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r - 分组统计测试 tidyverse

问题描述

我正在尝试对长格式数据进行 Wilcoxon 测试。我想用来dplyr::group_by()指定我想要进行测试的子集。

最终结果将是一个新列，其中将 Wilcoxon 检验的 p 值附加到原始数据框中。我见过的所有技术都需要总结数据框。我不想总结数据框。

请参阅重新格式化数据集以模仿我的数据的示例iris，最后是我执行任务的尝试。

我快接近了，但我想保留 Wilcoxon 测试之前的所有原始数据。

# Reformatting Iris to mimic my data.
long_format <- iris %>% 
  gather(key = "attribute", value = "measurement", -Species) %>%
  mutate(descriptor = 
           case_when(
    str_extract(attribute, pattern = "\\.(.*)") == ".Width" ~ "Width",
    str_extract(attribute, pattern = "\\.(.*)") == ".Length" ~ "Length")) %>%
  mutate(Feature = 
           case_when(
    str_extract(attribute, pattern = "^(.*?)\\.") == "Sepal." ~ "Sepal",
    str_extract(attribute, pattern = "^(.*?)\\.") == "Petal." ~ "Petal"))

# Removing no longer necessary column.
cleaned_up <- long_format %>% select(-attribute)

# Attempt using do(), but I lose important info like "measurement"
cleaned_up %>%
  group_by(Species, Feature) %>%
  do(w = wilcox.test(measurement~descriptor, data=., paired=FALSE)) %>% 
  mutate(Wilcox = w$p.value)

# This is an attempt with the dplyr experimental group_map function. If only I could just make this a new column appended to the original df in one step.

cleaned_up %>%
  group_by(Species, Feature) %>%
  group_map(~ wilcox.test(measurement~descriptor, data=., paired=FALSE)$p.value)

谢谢你的帮助。

标签： rdplyrstatistics

解决方案

模型对象可以包装在一个list

library(tidyverse)
cleaned_up %>%
   group_by(Species, Feature) %>%
   nest %>% 
   mutate(model = map(data, ~ 
          .x %>%
           transmute(w = list(wilcox.test(measurement~descriptor, 
               data=., paired=FALSE)))))

或者另一种选择是group_split进入list, 然后map通过list, 元素在应用模型后创建 'pval' 列

cleaned_up %>% 
    group_split(Species, Feature) %>%
    map_dfr(~ .x %>%
                 mutate(pval = wilcox.test(measurement~descriptor, 
               data=., paired=FALSE)$p.value))

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