Using R with Python in Quarto Documents: A Seamless Integration Guide

Integrating R with Python in Quarto Documents

=====================================================

Quarto is a document generation framework that allows users to create documents by combining code chunks in different programming languages. In this article, we will explore how to call an R object from Python within a Quarto document using the Reticulate package.

Introduction

R and Python are two popular programming languages widely used in data analysis and science. However, they have different syntaxes and ecosystems, making it challenging to integrate them seamlessly. Quarto addresses this issue by providing a way to run code chunks in multiple languages within a single document. In this article, we will focus on integrating R with Python using the Reticulate package.

Understanding Quarto’s Rendering Engines

Quarto has two rendering engines: Knitr and Jupyter. The choice of engine depends on the type of content being rendered. Knitr is suitable for documents that require statistical analysis and mathematical computations, while Jupyter is better suited for interactive visualizations and data exploration.

When using the Knitr engine, Quarto will use the Reticulate package to run Python code chunks. This allows users to leverage Python’s capabilities within their R workflow. Conversely, when using the Jupyter engine, Quarto will execute Python code directly, bypassing R entirely.

Calling R Objects from Python in Quarto Documents

To call an R object from Python within a Quarto document, we need to use the correct combination of Knitr and Reticulate settings.

Step 1: Install and Load Reticulate

First, we need to install and load the Reticulate package using the following code:

---
title: "Reticulate"
format: html
jupyter: python3
---

library(reticulate)

# Run Python code chunk

Step 2: Use Knitr Engine with Reticulate

To call an R object from Python, we need to use the Knitr engine and set up Reticulate correctly. We can do this by adding the following settings to our Quarto document:

---
title: "pandas"
format: html
jupyter: python3
knitr_engine: r
---

```{r}
data("penguins", package = "palmerpenguins")

import py.r

penguins = py.r.penguins
print(penguins.head())


In the code above, we use the Knitr engine and specify `r` as the engine. We then run an R code chunk using the `data()` function from the `palmerpenguins` package. The Python code chunk imports the `py.r` module and calls the `penguins` object using the `r.penguins` syntax.

### Step 3: Avoid Using IRkernel or rpy2

When using the Jupyter engine, we need to avoid using IRkernel or rpy2 modules in our R chunks. These modules will not be executed when rendering the document. Instead, we should use Python code chunks to run the necessary functionality.

Similarly, when using the Knitr engine with Reticulate, we can avoid using `reticulate::repl_python()` or `library(reticulate)` directly. The Reticulate package will take care of the magic conversion for us.

### Conclusion
--------------

Integrating R with Python in Quarto documents is a powerful feature that allows users to leverage multiple programming languages within a single workflow. By understanding the rendering engines and using the correct combination of Knitr and Reticulate settings, we can call R objects from Python seamlessly.

## Additional Resources
-------------------------

*   [R Markdown Documentation](https://rmarkdown.rstudio.com/)
*   [Quarto Documentation](https://quarto.org/docs/)
*   [Reticulate Package Documentation](https://retec.tau.ac.il/)

Last modified on 2023-05-19