How to Create Box Plots with Jitter Points in R ggplot2
Box plots display the median, quartiles, and range of a distribution. However, box plots can hide sample size and local distribution details. Overlaying jittered raw data points solves this, showing every individual observation.
In This Tutorial
0.Live Code: Box Plot with Overlaid Observations
1.Basic Box Plot (geom_boxplot)
2.Adding Raw Points (geom_jitter)
3.Avoiding Outlier Duplication
4.Violin Overlay Combinations
0. Live Code: Box Plot with Overlaid Observations
Distribution comparisons. Customize parameters using Python below, or upload your data to run R directly.
1. Basic Box Plot with geom_boxplot
Create standard box plots to visualize distribution quartiles:
R / ggplot2
ggplot(df, aes(x = group, y = measurement)) +
geom_boxplot(fill = "gray95", width = 0.5)2. Adding Raw Points with geom_jitter
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Overlay individual observations. Set width boundaries inside `geom_jitter` to prevent points spreading too far:
R / ggplot2
ggplot(df, aes(x = group, y = measurement)) +
geom_boxplot(fill = "gray95", width = 0.5) +
geom_jitter(color = "#4f46e5", alpha = 0.6, width = 0.15, size = 1.5)3. Avoiding Outlier Duplication
Crucial Tip: Disable Boxplot Outliers
When overlaying `geom_jitter()`, ggplot2 will plot outliers twice: once from the boxplot calculation and once as a jittered point. Set outlier.shape = NA in the boxplot layer to avoid double-plotting.
R / ggplot2
ggplot(df, aes(x = group, y = measurement)) +
geom_boxplot(outlier.shape = NA, fill = "gray95", width = 0.5) +
geom_jitter(color = "#4f46e5", alpha = 0.6, width = 0.15)4. Violin Overlay Combinations
Pairing a violin plot with a box plot inside is another premium scientific layout:
R / ggplot2
ggplot(df, aes(x = group, y = measurement)) +
geom_violin(fill = "gray90", color = "gray60", alpha = 0.5) +
geom_boxplot(width = 0.15, fill = "white", outlier.shape = NA)Chart gallery
Explore related formats
Review distribution formats.
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Box and Whisker Plot
Displays data distribution using quartiles, median, and outliers in a standardized format.
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 gene expression data for 4 genotypes
np.random.seed(42)
genotypes = ['WT', 'KO1', 'KO2', 'Mutant']
n_per_group = 20
Violin Plot
Combines box plots with kernel density to show distribution shape across groups.
Sample code / prompt
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from scipy.stats import f_oneway
# Generate exam score data for 3 groups
np.random.seed(42)
control = np.random.normal(72, 12, 50)
treatment_a = np.random.normal(78, 10, 50)Build This Box Plot Online
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PCA visualization workflow
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ANOVA with post-hoc brackets
Add statistically correct pairwise significance annotations.
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