Combine multiple plots into publication-ready multi-panel figures using patchwork, cowplot, or matplotlib GridSpec with shared legends and panel labels. Use when combining multiple plots into publication figures.
Reference examples tested with: ggplot2 3.5+, matplotlib 3.8+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturespackageVersion('<pkg>') then ?function_name to verify parametersIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Combine multiple plots into one figure" → Arrange subplots into a publication-ready multi-panel layout with shared legends and consistent sizing.
patchwork::wrap_plots(), cowplot::plot_grid()matplotlib.pyplot.subplots(), fig.add_subplot()library(patchwork)
p1 <- ggplot(df, aes(x, y)) + geom_point()
p2 <- ggplot(df, aes(group, value)) + geom_boxplot()
p3 <- ggplot(df, aes(x)) + geom_histogram()
# Horizontal
p1 + p2 + p3
# Vertical
p1 / p2 / p3
# Mixed layouts
(p1 | p2) / p3
(p1 + p2) / (p3 + p4)
# Automatic labels
(p1 + p2 + p3) + plot_annotation(tag_levels = 'A')
# Custom labels
(p1 + p2 + p3) + plot_annotation(tag_levels = list(c('A', 'B', 'C')))
# Label styling
(p1 + p2) + plot_annotation(
tag_levels = 'A',
tag_prefix = '(',
tag_suffix = ')',
theme = theme(plot.tag = element_text(face = 'bold', size = 14))
)
# Width ratios
p1 + p2 + plot_layout(widths = c(2, 1))
# Height ratios
p1 / p2 + plot_layout(heights = c(1, 2))
# Complex grid
layout <- "
AAB
AAB
CCC
"
p1 + p2 + p3 + plot_layout(design = layout)
# Fixed dimensions
p1 + p2 + plot_layout(widths = unit(c(5, 3), 'cm'))
# Collect legends
(p1 + p2 + p3) + plot_layout(guides = 'collect')
# Position at bottom
(p1 + p2) + plot_layout(guides = 'collect') &
theme(legend.position = 'bottom')
# Keep individual legends
(p1 + p2) + plot_layout(guides = 'keep')
# Add inset
p1 + inset_element(p2, left = 0.6, bottom = 0.6, right = 1, top = 1)
# Multiple insets
p1 +
inset_element(p2, 0.6, 0.6, 1, 1) +
inset_element(p3, 0.02, 0.6, 0.4, 1)
library(cowplot)
# Simple grid
plot_grid(p1, p2, p3, ncol = 3, labels = 'AUTO')
# With labels
plot_grid(p1, p2, labels = c('A', 'B'), label_size = 14)
# Relative widths
plot_grid(p1, p2, rel_widths = c(2, 1))
# Nested grids
top_row <- plot_grid(p1, p2, ncol = 2)
bottom_row <- p3
plot_grid(top_row, bottom_row, nrow = 2, labels = c('', 'C'))
library(patchwork)
# Same axis limits
(p1 + p2) & xlim(0, 10) & ylim(0, 100)
# Same theme
(p1 + p2 + p3) & theme_minimal()
# Same color scale
(p1 + p2) & scale_color_viridis_d()
# Add blank panel
p1 + plot_spacer() + p2
# With layout
layout <- "
AB#
CCC
"
p1 + p2 + p3 + plot_layout(design = layout)
(p1 + p2 + p3) +
plot_annotation(
title = 'Main Title',
subtitle = 'Subtitle text',
caption = 'Data source: ...',
theme = theme(
plot.title = element_text(face = 'bold', size = 16),
plot.subtitle = element_text(size = 12, color = 'grey40')
)
)
# Combine and save
combined <- (p1 | p2) / (p3 | p4) +
plot_annotation(tag_levels = 'A') &
theme(plot.tag = element_text(face = 'bold'))
ggsave('figure.pdf', combined, width = 10, height = 8)
ggsave('figure.png', combined, width = 10, height = 8, dpi = 300)
# For specific journal dimensions
ggsave('figure.pdf', combined, width = 180, height = 150, units = 'mm')
Goal: Assemble a complete multi-panel figure combining volcano, PCA, heatmap, and boxplot panels with consistent theming.
Approach: Create individual themed ggplot objects, arrange with patchwork operators (| for horizontal, / for vertical), add panel labels, collect shared legends, and export at journal-specified dimensions.
# Create themed plots
theme_pub <- theme_bw(base_size = 10) +
theme(
panel.grid = element_blank(),
legend.position = 'none'
)
p_volcano <- create_volcano(res) + theme_pub + ggtitle('Volcano Plot')
p_pca <- create_pca(vsd) + theme_pub + ggtitle('PCA')
p_heatmap <- wrap_elements(pheatmap_grob)
p_boxplot <- create_boxplot(expr_df) + theme_pub + ggtitle('Expression')
# Assemble
figure <- (p_volcano | p_pca) / (p_heatmap | p_boxplot) +
plot_annotation(tag_levels = 'A') +
plot_layout(guides = 'collect') &
theme(
plot.tag = element_text(face = 'bold', size = 12),
legend.position = 'bottom'
)
ggsave('Figure1.pdf', figure, width = 180, height = 160, units = 'mm')
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
fig = plt.figure(figsize=(12, 8))
gs = GridSpec(2, 3, figure=fig)
ax1 = fig.add_subplot(gs[0, 0]) # Top left
ax2 = fig.add_subplot(gs[0, 1:]) # Top right, spans 2 columns
ax3 = fig.add_subplot(gs[1, :]) # Bottom, spans all columns
# Add plots to each axis
ax1.plot(x, y)
ax2.scatter(x, y)
ax3.bar(x, y)
plt.tight_layout()
# Add panel labels
for ax, label in zip([ax1, ax2, ax3], ['A', 'B', 'C']):
ax.text(-0.1, 1.1, label, transform=ax.transAxes,
fontsize=14, fontweight='bold', va='top')
# matplotlib 3.4+ subfigures for complex layouts
fig = plt.figure(figsize=(12, 8))
subfigs = fig.subfigures(1, 2, width_ratios=[2, 1])
axs_left = subfigs[0].subplots(2, 1)
ax_right = subfigs[1].subplots(1, 1)
# Python
fig.savefig('figure1.pdf', bbox_inches='tight')
fig.savefig('figure1.png', dpi=300, bbox_inches='tight')