如何修改Raincloud图的Y轴以展示每组观测样本数量？

解决方案 #

核心思路是先统计每个物种的样本量，再将物种名称与样本量合并成新的Y轴刻度标签，具体有两种实现方式：

方法1：预处理数据生成带样本量的标签

先统计每个物种的样本数，生成包含样本量的新标签列，然后直接将该列作为Y轴映射变量：

library(dplyr)
library(ggplot2)
library(ggdist)

# 统计每个物种的样本量并生成带样本量的标签
species_labels <- iris %>%
  count(Species) %>%
  mutate(label = paste(Species, paste0("(n=", n, ")"), sep = "\n"))

# 合并数据并绘图
Rc <- iris %>%
  left_join(species_labels, by = "Species") %>%
  group_by(Species) %>%
  mutate(mean = mean(Petal.Length),
         se = sd(Petal.Length)/sqrt(length(Petal.Length))) %>%
  ungroup() %>%
  ggplot(aes(x = Petal.Length, y = label)) +
  stat_slab(aes(fill = Species)) +
  stat_dots(aes(color = Species), side = "bottom", shape = 16) +
  scale_fill_brewer(palette = "Set1", aesthetics = c("fill", "color")) +
  geom_errorbar(aes(xmin = mean - 1.96 * se,
                    xmax = mean + 1.96 * se), width = 0.2) +
  stat_summary(fun = mean, geom = "point", shape = 16, size = 3.0) +
  theme_bw(base_size = 10) +
  theme(legend.position = "top") +
  labs(title = "Raincloud plot with ggdist",
       x = "Petal Length",
       y = "Species (Sample Size)")

print(Rc)

方法2：通过scale_y_discrete动态替换标签

不需要修改原始数据，直接在绘图时通过scale_y_discrete的labels参数动态生成带样本量的标签：

library(dplyr)
library(ggplot2)
library(ggdist)

# 提前统计每个物种的样本量
species_counts <- iris %>% count(Species)

# 绘图并替换Y轴标签
Rc <- iris %>%
  group_by(Species) %>%
  mutate(mean = mean(Petal.Length),
         se = sd(Petal.Length)/sqrt(length(Petal.Length))) %>%
  ungroup() %>%
  ggplot(aes(x = Petal.Length, y = Species)) +
  stat_slab(aes(fill = Species)) +
  stat_dots(aes(color = Species), side = "bottom", shape = 16) +
  scale_fill_brewer(palette = "Set1", aesthetics = c("fill", "color")) +
  geom_errorbar(aes(xmin = mean - 1.96 * se,
                    xmax = mean + 1.96 * se), width = 0.2) +
  stat_summary(fun = mean, geom = "point", shape = 16, size = 3.0) +
  # 动态生成带样本量的Y轴标签
  scale_y_discrete(
    labels = function(species_name) {
      sample_size <- species_counts$n[species_counts$Species == species_name]
      paste(species_name, paste0("(n=", sample_size, ")"), sep = "\n")
    }
  ) +
  theme_bw(base_size = 10) +
  theme(legend.position = "top") +
  labs(title = "Raincloud plot with ggdist",
       x = "Petal Length",
       y = "Species (Sample Size)")

print(Rc)

两种方法都能实现需求，方法1适合需要多次复用带样本量标签的场景，方法2更轻量化，不需要修改原始数据结构。

内容的提问来源于stack exchange，提问作者user21215346