问题

I want to apply pairwise.wilcox.test function using dplyr package. I am using the following code

library(tidyverse)
tbl_df(df)%>% 
  pivot_longer(cols = -Class, names_to = "Wavelengths", values_to = "value") %>% 
  group_by(Wavelengths) %>% 
  summarize(pairwise.wilcox.test(value, as.factor(Class), p.adjust.method="bonf")$p.value)

It is giving me the following error

Error: Column pairwise.wilcox.test(try$value, as.factor(try$Class), p.adjust.method = "bonf")$p.value must be length 1 (a summary value), not 16

How to eliminate the error?

Update

As suggested by @duckmayr, I have updated my dplyr package (Newest version 1.0.0) and the error is not coming and it is giving me the following output

#> `summarise()` regrouping output by 'Wavelengths' (override with `.groups` argument)
#> # A tibble: 16 x 2
#> # Groups:   Wavelengths [4]
#>    Wavelengths pval[,1]    [,2]   [,3]   [,4]
#>    <chr>          <dbl>   <dbl>  <dbl>  <dbl>
#>  1 WV_350        1      NA      NA     NA    
#>  2 WV_350        0.0794  0.0794 NA     NA    
#>  3 WV_350        0.0794  0.0794  1     NA    
#>  4 WV_350        0.0794  0.0794  0.749  0.556
#>  5 WV_351        1      NA      NA     NA    
#>  6 WV_351        0.0794  0.0794 NA     NA    
#>  7 WV_351        0.0794  0.0794  1     NA    
#>  8 WV_351        0.0794  0.0794  0.556  0.556
#>  9 WV_352        1      NA      NA     NA    
#> 10 WV_352        0.0794  0.0794 NA     NA    
#> 11 WV_352        0.0794  0.0794  1     NA    
#> 12 WV_352        0.0794  0.0794  0.556  0.749
#> 13 WV_353        1      NA      NA     NA    
#> 14 WV_353        0.0794  0.0794 NA     NA    
#> 15 WV_353        0.0794  0.0794  1     NA    
#> 16 WV_353        0.0794  0.0794  0.556  0.317

As you can see from the above that the output is not coming in proper shape i.e. one more column of class should come. As of now, it is showing only class 1. I want to melt the output farther like Wavelengths Group1 Group2 pvalue, so that the output comes in a better format.

Data

df = structure(list(Class = c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 
3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5), WV_350 = c(0.0196, 0.0206, 
0.023, 0.0264, 0.029, 0.0201, 0.0181, 0.0216, 0.0225, 0.019, 
0.0165, 0.0121, 0.0129, 0.0123, 0.0149, 0.0137, 0.0116, 0.0151, 
0.0138, 0.0167, 0.0149, 0.0112, 0.0107, 0.01, 0.0099), WV_351 = c(0.0197, 
0.0206, 0.0229, 0.0265, 0.029, 0.0199, 0.0183, 0.0216, 0.0225, 
0.0187, 0.0165, 0.0118, 0.0127, 0.0122, 0.0148, 0.0138, 0.0114, 
0.0145, 0.0132, 0.0164, 0.0144, 0.0108, 0.01, 0.0093, 0.0095), 
    WV_352 = c(0.0199, 0.0207, 0.0233, 0.027, 0.0299, 0.0203, 
    0.0186, 0.0219, 0.0232, 0.019, 0.0169, 0.0124, 0.0133, 0.0126, 
    0.0152, 0.0145, 0.0118, 0.0148, 0.0132, 0.0168, 0.0148, 0.0111, 
    0.0102, 0.0096, 0.0098), WV_353 = c(0.0204, 0.0213, 0.0238, 
    0.0277, 0.0307, 0.0208, 0.0194, 0.0229, 0.0241, 0.0199, 0.0173, 
    0.013, 0.0142, 0.0134, 0.0161, 0.0152, 0.0126, 0.0153, 0.0137, 
    0.0175, 0.0151, 0.0116, 0.0105, 0.01, 0.0098)), row.names = c(NA, 
25L), class = "data.frame")

回答1:

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)

来源：https://stackoverflow.com/questions/62129863/how-to-melt-pairwise-wilcox-test-output-using-dplyr

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