只需使用fo rmula和data作为函数中的参数，并使用...作为其他参数，如bracket.nudge.y=，以调整愚蠢的n.s空白。

dunn_bxp <- \(fo, data, plot=TRUE, ...) {
  vars <- all.vars(fo)
  dnn <- rstatix::dunn_test(fo, data=data, p.adjust.method="BH")
  dnn_p <- rstatix::add_xy_position(dnn, x=vars[2])
  if (plot) {
    p <- ggpubr::ggboxplot(data, x=vars[2], y=vars[1]) + 
      ggpubr::stat_pvalue_manual(dnn_p, label="p.adj.signif", hide.ns=TRUE, ...)
    print(p)
    return(invisible(as.data.frame(dnn[1:8])))
  } else {
    return(as.data.frame(dnn[1:8]))
  }
}

如果是plot=TRUE（默认值），则绘制并以不可见的方式返回统计信息。

r <- dunn_bxp(fo=observed ~ month, data=df, bracket.nudge.y=-1000)
head(r)
#        .y. group1 group2 n1 n2  statistic           p      p.adj
# 1 observed    Apr    Aug  3  3 -0.6199874 0.535266078 0.71803318
# 2 observed    Apr    Dec  3  3 -2.7124449 0.006678888 0.08816133
# 3 observed    Apr    Feb  3  3 -1.1237272 0.261128784 0.50954093
# 4 observed    Apr    Jan  3  3 -1.7049654 0.088200884 0.32174752
# 5 observed    Apr    Jul  3  3 -1.7049654 0.088200884 0.32174752
# 6 observed    Apr    Jun  3  3  0.7749843 0.438348961 0.64291181

如果是plot=FALSE，则只返回统计信息。

head(dunn_bxp(fo=observed ~ month, data=df, plot=FALSE))
#        .y. group1 group2 n1 n2  statistic           p      p.adj
# 1 observed    Apr    Aug  3  3 -0.6199874 0.535266078 0.71803318
# 2 observed    Apr    Dec  3  3 -2.7124449 0.006678888 0.08816133
# 3 observed    Apr    Feb  3  3 -1.1237272 0.261128784 0.50954093
# 4 observed    Apr    Jan  3  3 -1.7049654 0.088200884 0.32174752
# 5 observed    Apr    Jul  3  3 -1.7049654 0.088200884 0.32174752
# 6 observed    Apr    Jun  3  3  0.7749843 0.438348961 0.64291181

• 数据：*

set.seed(557)
df <- data.frame(observed=round(rep(runif(12, 500, 800), each=3) + rep.int(rnorm(12, 0, 64), 3)),
                 month=rep(month.abb, each=3))

qdread展示了一个超级流畅的方法。我有一个不同的方法，使用for循环。因为你没有发布一个可重复的例子，我无法测试我的代码，它可能会产生错误，但它应该给予你一个想法。祝你好运！

list <- list() # creates empty list
count<-1       # creates an indicator starting with one, increasing by one every iteration, first plot will be saved as first item of list, second plot as second item, ...

for (i in c("Observed","Shannon","InvSimpson","Evenness")){            # i will take the first value in c(...) in the first iteration
                                                                       # the second value in the second iteration, ...
  list[[count]] <- eval(parse(
    text=paste0('dunn_test(',i,'~ Month, data=df, p.adjust.method="BH")') #paste0 pasts a string, eval(parse(text="string")) executes the string
    ))
  list[[count]] <- list[[count]] %>% arrange(p.adj)
  list[[count]] <- list[[count]] %>% add_xy_position(x = "Month")
  list[[count]] <- eval(parse(text=paste0('ggboxplot(df, x =,',i,', y = paste0(i)) + 
                                stat_pvalue_manual(',list[[count]],', label = "p.adj.signif", hide.ns = TRUE)')))
  count<- count+1 # count indicator increases by 1
}

这是一个以字符串为参数的函数。你可以为每个变量调用这个函数。

dunn_boxplot <- function(yvariable) {
  dunn <- dunn_test(formula(paste(yvariable, "~ Month")), data=df, p.adjust.method="BH") %>%
    arrange(p.adj) %>%
    add_xy_position(x = "Month")
  bxp <- ggboxplot(df, x = "Month", y = yvariable) + 
    stat_pvalue_manual(dunn, label = "p.adj.signif", hide.ns = TRUE)
}

按如下方式调用该函数。对其他三个变量重复此操作。

obs_bxp <- dunn_boxplot("Observed")

需要注意的关键是，您的四个代码块仅在y变量名称上有所不同。因此，它是作为参数传递的。您需要使用formula(paste(...)将公式组装为字符串，然后将其强制转换为formula类。