r - 使用 `gpplot2` 使用多个因子变量复制 `sciplot`

Question

我非常喜欢使用 sciplot 来获取实验数据，因为我不必手动计算误差线。过去，我用它来对两个因子变量进行分组，例如:

plot1<-bargraph.CI(
  df$factor1,   #categorical factor for the x-axis
  df$y,         #numerical DV for the y-axis
  df$factor2    #grouping factor
)

但是，我现在需要对三个因子变量进行分组。 sciplot 文档表明这在 sciplot.

中是不可能的

那么，现在是问问题的必要时间了……我究竟该如何使用 ggplot2 来做到这一点？具体来说，是否有一种简约的方法来生成带有超过 3 个因子变量的误差条的图形？我在网上四处搜寻，但在寻找优雅的解决方案时不断出现问题。

示例数据如下:

factor1          factor2             factor3     y
More expensive   Least important     Blue        1
Less expensive   Most important      Blue        0
Same cost        Least important     Red         1
More expensive   Least important     Red         0
Less expensive   Most important      Red         1
Same cost        Least important     Blue        1
More expensive   Least important     Red         1
Less expensive   Least important     Blue        0
Same cost        Most important      Red         1

Answer 1

您可以通过两次调用 stat_summary 来复制(在一定程度上)sciplot。

您可以将两个因素级别合并为一个interaction(使用interaction)或使用faceting。

我将使用 ToothGrowth，它包含在带有基础 R 的数据集包中。

# add third factor
ToothGrowth$F3 <- letters[1:2]
# coerce dose to a factor
ToothGrowth$dose <- factor(ToothGrowth$dose, levels = c(0.5,1,2))

# interaction on the x axis
 ggplot(ToothGrowth, aes(y = len, x = interaction(supp, F3))) + 
  stat_summary(fun.y = 'mean', fun.ymin = function(x) 0, geom = 'bar', 
    aes(fill =dose), position = 'dodge') +
  stat_summary(fun.ymin = function(x) mean(x) - sd(x), 
    fun.ymax = function(x) mean(x) + sd(x),  position ='dodge', 
    geom = 'errorbar', aes(group = dose))

# facetting on the third factor
ggplot(ToothGrowth, aes(y = len, x = supp )) + 
  stat_summary(fun.y = 'mean', fun.ymin = function(x) 0, geom = 'bar', 
   aes(fill =dose), position = 'dodge') +
  stat_summary(fun.ymin = function(x) mean(x) - sd(x), 
               fun.ymax = function(x) mean(x) + sd(x), position ='dodge', 
               geom = 'errorbar', aes(group = dose))+
  facet_wrap(~F3)

ggplot(ToothGrowth, aes(y = len, x = supp)) + 
  stat_summary(fun.y = 'mean', fun.ymin = function(x) 0, 
               geom = 'bar', aes(fill =interaction(dose, F3)), 
               position = 'dodge') +
  stat_summary(fun.ymin = function(x) mean(x) - sd(x), 
               fun.ymax = function(x) mean(x) + sd(x), 
               position ='dodge', geom = 'errorbar', 
               aes(fill =interaction(dose, F3)))