tools-only ggplot2 Fundamentals - Usage Guide
Overview
ggplot2 is a declarative visualization system based on the Grammar of Graphics. Build publication-quality figures layer by layer.
Prerequisites
install.packages(c('ggplot2', 'patchwork', 'ggrepel', 'scales', 'RColorBrewer', 'viridis'))
Quick Start
Tell your AI agent what you want to do:
- "Create a volcano plot with labeled significant genes"
- "Make a multi-panel figure with panels A, B, C"
- "Set up a consistent theme for all my figures"
Example Prompts
Basic Plots
"Create a scatter plot of gene expression vs significance"
"Make a bar chart showing sample counts by condition"
Customization
"Apply a Nature-style theme to my plot"
"Add a regression line with confidence interval"
Publication Export
"Export my figure at 300 DPI for journal submission"
"Save as vector PDF for publication"
What the Agent Will Do
- Define data mapping with aesthetics (x, y, color, size)
- Add appropriate geoms (points, lines, bars)
- Customize scales, labels, and theme
- Save at publication-quality resolution
Grammar of Graphics
| Component | Description |
|---|---|
| Data | The dataset to visualize |
| Aesthetics | Mappings (x, y, color, size) |
| Geoms | Visual elements (points, lines, bars) |
| Scales | How data maps to aesthetics |
| Facets | Small multiples |
| Theme | Visual styling |
Tips
- Always include axis labels with units
- Use colorblind-friendly palettes (viridis)
- Export at 300+ DPI for publication
- Use vector format (PDF) when possible
- Keep font size readable (8pt minimum)
Related Skills
- differential-expression/de-visualization - Specialized plots
- reporting/rmarkdown-reports - Embed in reports