Running VS Code

VS Code has a visual coding and development GUI that runs locally. It works at the level of a folder where your project code and other file artifacts live, similar to the way you’d organize a project with Git. In fact, it integrates smoothly with Git. As a Microsoft product, it is positioned for Azure Cloud inter-operability, making it possible to build Cloud systems at scale — an entirely different topic covered in my article Simply Python.

So download VS Code for your Windows, Mac or Linux machine if you don’t have it already. This is its default layout:

The interface is partitioned into panes and bars. The main editor pane also shows previews and can be split horizontally and vertically ad infinitum. Below it is the terminal, which can also show other scrolling output. The icons on the far left compose the activity bar that determines what is displayed in the pane immediately to its right, which here shows the File Explorer and Outline. For a tour of the interface and its basic features, see the Getting Started page on the VS Code website.

How to run R with Python in VS Code

There is more than one way to do this. These are the various ways covered in this article:

1. Use VS Code’s R Extension and Debugger to run .R files.
2. Run R code in Jupyter Notebooks in VS Code.
3. Run R code using rpy2 in Python Jupyter Notebooks.
4. Run .qmd Quarto markdown documents.

1. Running R files

.libPaths()

[1] "/Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library"

install.packages("languageserver")

2. Running Jupyter R Notebooks

The second engine for running R uses the Jupyter Notebook integration with VS Code. Conventional Jupyter notebooks can run R if they have an R kernel installed that connects them with the R executable that runs on your machine. To set this up, at the R prompt install the IRkernel package:

install.packages('IRkernel')
IRkernel::installspec()     # to register the kernel, making it visible to Jupyter.

Now restart VS Code and create a Jupyter Notebook from the New File... menu item. To switch to the R kernel, click the “Select Kernel” button above the notebook to the right. Then, in the menu-bar popup, select the R executable, and the “MagicPython” dropdown menu on the notebook cell will change to R. If no R kernel is shown, Choose “Select Another Kernel…” to search the file system for one. Once you’ve found one, it will appear next time in the kernel selection menu.

As with Python, you have a choice with new cells to create them as either R or Markdown cells. In a notebook, a cell’s graphic output will appear below the cell rather than in a separate pane.

3. Notebooks for R <-> Python interoperability

Alas, your R notebook is for R code only; there is no option to mix notebook cells of different languages. This section discusses how it is possible to create notebooks that can switch back and forth between cells running different languages in the same notebook.

pip install rpy2

%load_ext rpy2.ipython

%%R -i my_df

%%R -o my_df

4. Quarto: An alternative to multi-lingual notebooks

Quarto is designed to build beautiful documents interspersed with embedded, functioning code. In contrast, Jupyter notebooks are intended to be a programming environment, with a sequence of code cells alternated with text. The line between them gets blurred as more features are added to both. Quarto evolved from R Markdown, as a multi-purpose document generation tool, combining the best of existing tools with the ability to run code. In fact, .Rmd “R Markdown” files, an extension of standard Markdown, find a natural extension with Quarto .qmd files. Quarto can be run from the command line as a batch task that knits .qmd files into HTML, PDF, or other formats. However, it is fully integrated with RStudio and VS Code so that one never needs to resort to the command line.

```{r}
library(reticulate)
r_df<- py$python_df
```

```{python}
python_df = r.r_df
```

---
title: "Folded code example"
format: html
code-fold: true
code-summary: "The code"
---

Bottom line: Which environment to use for which purpose?

This post is a brief survey of the existing R <-> Python integrations available for the VS Code development environment. Here are my thoughts about when they come in useful.

1. Running R code in VS Code is a work-alike to running RStudio. RStudio is a dedicated R development environment with some recent additions to accommodate Python. Conversely, VS Code competes as a multi-language project development environment, with extensions for notebook, Python, and R programming. I find RStudio more intuitive and R-ish, and having to suffer multiple ways to accomplish similar tasks in VS Code adds needless complication to the interface. But if you are familiar with VS Code and don’t want to learn another tool, VS Code runs R code just fine.

2. When I need some R code capabilities not available in Python (yes, R still has better statistics and graphics libraries), I use rpy2 to extend my Jupyter notebooks. This way I can still use the extensive features of VS Code such as evolving my (pure) notebooks into Python modules. On the other hand just running R notebooks in VS Code has no advantages over running Quarto documents.

3. I’m learning Quarto not only for its “polyglot” language features but also for its extensive and varied document creation possibilities. This blog is actually created as a .qmd document! It remains to be seen if VS Code will equal the ease of Quarto development in RStudio as I get more proficient with it.

About the author

Recently, after departing his Data Scientist position at Microsoft, John Mark Agosta began teaching the “Math Foundations” course for the SJSU online Master’s program and founded Fondata, LLC to pursue his interest in probabilistic graphical models and related Bayesian modeling methods.