ggbarplot中最后一条柱形的mean_se误差线与柱顶不对齐问题求助

我用ggpubr绘制柱状图，用来比较Condition变量，同时按Geno分面。图表整体效果没问题，但最右侧的柱形存在异常：它的mean_se误差线落在了柱形阴影区域的中间。

绘图代码 #

plot = ggbarplot(filtered_df, 
                 x = "Condition", 
                 y = "ScrapeNitrite", 
                 fill = "Condition",
                 add = c("mean_se", "jitter"),
                 facet.by="Geno",
                 palette = c("white", "grey", "darkgrey","black")) +
  scale_y_continuous(limits = c(0,100), expand = c(0, 0)) +
  theme_prism()

数据集 #

# A tibble: 16 × 3
   Condition ScrapeNitrite Geno 
   <fct>             <dbl> <fct>
 1 naïve             16.9  FF   
 2 naïve              9.28 FF   
 3 DSS               15.2  dIEC 
 4 DSS               23.6  FF   
 5 DSS               48.8  FF   
 6 DSS               37.7  FF   
 7 DSS               34.7  FF   
 8 DSS               35.7  FF   
 9 naïve              5.09 dIEC 
10 DSS               35.8  dIEC 
11 DSS                6.33 dIEC 
12 DSS              151.   dIEC 
13 DSS               13.5  dIEC 
14 DSS               -2.84 dIEC 
15 DSS               20.8  dIEC 
16 naïve             28.2  FF   

原因 #

你设置了scale_y_continuous(limits = c(0,100))，但dIEC组的DSS条件里有个151的异常值，还有-2.84的负值。ggpubr计算mean_se时基于原始全量数据，均值和标准差被151拉高，但柱形因y轴限制被截断到100，导致误差线看起来落在柱形中间。

解决办法 #

有三种可行思路：

1. 截断数据后绘图：过滤掉超出y轴范围的数据，让统计计算和可视化基于同一数据集：

# 过滤0-100之间的数据
filtered_df_truncated = filtered_df %>% 
  filter(ScrapeNitrite >= 0, ScrapeNitrite <= 100)

# 用过滤后的数据重新绘图
plot = ggbarplot(filtered_df_truncated, 
                 x = "Condition", 
                 y = "ScrapeNitrite", 
                 fill = "Condition",
                 add = c("mean_se", "jitter"),
                 facet.by="Geno",
                 palette = c("white", "grey", "darkgrey","black")) +
  scale_y_continuous(limits = c(0,100), expand = c(0, 0)) +
  theme_prism()

2. 用坐标截断替代轴限制：改用coord_cartesian设置显示范围，统计计算仍用原始数据，误差线会对应真实均值：

plot = ggbarplot(filtered_df, 
                 x = "Condition", 
                 y = "ScrapeNitrite", 
                 fill = "Condition",
                 add = c("mean_se", "jitter"),
                 facet.by="Geno",
                 palette = c("white", "grey", "darkgrey","black")) +
  coord_cartesian(ylim = c(0,100), expand = FALSE) +
  theme_prism()

3. 修正/移除异常值：如果151是录入错误，直接修正或删除该数据点，统计结果会更贴合实际分布。

内容的提问来源于stack exchange，提问作者Bryce Hunt