Quarto Reports - Usage Guide

Quarto Reports - Usage Guide

Overview

Quarto is a next-generation scientific publishing system supporting R, Python, Julia, and Observable with enhanced features over R Markdown.

Prerequisites

# Install Quarto - download from https://quarto.org/docs/download/
quarto check

# For Python
pip install jupyter matplotlib

# For R
install.packages(c('knitr', 'rmarkdown'))

Quick Start

Tell your AI agent what you want to do:

• "Create a Quarto document for my Python analysis"
• "Set up a multi-format report (HTML + PDF)"
• "Build a Quarto website for my project"

Example Prompts

Basic Documents

"Create a Quarto document for my scRNA-seq analysis in Python"

"Convert my Jupyter notebook to a Quarto report with better formatting"

Multi-Format Output

"Set up a Quarto project that renders to both HTML and PDF"

"Create a report that generates Word output for my collaborators"

Interactive Features

"Add interactive Plotly figures to my Quarto document"

"Create a Quarto dashboard with tabsets for different samples"

Projects and Websites

"Set up a Quarto website to document my analysis pipeline"

"Create a Quarto book for my lab protocol collection"

What the Agent Will Do

1. Create a .qmd file with appropriate YAML front matter
2. Configure code cell options using #| syntax
3. Set up cross-references for figures, tables, and equations
4. Configure rendering for your target output formats
5. Add callouts, tabs, or other layout features as needed

Quarto vs R Markdown

Tips

• Use #| freeze: true to cache computed outputs across renders
• Cross-references use @fig-label syntax for figures and @tbl-label for tables
• Use callout blocks (note, warning, tip) for important information
• Set execute: freeze: auto in _quarto.yml for project-wide caching
• Preview live changes with quarto preview during development

Related Skills

• reporting/rmarkdown-reports - R Markdown for R-only workflows
• data-visualization/ggplot2-fundamentals - Creating visualizations for reports