Why Quarto?

As described in the quarto documentation: Quarto is a new, open-source, scientific, and technical publishing system. It is a multi-language, next generation version of R Markdown from RStudio, with many new features and capabilities. Like R Markdown, Quarto uses Knitr to execute R code, and is therefore able to render most existing Rmd files without modification.

Data scientists are pretty excited about the introduction of Quarto, and since it represents the future of R Markdown, we will conduct SDS 264 using Quarto. Intriguing Quarto features that have been cited include:

• collaborate across coding languages
• open source
• dozens of export types, including websites, books, and blogs
• built-in YAML assistance (since it’s a notoriously finicky language)
• it’s installed like other software and automatically included in new versions of RStudio
• free publishing through quartopub.com
• while Rmd will be maintained and updated, cool new features will appear in Quarto
• there are plans for future simultaneous editing like in google docs

From Posit and the developers of Rstudio and Quarto https://charlotte.quarto.pub/cascadia/, Quarto (compared to RMarkdown):

• Offers “batteries included” shared syntax across formats, while R Markdown grew into a large ecosystem with varying syntax
• Allows you to choose your own editor and your preferred data science language
• Comes with richer features out of the box
• Is actively developed

In other words, Quarto unifies and extends R Markdown:

• Consistent implementation of attractive and handy features across outputs: tabsets, code-folding, syntax highlighting, etc.
• More accessible defaults as well as better support for accessibility
• Support for other languages like Python, Julia, Observable, and more via Jupyter engine for executable code chunks.

Here’s a cool example from the Quarto documentation, showing features like cross-referencing of figures, chunk options using the hash-pipe format, collapsed code, and easy figure legends:

Air Quality

Figure 1 further explores the impact of temperature on ozone level.

library(ggplot2)

ggplot(airquality, aes(Temp, Ozone)) + 
  geom_point() + 
  geom_smooth(method = "loess")

Figure 1: Temperature and ozone level.