Annotations & Callouts: Guiding the Viewer with Additional Insights
Sometimes the story lives in a single point, line, or trend — and annotations help you draw attention to it. They turn raw data into clear communication.
You’ll explore annotate(), geom_text(), and geom_label() for direct text notes, along with arrows, boxes, and shapes to call out important findings. Learn to emphasize without overwhelming.
Before You Start 🧰
We’re using custom fonts in this lesson to make these plots look their best. To see everything correctly in the sandbox, please install Rethink Sans and Domine on your system! Then install the font files like any other system font.
🗒️ Sticky Notes
Once your plot is in place, annotations are where the storytelling truly begins. They help you point out what’s interesting, surprising, or worth a second look. A tiny note, an arrow, or a gentle highlight can turn a confusing pattern into an “Aha!” moment.
Think of annotations as your guiding voice inside the visualization, helping lead viewers exactly where you want them to look. They work best when they feel subtle, intentional, and neatly integrated into the visual flow.
In this lesson, we’ll focus on annotation techniques built into ggplot2 that allow you to add geometries like text, boxes, or arrows to you plot. We’ll put them right to work on our Simpsons heatmap from the previous lesson on titles and captions— a perfect playground for adding little insights and big clarifications.
💬 Let the Data Talk
When annotating data points, the respective geoms geom_text() and geom_label() are the way to go. They behave like any other layer: you pass data, map aesthetics, and decide where the text should appear. You can label all values or highlight only selected values to label only those that matter most..