The tiny Quarto guide

Author

Julius Fenn

Published

May 16, 2025

Background Information

This is an R Markdown document. Instructions for writing these documents and background information can be found in the book written by Xie, Allaire, and Grolemund (2018) When you execute code within the document, the results appear beneath the code. This file contains some instructions how to write code and texts in dynamic Quarto documents. For more detailed information, refer to the Quarto Markdown Basics.

📄 Introduction to Quarto Markdown

Quarto enables you to create dynamic documents by combining plain text with executable code. Understanding Markdown syntax is essential for formatting your documents effectively. Below are the fundamental elements:

1. Headings

Use the # symbol to create headings. The number of # symbols indicates the heading level:

# Heading Level 1
## Heading Level 2
### Heading Level 3

2. Emphasis

  • Italic: Wrap text with one asterisk or underscore: *italic* or _italic_
  • Bold: Wrap text with two asterisks or underscores: **bold** or __bold__
  • Bold and Italic: Combine both: ***bold and italic***

3. Lists

  • Unordered Lists: Use -, *, or + followed by a space:

    - Item 1
- Item 2

  • Ordered Lists: Use numbers followed by a period:

    1. First item
2. Second item

5. Blockquotes

Use > to create blockquotes:

> This is a blockquote.

6. Code

  • Inline Code: Use backticks ` to enclose code within a line:

    I can now compute the mean from 1 to 5 within my document: 3

  • Code Blocks: Use triple backticks to create code blocks. It is possible to apply different programming languages:

like R:

summary(cars)

or Python:

1 + 1

7. Tables

Create tables using pipes | and hyphens -:

| Column 1 | Column 2 |
|----------|----------|
| Data 1   | Data 2   |

8. Mathematical Notation

Quarto supports LaTeX-style math expressions:

  • Inline Math: Use single dollar signs: $...$

    Example:

    The equation of a line is $y = mx + b$.

  • Display Math: Use double dollar signs:

    $$
E = mc^2
$$

9. Callout Blocks

Highlight important information using callout blocks:

::: note
This is a note.
:::

::: warning
This is a warning.
:::

10. YAML Front Matter

At the beginning of your .qmd file, include metadata enclosed by ---:

---
title: "The tiny Quarto guide"
author: "Julius Fenn"
date: today
format:
  html:
    toc: true
    toc-depth: 3
    html-math-method: katex
bibliography: Library_subset.bib
biblio-style: apalike
link-citations: true
---

Ensure proper indentation and spacing in the YAML section to avoid errors.

Real Application Case

Please read Quarto Using R and play around with the following code:

Let’s demonstrate a real-world example using the built-in iris dataset, which contains measurements of different iris flower species.

We begin by displaying the first few rows of the dataset. We’ll use a code chunk with echo = TRUE to display both the code and its output:

# Show the first six rows of the iris dataset
head(iris)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

If you want to show only the output and not the code, use the chunk option echo = FALSE:

  Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
 Median :5.800   Median :3.000   Median :4.350   Median :1.300  
 Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
       Species  
 setosa    :50  
 versicolor:50  
 virginica :50

✅ echo = FALSE is useful in final reports where you want to focus on the results without showing the underlying R code.

You can also suppress the output using eval = FALSE, which means the code won’t be run:

# This code won't be executed
plot(iris$Sepal.Length, iris$Sepal.Width)

Now, let’s perform a simple data analysis:

Mean Sepal Length by Species

We can compute the average sepal length for each species using the aggregate() function. Here, we use message = FALSE and warning = FALSE to suppress potential messages or warnings.

agg_means <- aggregate(Sepal.Length ~ Species, data = iris, mean)
agg_means
     Species Sepal.Length
1     setosa        5.006
2 versicolor        5.936
3  virginica        6.588

💡 message = FALSE and warning = FALSE are useful for cleaner outputs, especially when functions produce startup messages or minor warnings.

Plotting

We now generate a boxplot of Sepal Length by Species and interpret the result.

boxplot(Sepal.Length ~ Species, data = iris,
        main = "Sepal Length by Species",
        xlab = "Species", ylab = "Sepal Length (cm)",
        col = c("lightblue", "lightgreen", "lightpink"))

Boxplot of Sepal Length by Species

The highest average sepal length is 6.588

🧠 Inline code lets us reference live results directly in text. This ensures your interpretations remain in sync with your data.

Global Chunk Options

To avoid repeating the same chunk options (like echo = FALSE, message = FALSE, etc.) in every code block, you can set them globally at the beginning of your document using the knitr::opts_chunk$set() function.

This is done in a setup chunk, which is typically not shown in the output.

knitr::opts_chunk$set(
  echo = FALSE,
  include = FALSE,
  message = FALSE,
  warning = FALSE,
  fig.align = "center"
)

Code is evaluated, but not shown or included:

References

Xie, Yihui, J. J. Allaire, and Garrett Grolemund. 2018. R Markdown: The Definitive Guide. New York: Chapman; Hall/CRC. https://doi.org/10.1201/9781138359444.