如何用R绘制带误差线及原始数据可视化的分组折线图

想要绘制带误差线的跨分组折线图，同时叠加原始数据可视化（如小提琴图、雨云图），弥补SD/SE/CI等汇总统计无法呈现完整数据分布的缺陷。以下基于你提供的代码，给出三种实现方式：

方式1：折线图叠加小提琴图 #

利用原始数据d1绘制小提琴图，通过position_dodge保证分组与折线图对齐，先画小提琴图作为底层，再叠加折线和误差线：

library(dplyr)
library(ggplot2)

# 生成数据
d1 <-  data.frame(time_serie = as.factor(rep(rep(1:3, each = 6), 3)),
                  treatment = as.factor(rep(c("HIGH", "MEDIUM", "LOW"), each = 18)),
                  value = runif(54, 1, 10))

# 计算汇总统计
d2 <- d1 %>%
  group_by(time_serie,treatment) %>%
  summarise(mean_value = mean(value), 
            sd_value = sd(value)) %>%
  ungroup()

# 绘图：小提琴图+折线误差线
ggplot() +
  # 底层小提琴图，position_dodge与后续图层保持一致
  geom_violin(data = d1, 
              aes(x = time_serie, y = value, fill = treatment),
              position = position_dodge(0.3), alpha = 0.3) +
  # 误差线
  geom_errorbar(data = d2,
                aes(x = time_serie, y = mean_value, 
                    ymin = mean_value - sd_value, ymax = mean_value + sd_value,
                    color = treatment),
                width = .2, position = position_dodge(0.3), size =1) +
  # 均值点
  geom_point(data = d2,
             aes(x = time_serie, y = mean_value, color = treatment),
             position = position_dodge(0.3), size = 3) +
  # 折线
  geom_line(data = d2,
            aes(x = time_serie, y = mean_value, color = treatment, group = treatment),
            position=position_dodge(0.3), size =1) +
  theme_bw()

方式2：折线图叠加雨云图 #

雨云图结合了小提琴图（分布趋势）和散点（原始数据），需借助ggdist包实现，视觉信息更丰富：

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

# 数据部分同前
d1 <-  data.frame(time_serie = as.factor(rep(rep(1:3, each = 6), 3)),
                  treatment = as.factor(rep(c("HIGH", "MEDIUM", "LOW"), each = 18)),
                  value = runif(54, 1, 10))

d2 <- d1 %>%
  group_by(time_serie,treatment) %>%
  summarise(mean_value = mean(value), 
            sd_value = sd(value)) %>%
  ungroup()

# 绘图：雨云图+折线误差线
ggplot() +
  # 雨云图（整合小提琴+散点）
  geom_raincloud(data = d1,
                 aes(x = time_serie, y = value, fill = treatment, color = treatment),
                 position = position_dodge(0.3), alpha = 0.3) +
  # 折线与误差线
  geom_errorbar(data = d2,
                aes(x = time_serie, y = mean_value, 
                    ymin = mean_value - sd_value, ymax = mean_value + sd_value,
                    color = treatment),
                width = .2, position = position_dodge(0.3), size =1) +
  geom_point(data = d2,
             aes(x = time_serie, y = mean_value, color = treatment),
             position = position_dodge(0.3), size = 3) +
  geom_line(data = d2,
            aes(x = time_serie, y = mean_value, color = treatment, group = treatment),
            position=position_dodge(0.3), size =1) +
  theme_bw()

方式3：折线图叠加抖动散点 #

如果追求简洁直观，直接用抖动散点展示原始数据，避免点重叠：

library(dplyr)
library(ggplot2)

d1 <-  data.frame(time_serie = as.factor(rep(rep(1:3, each = 6), 3)),
                  treatment = as.factor(rep(c("HIGH", "MEDIUM", "LOW"), each = 18)),
                  value = runif(54, 1, 10))

d2 <- d1 %>%
  group_by(time_serie,treatment) %>%
  summarise(mean_value = mean(value), 
            sd_value = sd(value)) %>%
  ungroup()

ggplot() +
  # 抖动散点（避免点重叠）
  geom_jitter(data = d1,
              aes(x = time_serie, y = value, color = treatment),
              position = position_dodge(jitter.width = 0.2, width = 0.3),
              size = 2, alpha = 0.5) +
  # 折线与误差线
  geom_errorbar(data = d2,
                aes(x = time_serie, y = mean_value, 
                    ymin = mean_value - sd_value, ymax = mean_value + sd_value,
                    color = treatment),
                width = .2, position = position_dodge(0.3), size =1) +
  geom_point(data = d2,
             aes(x = time_serie, y = mean_value, color = treatment),
             position = position_dodge(0.3), size = 3) +
  geom_line(data = d2,
            aes(x = time_serie, y = mean_value, color = treatment, group = treatment),
            position=position_dodge(0.3), size =1) +
  theme_bw()

关键注意点 #

• 所有图层的position_dodge(width = 0.3)数值要统一，保证分组对齐
• 原始数据图层（小提琴/散点）放在最底层，避免遮挡折线和误差线
• 可通过调整alpha（透明度）、size（元素大小）优化视觉层次

内容的提问来源于stack exchange，提问作者Muhammad Aaqib Shamim