R for Data Science, 2nd Edition

Chapter 28. Quarto

Introduction

Quarto provides a unified authoring framework for data science, combining your code, its results, and your prose. Quarto documents are fully reproducible and support dozens of output formats, such as PDFs, Word files, presentations, and more.

Quarto files are designed to be used in three ways:

• For communicating to decision-makers, who want to focus on the conclusions, not the code behind the analysis

• For collaborating with other data scientists (including future you!), who are interested in both your conclusions and how you reached them (i.e., the code)

• As an environment in which to do data science, as a modern-day lab notebook where you can capture not only what you did but also what you were thinking

Quarto is a command-line interface tool, not an R package. This means that help is, by and large, not available through ?. Instead, as you work through this chapter and use Quarto in the future, you should refer to the Quarto documentation.

If you’re an R Markdown user, you might be thinking, “Quarto sounds a lot like R Markdown.” You’re not wrong! Quarto unifies the functionality of many packages from the R Markdown ecosystem (rmarkdown, bookdown, distill, xaringan, etc.) into a single consistent system as well as extends it with native support for multiple programming languages such as Python and Julia in addition to R. In a way, Quarto reflects everything that was learned from expanding and supporting the R Markdown ecosystem for a decade. ...