How to melt pairwise.wilcox.test output using dplyr?

Question

I want to apply pairwise.wilcox.test for multiple independent variables at a time and then want to have the output in long format. For a particular Wavelength,

Answer 1

I could able to solve the problem using rstatix package which "provides a simple and intuitive pipe friendly framework, coherent with the 'tidyverse' design philosophy for performing basic statistical tests".

library(tidyverse)
library(rstatix)

as_tibble(df)%>% 
  pivot_longer(cols = -Class, names_to = "Wavelengths", values_to = "value") %>% 
  mutate(Class = as.factor(Class)) %>% 
  group_by(Wavelengths) %>% 
  pairwise_wilcox_test(value~Class, p.adjust.method="bonf")

which returns the following output

#> # A tibble: 40 x 10
#>   Wavelengths .y.   group1 group2    n1    n2 statistic     p p.adj
#> * <chr>       <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl> <dbl>
#> 1 WV_350      value 1      2          5     5      20   0.151 1    
#> 2 WV_350      value 1      3          5     5      25   0.008 0.079
#> 3 WV_350      value 1      4          5     5      25   0.008 0.079
#> 4 WV_350      value 1      5          5     5      25   0.008 0.079
#> 5 WV_350      value 2      3          5     5      25   0.008 0.079
#> 6 WV_350      value 2      4          5     5      25   0.008 0.079
#> 7 WV_350      value 2      5          5     5      25   0.008 0.079
#> 8 WV_350      value 3      4          5     5      10   0.69  1    
#> 9 WV_350      value 3      5          5     5      21.5 0.075 0.749
#> 10 WV_350      value 4      5          5     5      22   0.056 0.556
#> # ... with 30 more rows, and 1 more variable: p.adj.signif <chr>

Answer 2

A bit verbose and Im sure it could be made more efficient:

library(tidyverse)
library(broom)
res <- 
  tbl_df(df)%>% 
  pivot_longer(cols = -Class, names_to = "Wavelengths", values_to = "value") %>% 
  group_by(Wavelengths) %>% 
  summarise(pw_wt = list(pairwise.wilcox.test(value,as.factor(Class),
                                              p.adjust.method = "bonf")$p.value)) %>% 
  ungroup() %>% 
  mutate(pw_wt_t = map(pw_wt, broom::tidy)) %>% 
  unnest(pw_wt_t)