📦 quartify

🇫🇷 Version française

TL;DR

• 💻 English presentation
• 📖 Official documentation
• 🌐 Online version

Description

quartify is an R package that automatically converts R scripts into Quarto markdown documents (.qmd).

The package facilitates the transformation of your R analyses into reproducible and well-structured Quarto documents, preserving the logical structure of your code through RStudio code sections. It recognizes the standard RStudio code section syntax (####, ====, ----) to create properly indented navigation structures.

Differences with knitr::spin() and Quarto render-scripts

knitr::spin() and Quarto’s render-scripts feature both allow rendering R scripts, but they require inserting Markdown structure directly into comments (#', headers, chunk options, etc.).

quartify takes a different philosophy: it imposes no Markdown syntax in comments and works on completely standard R scripts. Comments remain comments, code remains code.

The goal is to keep clean, conventional scripts while making them convertible to .qmd — and when multiple scripts are selected, to automatically assemble them into a structured Quarto book without any source code rewriting.

Use Cases

If you have a working R script that contains comments, you may want to generate a Quarto document from this script that will allow you to automatically produce displayable HTML documentation. This is particularly useful for:

• Documentation: Automatically generate documentation from your commented code
• Code review: Present your code in a more readable format for stakeholders who prefer formatted documents over raw scripts

Features

• Automatic conversion: Transforms your R scripts (.R) into Quarto documents (.qmd)

• Multiple file conversion: Convert single files, multiple files, or entire directories at once

• Quarto Book creation: Automatically generate navigable Quarto books from directories (enabled by default)

• RStudio code sections support: Recognizes RStudio code sections (####, ====, ----) and converts them to proper markdown headers with correct indentation levels

• Comment preservation: Regular comments are converted into explanatory text

• Code organization: R code is automatically organized into executable blocks

• Customizable YAML header: Ability to define title, author, and other parameters

• Table of contents: Automatic generation of a table of contents in the Quarto document with proper depth

• French language support: Displays "Sommaire" instead of "Table of contents" when language is set to French

• Automatic HTML rendering: Optionally renders the .qmd file to HTML and opens it in your browser

• Customizable themes: Choose from 25+ Quarto themes to customize the appearance of your HTML documents

• Source line numbers: Optionally display original line numbers from the R script in code chunks for traceability

• Code quality integration: Optional integration with styler and lintr to show code formatting suggestions and quality issues in interactive tabsets

• Roxygen2 documentation support: Automatically converts roxygen2 documentation blocks (#') into callout-note sections with function names

• RStudio snippets support: Quick metadata and structure insertion using customizable code snippets

• Special features: Support for Mermaid diagrams, callouts (note, tip, warning, etc.), and tabsets for organizing content

• Output directory customization: Specify custom output directories for book generation (defaults to _book)

• Web deployment ready: Includes quartify_app_web() for deploying on web servers with file upload/download capabilities

Installation

You can install the development version of quartify from GitHub:

# install.packages("devtools")
devtools::install_github("ddotta/quartify")

Usage

🌐 Try the Online Web Application!

No installation required! Use quartify directly in your browser:

→ https://quartify.lab.sspcloud.fr/ ←

The web version allows you to:

• ✅ Upload one or multiple R scripts directly from your computer OR select a directory containing R scripts for batch conversion
• ✅ Create Quarto Books automatically from multiple files with navigation structure
• ✅ Configure conversion options (title, author, theme, book creation, etc.)
• ✅ Download generated .qmd and .html files (or .zip archive for batch mode)
• ✅ No R installation required!

---

Interactive Shiny Interface (for R users)

quartify also provides an interactive Shiny interface that works in any R environment:

Option 1: Standalone App (works in most IDEs)

library(quartify)
quartify_app()  # Opens in your default web browser

This launches a browser-based interface where you can:

• Convert one or multiple files OR an entire directory of R scripts
• Create Quarto Books with automatic navigation (enabled by default for directories)
• Select custom output directory for book generation
• Select input files/directories using a file browser
• Choose output file location for individual files
• Customize document title, author, and theme
• Toggle rendering and display options
• Switch between English/French interface

Perfect for users of Positron, VS Code, or any IDE that supports R!

Option 2: RStudio Add-in

If you use RStudio, you can also access the same interface through:

1. Open your R script in RStudio
2. Go to Addins menu → Quartify → Convert R Script to Quarto
3. A dialog window will appear with the same options as the standalone app
4. Click GENERATE to convert your script

The interface automatically detects your R session language preferences and displays all labels in English or French accordingly. You can change the language at any time using the EN/FR buttons. The output format is always HTML.

Basic example

library(quartify)

# Convert an R script to a Quarto document and render to HTML
rtoqmd("my_script.R", "my_document.qmd")

# Convert only, without rendering to HTML
rtoqmd("my_script.R", "my_document.qmd", render = FALSE)

Customization

# With title and author customization
rtoqmd("my_script.R",
output_file = "my_document.qmd",
title = "My statistical analysis",
author = "Your name",
format = "html",
theme = "cosmo",              # Quarto theme (optional)
render = TRUE,                # Render to HTML
output_html_file = "docs/my_analysis.html",  # Custom HTML location
open_html = TRUE,             # Open HTML in browser
number_sections = TRUE)       # Number sections automatically

Using the example files

Example files are included in the package to test the function:

# Locate the basic example file
example_file <- system.file("examples", "example.R", package = "quartify")

# Convert the example file
rtoqmd(example_file, "test_output.qmd")

# Try the Mermaid diagrams example
mermaid_file <- system.file("examples", "example_mermaid.R", package = "quartify")
rtoqmd(mermaid_file, "test_mermaid.qmd", render = TRUE)

Batch conversion

Using the Web Interface

In quartify_app_web() (or the online app), you can:

1. Select "Batch (multiple files)" mode
2. Choose your input method:

• Upload files: Upload multiple R scripts at once
• Select directory: Browse and select a directory containing your R scripts

1. The app will convert all R scripts and create a downloadable .zip archive

This is perfect for converting entire projects without writing any R code!

Using R Functions

Convert all R scripts in a directory (including subdirectories):

# Convert all R scripts in a directory
rtoqmd_dir("path/to/scripts")

# Convert and render all scripts to custom HTML directory
rtoqmd_dir("path/to/scripts",
render = TRUE,
output_html_dir = "docs/html")

# With custom settings
rtoqmd_dir("path/to/scripts",
author = "Data Team",
exclude_pattern = "test_.*\\.R$")

Code Quality Integration

quartify optionally integrates with styler and lintr to help you improve code quality:

Features

• use_styler: Shows formatted code according to the tidyverse style guide
• use_lintr: Identifies code quality issues and potential problems
• apply_styler: Directly applies styling to your original R script (⚠️ modifies source file)

When quality issues are detected, quartify creates interactive tabsets in the HTML output with:

• Original Code: Your original code
• Styled Code: Formatted version (if use_styler = TRUE and changes detected)
• Lint Issues: Quality warnings (if use_lintr = TRUE and issues found)

Installation

These packages are optional and only needed if you want to use code quality features:

install.packages(c("styler", "lintr"))

Examples

# Show styling suggestions in tabs
rtoqmd("my_script.R", "output.qmd",
use_styler = TRUE)

# Show both styling and lint issues
rtoqmd("my_script.R", "output.qmd",
use_styler = TRUE,
use_lintr = TRUE)

# Apply styling directly to source file (⚠️ modifies original)
rtoqmd("my_script.R", "output.qmd",
apply_styler = TRUE)

In Shiny Apps

All three Shiny applications (rtoqmd_addin(), quartify_app(), and quartify_app_web()) include checkboxes for these options in the interface.

📖 For detailed information, see:

• Code Quality Guide
• Advanced Features Vignette - Complete guide with examples
• Package vignettes

Source R script format

For the conversion to work properly, structure your R script using RStudio code sections:

# Title : Iris Data Analysis
#
# Author : Jane Doe
#
# Date : 2025-11-28
#
# Description : Explore differences between iris species
#

library(dplyr)

## Title 2 ####

### Title 3 ====

# Start of statistical processing
# Counting the number of observations by species

iris |>
count(Species)

### Title 3 ====

# Filter the data.frame on Species "setosa"

iris |>
filter(Species == "setosa")

#### Title 4 ----

# Select column Species

iris %>%
# Select a column
select(Species)

Commenting Rules

0. Document Metadata (Optional)

You can define metadata directly in your R script using special comments at the beginning:

• Title: # Title : My title or # Titre : Mon titre
• Author: # Author : My name or # Auteur : Mon nom
• Date: # Date : YYYY-MM-DD
• Description: # Description : Your description

💡 RStudio Snippet: Use install_quartify_snippets() function to install quartify’s snippets or create a snippet for quick metadata insertion (Tools > Edit Code Snippets > R):

snippet header
# Title : ${1}
#
# Author : ${2}
#
# Date : ${3}
#
# Description : ${4}
#

Type header + Tab in your script to insert the metadata structure.

Behavior:

• Metadata found in script overrides function parameters
• Metadata lines are removed from document body (only in YAML)
• If no metadata in script, function parameters are used

📝 Note: The Description field can span multiple lines. To continue the description, start the next line with # followed by at least one space. Continuation lines are automatically concatenated. Example:

# Description : This analysis explores differences between iris species
# using various statistical methods and visualization techniques
# to identify patterns and correlations.

quartify recognizes three types of lines in your R script:

1. Code Sections (Headers)

RStudio code sections become markdown headers. Critical: trailing symbols must be at least 4 characters long:

• ## Title ---- → Level 2 header (at least 4 #, = or - at the end)
• ### Title ---- → Level 3 header (at least 4 #, = or - at the end)
• #### Title ---- → Level 4 header (at least 4 #, = or - at the end)

Note: You can use #, =, or - interchangeably as trailing symbols (e.g., ## Title ==== or ### Title ---- will work).

2. Regular Comments (Text)

Single # comments at the start of a line (no leading space) become explanatory text:

# This is a standalone comment
# It becomes plain text in the Quarto document

⚠️ Important: For a comment to be converted to text, the line must start with # without any leading space. Indented comments (with spaces before #) remain in the code.

💡 Tip: To split a long chunk into multiple parts, insert a comment at the start of a line (no space before #) between two code blocks. This comment will be converted to text and naturally create two separate chunks.

Tip: Use RStudio’s Comment/Uncomment shortcut (Ctrl+Shift+C on Windows/Linux or Cmd+Shift+C on Mac) to quickly add or remove comments.

3. Code Lines

Uncommented lines become executable R code chunks:

iris |> filter(Species == "setosa")

4. Inline Comments (Within Code)

Comments within code blocks are preserved inside the R code chunk:

iris %>%
# This comment stays in the code block
select(Species)

5. Callouts

Callouts are special blocks that highlight important information. Five types are supported: note, tip, warning, caution, important.

Syntax in R script:

# callout-note - Important Note
# This is the content of the callout.
# It can span multiple lines.

# Empty line or code ends the callout
x <- 1

Converts to Quarto:

::: {.callout-note title="Important Note"}
This is the content of the callout.
It can span multiple lines.
:::

Without title:

# callout-tip
# This is a tip without a title.

Callouts end when encountering an empty line, code, or another section.

6. Mermaid Diagrams

Create flowcharts, sequence diagrams, and other visualizations using Mermaid syntax.

Syntax in R script:

#| mermaid
#| eval: true
flowchart TD
A[Start] --> B[Process]
B --> C{Decision}
C -->|Yes| D[End]
C -->|No| B

Converts to Quarto Mermaid chunk with proper formatting for diagram rendering in HTML output.

Supported diagram types:

• Flowcharts (flowchart TD, flowchart LR)
• Sequence diagrams (sequenceDiagram)
• Class diagrams (classDiagram)
• State diagrams, Gantt charts, and more

Rules:

• Start with #| mermaid comment
• Add chunk options with #| (e.g., #| eval: true)
• Diagram content follows without # prefix
• Chunk ends at first blank line or comment

See complete example in inst/examples/example_mermaid.R

7. Tabsets

Organize related content in interactive tabs for better presentation and navigation.

Syntax in R script:

# tabset

# tab - Summary
# Display summary statistics

summary(data)

# tab - Structure
# Show data structure

str(data)

# tab - Preview
# First rows of data

head(data)

Converts to Quarto tabset with interactive tabs in HTML output.

Rules:

• Start with # tabset comment to begin the tabset container
• Define each tab with # tab - Tab Title
• Add content (comments and code) after each tab declaration
• Tabset closes automatically at next section or end of file
• Tabs can contain text, code chunks, and any other content

See complete example in inst/examples/example_tabset.R

Important rules:

• Always include a space after # for comments
• Section headers MUST have at least 4 trailing symbols
• Standalone comments with # at line start → become text outside code blocks
• Inline comments within code → stay inside code blocks
• Callouts → # callout-TYPE or # callout-TYPE - Title
• Mermaid diagrams → #| mermaid followed by options and diagram content
• Tabsets → # tabset then # tab - Title for each tab
• Consecutive code lines are grouped in the same block
• Empty lines between blocks are ignored

This follows the RStudio code sections convention which provides proper indentation in the RStudio document outline navigation.

Quarto Themes

Customize the appearance of your HTML documents with Quarto themes. The package supports all available Bootswatch themes:

Light themes: cosmo, flatly, journal, litera, lumen, lux, materia, minty, morph, pulse, quartz, sandstone, simplex, sketchy, spacelab, united, vapor, yeti, zephyr

Dark themes: darkly, cyborg, slate, solar, superhero

Example:

# Use the "flatly" theme
rtoqmd("my_script.R", theme = "flatly")

# Use the dark "darkly" theme
rtoqmd("my_script.R", theme = "darkly")

For more information about themes, see the Quarto documentation.

Output and documentation

The generated .qmd document contains:

• A complete YAML header with table of contents configuration
• Properly structured headers from RStudio code sections
• Textual explanations from your comments
• Non-executable code chunks for static documentation

📝 For a complete example of the generated output, see the Getting Started vignette

CI/CD Integration

Use quartify in your CI/CD pipelines to automatically generate documentation:

GitHub Actions (.github/workflows/generate-docs.yml):

- name: Generate documentation
run: |
library(quartify)
rtoqmd_dir("scripts/", render = TRUE, author = "Data Team")
shell: Rscript {0}

- uses: actions/upload-artifact@v4
with:
name: documentation
path: |
scripts/**/*.qmd
scripts/**/*.html

GitLab CI (.gitlab-ci.yml):

generate-docs:
image: ddottaagr/quartify:latest
script:
- R -e "quartify::rtoqmd_dir('scripts/', render = TRUE, author = 'Data Team')"
artifacts:
paths:
- scripts/**/*.qmd
- scripts/**/*.html

📘 Full CI/CD guide with complete examples: CI/CD Integration

License

MIT