Small Multiples: Comparing Subsets with Facets
Small multiples, also known as facets, are a powerful way to compare patterns across groups by repeating the same chart for different subsets of your data. Instead of squeezing everything into a single crowded panel, you can lay out a grid of consistent plots that make similarities and differences easier to spot at a glance.
In this lesson, you’ll learn how to use faceting to break data into meaningful subsets, control the layout and scales of your panels, and decide when small multiples are the right tool for your story.
🧩 Many Charts for One Question
You've probably seen charts where the same chart type repeats in a tidy grid, each panel showing data for a different group or time period.
This approach — splitting one crowded plot into many consistent panels so you can compare patterns side-by-side — is called small multiples . Data viz practitioners, from analytic labs to newsrooms, love them for comparing economic trends, election results, or sports stats as readers instantly grasp variation without hunting through legends or toggling filters.
Their magic lies in repetition: by keeping axes, colors, and scales identical across panels, differences between groups pop out immediately. Instead of squeezing everything into one noisy panel, small multiples give each group breathing room while letting your audience scan the full picture at once.
Edward Tufte popularized the term small multiples in his 1990 book “Envisioning Information.” However, the concept of using multiple similar charts to compare data across categories has been around for much longer.
Tufte stated that the most important question for quantitative reasoning is “Compared to what?”, concluding that:
“For a wide range of problems in data presentation, small multiples are the best design solution.”
He argued that showing many small, consistent charts enforces comparisons and allows viewers to see patterns and trends across categories without the clutter of a single, complex graph.
Small multiples also solve the qualitative color conundrum from our “Color Choice” lesson when dealing with many categories: Instead of straining to match dots to an 8+ color legend, each category gets its own panel, making it easy to spot differences without needing color at all.
ggplot2 for the first time — the start of a 💙 story.And here's the best part: ggplot2 makes small multiples incredibly easy. Just one function call transforms your single crowded plot into a clean grid of comparable panels, one for each group or combination.
🧱 Pick Your Favorite Facet
In ggplot2, there are two ways to create small multiples: facet_wrap() and facet_grid(). Both split data by categorical variables into separate panels, but they arrange and handle your splits differently.
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