Introduction to Quarto for Research

This workshop will introduce you to Quarto, an open-source scientific and technical publishing system that allows you to combine narrative text, code, images, and tables. We’ll learn how to use Quarto for authoring journal articles.
Published

May 9, 2024

Session overview

Part 1 (morning)

  • Introduction to Quarto
    • What is Quarto?
    • Why is Quarto good for reproducible research?
    • How does Quarto compare to alternatives?
  • Making your first Quarto document
    • Output types
    • YAML options (document properties)
    • Document rendering
    • Quarto in RStudio
  • Document content
    • External image files
    • Tables
    • Links
    • Markdown syntax: Bullet points, lists, bold text
  • Code blocks (maybe afternoon depending on time)
    • Plots
    • Tables

Part 2 (afternoon)

  • Referencing
    • Inline code and referencing results
    • Figure and table references
    • Cross references
    • Citations
  • Formatting
    • Journal templates (e.g. APA style)
    • Output formats (Word, LaTeX)
    • Manuscript format
  • Collaborating with Quarto
    • GitHub
    • {trackdown}
    • VSCode Live Share
  • Discussion
    • Nice features
    • Tricky things
    • Quarto and R versions

Prerequisites

Quarto

  • You should have a working installation of Quarto.
    • If you have installed a recent version of RStudio IDE (after Jul 2022), you should already have Quarto installed.
    • If you click on the Terminal tab in RStudio (next to the Console) and run quarto --version this will tell you if Quarto is installed and what version you have.
    • You can also install a more recent version if required. See quarto.org/docs/get-started to download Quarto.
    • You should also have access to a text editor or IDE (e.g. RStudio, VSCode, Emacs). RStudio is recommended for this workshop.
    • If you have any issues installing R, RStudio, or Quarto, you may wish to use posit.cloud for the session.

R

  • You should have a working installation of R.
  • You do not need to be experienced in R for this workshop. Code examples will be provided.
  • Packages used:
    • {psych} (for bfi data only). This can be read in with bfi <- read.csv("https://vincentarelbundock.github.io/Rdatasets/csv/psych/bfi.csv") instead.

Other

  • You may wish to bring a laptop charger as the workshop will run for most of the day.