How to use geom_tile() in R

ggplot2

ggplot2 geom_tile()

Learn how to use geom_tile() in R with practical examples. Step-by-step guide with code you can copy and run immediately.

Published

February 21, 2026

Introduction

The geom_tile() function in ggplot2 creates rectangular tiles or heatmaps by plotting filled rectangles at specified x and y coordinates. This visualization is particularly useful for displaying correlations, creating calendar heatmaps, or showing relationships between categorical variables through color intensity.

Getting Started

library(tidyverse)
library(palmerpenguins)

Example 1: Basic Usage

The Problem

We want to create a simple heatmap showing the relationship between two categorical variables. Let’s visualize how penguin species are distributed across different islands using tile colors.

Step 1: Prepare the data

We need to count the number of penguins by species and island to create our tile data.

penguin_counts <- penguins |>
  filter(!is.na(species), !is.na(island)) |>
  count(species, island, name = "count")

penguin_counts

This creates a summary table with species, island, and the count of penguins for each combination.

Step 2: Create basic tiles

Now we’ll create our first heatmap using geom_tile() with the count values determining the fill color.

ggplot(penguin_counts, aes(x = island, y = species)) +
  geom_tile(aes(fill = count)) +
  labs(title = "Penguin Distribution by Species and Island")

The tiles show darker colors for higher penguin counts, creating an immediate visual representation of the distribution patterns.

Step 3: Improve the appearance

Let’s enhance the visualization with better colors and formatting.

ggplot(penguin_counts, aes(x = island, y = species)) +
  geom_tile(aes(fill = count), color = "white", linewidth = 0.5) +
  scale_fill_gradient(low = "lightblue", high = "darkblue") +
  labs(title = "Penguin Distribution by Species and Island",
       x = "Island", y = "Species", fill = "Count") +
  theme_minimal()

Heatmap in R using geom_tile() in ggplot2 showing penguin distribution by species and island with blue fill gradient

The white borders and improved color scheme make the tiles more distinct and visually appealing.

Example 2: Practical Application

The Problem

We want to create a correlation heatmap to understand relationships between numeric variables in our dataset. This is commonly used in data analysis to identify which variables are strongly related to each other.

Step 1: Calculate correlations

First, we’ll compute correlations between numeric penguin measurements.

penguin_cor <- penguins |>
  select(bill_length_mm, bill_depth_mm, 
         flipper_length_mm, body_mass_g) |>
  na.omit() |>
  cor()

This creates a correlation matrix showing how each numeric variable relates to the others.

Step 2: Convert to long format

We need to reshape the correlation matrix for use with ggplot2.

cor_data <- penguin_cor |>
  as.data.frame() |>
  rownames_to_column("var1") |>
  pivot_longer(-var1, names_to = "var2", values_to = "correlation")

The long format allows ggplot2 to properly map variables to x and y aesthetics.

Step 3: Create correlation heatmap

Now we’ll build a professional-looking correlation heatmap.

ggplot(cor_data, aes(x = var1, y = var2)) +
  geom_tile(aes(fill = correlation)) +
  scale_fill_gradient2(low = "red", mid = "white", high = "blue",
                       midpoint = 0) +
  theme_minimal()

The diverging color scale helps identify positive (blue) and negative (red) correlations clearly.

Step 4: Add correlation values

Let’s include the actual correlation values on each tile for precise reading.

ggplot(cor_data, aes(x = var1, y = var2)) +
  geom_tile(aes(fill = correlation)) +
  geom_text(aes(label = round(correlation, 2)), color = "black") +
  scale_fill_gradient2(low = "red", mid = "white", high = "blue",
                       midpoint = 0)

The text labels provide exact correlation values while maintaining the visual impact of the color coding.

Step 5: Polish the final visualization

Finally, let’s improve the labels and overall appearance.

ggplot(cor_data, aes(x = var1, y = var2)) +
  geom_tile(aes(fill = correlation), color = "gray") +
  geom_text(aes(label = round(correlation, 2))) +
  scale_fill_gradient2(low = "red", mid = "white", high = "blue", midpoint = 0) +
  labs(title = "Penguin Measurements Correlation Heatmap",
       x = "", y = "", fill = "Correlation") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Correlation heatmap in R using geom_tile() and geom_text() in ggplot2 showing pairwise correlations between penguin bill, flipper, and body mass measurements

The rotated x-axis labels prevent overlapping and the clean theme creates a professional appearance.

Summary

geom_tile() creates rectangular heatmaps perfect for displaying relationships between categorical or continuous variables
Always prepare your data in the correct format with x, y, and fill variables clearly defined
Use scale_fill_gradient() for single-direction color schemes and scale_fill_gradient2() for diverging scales
Add geom_text() to display exact values on tiles when precision is important
White or gray borders between tiles improve readability and visual separation

--- title: "How to use geom_tile() in R" description: "Learn how to use geom_tile() in R with practical examples. Step-by-step guide with code you can copy and run immediately." date: 2026-02-21 categories: ['ggplot2', 'ggplot2 geom_tile()'] format: html: code-fold: false code-tools: true --- ## Introduction The `geom_tile()` function in ggplot2 creates rectangular tiles or heatmaps by plotting filled rectangles at specified x and y coordinates. This visualization is particularly useful for displaying correlations, creating calendar heatmaps, or showing relationships between categorical variables through color intensity. ## Getting Started ```r library(tidyverse) library(palmerpenguins) ``` ## Example 1: Basic Usage ### The Problem We want to create a simple heatmap showing the relationship between two categorical variables. Let's visualize how penguin species are distributed across different islands using tile colors. ### Step 1: Prepare the data We need to count the number of penguins by species and island to create our tile data. ```r penguin_counts <- penguins |> filter(!is.na(species), !is.na(island)) |> count(species, island, name = "count") penguin_counts ``` This creates a summary table with species, island, and the count of penguins for each combination. ### Step 2: Create basic tiles Now we'll create our first heatmap using `geom_tile()` with the count values determining the fill color. ```r ggplot(penguin_counts, aes(x = island, y = species)) + geom_tile(aes(fill = count)) + labs(title = "Penguin Distribution by Species and Island") ``` The tiles show darker colors for higher penguin counts, creating an immediate visual representation of the distribution patterns. ### Step 3: Improve the appearance Let's enhance the visualization with better colors and formatting. ```r ggplot(penguin_counts, aes(x = island, y = species)) + geom_tile(aes(fill = count), color = "white", linewidth = 0.5) + scale_fill_gradient(low = "lightblue", high = "darkblue") + labs(title = "Penguin Distribution by Species and Island", x = "Island", y = "Species", fill = "Count") + theme_minimal() ``` ![Heatmap in R using geom_tile() in ggplot2 showing penguin distribution by species and island with blue fill gradient](/images/ggplot2/geom-tile-in-r-penguin-counts-heatmap-ggplot.png) The white borders and improved color scheme make the tiles more distinct and visually appealing. ## Example 2: Practical Application ### The Problem We want to create a correlation heatmap to understand relationships between numeric variables in our dataset. This is commonly used in data analysis to identify which variables are strongly related to each other. ### Step 1: Calculate correlations First, we'll compute correlations between numeric penguin measurements. ```r penguin_cor <- penguins |> select(bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g) |> na.omit() |> cor() ``` This creates a correlation matrix showing how each numeric variable relates to the others. ### Step 2: Convert to long format We need to reshape the correlation matrix for use with ggplot2. ```r cor_data <- penguin_cor |> as.data.frame() |> rownames_to_column("var1") |> pivot_longer(-var1, names_to = "var2", values_to = "correlation") ``` The long format allows ggplot2 to properly map variables to x and y aesthetics. ### Step 3: Create correlation heatmap Now we'll build a professional-looking correlation heatmap. ```r ggplot(cor_data, aes(x = var1, y = var2)) + geom_tile(aes(fill = correlation)) + scale_fill_gradient2(low = "red", mid = "white", high = "blue", midpoint = 0) + theme_minimal() ``` The diverging color scale helps identify positive (blue) and negative (red) correlations clearly. ### Step 4: Add correlation values Let's include the actual correlation values on each tile for precise reading. ```r ggplot(cor_data, aes(x = var1, y = var2)) + geom_tile(aes(fill = correlation)) + geom_text(aes(label = round(correlation, 2)), color = "black") + scale_fill_gradient2(low = "red", mid = "white", high = "blue", midpoint = 0) ``` The text labels provide exact correlation values while maintaining the visual impact of the color coding. ### Step 5: Polish the final visualization Finally, let's improve the labels and overall appearance. ```r ggplot(cor_data, aes(x = var1, y = var2)) + geom_tile(aes(fill = correlation), color = "gray") + geom_text(aes(label = round(correlation, 2))) + scale_fill_gradient2(low = "red", mid = "white", high = "blue", midpoint = 0) + labs(title = "Penguin Measurements Correlation Heatmap", x = "", y = "", fill = "Correlation") + theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) ``` ![Correlation heatmap in R using geom_tile() and geom_text() in ggplot2 showing pairwise correlations between penguin bill, flipper, and body mass measurements](/images/ggplot2/geom-tile-in-r-correlation-heatmap-with-values-ggplot.png) The rotated x-axis labels prevent overlapping and the clean theme creates a professional appearance. ## Summary - `geom_tile()` creates rectangular heatmaps perfect for displaying relationships between categorical or continuous variables - Always prepare your data in the correct format with x, y, and fill variables clearly defined - Use `scale_fill_gradient()` for single-direction color schemes and `scale_fill_gradient2()` for diverging scales - Add [`geom_text()`](/ggplot2/how-to-use-geomtext-in-r.html) to display exact values on tiles when precision is important - White or gray borders between tiles improve readability and visual separation --- ## Related Posts - [How to use geom_point() in R](/ggplot2/how-to-use-geompoint-in-r.html) - [How to use geom_area() in R](/ggplot2/how-to-use-geomarea-in-r.html) - [How to use geom_errorbar() in R](/ggplot2/how-to-use-geomerrorbar-in-r.html) - [How to use select() in R](/dplyr/how-to-use-select-in-r.html) - [How to use mutate() in R](/dplyr/how-to-use-mutate-in-r.html)