How to Make Heatmaps in R with ggplot2 (geom_tile)
Heatmaps are excellent for summarizing large correlation matrices, gene expression grids, or spatial intensities. In ggplot2, heatmaps are built using the geom_tile() layer, mapping two categorical columns to the x and y axes, and a continuous variable to the fill aesthetic.
In This Tutorial
0.Live Code: Heatmap with Annotations
1.Basic Grid (geom_tile)
2.Applying Diverging Color Scales
3.Adding Values as Text
4.Masking Symmetrical Triangles
0. Live Code: Heatmap with Annotations
Correlation matrices. Customize parameters using Python below, or upload your data to run R directly.
1. Basic Heatmap Grid with geom_tile
Specify categorical variables for coordinate grids, and map intensity to the `fill` aesthetic:
R / ggplot2
# Basic heat grid
ggplot(df, aes(x = Var1, y = Var2, fill = Correlation)) +
geom_tile(color = "white", size = 0.1)Try it
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2. Applying Diverging Color Scales
For correlation matrices, use diverging scales like `scale_fill_gradient2()` centered at zero to clearly separate positive and negative correlations:
R / ggplot2
ggplot(df, aes(x = Var1, y = Var2, fill = Correlation)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "royalblue4", mid = "white", high = "red4", midpoint = 0, limit = c(-1, 1))3. Adding Values as Text annotations
Overlay numeric values directly inside cells using geom_text():
R / ggplot2
ggplot(df, aes(x = Var1, y = Var2, fill = Correlation)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0) +
geom_text(aes(label = round(Correlation, 2)), color = "black", size = 3) +
theme_minimal()4. Masking Symmetrical Triangles
Filer the correlation data frame beforehand using `lower.tri()` or `upper.tri()` to keep only one half and avoid redundant duplicates.
Chart gallery
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Scatterplot
Displays values for two variables as points on a Cartesian coordinate system.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import pandas as pd
# Generate sample data
np.random.seed(42)
n_samples = 200
height = np.random.normal(170, 8, n_samples)
weight = height * 0.6 + np.random.normal(0, 8, n_samples) - 50
Bar Chart
Compares categorical data using rectangular bars with heights proportional to values.
Sample code / prompt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
# Generate performance scores for 5 treatment groups
np.random.seed(42)
groups = ['Control', 'Treatment A', 'Treatment B', 'Treatment C', 'Treatment D']
n_samples = 30Build This Correlation Heatmap Online
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Hands-on experience in silicon photonics, semiconductor fabrication (DRIE/ICP-RIE), optical simulation, and data-driven analysis. Built Plotivy to help researchers focus on discoveries instead of data struggles.
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Apply the techniques from this article to your own datasets. Upload CSV, Excel, or paste data directly.