# 安装必要的包
if (!requireNamespace("palmerpenguins", quietly = TRUE)) {
install.packages("palmerpenguins")
}
if (!requireNamespace("patchwork", quietly = TRUE)) {
install.packages("patchwork")
}
# 加载包
library(ggplot2)
library(dplyr)
library(patchwork)
library(palmerpenguins)边缘分布散点图
散点图展示两个变量如何共同变化，却隐藏了它们各自的分布——右上角的一团点云可能藏着一个双峰变量和一个偏态变量。边缘分布图（marginal plots）解决了这一问题：把 x 变量的分布沿上边缘、y 变量的分布沿右边缘附加到散点图上（直方图、密度或箱线图），读者在一张图中同时看到联合关系与两个边缘分布 [1]。这一设计在临床与生物医学散点图中非常流行——例如肿瘤论文里的生物标志物相关性面板。
本教程用 ggplot2 + patchwork [2] 手工搭建该版式。装配过程完全显式（哪些标度、哪些主题、哪些边距），不依赖特定包版本，且可直接迁移到任意面板组合。关于常用一行代码方案 ggExtra::ggMarginal() 的说明见下方 callout。
本教程涵盖两种工作流：
- 带边缘直方图的散点图；
- 按物种分层、共享色标的边缘密度图。
Example
Setup
系统要求：跨平台（Linux/MacOS/Windows）
编程语言：R
依赖包：
ggplot2；dplyr；patchwork；palmerpenguins
sessioninfo::session_info("attached")─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.6.2 (2026-10-29)
os Ubuntu 24.04.5 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate C.UTF-8
ctype C.UTF-8
tz UTC
date 2026-10-31
pandoc 3.1.3 @ /usr/bin/ (via rmarkdown)
quarto 1.10.20 @ /usr/local/bin/quarto
─ Packages ───────────────────────────────────────────────────────────────────
! package * version date (UTC) lib source
P dplyr * 1.2.1 2026-04-03 [?] RSPM
P ggplot2 * 4.0.3.9000 2026-10-30 [?] Github (tidyverse/ggplot2@b638d65)
P palmerpenguins * 0.1.1 2022-08-15 [?] RSPM
P patchwork * 1.3.2 2025-08-25 [?] RSPM
[1] /home/runner/.cache/R/renv/library/Bizard-5140bdc6/linux-ubuntu-noble/R-4.6/x86_64-pc-linux-gnu
[2] /home/runner/work/_temp/Library
[3] /opt/R/4.6.2/lib/R/library
* ── Packages attached to the search path.
P ── Loaded and on-disk path mismatch.
──────────────────────────────────────────────────────────────────────────────Data Preparation
使用 palmerpenguins 包的 penguins 数据 [3]：344 只南极企鹅（三个物种）的形态学测量。喙长与鳍长在 342 只中完整——是该版式的经典相关变量对。
data(penguins, package = "palmerpenguins")
peng <- penguins |>
filter(!is.na(bill_length_mm), !is.na(flipper_length_mm))
xr <- range(peng$bill_length_mm)
yr <- range(peng$flipper_length_mm)
cat("Rows used:", nrow(peng), "\n")Rows used: 342 Visualization
1. 带边缘直方图的散点图
诀窍是三个共享标度的图：主散点图、上侧直方图（相同的 x 标度）、右侧直方图（相同的 y 标度，且 x 轴反转使条形朝散点生长）。patchwork 的 design 字符串放置三个面板。
nb <- 28
pmain <- ggplot(peng, aes(bill_length_mm, flipper_length_mm)) +
geom_point(color = "#2166AC", alpha = 0.6, size = 2) +
scale_x_continuous(limits = xr) +
scale_y_continuous(limits = yr) +
labs(x = "Bill length (mm)", y = "Flipper length (mm)") +
theme_bw(base_size = 11)
ptop <- ggplot(peng, aes(bill_length_mm)) +
geom_histogram(bins = nb, fill = "#2166AC", alpha = 0.75) +
scale_x_continuous(limits = xr) +
labs(x = NULL, y = "Count") +
theme_bw(base_size = 10) +
theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(),
panel.grid.minor = element_blank())
pright <- ggplot(peng, aes(y = flipper_length_mm)) +
geom_histogram(bins = nb, fill = "#2166AC", alpha = 0.75) +
scale_y_continuous(limits = yr) +
scale_x_continuous(trans = "reverse") +
labs(x = "Count", y = NULL) +
theme_bw(base_size = 10) +
theme(axis.text.y = element_blank(), axis.ticks.y = element_blank(),
panel.grid.minor = element_blank())
design <- "AB
CD"
p1 <- wrap_plots(A = ptop, B = plot_spacer(), C = pmain, D = pright,
design = design) +
plot_layout(widths = c(3, 1), heights = c(1, 3))
p1
注意边缘直方图携带了散点图无法表达的信息：鳍长是双峰的，尽管散点看起来像一团连续的云。
2. 分组边缘密度图
pal <- c(Adelie = "#4E79A7", Chinstrap = "#F28E2B", Gentoo = "#E15759")
pmain_g <- ggplot(peng, aes(bill_length_mm, flipper_length_mm, color = species)) +
geom_point(alpha = 0.6, size = 2) +
scale_color_manual(values = pal) +
scale_x_continuous(limits = xr) + scale_y_continuous(limits = yr) +
labs(x = "Bill length (mm)", y = "Flipper length (mm)") +
theme_bw(base_size = 11) +
theme(legend.position = c(0.72, 0.22), legend.title = element_blank(),
legend.background = element_rect(fill = "white", color = "grey80",
linewidth = 0.2))
ptop_g <- ggplot(peng, aes(bill_length_mm, fill = species)) +
geom_density(alpha = 0.45, linewidth = 0.4) +
scale_fill_manual(values = pal) +
scale_x_continuous(limits = xr) +
labs(x = NULL, y = NULL) +
theme_bw(base_size = 10) +
theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(),
axis.text.y = element_blank(), panel.grid.minor = element_blank())
pright_g <- ggplot(peng, aes(y = flipper_length_mm, fill = species)) +
geom_density(alpha = 0.45, linewidth = 0.4) +
scale_fill_manual(values = pal) +
scale_y_continuous(limits = yr) +
scale_x_continuous(trans = "reverse") +
labs(x = NULL, y = NULL) +
theme_bw(base_size = 10) +
theme(axis.text.y = element_blank(), axis.ticks.y = element_blank(),
axis.text.x = element_blank(), panel.grid.minor = element_blank())
p2 <- wrap_plots(A = ptop_g, B = plot_spacer(), C = pmain_g, D = pright_g,
design = design) +
plot_layout(widths = c(3, 1), heights = c(1, 3))
p2
这正是该版式的价值所在：Adelie 与 Chinstrap 在鳍长（右边缘）上不可分，但上边缘把二者在喙长上干净地分开——这一事实在散点图中完全不可见。
提示实践要点
- 三个面板保持完全相同的标度：若边缘分布与散点轴不对齐，图就不诚实。用
scale_*_continuous(limits = range(...))在每个面板冻结范围。 - 侧面板反转坐标轴（
scale_x_continuous(trans = "reverse")），让条形朝散点生长——这是标准惯例。 - 一行代码即可完成：
ggExtra::ggMarginal(p)[1]——但截至 2026-10，该包的边缘面板在 ggplot2 ≥ 4.0 下会塌陷（渲染为黑色矩形）；遇到此问题请回退到上面的手工装配方案，后者与版本无关。 - n < ~30 时，把直方图换成边缘地毯/散点——小样本的分箱计数会误导。
Application
边缘增强散点图常见于方法比较与生物标志物论文：两种检测的相关性图（边缘放各自分布）、剂量-反应面板、PCA/t-SNE 投影配各轴得分分布。下图模拟典型论文版式：(A) 合并队列的边缘直方图，(B) 按亚群分组的边缘密度图。
Reference
[1] Attali D, Baker C. ggExtra: add marginal histograms to ‘ggplot2’, and more ‘ggplot2’ enhancements. https://github.com/daattali/ggExtra
[2] Pedersen TL. patchwork: the composer of plots. R package version 1.3.2. 2025. https://CRAN.R-project.org/package=patchwork
[3] Horst AM, Hill AP, Gorman KB. palmerpenguins: Palmer Archipelago (Antarctica) penguin data. R package version 0.1.1. 2022. doi:10.5281/zenodo.3965738