Working with R Scripts

This vignette demonstrates how to work with R scripts in Quarto workflows using the quarto R package. The package provides two main functions for this purpose:

Extracting R Code from Quarto Documents

Lifecycle: experimental 

The qmd_to_r_script() function allows you to extract R code cells from .qmd files and convert them to .R scripts. This is particularly useful for:

Basic Usage

library(quarto)

# Extract R code from a Quarto document to an R script
# It will output my-analysis.R
qmd_to_r_script("my-analysis.qmd"")

The function preserves important metadata from your Quarto document:

It also have some important limitations:

Example: Converting a Simple Quarto Document

Let’s create a sample Quarto document to demonstrate:

example.qmd
# Sample Quarto document content
---
title: "My Analysis"
author: "Data Scientist"
format: html
---

# Introduction

This is a sample analysis.

```{r}
#| label: setup
#| message: false
library(ggplot2)
library(dplyr)
```

```{r}
#| label: data-viz
#| fig-width: 8
#| fig-height: 6
mtcars |>
  ggplot(aes(x = wt, y = mpg)) +
  geom_point() +
  geom_smooth()
```

Now let’s extract the R code:

library(quarto)

# Extract R code to a script
r_script <- qmd_to_r_script(qmd_file)

Let’s see what the generated R script looks like:

example.R
#' ---
#' title: My Analysis
#' author: Data Scientist
#' format: html
#' ---
#' 

#| label: setup
#| message: false
library(ggplot2)
library(dplyr)

#| label: data-viz
#| fig-width: 8
#| fig-height: 6
mtcars |>
  ggplot(aes(x = wt, y = mpg)) +
  geom_point() +
  geom_smooth()

Working with Mixed-Language Documents

When working with documents that contain multiple languages (R, Python, JavaScript, etc.), qmd_to_r_script() will:

  1. Extract only the R code cells
  2. Provide informative messages about non-R cells
  3. Return NULL if no R cells are found
mixed.qmd
---
title: "Mixed Language Analysis"
format: html
---

```{r}
#| label: r-analysis
data <- mtcars
summary(data)
```

```{python}
#| label: python-analysis
import pandas as pd
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
print(df.head())
```

```{ojs}
//| label: js-viz
Plot.plot({
  marks: [Plot.dot(data, {x: "x", y: "y"})]
})
```

The function will inform you about the non-R cells and extract only the R code:

# Extract R code from mixed-language document
mixed_r_script <- qmd_to_r_script(mixed_qmd)
#> Extracting only R code cells from
#> 'C:\Users\chris\AppData\Local\Temp\RtmpuAG5TU\quarto-r-scripts-vignette59a8e7daf/mixed.qmd'.
#> → Other languages will be ignored (found python and ojs).

The resulting R script will contain only the R code cell:

mixed.R
#' ---
#' title: Mixed Language Analysis
#' format: html
#' ---
#' 

#| label: r-analysis
data <- mtcars
summary(data)

Adding YAML Metadata to R Scripts

The add_spin_preamble() function helps you add YAML metadata to existing R scripts, making them compatible with Quarto’s script rendering feature.

Basic Usage

# Add a simple title to an R script
add_spin_preamble("my-script.R", title = "My Analysis")

# Add custom YAML metadata
add_spin_preamble("my-script.R", 
                  preamble = list(
                    title = "Advanced Analysis",
                    author = "Data Scientist",
                    format = "html",
                    execute = list(echo = TRUE, warning = FALSE)
                  ))

Example: Preparing a Script for Quarto Rendering

simple.R
# Load required libraries
library(ggplot2)
library(dplyr)

# Analyze mtcars data
mtcars |>
  group_by(cyl) |>
  summarise(avg_mpg = mean(mpg), .groups = "drop")

# Create visualization
ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_boxplot() +
  labs(title = "MPG by Number of Cylinders",
       x = "Cylinders", y = "Miles per Gallon")

Now add a YAML preamble:

# Add YAML metadata for Quarto rendering
add_spin_preamble(simple_script, 
                  title = "Car Analysis",
                  preamble = list(
                    author = "R User",
                    format = list(
                      html = list(
                        code_fold = TRUE,
                        theme = "cosmo"
                      )
                    )
                  ))
#> Added spin preamble to
#> 'C:\Users\chris\AppData\Local\Temp\RtmpuAG5TU\quarto-r-scripts-vignette59a8e7daf/simple.R'.

The updated script now has YAML metadata:

simple.R
#' ---
#' author: R User
#' format:
#'   html:
#'     code_fold: true
#'     theme: cosmo
#' title: Car Analysis
#' ---
#' 

# Load required libraries
library(ggplot2)
library(dplyr)

# Analyze mtcars data
mtcars |>
  group_by(cyl) |>
  summarise(avg_mpg = mean(mpg), .groups = "drop")

# Create visualization
ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_boxplot() +
  labs(title = "MPG by Number of Cylinders",
       x = "Cylinders", y = "Miles per Gallon")

This script can now be rendered with Quarto:

# Render the R script as a Quarto document
quarto_render(simple_script)

Integration with Existing Workflows

These functions work seamlessly with other quarto package functions:

# Complete workflow example
library(quarto)

# 1. Extract R code from Quarto document
extracted_script <- qmd_to_r_script("analysis.qmd", output = "analysis.R")

# 2. Add additional metadata if needed
add_spin_preamble(extracted_script, 
                  title = "Extracted Analysis",
                  preamble = list(format = "pdf"))

# 3. Render the script
quarto_render(extracted_script)

# 4. Preview the output
quarto_preview(extracted_script)

Summary

The qmd_to_r_script() and add_spin_preamble() functions provide a powerful toolkit for working with R scripts in Quarto workflows. Whether you’re extracting code from existing documents or preparing scripts for Quarto rendering, these functions help bridge the gap between narrative documents and standalone scripts.

For more advanced usage and additional options, see the function documentation with ?qmd_to_r_script and ?add_spin_preamble.