D3: Publication-Quality Figures

Practical workshop — bring your laptop

Friday Workshop — ~50 minutes. Bring your laptop with R and ggplot2 installed. We’ll work through real examples together. If you have a figure from your own research that you’d like to improve, bring it.

Why This Matters

A figure in a published paper is the first thing a reader actually looks at. Before they read the abstract, before they check the methods — they look at the figures. A clear, well-made figure communicates at a glance. A cluttered or confusing figure makes referees skeptical before they’ve read a word.

Most default ggplot2 output is not publication-ready. This session is about closing that gap.

What We’ll Cover

Typography and layout - base_size in themes — stop using the default - element_text() for font control - Axis label sizing and angle - plot.title, plot.subtitle, plot.caption — when to use each

Color - Avoid default ggplot2 colors in publication (they don’t print well in grayscale) - Colorblind-safe palettes: scale_color_brewer(), scale_color_viridis_d() - When to use color vs. shape vs. linetype - Manual palettes: scale_color_manual()

Composing multi-panel figures - patchwork package: p1 + p2, p1 / p2, (p1 | p2) / p3 - Adding panel labels (A, B, C): plot_annotation(tag_levels = "A") - Shared legends: plot_layout(guides = "collect")

Exporting - ggsave() — resolution, dimensions, file format - PDF vs PNG: when to use each - Fonts in exported PDFs (the cairo_pdf device)

Workshop Exercises

# 1. Start with this (bad figure) and make it publication-ready
library(ggplot2)
ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point()

# 2. Make a two-panel figure with shared color legend
# Panel A: Sepal.Length vs Petal.Length
# Panel B: distribution of Sepal.Length by species
# Hint: install.packages("patchwork")

Key Packages

install.packages(c("ggplot2", "patchwork", "ggrepel", "scales"))

patchwork — composing multi-panel layouts
ggrepel — non-overlapping text labels (geom_label_repel())
scales — axis formatting (comma, percent, scientific)

Resources

ggplot2 book (free online) — Hadley Wickham
Fundamentals of Data Visualization (free) — Claus Wilke. Best reference for what to visualize, not just how.
patchwork documentation
ColorBrewer palettes — browser tool for choosing safe color palettes

Tip

Bring a figure you’ve already made — from your research, from a homework, from anywhere. We’ll spend the last 15 minutes making targeted improvements to real figures from the room.

--- title: "D3: Publication-Quality Figures" subtitle: "Practical workshop — bring your laptop" --- ::: {.week-header} **Friday Workshop — ~50 minutes.** Bring your laptop with R and ggplot2 installed. We'll work through real examples together. If you have a figure from your own research that you'd like to improve, bring it. ::: --- ## Why This Matters A figure in a published paper is the first thing a reader actually looks at. Before they read the abstract, before they check the methods — they look at the figures. A clear, well-made figure communicates at a glance. A cluttered or confusing figure makes referees skeptical before they've read a word. Most default ggplot2 output is not publication-ready. This session is about closing that gap. --- ## What We'll Cover **Typography and layout** - `base_size` in themes — stop using the default - `element_text()` for font control - Axis label sizing and angle - `plot.title`, `plot.subtitle`, `plot.caption` — when to use each **Color** - Avoid default ggplot2 colors in publication (they don't print well in grayscale) - Colorblind-safe palettes: `scale_color_brewer()`, `scale_color_viridis_d()` - When to use color vs. shape vs. linetype - Manual palettes: `scale_color_manual()` **Composing multi-panel figures** - `patchwork` package: `p1 + p2`, `p1 / p2`, `(p1 | p2) / p3` - Adding panel labels (A, B, C): `plot_annotation(tag_levels = "A")` - Shared legends: `plot_layout(guides = "collect")` **Exporting** - `ggsave()` — resolution, dimensions, file format - PDF vs PNG: when to use each - Fonts in exported PDFs (the `cairo_pdf` device) --- ## Workshop Exercises ```r # 1. Start with this (bad figure) and make it publication-ready library(ggplot2) ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) + geom_point() # 2. Make a two-panel figure with shared color legend # Panel A: Sepal.Length vs Petal.Length # Panel B: distribution of Sepal.Length by species # Hint: install.packages("patchwork") ``` --- ## Key Packages ```r install.packages(c("ggplot2", "patchwork", "ggrepel", "scales")) ``` - `patchwork` — composing multi-panel layouts - `ggrepel` — non-overlapping text labels (`geom_label_repel()`) - `scales` — axis formatting (`comma`, `percent`, `scientific`) --- ## Resources - [ggplot2 book (free online)](https://ggplot2-book.org) — Hadley Wickham - [Fundamentals of Data Visualization (free)](https://clauswilke.com/dataviz/) — Claus Wilke. Best reference for *what* to visualize, not just *how*. - [patchwork documentation](https://patchwork.data-imaginist.com) - [ColorBrewer palettes](https://colorbrewer2.org) — browser tool for choosing safe color palettes --- ::: {.callout-tip} Bring a figure you've already made — from your research, from a homework, from anywhere. We'll spend the last 15 minutes making targeted improvements to real figures from the room. :::