R ggplot2：具有显着性水平的盒图（超过2个组：kruskal.test和wilcox.test成对）和多个方面 [英] R ggplot2: boxplots with significance level (more than 2 groups: kruskal.test and wilcox.test pairwise) and multiple facets

问题描述

继续阅读

这个想法是执行一个Kruskal- Wallis测试跨越变量组（Sepal.Length，Sepal.Width，Petal.Length，Petal.Width）和它们之间的成对Wilcoxon测试，由Species和treat定义的PER FACET

这很可能涉及更新注释，就像我以前的问题一样。

换句话说，我想要做的和

Kruskal.wallis可以是包括通过添加

  library（ggpubr）
 stat_compare_means（test =kruskal.test）
  

Following up on this question, I am trying to make boxplots and pairwise comparisons to show levels of significance (only for the significant pairs) again, but this time I have more than 2 groups to compare and more complicated facets.

I am going to use the iris dataset here for illustration purposes. Check the MWE below where I add an additional "treatment" variable.

library(reshape2)
library(ggplot2)
data(iris)
iris$treatment <- rep(c("A","B"), length(iris$Species)/2)
mydf <- melt(iris, measure.vars=names(iris)[1:4])
ggplot(mydf, aes(x=variable, y=value, fill=Species)) + geom_boxplot() +
stat_summary(fun.y=mean, geom="point", shape=5, size=4) +
facet_grid(treatment~Species, scales="free", space="free_x") +
theme(axis.text.x = element_text(angle=45, hjust=1)) 

This produces the following plot:

The idea would be to perform a Kruskal-Wallis test across the "variable" groups (Sepal.Length, Sepal.Width, Petal.Length, Petal.Width), and pairwise Wilcoxon tests between them, PER FACET defined by "Species" and "treatment".

It would most likely involve updating the annotation like in my previous question.

In other words, I want to do the same as in this other question I posted, but PER FACET.

I am getting horribly confused and stuck, though the solution should be quite similar... Any help would be appreciated!! Thanks!!

解决方案

You can try

library(ggsignif)
ggplot(mydf,aes(x=variable, y=value)) + 
  geom_boxplot(aes(fill=Species)) + # define the fill argument here
  facet_grid(treatment~Species) +
  ylim(0,15)+
  theme(axis.text.x = element_text(angle=45, hjust=1)) +
  geom_signif(test="wilcox.test", comparisons = combn(levels(mydf$variable),2, simplify = F)[-4],
              step_increase = 0.2)

Kruskal.wallis can be included by adding

library(ggpubr)
stat_compare_means(test="kruskal.test")

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