lab using cmd: jupyter notebook or jupyter lab

Run jupyter notebook in browser

  • In cmd: jupyter notebook

Run R with jupyter notebook in VS Code (Visual Studio code)

  • Install VS Code Download VS Code

  • Install R extension in VS code: R Extension for Visual Studio Code (REditorSupport)

  • Install radian in cmd for syntax highlighting: pip3 install -U radian

  • Open R from Start Menu

  • Install packages in R (not RStudio) without compilation: install.packages(c('languageserver', 'httpgd', 'repr', 'IRdisplay', 'evaluate', 'crayon', 'pbdZMQ', 'devtools', 'uuid', 'digest'))

  • Install R kernel for jupyter in R (not RStudio): devtools::install_github('IRkernel/IRkernel')

  • Link R kernel to jupyter notebook in R (not RStudio): IRkernel::installspec(user = FALSE)

  • Open a file in VS code with .ipynb extension and select kernel (jupyer kernel > R kernel)

Now your jupyter notebook is ready for both python and R. You can also create and work with R file with .R extension in vs code. At the end, you can export the .ipynb (jupyter notebook) file as html by pressing ctrl+shift+P and search for ‘jupyter: export to html’. Open this html file in RStudio to publish. In addition, you can read some tips on working with markdown in Markdown cheat sheet.