如何使用 dplyr 分组进行统计测试，然后用扫帚打扫

Question

我有以下数据框：

library(tidyverse)

dat <- structure(list(charge.Group3 = c(0.167, 0.167, 0.1, 0.067, 0.033, 
0.033, 0.067, 0.133, 0.2, 0.067, 0.133, 0.114, 0.167, 0.033, 
0.1, 0.033, 0.133, 0.267, 0.133, 0.233, 0.1, 0.167, 0.067, 0.133, 
0.1, 0.133, 0.1, 0.133, 0.1, 0.067, 0.167, 0), hydrophobicity.Group3 = c(0.267, 
0.467, 0.067, 0.167, 0.267, 0.1, 0.367, 0.233, 0.367, 0.233, 
0.133, 0.205, 0.333, 0.267, 0.267, 0.067, 0.133, 0.3, 0.233, 
0.267, 0.5, 0.333, 0.2, 0.5, 0.5, 0.4, 0.033, 0.3, 0.233, 0.5, 
0.233, 0.033), class = c("Negative", "Negative", "Positive", 
"Positive", "Positive", "Positive", "Positive", "Negative", "Positive", 
"Positive", "Positive", "Positive", "Positive", "Positive", "Negative", 
"Positive", "Negative", "Negative", "Negative", "Negative", "Negative", 
"Negative", "Negative", "Negative", "Negative", "Negative", "Positive", 
"Positive", "Positive", "Negative", "Positive", "Negative")), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -32L))

dat
#> # A tibble: 32 x 3
#>    charge.Group3 hydrophobicity.Group3 class   
#>            <dbl>                 <dbl> <chr>   
#>  1         0.167                 0.267 Negative
#>  2         0.167                 0.467 Negative
#>  3         0.1                   0.067 Positive
#>  4         0.067                 0.167 Positive
#>  5         0.033                 0.267 Positive
#>  6         0.033                 0.1   Positive
#>  7         0.067                 0.367 Positive
#>  8         0.133                 0.233 Negative
#>  9         0.2                   0.367 Positive
#> 10         0.067                 0.233 Positive
#> # ... with 22 more rows

我想为每个特征做些什么：charge.Group3 和 hydrophobicity.Group3，在 Negative 和 positive class 之间执行 wilcox.test。最后将 p 值作为数据框或 tibble：

features                      pvalue
charge.Group3                 0.1088  
hydrophobicity.Group3         0.03895
# I do by hand.

请注意，实际上有不止 2 个特征。我怎样才能做到这一点？

Answer 1

这是使用 dplyr::summarize_at 和 tidyr::gather 的方法：

library(tidyverse)
dat %>%
  summarize_at(c("charge.Group3","hydrophobicity.Group3"),
               ~wilcox.test(.x ~ .y)$p.value, .$class) %>%
  gather(features, pvalue)

# # A tibble: 2 x 2
#                features pvalue
#                   <chr>  <dbl>
# 1         charge.Group3  0.109
# 2 hydrophobicity.Group3  0.039

总结除class以外的所有变量：

dat %>%
  summarize_at(vars(-class),
               ~wilcox.test(.x ~ .y)$p.value,
               .$class) %>%
  gather(features,pvalue)

Answer 2

如果您只需要检验的 p 值，则不需要使用 broom。

library(tidyverse)


dat %>% 
  gather(group, value, -class) %>%    # reshape data            
  nest(-group) %>%                    # for each group nest data
  mutate(pval = map_dbl(data, ~wilcox.test(value ~ class, data = .)$p.value)) %>%  # get p value for wilcoxon test
  select(-data)                       # remove data column


# # A tibble: 2 x 2
#   group                   pval
#   <chr>                  <dbl>
# 1 charge.Group3         0.109 
# 2 hydrophobicity.Group3 0.0390

首先重塑将使您能够应用此过程，无论您有多少列，假设 class 是唯一的额外变量。

或者您甚至可以避免使用 map，因为 @Moody_Mudskipper 建议使用

dat %>% 
  gather(group, value, -class) %>% 
  group_by(group) %>% 
  summarize(results = wilcox.test(value ~ class)$p.value)

如果你真的想参与broom那么你可以

library(broom)

dat %>% 
   gather(group, value, -class) %>%  
   nest(-group) %>%                  
   mutate(results = map(data, ~tidy(wilcox.test(value ~ class, data = .)))) %>%
   select(-data) %>%
   unnest(results)

# # A tibble: 2 x 5
# group                 statistic p.value method                                            alternative
#   <chr>                     <dbl>   <dbl> <chr>                                             <chr>      
# 1 charge.Group3              170.  0.109  Wilcoxon rank sum test with continuity correction two.sided  
# 2 hydrophobicity.Group3      183   0.0390 Wilcoxon rank sum test with continuity correction two.sided

还有 returns 列，但如果需要，您可以保留 p 值。

如何使用 dplyr 分组进行统计测试，然后用扫帚打扫

How to perform statistical test using dplyr grouping and then make tibble with broom

r

tidyverse

broom