ggplot2 Color Palettes: How to Customize Colors in R Plots
Color is a primary aesthetic dimension in statistical graphs. In R's ggplot2, scale mapping controls color assignments. Correctly distinguishing color categories is essential to represent multiple experimental groups.
In This Guide
0.Live Code: Palette Customization
1.Discrete Manual Scales (scale_color_manual)
2.ColorBrewer Schemes (scale_fill_brewer)
3.Perceptually Uniform Gradients (scale_color_viridis_c)
4.Colorblind-Safe Design Best Practices
0. Live Code: Palette Customization
Palette transitions. Customize parameters using Python below, or upload your data to run R directly.
1. Discrete Manual Scales (scale_color_manual)
To specify exact hex codes for categorical factors, use scale_color_manual() for outlines and scale_fill_manual() for filled shapes:
R / ggplot2
# Manual discrete color mapping
ggplot(df, aes(x = group, y = measurement, color = treatment)) +
geom_point() +
scale_color_manual(values = c("Placebo" = "#94a3b8", "Drug A" = "#4f46e5", "Drug B" = "#059669"))Try it
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2. ColorBrewer Schemes (scale_fill_brewer)
Access pre-designed ColorBrewer palettes optimized for sequential, diverging, or qualitative variables:
R / ggplot2
# Qualitative ColorBrewer palette
ggplot(df, aes(x = group, y = measurement, fill = treatment)) +
geom_col() +
scale_fill_brewer(palette = "Set2")3. Perceptually Uniform Gradients (scale_color_viridis_c)
For continuous variables, use viridis scales which map intensities uniformly:
R / ggplot2
# Continuous viridis scale
ggplot(df, aes(x = concentration, y = speed, color = temperature)) +
geom_point() +
scale_color_viridis_c(option = "plasma")4. Colorblind-Safe Design Best Practices
Avoid pure red-green combinations. Utilize double encoding: represent groupings using different shapes and dashed lines alongside color changes.
Chart gallery
Explore related formats
Review visual formats.
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 Color-Mapped Plot Online
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