我创建了一个小的示例数据集：

value <- rnorm(100, mean = 100, sd = 36)
group <- c(rep(c("A", "B", "C"), 33), "C")
gender <- c(rep(c("M", "F"), 50))  
test <- cbind(value, group, gender)
test <- as.data.table(test)

我想看看两个因子之间的平均值是否不同。所以我基本上想比较下面所有六个平均值：

> testSummary <- test %>%
+   group_by(group, gender) %>%
+   get_summary_stats(value, type = "common")
Error in `mutate()`:
ℹ In argument: `data = map(.data$data, .f, ...)`.
Caused by error in `map()`:
ℹ In index: 1.
Caused by error in `get_selected_vars()`:
! Can't subset columns with `value`.
✖ Can't convert from `value` <double> to <integer> due to loss of precision.
Run `rlang::last_error()` to see where the error occurred.
> testSummary
# A tibble: 6 × 12
  group gender variable     n   min   max median   iqr  mean    sd    se    ci
  <dbl>  <dbl> <fct>    <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1     1      1 value       16  70.1  155.   112.  41.5 112.   28.1  7.03  15.0
2     1      2 value       17  61.4  193.   106.  43.5 105.   34.4  8.33  17.7
3     2      1 value       17  10.2  162.   107.  51.7  98.8  42.7 10.3   21.9
4     2      2 value       16  48.3  179.   102.  63.2 110.   42.4 10.6   22.6
5     3      1 value       17  66.2  165.   109.  22.3 108.   24.5  5.93  12.6
6     3      2 value       17  53.8  182.   115.  35.6 108.   33.1  8.02  17.0

假设我的数据呈非正态分布，如果6个汇总组的平均值不同，如何进行比较？
我想我需要一个成对比较，但是函数只接受两个参数，所以我需要运行两次吗？有没有办法一步完成？

Pairwise comparisons using Wilcoxon rank sum test with continuity correction 

data:  as.numeric(test$value) and test$gender 

  F   
M 0.67

P value adjustment method: holm 
> pairwise.wilcox.test(as.numeric(test$value), g = test$group)

    Pairwise comparisons using Wilcoxon rank sum exact test 

data:  as.numeric(test$value) and test$group 

  A    B   
B 0.80 -   
C 0.32 0.30

P value adjustment method: holm