2  Positron

Writing code in R, Python, Julia, or other languges benefit from an interface and ecosystem that helps you accomplish your data analysis tasks. By default, R comes with an interface that you could use to do all your work.

Figure 2.1: The stock R application, bundled with each distribution.

It is also possible to use R directly from a terminal app. If you use linux or mac, you can access R from your local terminal application. On Windows, there is a more complicated way of doing it but—I do not undersand Windows so I cannot help with this one.

Figure 2.2: Running R in a terminal locally.

Integrated Development Environment

There are several GUI environments that you can use to interface with R beyond the built-in interface. Since February 2011, the RStudio IDE has been a popular interface for R and its development has gone hand-in-hand with the rise of the modern R reproducible reserach stack (e.g., knitr \(\to\) R Markdown \(\to\) tidyverse \(\to\) Quarto). One of the benefits of this interface is that it made it much easier to integrate data, analysis, interpretation, and distribution into a single interface and framework—enhancing reproducibility.

Figure 2.3: The main interface for RStudio.

In 2023, the next generation of IDE, named Positron focusing specifically on R, Python, Julia, and Quarto was released. This IDE is a fork of the very popular VSCode interface and is designed to support data science workflows across several languages (e.g., you can mix R, Python, Julia, SQL, etc.).

Figure 2.4: The Positron integrated development environment showing this very page!

This text was written entirely using the Positron IDE, as a way to help me switch from RStudio in my own workflows and teaching. This book does not require you to use any specific interface—though I will have been known to give 10,000 bonus points if you are using emacs or vi in my class… 😏