GC3 Roundtable #3 — Layout and Composition of Multiple Plots

From Single Figures to Cohesive Visual Stories

Author

Jimmy Zhang

Published

November 6, 2025

Why layout matters

  • Last time: group comparisons (e.g., violin/box/raincloud).
  • Today: how multiple plots work together to tell one story.
  • Good layout ⇒ stronger visual hierarchy, comparability, and clarity.
  • Bad layout ⇒ clutter, duplicated elements, and misinterpretation.
Note

Goal: Compose multi-panel figures that are publication-ready and reproducible.

What we’ll cover

  1. Patchwork grammar for multi-plot composition
  2. Advanced control: widths/heights, custom designs, legend & axis collection
  3. Design principles: hierarchy, accessibility, and story flow
  4. Demos: diagnostics dashboard, group comparisons, publication figure with tags
  5. Exporting & common pitfalls

Packages for layout

Package Strength Example
patchwork Intuitive grammar & guide/axis collection (p1 | p2) / p3
cowplot Precise alignment; plot grids plot_grid(p1, p2)
gridExtra Base grid layouts grid.arrange(p1, p2)

We’ll primarily use patchwork.

Data used in demos

We’ll mix simulated data and palmerpenguins.

# Simulated example data (two groups, simple outcome)
set.seed(42)
n <- 400
simdat <- tibble(
  group = sample(c("Control", "Treatment"), n, TRUE),
  x = rnorm(n),
  y = 0.4 * (group == "Treatment") + 0.6 * x + rnorm(n, sd = 0.8)
)

# A small model for diagnostic plots later
mod <- lm(y ~ x + group, data = simdat)
simdat$.fitted <- fitted(mod)
simdat$.resid  <- resid(mod)

# Penguins subset (complete cases)
peng <- penguins |>
  select(species, island, bill_length_mm, bill_depth_mm, body_mass_g, flipper_length_mm, sex) |>
  drop_na()

Patchwork basics: horizontal & vertical

p1 <- ggplot(simdat, aes(group, y, fill = group)) +
  geom_violin(trim = FALSE, alpha = 0.6) +
  geom_boxplot(width = 0.2, outlier.shape = NA) +
  labs(title = "Outcome by Group") +
  theme(legend.position = "none")

p2 <- ggplot(simdat, aes(x, y, color = group)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "x vs y by Group")
# Side-by-side (horizontal)
p1 | p2
`geom_smooth()` using formula = 'y ~ x'

# Stacked (vertical)
p1 / p2
`geom_smooth()` using formula = 'y ~ x'

Patchwork: nesting & layout

# Mixed nesting
(p1 | p2) / (p2 | p1)
`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'

# Specify grid dimensions
(p1 | p2 | p1) + plot_layout(ncol = 3)
`geom_smooth()` using formula = 'y ~ x'

# Control relative widths / heights
(p1 | p2) + plot_layout(widths = c(1.2, 1))
`geom_smooth()` using formula = 'y ~ x'

Custom designs & spacers

# Custom layout design string
design <- "
AAB
CCD
"

p3 <- ggplot(simdat, aes(x = .fitted, y = .resid, color = group)) +
  geom_point(alpha = 0.6) +
  geom_hline(yintercept = 0, linetype = 2) +
  labs(title = "Residuals vs Fitted")

p4 <- ggplot(simdat, aes(sample = .resid, color = group)) +
  stat_qq(alpha = 0.6) +
  stat_qq_line() +
  labs(title = "Q-Q Plot (Residuals)")

p1 + p2 + p3 + p4 + plot_layout(design = design)
`geom_smooth()` using formula = 'y ~ x'

# Spacers for intentional whitespace
p1 | plot_spacer() | p2
`geom_smooth()` using formula = 'y ~ x'

Collecting legends & axes

# Legends: collect across panels
(p2 | p3 | p4) +
  plot_layout(guides = "collect") +
  plot_annotation(title = "Diagnostics with Shared Legend")
`geom_smooth()` using formula = 'y ~ x'

# Global theme tweaks via '&'
((p2 | p3) / p4) &
  theme(legend.position = "bottom",
        plot.title.position = "plot")
`geom_smooth()` using formula = 'y ~ x'

Demo: Diagnostics dashboard

diag1 <- ggplot(simdat, aes(.fitted, .resid, color = group)) +
  geom_point(alpha = 0.6) +
  geom_hline(yintercept = 0, linetype = 2) +
  labs(x = "Fitted", y = "Residuals", title = "Residuals vs Fitted")

diag2 <- ggplot(simdat, aes(x = .resid, fill = group)) +
  geom_histogram(position = "identity", alpha = 0.5, bins = 30) +
  labs(x = "Residual", title = "Residual Distribution")

diag3 <- ggplot(simdat, aes(sample = .resid, color = group)) +
  stat_qq(alpha = 0.5) + stat_qq_line() +
  labs(title = "Q–Q Plot")

(diag1 | diag2 | diag3) +
  plot_layout(guides = "collect") +
  plot_annotation(title = "Model Diagnostics Overview")

Demo: Group comparisons panel (penguins)

gA <- ggplot(peng, aes(species, bill_length_mm, fill = species)) +
  geom_violin(trim = FALSE, alpha = 0.6) +
  geom_boxplot(width = 0.15, outlier.shape = NA) +
  labs(title = "Bill Length by Species", x = NULL, y = "mm")

gB <- ggplot(peng, aes(species, bill_depth_mm, fill = species)) +
  geom_violin(trim = FALSE, alpha = 0.6) +
  geom_boxplot(width = 0.15, outlier.shape = NA) +
  labs(title = "Bill Depth by Species", x = NULL, y = "mm")

gC <- ggplot(peng, aes(species, body_mass_g, fill = species)) +
  geom_violin(trim = FALSE, alpha = 0.6) +
  geom_boxplot(width = 0.15, outlier.shape = NA) +
  labs(title = "Body Mass by Species", x = NULL, y = "g")

(gA | gB | gC) +
  plot_layout(guides = "collect") +
  plot_annotation(title = "Morphological Differences Among Penguin Species")

Publication-ready figure: tags & hierarchy

pub <- ((gA | gB) / (gC | diag1)) +
  plot_layout(guides = "collect", heights = c(1, 1.1)) +
  plot_annotation(title = "Figure 2. Study Results Summary",
                  tag_levels = "A")

pub

Mixing plots with a table

tbl <- peng |>
  group_by(species) |>
  summarize(n = n(),
            mean_mass = round(mean(body_mass_g), 1),
            mean_bill = round(mean(bill_length_mm), 1),
            .groups = "drop") |>
  arrange(desc(n)) |>
  gt() |>
  tab_header(title = "Sample Summary by Species")

# Render table to graphic (cowplot) for composition
tbl_grob <- cowplot::ggdraw() + cowplot::draw_grob(grid::grid.grabExpr(print(tbl)))
<div id="obmldgmsmz" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
  <style>#obmldgmsmz table {
  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}

#obmldgmsmz thead, #obmldgmsmz tbody, #obmldgmsmz tfoot, #obmldgmsmz tr, #obmldgmsmz td, #obmldgmsmz th {
  border-style: none;
}

#obmldgmsmz p {
  margin: 0;
  padding: 0;
}

#obmldgmsmz .gt_table {
  display: table;
  border-collapse: collapse;
  line-height: normal;
  margin-left: auto;
  margin-right: auto;
  color: #333333;
  font-size: 16px;
  font-weight: normal;
  font-style: normal;
  background-color: #FFFFFF;
  width: auto;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #A8A8A8;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #A8A8A8;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
}

#obmldgmsmz .gt_caption {
  padding-top: 4px;
  padding-bottom: 4px;
}

#obmldgmsmz .gt_title {
  color: #333333;
  font-size: 125%;
  font-weight: initial;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
  border-bottom-color: #FFFFFF;
  border-bottom-width: 0;
}

#obmldgmsmz .gt_subtitle {
  color: #333333;
  font-size: 85%;
  font-weight: initial;
  padding-top: 3px;
  padding-bottom: 5px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-color: #FFFFFF;
  border-top-width: 0;
}

#obmldgmsmz .gt_heading {
  background-color: #FFFFFF;
  text-align: center;
  border-bottom-color: #FFFFFF;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#obmldgmsmz .gt_bottom_border {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#obmldgmsmz .gt_col_headings {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#obmldgmsmz .gt_col_heading {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  overflow-x: hidden;
}

#obmldgmsmz .gt_column_spanner_outer {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  padding-top: 0;
  padding-bottom: 0;
  padding-left: 4px;
  padding-right: 4px;
}

#obmldgmsmz .gt_column_spanner_outer:first-child {
  padding-left: 0;
}

#obmldgmsmz .gt_column_spanner_outer:last-child {
  padding-right: 0;
}

#obmldgmsmz .gt_column_spanner {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 5px;
  overflow-x: hidden;
  display: inline-block;
  width: 100%;
}

#obmldgmsmz .gt_spanner_row {
  border-bottom-style: hidden;
}

#obmldgmsmz .gt_group_heading {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  text-align: left;
}

#obmldgmsmz .gt_empty_group_heading {
  padding: 0.5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: middle;
}

#obmldgmsmz .gt_from_md > :first-child {
  margin-top: 0;
}

#obmldgmsmz .gt_from_md > :last-child {
  margin-bottom: 0;
}

#obmldgmsmz .gt_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  margin: 10px;
  border-top-style: solid;
  border-top-width: 1px;
  border-top-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  overflow-x: hidden;
}

#obmldgmsmz .gt_stub {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 5px;
  padding-right: 5px;
}

#obmldgmsmz .gt_stub_row_group {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 5px;
  padding-right: 5px;
  vertical-align: top;
}

#obmldgmsmz .gt_row_group_first td {
  border-top-width: 2px;
}

#obmldgmsmz .gt_row_group_first th {
  border-top-width: 2px;
}

#obmldgmsmz .gt_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#obmldgmsmz .gt_first_summary_row {
  border-top-style: solid;
  border-top-color: #D3D3D3;
}

#obmldgmsmz .gt_first_summary_row.thick {
  border-top-width: 2px;
}

#obmldgmsmz .gt_last_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#obmldgmsmz .gt_grand_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#obmldgmsmz .gt_first_grand_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: double;
  border-top-width: 6px;
  border-top-color: #D3D3D3;
}

#obmldgmsmz .gt_last_grand_summary_row_top {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-bottom-style: double;
  border-bottom-width: 6px;
  border-bottom-color: #D3D3D3;
}

#obmldgmsmz .gt_striped {
  background-color: rgba(128, 128, 128, 0.05);
}

#obmldgmsmz .gt_table_body {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#obmldgmsmz .gt_footnotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#obmldgmsmz .gt_footnote {
  margin: 0px;
  font-size: 90%;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
}

#obmldgmsmz .gt_sourcenotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#obmldgmsmz .gt_sourcenote {
  font-size: 90%;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
}

#obmldgmsmz .gt_left {
  text-align: left;
}

#obmldgmsmz .gt_center {
  text-align: center;
}

#obmldgmsmz .gt_right {
  text-align: right;
  font-variant-numeric: tabular-nums;
}

#obmldgmsmz .gt_font_normal {
  font-weight: normal;
}

#obmldgmsmz .gt_font_bold {
  font-weight: bold;
}

#obmldgmsmz .gt_font_italic {
  font-style: italic;
}

#obmldgmsmz .gt_super {
  font-size: 65%;
}

#obmldgmsmz .gt_footnote_marks {
  font-size: 75%;
  vertical-align: 0.4em;
  position: initial;
}

#obmldgmsmz .gt_asterisk {
  font-size: 100%;
  vertical-align: 0;
}

#obmldgmsmz .gt_indent_1 {
  text-indent: 5px;
}

#obmldgmsmz .gt_indent_2 {
  text-indent: 10px;
}

#obmldgmsmz .gt_indent_3 {
  text-indent: 15px;
}

#obmldgmsmz .gt_indent_4 {
  text-indent: 20px;
}

#obmldgmsmz .gt_indent_5 {
  text-indent: 25px;
}

#obmldgmsmz .katex-display {
  display: inline-flex !important;
  margin-bottom: 0.75em !important;
}

#obmldgmsmz div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
  height: 0px !important;
}
</style>
  <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
  <thead>
    <tr class="gt_heading">
      <td colspan="4" class="gt_heading gt_title gt_font_normal gt_bottom_border" style>Sample Summary by Species</td>
    </tr>
    
    <tr class="gt_col_headings">
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="species">species</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="n">n</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="mean_mass">mean_mass</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="mean_bill">mean_bill</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td headers="species" class="gt_row gt_center">Adelie</td>
<td headers="n" class="gt_row gt_right">146</td>
<td headers="mean_mass" class="gt_row gt_right">3706.2</td>
<td headers="mean_bill" class="gt_row gt_right">38.8</td></tr>
    <tr><td headers="species" class="gt_row gt_center">Gentoo</td>
<td headers="n" class="gt_row gt_right">119</td>
<td headers="mean_mass" class="gt_row gt_right">5092.4</td>
<td headers="mean_bill" class="gt_row gt_right">47.6</td></tr>
    <tr><td headers="species" class="gt_row gt_center">Chinstrap</td>
<td headers="n" class="gt_row gt_right">68</td>
<td headers="mean_mass" class="gt_row gt_right">3733.1</td>
<td headers="mean_bill" class="gt_row gt_right">48.8</td></tr>
  </tbody>
  
  
</table>
</div>
((gA | gB) / (gC | tbl_grob)) +
  plot_layout(heights = c(1, 1.2)) +
  plot_annotation(title = "Panels + Table in a Single Figure")

Design guidelines (quick checklist)

  • Hierarchy: make the most important panel larger or top-left.
  • Consistency: same theme, fonts, and scales where comparisons are intended.
  • Economy: collect legends; avoid redundant labels.
  • Accessibility: colorblind-safe palettes; sufficient text size.
  • Whitespace: breathing room improves readability.

Common pitfalls

  • Overcrowding (too many tiny panels).
  • Misaligned axes / inconsistent scales.
  • Duplicate legends; forgotten units or labels.
  • Aspect ratio mismatches creating awkward spacing.

Export: resolution & format

# Vector export (PDF) for print
# ggsave("gc3_multipanel_figure.pdf", plot = pub, width = 11, height = 8.5, device = cairo_pdf)

# High-res PNG for slides
# ggsave("gc3_multipanel_figure.png", plot = pub, width = 11, height = 8.5, dpi = 300)

Tips: Always preview exported figures; ensure labels are legible at final size.

Mini hands-on (optional)

  • Take any three plots from your work.
  • Compose with patchwork: set widths/heights, collect legends, add a title.
  • Aim for one coherent message per figure.

Takeaways

  • Layout decisions shape interpretation.
  • patchwork provides a clean grammar for multi-plot composition.
  • Think in stories: each panel a sentence; the figure the paragraph.

Q&A

Questions, edge cases, or examples from your projects?