这可能不是最优雅的方法,但您可以在 ggplot2 之外计算 p 值,并使用 ifelse 语句,赋予您可以使用 scale_fill_identity 调用的颜色模式。
这里是一个使用虚拟示例的示例:
df <- data.frame(Xval = rep(c("A","B"),each = 50),
Yval = c(sample(1:50,50), sample(50:100,50)))
我在这里使用了dplyr 管道序列,但你可以在base r 中轻松做到这一点:
library(dplyr)
library(ggplot2)
df %>% mutate(pval = t.test(Yval~Xval)$p.value) %>%
group_by(Xval) %>% mutate(Mean = mean(Yval)) %>%
ungroup() %>%
mutate(Color = ifelse(pval < 0.05 & Mean == max(Mean), "blue","green")) %>%
ggplot(aes(x = Xval, y = Yval, fill = Color))+
geom_boxplot()+
stat_compare_means(method = "t.test")+
scale_fill_identity()
使用您的示例:
df1 %>% mutate(pval = t.test(time~type)$p.value) %>%
group_by(type) %>% mutate(Mean = mean(time)) %>%
ungroup() %>%
mutate(Color = ifelse(pval < 0.05 & Mean == max(Mean), "blue","green")) %>%
ggplot(aes(x = type, y = time, fill = Color))+
geom_boxplot()+
stat_compare_means(method = "t.test")+
scale_fill_identity()