Smart Label Placement: Minimizing Effort for Clear Annotations
Well-placed labels guide the eye, instantly clarifying patterns and reducing the mental effort required to understand a plot. But manually positioning them often involves tedious fine-tuning to avoid overlaps, highlight certain groups, or craft callouts that look professional.
In this lesson, you’ll learn to automate label placement using smart tools that handle the heavy lifting for you. You’ll master techniques for annotating points, lines, and groups — ensuring your callouts remain readable, organized, and professional with far less effort.
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.
🏭 More Flexible, More Efficient
Callouts and direct annotations are often the last thing we add and often the detail that requires the most effort to fine-tune .
Overlapping or cut-off labels, tedious manual additions of shapes, and endless justification, nudging, and line-break tweaks can cause frustration and quickly eat up a lot of time.
As a result, we may decide to skip this important step altogether due to time limitations — but it usually comes at a cost. Without guidance, viewers are left to guess what matters most and why it is important.
This is when more flexible and efficient approaches become essential. We’ll focus on tools that handle spacing, alignment, and styling automatically — so annotations stay readable without constant fine-tuning, making not only your coding work more efficient but also your charts look nicer 💪
🧲 Positioning Pointers
We’ll start where things usually go wrong: overlapping labels.
Scatterplots and other point-based charts are a common place where labels start to clash. With many categories or densely packed points, text often overlaps, gets cut off, or requires endless manual nudging and justification — making annotations frustratingly tedious to add.
This is where smarter direct-labeling approaches come in handy. They automatically adjust positions so labels stay readable and clearly associated with their points, even as the data or aesthetics change.
🧰 The ggrepel Package
We’ll start with the {ggrepel} package, which moves text out of each other’s way while keeping a clear visual link to the corresponding points. It’s perfect for tackling crowded scatterplots efficiently!
Let’s explore this with a first example. In the scatter plot below, showing in 2023, direct labeling is clearly the preferred approach given the large number of categories encoded — but some labels overlap, and one even gets cut off on the right. Justification and nudges are already applied, but placing everything cleanly without overlaps would require tedious extra steps like defining the positions of each label manually...