intro.qmd

# Introduction

## Language Agnostic Data Science

The trend of the past few years is that Data Science is becoming more language-agnostic.

Tools such as [Quarto](https://quarto.org/), [Apache Arrow](https://arrow.apache.org/)... enable working across languages with less friction.

RStudio (the company) renaming itself to "Posit" was also a clear statement of intent. Tools like [Shiny](https://shiny.posit.co/py/) and [Pins](https://rstudio.github.io/pins-python/) are being ported to Python, and the latest exciting installment is Posit's new language agnostic data science IDE, [Positron](https://github.com/posit-dev/positron).

We often receive questions from R users on whether we can deliver an "Introduction to Python" course, or something similar. While yes, we can certainly do that, the question really is... *why?*

Another aspect to this we have noticed, is that a lot of "Introduction to Python" courses are almost always aimed at complete beginners of programming *full stop*. Most, if not all R users know full-well what a variable or a dataframe is and how to `print("Hello World!")`. These courses are not pitched at the right level.

Returning to the question of why R users might want to learn Python, the answer is that there could be a plethora of reasons. But sometimes, simply being able to use R in combination with Python could be enough to achieve your aims.

In other scenarios, you **will** need to learn some Python, but a quick crash course on the differences between R and Python and the particular topic you're interested in could be enough.

This book aims to provide some guidance on where R and Python are different, but also show the ever-increasing range of methods to combine the two languages together. This book is split in two major sections, **Python for the R user** and **Using R and Python together**.

## Python for the R user

This chapter attempts to minimally break down the differences between R and Python, written for the R user.

Topics:

-   matplotlib, instead of ggplot2
-   numpy, instead of dplyr

## Using R and Python together

This chapter outlines the range of tools available to implement R and Python together, and allow you to collaborate with Python users, without actually learning much Python.

Topics:

-   Language bridges: reticulate / rpy2
-   Quarto: language agnostic publishing
-   Web Applications: Shiny for R / Shiny for Python
-   In Production: Shell execution
-   In Production: Managing environments