One plot is rarely enough. Whether you are comparing species across islands, aligning a scatter plot next to a box plot, or building a manuscript figure with labeled panels, multi-panel layouts are a daily tool in data visualization. R gives you two broad strategies: faceting (splitting one plot by a variable) and plot composition (gluing separate plots together). This post covers both, using the Palmer Penguins dataset throughout.

Setup

Code

# Uncomment to install any missing packages
# install.packages(c("ggplot2", "dplyr", "palmerpenguins", "patchwork", "cowplot"))

library(ggplot2)
library(dplyr)
library(palmerpenguins)
library(patchwork)
library(cowplot)

penguins <- palmerpenguins::penguins

Faceting: Splitting One Plot by a Variable

Faceting is built into ggplot2. You write a single ggplot() call, then add facet_wrap() or facet_grid() to automatically replicate the plot for each level of a grouping variable. All panels share the same axes by default, making comparisons easy.

facet_wrap

facet_wrap() lays panels out in a ribbon that wraps to a new row once it hits a set number of columns. It is the go-to choice when you have one grouping variable and want ggplot2 to handle the layout for you.

Code

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) +
  geom_point(alpha = 0.7, na.rm = TRUE) +
  facet_wrap(~species) +
  scale_color_brewer(palette = "Set2") +
  labs(
    title = "Bill dimensions by species",
    x = "Bill length (mm)",
    y = "Bill depth (mm)",
    color = "Species"
  ) +
  theme_bw() +
  theme(legend.position = "none")

The ~species formula tells ggplot2 which variable to facet on. By default it picks the number of columns automatically, but you can override that with ncol or nrow.

Code

ggplot(penguins, aes(x = body_mass_g, fill = sex)) +
  geom_histogram(bins = 20, na.rm = TRUE, position = "identity", alpha = 0.7) +
  facet_wrap(~species, ncol = 1) +
  scale_fill_brewer(palette = "Pastel1", na.value = "grey70") +
  labs(
    title = "Body mass distribution by species",
    x = "Body mass (g)",
    y = "Count",
    fill = "Sex"
  ) +
  theme_bw()

Free scales

By default all panels share the same axis limits (scales = "fixed"). Set scales = "free", "free_x", or "free_y" when the ranges differ dramatically between groups and you care more about within-panel pattern than cross-panel comparison.

Code

ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point(aes(color = island), alpha = 0.7, na.rm = TRUE) +
  geom_smooth(method = "lm", se = FALSE, na.rm = TRUE, color = "grey30") +
  facet_wrap(~species, scales = "free") +
  scale_color_brewer(palette = "Dark2") +
  labs(
    title = "Flipper length vs body mass (free scales)",
    x = "Flipper length (mm)",
    y = "Body mass (g)",
    color = "Island"
  ) +
  theme_bw()

facet_grid

facet_grid() creates a strict rows × columns grid defined by two variables. Use it when you want to cross-tabulate two grouping variables and keep the layout perfectly aligned.

Code

ggplot(
  penguins |> filter(!is.na(sex)),
  aes(x = bill_length_mm, y = bill_depth_mm)
) +
  geom_point(aes(color = species), alpha = 0.7, na.rm = TRUE) +
  facet_grid(sex ~ species) +
  scale_color_brewer(palette = "Set2") +
  labs(
    title = "Bill dimensions by species and sex",
    x = "Bill length (mm)",
    y = "Bill depth (mm)",
    color = "Species"
  ) +
  theme_bw() +
  theme(legend.position = "bottom")

The formula sex ~ species puts sex levels along rows and species levels across columns. Use . to suppress one dimension: ~ species gives one row of columns, sex ~ . gives one column of rows.

Code

# One row of columns (equivalent to facet_wrap with fixed layout)
ggplot(penguins, aes(x = body_mass_g, y = flipper_length_mm)) +
  geom_point(aes(color = sex), alpha = 0.7, na.rm = TRUE) +
  facet_grid(. ~ island) +
  scale_color_brewer(palette = "Set1", na.value = "grey70") +
  labs(
    title = "Body mass vs flipper length by island",
    x = "Body mass (g)",
    y = "Flipper length (mm)",
    color = "Sex"
  ) +
  theme_bw()

facet_wrap vs facet_grid at a glance

Feature	`facet_wrap`	`facet_grid`
Variables	One	Two (rows × columns)
Layout	Wraps automatically	Strict grid
Best for	One grouping variable	Crossing two variables
Free scales	Yes (`scales =`)	Yes (`scales =`)
Space control	`nrow` / `ncol`	Fixed by variable levels

Plot Composition: Combining Separate Plots

Sometimes you need panels that show different variables, geoms, or even different datasets side by side. That is where patchwork and cowplot come in — they let you take any ggplot objects and arrange them into a single figure.

Let us build a few reusable plots to combine throughout these examples.

Code

p_scatter <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm,
                                   color = species)) +
  geom_point(alpha = 0.7, na.rm = TRUE) +
  scale_color_brewer(palette = "Set2") +
  labs(x = "Bill length (mm)", y = "Bill depth (mm)", color = "Species") +
  theme_bw()

p_box <- ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) +
  geom_boxplot(na.rm = TRUE, show.legend = FALSE) +
  scale_fill_brewer(palette = "Set2") +
  labs(x = "Species", y = "Body mass (g)") +
  theme_bw()

p_hist <- ggplot(penguins, aes(x = flipper_length_mm, fill = species)) +
  geom_histogram(bins = 25, alpha = 0.7, na.rm = TRUE, position = "identity") +
  scale_fill_brewer(palette = "Set2") +
  labs(x = "Flipper length (mm)", y = "Count", fill = "Species") +
  theme_bw()

p_bar <- ggplot(penguins, aes(x = island, fill = species)) +
  geom_bar(position = "dodge") +
  scale_fill_brewer(palette = "Set2") +
  labs(x = "Island", y = "Count", fill = "Species") +
  theme_bw()

patchwork

patchwork extends ggplot2 with a simple operator-based syntax. After loading the package, you can combine plots with +, /, and | exactly like arithmetic.

Code

# Side by side
p_scatter + p_box

Code

# Stacked
p_scatter / p_box

Layouts with `plot_layout()`

plot_layout() gives you fine-grained control over the grid, including relative widths, heights, and shared guides.

Code

(p_scatter | p_box | p_hist) +
  plot_layout(widths = c(2, 1, 1))

Code

# A 2×2 grid with shared legend collected at the bottom
(p_scatter + p_box) / (p_hist + p_bar) +
  plot_layout(guides = "collect") &
  theme(legend.position = "bottom")

Panel labels with `plot_annotation()`

plot_annotation() adds a title and subtitle to the whole figure, plus automatic A, B, C… labels on each panel — essential for publications.

Code

(p_scatter + p_box) / (p_hist + p_bar) +
  plot_annotation(
    title = "Palmer Penguins: a four-panel summary",
    subtitle = "Bill dimensions, body mass, flipper distribution, and island counts",
    tag_levels = "A"
  ) +
  plot_layout(guides = "collect") &
  theme(legend.position = "bottom")

Nesting layouts

You can nest patchwork assemblies inside each other using wrap_plots() or by grouping with parentheses.

Code

right_column <- p_box / p_bar

p_scatter | right_column

cowplot

cowplot (by Claus Wilke) takes a slightly different approach. Its main function is plot_grid(), which accepts a list of plots and arranges them with precise alignment control. It is especially useful when panels have different axis structures and you need exact pixel-level alignment.

Code

plot_grid(p_scatter, p_box, ncol = 2)

Code

# Add panel labels
plot_grid(p_scatter, p_box, p_hist, p_bar,
          labels = c("A", "B", "C", "D"),
          label_size = 12,
          ncol = 2)

Controlling relative sizes

Use rel_widths and rel_heights to weight panels.

Code

plot_grid(p_scatter, p_box,
          ncol = 2,
          rel_widths = c(2, 1),
          labels = "AUTO")

Shared legends with `get_legend()`

A common cowplot pattern is to pull the legend out of one plot and place it as its own element using get_legend().

Code

# Build plots without their own legends
p_scatter_noleg <- p_scatter + theme(legend.position = "none")
p_hist_noleg    <- p_hist    + theme(legend.position = "none")

# Extract legend from the original scatter
legend <- get_legend(p_scatter)

# Arrange plots + legend
top_row <- plot_grid(p_scatter_noleg, p_hist_noleg,
                     ncol = 2, labels = c("A", "B"))

plot_grid(top_row, legend,
          ncol = 1,
          rel_heights = c(1, 0.15))

Aligning axes

align = "hv" forces horizontal and vertical axis alignment across panels — critical when mixing plots with different title heights or legend sizes.

Code

plot_grid(p_scatter, p_box, p_hist,
          align  = "hv",
          ncol   = 3,
          labels = "AUTO")

patchwork vs cowplot at a glance

Feature	`patchwork`	`cowplot`
Syntax	Operators (`+`, `/`, `\|`)	`plot_grid()` function
Learning curve	Very low	Low
Shared legend	`plot_layout(guides = "collect")`	`get_legend()` + `plot_grid()`
Axis alignment	Automatic	`align = "hv"`
Panel labels	`plot_annotation(tag_levels =)`	`labels =` argument
Nesting	Parentheses / `wrap_plots()`	Nested `plot_grid()` calls
Best for	Quick layouts, publications	Precise alignment, mixed axes

Putting It All Together

You can even combine faceting and composition. Here we facet a scatter plot by island, then place it alongside a summary box plot.

Code

p_faceted <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm,
                                   color = species)) +
  geom_point(alpha = 0.7, na.rm = TRUE) +
  facet_wrap(~island) +
  scale_color_brewer(palette = "Set2") +
  labs(
    title = "Bill dimensions by island",
    x = "Bill length (mm)",
    y = "Bill depth (mm)",
    color = "Species"
  ) +
  theme_bw() +
  theme(legend.position = "none")

p_summary <- ggplot(penguins, aes(x = species, y = bill_length_mm,
                                   fill = species)) +
  geom_violin(na.rm = TRUE, alpha = 0.7, show.legend = FALSE) +
  geom_boxplot(width = 0.15, na.rm = TRUE, show.legend = FALSE) +
  scale_fill_brewer(palette = "Set2") +
  labs(
    title = "Bill length by species",
    x = "Species",
    y = "Bill length (mm)"
  ) +
  theme_bw()

p_faceted + p_summary +
  plot_layout(widths = c(2, 1)) +
  plot_annotation(
    title   = "Penguin bill dimensions: island facets + species summary",
    tag_levels = "A"
  )

Quick Reference

Goal	Tool	Key argument
Split one plot by one variable	`facet_wrap(~var)`	`ncol`, `nrow`, `scales`
Cross two grouping variables	`facet_grid(r ~ c)`	`scales`, `space`
Place plots side by side	`p1 \\| p2` (patchwork)	—
Stack plots vertically	`p1 / p2` (patchwork)	—
Control widths/heights	`plot_layout()`	`widths`, `heights`
Add figure title + labels	`plot_annotation()`	`title`, `tag_levels`
Arrange with precise alignment	`plot_grid()` (cowplot)	`align`, `rel_widths`
Shared legend (cowplot)	`get_legend()`	—

Multi-panel layouts take a little practice, but once you have the patterns down they become second nature. Start with facet_wrap whenever a single variable is doing the grouping, reach for facet_grid when you are crossing two factors, and pull in patchwork or cowplot whenever you need to combine fundamentally different plots into one cohesive figure.

--- title: "Multi-Panel Plots in R" subtitle: "Combining and faceting figures with patchwork, cowplot, facet_wrap, and facet_grid" author: "Noah Weidig" date: "2026-03-28" categories: [code, ggplot2, dataviz] description: "Learn four powerful approaches to multi-panel plots in R — use facet_wrap and facet_grid to split a single dataset by groups, or reach for patchwork and cowplot to stitch together entirely different figures into a polished layout." execute: warning: false message: false toc: true toc-depth: 2 code-fold: show share: permalink: "https://noahweidig.github.io/chartifyr/posts/multiplot/" --- One plot is rarely enough. Whether you are comparing species across islands, aligning a scatter plot next to a box plot, or building a manuscript figure with labeled panels, multi-panel layouts are a daily tool in data visualization. R gives you two broad strategies: **faceting** (splitting one plot by a variable) and **plot composition** (gluing separate plots together). This post covers both, using the Palmer Penguins dataset throughout. # Setup ```{r} #| label: setup # Uncomment to install any missing packages # install.packages(c("ggplot2", "dplyr", "palmerpenguins", "patchwork", "cowplot")) library(ggplot2) library(dplyr) library(palmerpenguins) library(patchwork) library(cowplot) penguins <- palmerpenguins::penguins ``` # Faceting: Splitting One Plot by a Variable Faceting is built into ggplot2. You write a single `ggplot()` call, then add `facet_wrap()` or `facet_grid()` to automatically replicate the plot for each level of a grouping variable. All panels share the same axes by default, making comparisons easy. ## facet_wrap `facet_wrap()` lays panels out in a ribbon that wraps to a new row once it hits a set number of columns. It is the go-to choice when you have **one grouping variable** and want ggplot2 to handle the layout for you. ```{r} #| label: facet-wrap-basic ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + geom_point(alpha = 0.7, na.rm = TRUE) + facet_wrap(~species) + scale_color_brewer(palette = "Set2") + labs( title = "Bill dimensions by species", x = "Bill length (mm)", y = "Bill depth (mm)", color = "Species" ) + theme_bw() + theme(legend.position = "none") ``` The `~species` formula tells ggplot2 which variable to facet on. By default it picks the number of columns automatically, but you can override that with `ncol` or `nrow`. ```{r} #| label: facet-wrap-ncol ggplot(penguins, aes(x = body_mass_g, fill = sex)) + geom_histogram(bins = 20, na.rm = TRUE, position = "identity", alpha = 0.7) + facet_wrap(~species, ncol = 1) + scale_fill_brewer(palette = "Pastel1", na.value = "grey70") + labs( title = "Body mass distribution by species", x = "Body mass (g)", y = "Count", fill = "Sex" ) + theme_bw() ``` ### Free scales By default all panels share the same axis limits (`scales = "fixed"`). Set `scales = "free"`, `"free_x"`, or `"free_y"` when the ranges differ dramatically between groups and you care more about within-panel pattern than cross-panel comparison. ```{r} #| label: facet-wrap-free-scales ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) + geom_point(aes(color = island), alpha = 0.7, na.rm = TRUE) + geom_smooth(method = "lm", se = FALSE, na.rm = TRUE, color = "grey30") + facet_wrap(~species, scales = "free") + scale_color_brewer(palette = "Dark2") + labs( title = "Flipper length vs body mass (free scales)", x = "Flipper length (mm)", y = "Body mass (g)", color = "Island" ) + theme_bw() ``` ## facet_grid `facet_grid()` creates a strict **rows × columns** grid defined by two variables. Use it when you want to cross-tabulate two grouping variables and keep the layout perfectly aligned. ```{r} #| label: facet-grid-basic ggplot( penguins |> filter(!is.na(sex)), aes(x = bill_length_mm, y = bill_depth_mm) ) + geom_point(aes(color = species), alpha = 0.7, na.rm = TRUE) + facet_grid(sex ~ species) + scale_color_brewer(palette = "Set2") + labs( title = "Bill dimensions by species and sex", x = "Bill length (mm)", y = "Bill depth (mm)", color = "Species" ) + theme_bw() + theme(legend.position = "bottom") ``` The formula `sex ~ species` puts `sex` levels along rows and `species` levels across columns. Use `.` to suppress one dimension: `~ species` gives one row of columns, `sex ~ .` gives one column of rows. ```{r} #| label: facet-grid-one-dim # One row of columns (equivalent to facet_wrap with fixed layout) ggplot(penguins, aes(x = body_mass_g, y = flipper_length_mm)) + geom_point(aes(color = sex), alpha = 0.7, na.rm = TRUE) + facet_grid(. ~ island) + scale_color_brewer(palette = "Set1", na.value = "grey70") + labs( title = "Body mass vs flipper length by island", x = "Body mass (g)", y = "Flipper length (mm)", color = "Sex" ) + theme_bw() ``` ### facet_wrap vs facet_grid at a glance | Feature | `facet_wrap` | `facet_grid` | |---|---|---| | Variables | One | Two (rows × columns) | | Layout | Wraps automatically | Strict grid | | Best for | One grouping variable | Crossing two variables | | Free scales | Yes (`scales =`) | Yes (`scales =`) | | Space control | `nrow` / `ncol` | Fixed by variable levels | # Plot Composition: Combining Separate Plots Sometimes you need panels that show **different variables, geoms, or even different datasets** side by side. That is where `patchwork` and `cowplot` come in — they let you take any ggplot objects and arrange them into a single figure. Let us build a few reusable plots to combine throughout these examples. ```{r} #| label: base-plots p_scatter <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species, fill = species)) + geom_point(alpha = 0.7, na.rm = TRUE, shape = 21) + scale_color_brewer(palette = "Set2") + scale_fill_brewer(palette = "Set2") + labs(x = "Bill length (mm)", y = "Bill depth (mm)", fill = "Species") + guides(color = "none") + theme_bw() p_box <- ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) + geom_boxplot(na.rm = TRUE, show.legend = FALSE) + scale_fill_brewer(palette = "Set2") + labs(x = "Species", y = "Body mass (g)") + theme_bw() p_hist <- ggplot(penguins, aes(x = flipper_length_mm, fill = species)) + geom_histogram(bins = 25, alpha = 0.7, na.rm = TRUE, position = "identity") + scale_fill_brewer(palette = "Set2") + labs(x = "Flipper length (mm)", y = "Count", fill = "Species") + theme_bw() p_bar <- ggplot(penguins, aes(x = island, fill = species)) + geom_bar(position = "dodge") + scale_fill_brewer(palette = "Set2") + labs(x = "Island", y = "Count", fill = "Species") + theme_bw() ``` ## patchwork **patchwork** extends ggplot2 with a simple operator-based syntax. After loading the package, you can combine plots with `+`, `/`, and `|` exactly like arithmetic. ```{r} #| label: patchwork-basic # Side by side p_scatter + p_box ``` ```{r} #| label: patchwork-stack # Stacked p_scatter / p_box ``` ### Layouts with `plot_layout()` `plot_layout()` gives you fine-grained control over the grid, including relative widths, heights, and shared guides. ```{r} #| label: patchwork-layout (p_scatter | p_box | p_hist) + plot_layout(widths = c(2, 1, 1), guides = "collect") & theme(legend.position = "bottom") ``` ```{r} #| label: patchwork-complex # A 2×2 grid with shared legend collected at the bottom (p_scatter + p_box) / (p_hist + p_bar) + plot_layout(guides = "collect") & theme(legend.position = "bottom") ``` ### Panel labels with `plot_annotation()` `plot_annotation()` adds a title and subtitle to the whole figure, plus automatic **A, B, C…** labels on each panel — essential for publications. ```{r} #| label: patchwork-annotation (p_scatter + p_box) / (p_hist + p_bar) + plot_annotation( title = "Palmer Penguins: a four-panel summary", subtitle = "Bill dimensions, body mass, flipper distribution, and island counts", tag_levels = "A" ) + plot_layout(guides = "collect") & theme(legend.position = "bottom") ``` ### Nesting layouts You can nest patchwork assemblies inside each other using `wrap_plots()` or by grouping with parentheses. ```{r} #| label: patchwork-nested right_column <- p_box / p_bar p_scatter | right_column ``` ## cowplot **cowplot** (by Claus Wilke) takes a slightly different approach. Its main function is `plot_grid()`, which accepts a list of plots and arranges them with precise alignment control. It is especially useful when panels have different axis structures and you need **exact pixel-level alignment**. ```{r} #| label: cowplot-basic plot_grid(p_scatter, p_box, ncol = 2) ``` ```{r} #| label: cowplot-labels # Add panel labels plot_grid(p_scatter, p_box, p_hist, p_bar, labels = c("A", "B", "C", "D"), label_size = 12, ncol = 2) ``` ### Controlling relative sizes Use `rel_widths` and `rel_heights` to weight panels. ```{r} #| label: cowplot-rel-widths plot_grid(p_scatter, p_box, ncol = 2, rel_widths = c(2, 1), labels = "AUTO") ``` ### Shared legends with `get_legend()` A common cowplot pattern is to pull the legend out of one plot and place it as its own element using `get_legend()`. ```{r} #| label: cowplot-shared-legend # Build plots without their own legends p_scatter_noleg <- p_scatter + theme(legend.position = "none") p_hist_noleg <- p_hist + theme(legend.position = "none") # Extract legend from the original scatter legend <- get_legend(p_scatter) # Arrange plots + legend top_row <- plot_grid(p_scatter_noleg, p_hist_noleg, ncol = 2, labels = c("A", "B")) plot_grid(top_row, legend, ncol = 1, rel_heights = c(1, 0.25)) ``` ### Aligning axes `align = "hv"` forces horizontal and vertical axis alignment across panels — critical when mixing plots with different title heights or legend sizes. ```{r} #| label: cowplot-align plot_grid(p_scatter + theme(legend.position = "bottom"), p_box, p_hist + theme(legend.position = "bottom"), align = "hv", ncol = 3, labels = "AUTO") ``` ## patchwork vs cowplot at a glance | Feature | `patchwork` | `cowplot` | |---|---|---| | Syntax | Operators (`+`, `/`, `|`) | `plot_grid()` function | | Learning curve | Very low | Low | | Shared legend | `plot_layout(guides = "collect")` | `get_legend()` + `plot_grid()` | | Axis alignment | Automatic | `align = "hv"` | | Panel labels | `plot_annotation(tag_levels =)` | `labels =` argument | | Nesting | Parentheses / `wrap_plots()` | Nested `plot_grid()` calls | | Best for | Quick layouts, publications | Precise alignment, mixed axes | # Putting It All Together You can even combine faceting *and* composition. Here we facet a scatter plot by island, then place it alongside a summary box plot. ```{r} #| label: combined p_faceted <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + geom_point(alpha = 0.7, na.rm = TRUE) + facet_wrap(~island) + scale_color_brewer(palette = "Set2") + labs( title = "Bill dimensions by island", x = "Bill length (mm)", y = "Bill depth (mm)", color = "Species" ) + theme_bw() + theme(legend.position = "none") p_summary <- ggplot(penguins, aes(x = species, y = bill_length_mm, fill = species)) + geom_violin(na.rm = TRUE, alpha = 0.7, show.legend = FALSE) + geom_boxplot(width = 0.15, na.rm = TRUE, show.legend = FALSE) + scale_fill_brewer(palette = "Set2") + labs( title = "Bill length by species", x = "Species", y = "Bill length (mm)" ) + theme_bw() p_faceted + p_summary + plot_layout(widths = c(2, 1)) + plot_annotation( title = "Penguin bill dimensions: island facets + species summary", tag_levels = "A" ) ``` # Quick Reference | Goal | Tool | Key argument | |---|---|---| | Split one plot by one variable | `facet_wrap(~var)` | `ncol`, `nrow`, `scales` | | Cross two grouping variables | `facet_grid(r ~ c)` | `scales`, `space` | | Place plots side by side | `p1 \| p2` (patchwork) | — | | Stack plots vertically | `p1 / p2` (patchwork) | — | | Control widths/heights | `plot_layout()` | `widths`, `heights` | | Add figure title + labels | `plot_annotation()` | `title`, `tag_levels` | | Arrange with precise alignment | `plot_grid()` (cowplot) | `align`, `rel_widths` | | Shared legend (cowplot) | `get_legend()` | — | Multi-panel layouts take a little practice, but once you have the patterns down they become second nature. Start with `facet_wrap` whenever a single variable is doing the grouping, reach for `facet_grid` when you are crossing two factors, and pull in `patchwork` or `cowplot` whenever you need to combine fundamentally different plots into one cohesive figure.

Stay in the loop

Setup

Faceting: Splitting One Plot by a Variable

facet_wrap

Free scales

facet_grid

facet_wrap vs facet_grid at a glance

Plot Composition: Combining Separate Plots

patchwork

Layouts with plot_layout()

Panel labels with plot_annotation()

Nesting layouts

cowplot

Controlling relative sizes

Shared legends with get_legend()

Aligning axes

patchwork vs cowplot at a glance

Putting It All Together

Quick Reference

Layouts with `plot_layout()`

Panel labels with `plot_annotation()`

Shared legends with `get_legend()`