Multi-Panel Plots in R: A Guide to facet_wrap and facet_grid in ggplot2
Creating multi-panel figures by manually positioning subplots is tedious. R's ggplot2 implements automatic sub-panels via faceting. It partitions the dataset by a categorical variable and replicates the plot structure across panels.
In This Guide
0.Live Code: Multi-Panel Faceted Layouts
1.Wrapping Panels (facet_wrap)
2.Faceted Grids (facet_grid)
3.Freeing Axis Scales (scales = "free")
4.Customizing Label Strips
0. Live Code: Multi-Panel Faceted Layouts
Visualizing segmented subgroups. Customize parameters using Python below, or upload your data to run R directly.
1. Wrapping Panels with facet_wrap
Use facet_wrap() to sequence panels horizontally and wrap them based on the `ncol` or `nrow` constraints:
R / ggplot2
ggplot(df, aes(x, y)) +
geom_point() +
facet_wrap(~ group_variable, ncol = 3)2. Faceted Grids with facet_grid
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Use facet_grid() to lay out panels matching a matrix grid (rows vs columns) of two variables:
R / ggplot2
# Rows ~ Columns
ggplot(df, aes(x, y)) +
geom_point() +
facet_grid(treatment ~ genotype)3. Freeing Axis Scales across Panels
By default, all panels share fixed x and y ranges. Set scales = "free" or scales = "free_y" to let individual panel axes auto-scale:
R / ggplot2
ggplot(df, aes(x, y)) +
geom_point() +
facet_wrap(~ group, scales = "free_y")4. Customizing Label Strips
Modify the background and text style of strip header labels using the `theme()` function:
R / ggplot2
ggplot(df, aes(x, y)) +
geom_point() +
facet_wrap(~ group) +
theme(
strip.background = element_rect(fill = "gray90", color = "gray70"),
strip.text = element_text(face = "bold", size = 10, color = "black")
)Chart gallery
Explore related formats
Review multi-panel layouts.
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
Line Graph
Displays data points connected by straight line segments to show trends over time.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
# Generate temperature data for 3 major US cities over 12 months
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
nyc = [30, 32, 40, 52, 65, 75, 82, 81, 74, 63, 50, 38]
miami = [65, 66, 70, 76, 82, 87, 90, 90, 87, 80, 72, 66]
chicago = [25, 27, 35, 48, 62, 72, 80, 79, 71, 60, 45, 32]
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