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geom_tile in ggplot2: Why Are My Tiles Uneven?

geom_tile with facet_grid can create uneven tiles with sparse data. Learn how to adjust height and width settings in ggplot2 v4.0.1.

byDev Solutions

January 20, 2026

Thumbnail showing uneven vs fixed tiles in a ggplot2 heatmap using facet_grid, with a red X and green check mark

Tile distortion in ggplot2 plots happens when axis scales or panel sizes are not equal.
Just using geom_tile with fixed width/height does not make all tiles the same size across panels.
Using facet_grid with scales = "fixed" lines up axis scales for tiles to look the same.
Adding complete() to sparse panels stops tiles from stretching if data points are missing.
Using geom_raster makes plots draw faster and makes sure tiles line up evenly.

Understanding and Fixing Uneven Tiles in Faceted Heatmaps Using `geom_tile()` and `facet_grid()` in `ggplot2`

You use geom_tile() with facet_grid() in ggplot2 to make a heatmap. You want a neat, even grid. But sometimes, one panel has tall tiles, and another has short, squashed ones. This difference in tile size is common. It can be annoying, especially when your data changes a lot from group to group. Luckily, if you know how ggplot2 works with panels and tiles, you can find and fix these problems. This guide will explain why this happens. It will also give you ways to make your heatmaps look neat and clear across all panels.

A Look at `geom_tile()` and `facet_grid()` Basics

geom_tile() is a common way to make heatmaps in ggplot2. This function makes rectangles, or "tiles." Their spots come from x and y settings. The fill color usually shows a third number, like how strong something is.

library(ggplot2)

df <- data.frame(
  x = rep(1:5, 2),
  y = c(1:5, 1:5),
  z = runif(10),
  group = rep(c("A", "B"), each = 5)
)

ggplot(df, aes(x, y, fill = z)) +
  geom_tile() +
  facet_grid(~group)

This geom_tile() part draws rectangles for each (x, y) pair. And facet_grid(~group) splits the data into different panels by the group variable.

In ggplot2, facet_grid() gives each panel its own coordinate system and layout. This means each panel's x and y ranges are worked out from the data inside it. If these ranges are different between panels, the tiles might look stretched or squashed.

The Real Problem: Panels That Don't Match Because of Sparse Data

When your data isn't even across groups (for example, one group has five y values and another only has two), ggplot2 will still fill the vertical space in sparser panels. It does this by stretching tiles along the y axis. This makes the tiles look wrong because fewer values are stretched to fill the same space as more values.

Here is an example that shows the problem:

library(dplyr)

df <- data.frame(
  x = rep(1:5, times = c(5, 2)),
  y = c(1:5, 1:2),
  z = runif(7),
  group = c(rep("Full", 5), rep("Sparse", 2))
)

ggplot(df, aes(x, y, fill = z)) +
  geom_tile() +
  facet_grid(~group)

The "Full" group looks right here. But the "Sparse" group looks too long. This is because the "Sparse" panel only has two y-values. Yet, ggplot2 stretches them up and down to fill the same panel height as the five y-values in "Full".

Fix #1: Set Width and Height of Tiles by Hand

One simple way is to give each tile a set size. Do this by setting the width and height in geom_tile(). This changes how big each tile is:

ggplot(df, aes(x, y, fill = z)) +
  geom_tile(width = 0.9, height = 0.9) +
  facet_grid(~group)

This can stop tiles from filling all the space. They now have a set size. But this is not a perfect fix. If panel axes still have different ranges, tiles might still look out of proportion. This is because ggplot2 does not change the data coordinates. It just draws smaller tiles in the space it has.

⚠️ Note: A set tile size does not mean set panel scales. The background coordinates can still stretch or squeeze tiles if panels differ.

Fix #2: Make Panel Axis Ranges Standard Using `scales = "fixed"`

To make tiles look even and the same size across all panels, you often need to lock the axes. By default, facet_grid() uses scales = "fixed". This makes all panels share the same axis limits. Make sure to put this in your code. Also, use it with fixed tile sizes for the best outcome:

ggplot(df, aes(x, y, fill = z)) +
  geom_tile(width = 0.9, height = 0.9) +
  facet_grid(~group, scales = "fixed")

This makes sure each panel covers the same x and y ranges. This fixes most problems with distortion, especially when data for each group is not very different.

🧠 Pro Tip: Fixed scales work well when your data groups have similar ranges. They also work when you are showing measurements in similar situations.

Fix #3: Fill Gaps in Sparse Panels Using `complete()` from `tidyr`

Sometimes, your data has groups with missing x, y pairs. This makes panels look "short" in one or both directions. You can fill in these missing pairs with NA using the complete() function from the tidyr package. This makes the panel layout even across all panels.

library(tidyr)

df_complete <- df %>% complete(group, x, y)

ggplot(df_complete, aes(x, y, fill = z)) +
  geom_tile(width = 0.9, height = 0.9) +
  facet_grid(~group)

This makes the dataset a full grid. It keeps the spacing the same, even if some cells are empty (have NA in them). It makes sure each panel has the same grid structure inside.

💡 Why It Works: With complete(), every group now takes up a grid of the same size. This is true even if some data points are missing. It keeps the visual shape correct.

Fix #4: Use `theme()` Layout Controls to Change Proportions

Sometimes, even after changing tile size and panel scales, layout problems remain. You can use the theme() function to make finer changes to the plot area:

ggplot(df, aes(x, y, fill = z)) +
  geom_tile(width = 0.9, height = 0.9) +
  facet_grid(~group) +
  theme(
    aspect.ratio = 1,
    panel.spacing = unit(0.5, "lines"),
    plot.margin = margin(10, 10, 10, 10)
  )

aspect.ratio = 1 makes the y-axis use the same scale per unit as the x-axis.
panel.spacing gives more space between panels.
plot.margin helps you control the white space better.

This is extra helpful for detailed plots where you want perfect look for talks or papers.

Stay Updated: `ggplot2` Version Improvements

facet_grid() and geom_tile() have changed in different ggplot2 versions. Starting with version 3.4.0, spacing across panels became more stable. Newer versions, like 4.0.1, handle spacing better, even with empty panels or missing factors.

install.packages("ggplot2")  # Get the newest features

Make sure your packages are current if your tiles are still not lined up. An update could fix problems you are trying to fix by hand.

Different Drawing: Use `geom_raster()` for Even Grids

If x and y are whole numbers (or spaced the same way), geom_raster() works better than geom_tile(). It lines up tiles right to the plot grid. This makes heatmaps faster to draw and look even.

ggplot(df_complete, aes(x, y, fill = z)) +
  geom_raster() +
  facet_grid(~group)

geom_raster() draws pixels fast. This is good for heatmaps with a lot of data and plots that look like matrices. But it needs a grid that is spaced evenly. Missing values and uneven gaps can make the plot look wrong.

🚨 Caution: geom_raster() does not draw tiles for NA values. Get your data ready for this.

When Not to Use Panels: Use Color for Grouping Instead

Too many panels can make a plot hard to read. This is true when group differences are small or data is sparse. A different way is to use color or fill to show which group is which:

ggplot(df, aes(x, y, fill = z, color = group)) +
  geom_tile(width = 0.9, height = 0.9)

This puts all your data into one panel. It keeps the structure but still shows groups apart. Think about this if you have little space or if separating groups is not the main point of your work.

🔎 Use Case: This is good for exploring data. You can look for patterns across groups instead of showing each group alone.

Fix Problems Smarter: Tools and Checks When Tiles Don't Work

When fixing layout problems in complex plots:

🧪 Use ggplot_build() to check scales and layout details inside:

p <- ggplot(df, aes(x, y, fill = z)) + geom_tile() + facet_grid(~group)
str(ggplot_build(p))

✅ Make sure your x and y ranges are the same across panels.
🧱 Look for sparse data that might need complete() to fill gaps.
🔄 Compare scales. Lock them with scales = "fixed".

Most times, strange-looking tiles are not a plot error. They just mean your layout ideas might need changing.

Myth-Busting: `ggplot2` Isn't Broken—You Just Need to Guide It

People often blame ggplot2 bugs for heatmap distortion. But these problems really come from how flexible the system is. ggplot2 works as it should. But it needs clear rules to make the plot you want. If you give it unclear spacing, you will get unclear results.

This is about how plots look and work. When you give ggplot2 clear axes, tile sizes, and padding, it gives you neat, easy-to-read plots.

Your Heatmap Design Checklist

Before you draw your final faceted heatmap, always think about these points:

✅ Are your x/y ranges the same across all panels?
✅ Are you using scales = "fixed" to make them uniform?
✅ Have you set width and height by hand so tiles don't get too big?
✅ Did you fill in missing combinations with tidyr::complete()?
✅ Are you using the newest ggplot2 version for stable layouts?

Doing these good practices will greatly cut down the time you spend fixing problems. It will also keep your plots looking the same and ready for showing.

Bringing It All Together

Faceted heatmaps in ggplot2 are a strong way to compare patterns between groups. But this works only if tiles line up correctly across panels. Uneven tiles are a problem you can fix. They should not stop you from using heatmaps or panels in your plots. The main points are to know how geom_tile() and facet_grid() work together, to make scales fixed, and to set up your data in neat, even ways.

Using complete() to fill gaps, setting fixed scales, and moving to geom_raster() for speed and alignment, these ways will help you make clear, lined-up, and good-looking heatmaps.