为什么ggplot不显示一个方框的错误条？

Question

我在R中做了一些带有ggplot的盒子图，我想知道为什么它不显示一个框图的错误栏呢？

代码就是这样的：

ID1.4.5.6.7[,"Time"] <- as.factor(ID1.4.5.6.7[,"Time"])
ggplot(data=ID1.4.5.6.7,aes(x=Time, y=mRNA, fill=Time)) +
  geom_boxplot(notch = TRUE) +
  
  stat_boxplot(geom="errorbar")+
  
  labs(title="mRNA vs Time", subtitle="Irradiated",x = "Time [min]",y = "mRNA")+
  theme(plot.title = element_text(hjust = 0.5),plot.subtitle = element_text(hjust = 0.5))

我不知道它是与代码相关的问题，还是与数据相关的问题，而不是问题。

structure(list(Gene = c("ID-1", "ID-1", "ID-1", "ID-1", "ID-1", 
"ID-1", "ID-1", "ID-1", "ID-1", "ID-1", "ID-1", "ID-1", "ID-1", 
"ID-1", "ID-1", "ID-4", "ID-4", "ID-4", "ID-4", "ID-4", "ID-4", 
"ID-4", "ID-4", "ID-4", "ID-4", "ID-4", "ID-4", "ID-4", "ID-4", 
"ID-4", "ID-4", "ID-5", "ID-5", "ID-5", "ID-5", "ID-5", "ID-5", 
"ID-5", "ID-5", "ID-5", "ID-5", "ID-5", "ID-5", "ID-5", "ID-5", 
"ID-5", "ID-5", "ID-5", "ID-5", "ID-6", "ID-6", "ID-6", "ID-6", 
"ID-6", "ID-6", "ID-6", "ID-6", "ID-6", "ID-6", "ID-6", "ID-6", 
"ID-6", "ID-6", "ID-6", "ID-6", "ID-6", "ID-6", "ID-7", "ID-7", 
"ID-7", "ID-7", "ID-7", "ID-7", "ID-7", "ID-7", "ID-7", "ID-7", 
"ID-7", "ID-7", "ID-7", "ID-7", "ID-7", "ID-7", "ID-7", "ID-7"
), mRNA = c(-0.181385669, -0.059647494, 0.104476117, -0.052190978, 
-0.040484945, 0.194226742, -0.501601326, 0.102342605, -0.127143845, 
-0.008523742, -0.102946211, -0.042894028, 0.002922923, -0.134394347, 
-0.214204393, -0.138122686, 0.203242361, 0.097935502, 0.147068146, 
-0.089430917, 0.331565412, -0.034572422, -0.129896329, 0.324191, 
0.470108479, -0.027268223, 0.232304713, 0.090348708, 0.070848402, 
0.181540708, -0.502255367, -0.267631441, -0.368647839, -0.040910404, 
-0.003983171, -0.003983171, -0.003983171, -0.14980589, -0.119449612, 
-0.309154214, -0.487589361, 0.272803506, -0.421733575, -0.467108567, 
0.024868338, -0.156025729, -0.044680175, -0.206716896, -0.272014193, 
-0.230499883, -0.238597397, -0.118130949, 0.349957464, 0.349957464, 
0.349957464, 0.172048587, -0.186226994, 0.16113822, -0.293029136, 
-0.111636253, -0.044189887, 0.081555274, -0.048106079, -0.05853566, 
0.010407814, -0.066981809, -0.09828484, -0.315190986, -0.005102456, 
0.221556197, 0.206584568, 0.206584568, 0.206584568, 0.102649006, 
-0.011777384, -0.36963487, -0.054853074, -0.230240699, -0.210508323, 
-0.208889919, -0.050763372, 0.023073782, -0.095118984, -0.091076071, 
-0.330257395), Time = structure(c(2L, 2L, 2L, 3L, 3L, 2L, 3L, 
3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 
4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 2L, 2L, 2L, 1L, 1L, 1L, 3L, 3L, 
2L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 2L, 2L, 2L, 1L, 1L, 1L, 
3L, 3L, 2L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 2L, 2L, 2L, 1L, 
1L, 1L, 3L, 2L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L), .Label = c("0", 
"20", "40", "60", "120"), class = "factor"), predicted_mRNA = c(-0.00551000342030954, 
-0.00551000342030954, -0.00551000342030954, -0.0302695238715682, 
-0.0302695238715682, -0.00551000342030954, -0.0302695238715682, 
-0.0302695238715682, -0.0550290443228268, -0.0550290443228268, 
-0.0550290443228268, -0.0550290443228268, -0.129307605676603, 
-0.129307605676603, -0.129307605676603, -0.00551000342030954, 
-0.00551000342030954, -0.00551000342030954, -0.0302695238715682, 
-0.0302695238715682, -0.00551000342030954, -0.0302695238715682, 
-0.0302695238715682, -0.0550290443228268, -0.0550290443228268, 
-0.0550290443228268, -0.0550290443228268, -0.129307605676603, 
-0.129307605676603, -0.129307605676603, -0.129307605676603, -0.00551000342030954, 
-0.00551000342030954, -0.00551000342030954, 0.0192495170309491, 
0.0192495170309491, 0.0192495170309491, -0.0302695238715682, 
-0.0302695238715682, -0.00551000342030954, -0.0302695238715682, 
-0.0302695238715682, -0.0550290443228268, -0.0550290443228268, 
-0.0550290443228268, -0.129307605676603, -0.129307605676603, 
-0.129307605676603, -0.129307605676603, -0.00551000342030954, 
-0.00551000342030954, -0.00551000342030954, 0.0192495170309491, 
0.0192495170309491, 0.0192495170309491, -0.0302695238715682, 
-0.0302695238715682, -0.00551000342030954, -0.0302695238715682, 
-0.0302695238715682, -0.0550290443228268, -0.0550290443228268, 
-0.0550290443228268, -0.0550290443228268, -0.129307605676603, 
-0.129307605676603, -0.129307605676603, -0.00551000342030954, 
-0.00551000342030954, -0.00551000342030954, 0.0192495170309491, 
0.0192495170309491, 0.0192495170309491, -0.0302695238715682, 
-0.00551000342030954, -0.0302695238715682, -0.0302695238715682, 
-0.0550290443228268, -0.0550290443228268, -0.0550290443228268, 
-0.0550290443228268, -0.129307605676603, -0.129307605676603, 
-0.129307605676603, -0.129307605676603)), row.names = c(NA, -85L
), class = "data.frame")

这里是dput(ID1.4.5.6.7)，所以是dataframe。

Answer 1

我建议采用这种方法，在这里您可以启用varwidth以查看错误栏。在这里，代码：

#Plot
ggplot(data=ID1.4.5.6.7,aes(x=Time, y=mRNA, fill=Time)) +
  geom_boxplot(varwidth = TRUE,notch=TRUE) +
  stat_boxplot(geom="errorbar")+
  labs(title="mRNA vs Time", subtitle="Irradiated",x = "Time [min]",y = "mRNA")+
  theme(plot.title = element_text(hjust = 0.5),plot.subtitle = element_text(hjust = 0.5))

输出：

​

​

Answer 2

因为方框没有错误条。盒子图只是五个数字的图形表示:最小，Q1 (第一个四分位数)，中位数，Q3 (第三个四分位数)和最大值。晶须(向上和向下的“条形”)只是以数据中的最小值(下一条)和最大值(上一条)结尾的行。“框”的底部边缘是Q1，上边是Q3。

可以安排一组数据，使最小值与Q1相同，最大值与Q3相同。或多或少，这似乎是在没有胡须的盒子里发生的事情。ggplot在盒子图中添加了一些额外的细节(“腰部”被拉了进来，算法上的调整导致了在0组顶部出现倒排的可能性)，但这似乎或多或少就是正在发生的事情。

编辑:这似乎是一个关于代码的问题，但实际上是关于统计的。交叉验证可能更好(虽然我认为它现在可能已经得到了充分的回答)。