Advanced Quarto Article

UC Davis Seroepidemiology Research Group (UCD-SERG)

2026-02-02

library(rpt)

1 Overview

This article demonstrates advanced features of Quarto for package documentation. Unlike vignettes, articles are only available on the documentation website and are not included in the package bundle.

2 When to Use Articles vs Vignettes

Vignettes should be used for:

• Core package documentation
• Essential usage examples
• Content that users need offline

Articles are better for:

• Extended tutorials
• Case studies and detailed examples
• Content with large datasets or external dependencies
• Supplementary material

3 Advanced Quarto Features

Cross-References

Quarto makes it easy to create cross-references. For example, see Figure 1 for a scatter plot and Table 1 for a summary table.

# Generate sample data
set.seed(123)
x <- rnorm(100)
y <- 2 * x + rnorm(100, 0, 0.5)

# Create scatter plot
plot(x, y, 
     main = "Example Scatter Plot",
     xlab = "X Variable",
     ylab = "Y Variable",
     pch = 16,
     col = rgb(0.2, 0.4, 0.6, 0.6))
abline(lm(y ~ x), col = "red", lwd = 2)

Figure 1: Scatter plot demonstrating cross-referencing

Summary Tables

data_summary <- data.frame(
  Statistic = c("Mean", "Median", "SD", "Min", "Max"),
  X = c(mean(x), median(x), sd(x), min(x), max(x)),
  Y = c(mean(y), median(y), sd(y), min(y), max(y))
)

knitr::kable(data_summary, digits = 3)

Table 1: Summary statistics for example data

Statistic	X	Y
Mean	0.090	0.127
Median	0.062	0.137
SD	0.913	1.865
Min	-2.309	-4.586
Max	2.187	4.675

As shown in Table 1, the variables have similar distributions.

Code Folding

You can make code chunks foldable:

# Prepare more complex data
complex_data <- data.frame(
  id = 1:50,
  group = rep(c("A", "B"), each = 25),
  value1 = rnorm(50, 100, 15),
  value2 = rnorm(50, 50, 10)
)

# Calculate group statistics
group_stats <- aggregate(
  cbind(value1, value2) ~ group, 
  data = complex_data, 
  FUN = function(x) c(mean = mean(x), sd = sd(x))
)

print(group_stats)
#>   group value1.mean value1.sd value2.mean value2.sd
#> 1     A   101.61247  14.63952   51.727220 10.047051
#> 2     B    98.13191  13.84514   53.261796  8.934998

Tabsets

• Plot View
• Summary View
• Raw Data

#>        id           group               value1           value2     
#>  Min.   : 1.00   Length:50          Min.   : 79.60   Min.   :32.43  
#>  1st Qu.:13.25   Class :character   1st Qu.: 89.29   1st Qu.:46.76  
#>  Median :25.50   Mode  :character   Median : 96.64   Median :51.04  
#>  Mean   :25.50                      Mean   : 99.87   Mean   :52.49  
#>  3rd Qu.:37.75                      3rd Qu.:107.93   3rd Qu.:59.34  
#>  Max.   :50.00                      Max.   :132.98   Max.   :72.93

#>    id group    value1   value2
#> 1   1     A 132.98216 46.24397
#> 2   2     A 119.68619 44.38124
#> 3   3     A  96.02282 46.56083
#> 4   4     A 108.14791 50.90497
#> 5   5     A  93.78490 65.98509
#> 6   6     A  92.85630 49.11435
#> 7   7     A  88.17096 60.80799
#> 8   8     A  91.08074 56.30754
#> 9   9     A 124.76361 48.86360
#> 10 10     A  99.18958 34.67098

Advanced Callouts

Important Considerations

When using this package in production:

1. Always validate input data
2. Check for missing values
3. Consider computational complexity

Performance Warning

Large datasets may require additional memory and processing time.

Columns Layout

Left Column

This demonstrates a two-column layout in Quarto.

• Feature 1
• Feature 2
• Feature 3

Right Column

You can place different content in each column.

4 Working with Package Functions

Here’s how to use the package’s example function with different inputs:

# Numeric vector
example_function(c(5, 10, 15, 20, 25))
#> [1] 15

# Using with generated data
random_data <- runif(10, min = 0, max = 100)
example_function(random_data)
#> [1] 47.42519

5 Code Annotations

Quarto supports code annotations for detailed explanations:

1
2data <- c(1, 2, 3, 4, 5)
3result <- example_function(data)
4print(result)
#> [1] 3

1

Load sample data

2

Create a simple numeric vector

3

Apply the example function from the package

4

Display the results

6 Mathematical Notation

Quarto excels at mathematical notation. Here’s an example of a statistical formula:

The standard error of the mean is calculated as:

\[ SE = \frac{s}{\sqrt{n}} \]

where \(s\) is the sample standard deviation and \(n\) is the sample size.

For a confidence interval:

\[ CI = \bar{x} \pm t_{\alpha/2, n-1} \cdot SE \]

7 Tips and Best Practices

Expanding tip section

Click to expand this section for additional tips:

1. Use descriptive chunk labels for better organization
2. Set global chunk options at the beginning
3. Use cross-references to link related content
4. Leverage callouts for important information
5. Consider code folding for long code blocks

8 Conclusion

This article demonstrates the advanced capabilities of Quarto for creating rich, interactive documentation for R packages.

9 Learn More

• Visit the package website
• Check out the GitHub repository
• Read the Quarto documentation

10 References