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我在下面有一个示例数据框。

example.df <- data.frame( 
species = sample(c("primate", "non-primate"), 50, replace = TRUE),
treated = sample(c("Yes", "No"), 50, replace = TRUE), 
gender = sample(c("male", "female"), 50, replace = TRUE), 
var1 = rnorm(50, 100, 5), resp=rnorm(50, 10,5), value1 = rnorm (50, 25, 5))

我想先分组treated，然后遍历数据中的所有数字变量以使用作为解释变量执行 dunn 测试（pairw.kw来自asbio包） ，提取数据框对象并将列和子列表中的列绑定到一个新的数据框对象。speciessummaryyesno

我已经在这里和这里使用部分“整洁”的方法获得了部分解决方案，效果很好。我只是在寻找更优雅的 tidyverse 解决方案，以帮助我学习成为更好的 R 用户。

任何帮助表示赞赏。

编辑：这是我从部分“整洁”解决方案中的代码获得的输出。

structure(list(var1.Diff = structure(1:2, .Label = c("-7.05229", 
"-2.25"), class = "factor"), var1.Lower = structure(1:2, .Label = 
c("-13.23198", 
"-8.25114"), class = "factor"), var1.Upper = structure(1:2, .Label = 
c("-0.87259", 
"3.75114"), class = "factor"), var1.Decision = structure(1:2, .Label = 
 c("Reject H0", 
 "FTR H0"), class = "factor"), var1.Adj..P.value = structure(1:2, .Label = 
 c("0.025305", 
 "0.462433"), class = "factor"), resp.Diff = structure(1:2, .Label = 
 c("1.10458", 
 "0"), class = "factor"), resp.Lower = structure(1:2, .Label = c("-5.07512", 
 "-6.00114"), class = "factor"), resp.Upper = structure(1:2, .Label = 
 c("7.28427", 
 "6.00114"), class = "factor"), resp.Decision = structure(c(1L, 
 1L), .Label = "FTR H0", class = "factor"), resp.Adj..P.value = 
 structure(1:2, .Label = c("0.726092", 
 "1"), class = "factor"), effect.Diff = structure(1:2, .Label = 
 c("-1.27451", 
 "-0.5625"), class = "factor"), effect.Lower = structure(1:2, .Label = 
 c("-7.4542", 
 "-6.56364"), class = "factor"), effect.Upper = structure(1:2, .Label = 
 c("4.90518", 
 "5.43864"), class = "factor"), effect.Decision = structure(c(1L, 
 1L), .Label = "FTR H0", class = "factor"), effect.Adj..P.value = 
 structure(1:2, .Label = c("0.686047", 
 "0.85424"), class = "factor")), row.names = c("No", "Yes"), class = 
 "data.frame")
4

1 回答 1

5

这是一种同时运行多个测试的简洁方法。

library("asbio")
#> Loading required package: tcltk
library("tidyverse")

# It is good practice to set the seed before simulating random data
# Otherwise can't compare
set.seed(12345)

example.df <- tibble(
  species = sample(c("primate", "non-primate"), 50, replace = TRUE),
  treated = sample(c("Yes", "No"), 50, replace = TRUE),
  gender = sample(c("male", "female"), 50, replace = TRUE),
  var1 = rnorm(50, 100, 5), resp=rnorm(50, 10,5), value1 = rnorm (50, 25, 5)) %>%
  # Make sure species is a factor as required by `pair.kw`
  mutate(species = factor(species))

example.df %>%
  # Nest the data for each treatment group
  nest(- treated) %>%
  # Run the test on each treatment group
  mutate(test = map(data, ~ pairw.kw(.$var1, .$species, conf = 0.95))) %>%
  # There is no broom::tidy method for objects of class pairw
  # but we can extract the summary ourselves
  mutate(summary = map(test, pluck, "summary")) %>%
  # Cast all the factor columns in the summary table to character, to
  # avoid a warning about converting factors to characters.
  mutate(summary = map(summary, mutate_if, is.factor, as.character)) %>%
  unnest(summary)
#> # A tibble: 2 x 8
#>   treated data        test     Diff    Lower  Upper Decision `Adj. P-value`
#>   <chr>   <list>      <list>   <chr>   <chr>  <chr> <chr>    <chr>         
#> 1 No      <tibble [2~ <S3: pa~ -1.705~ -7.99~ 4.58~ FTR H0   0.595163      
#> 2 Yes     <tibble [2~ <S3: pa~ -1.145~ -6.45~ 4.16~ FTR H0   0.672655

将这种方法扩展为运行 6 个测试很简单，每个测试对应一个治疗组和一个响应变量 ( var1,value1或resp) 的组合。例如，我们可以将 tibble 从宽格式（三个响应列）转换为窄格式（三个响应按行堆叠），然后按照上面的方法进行。

example.df %>%
  # From wide format to narrow format
  gather(varname, y, value1, var1, resp) %>%
  # Nest the data for each treatment group and each variable
  nest(- treated, - varname) %>%
  # Run 6 tests = (number of treatments) * (number of response variables)
  mutate(test = map(data, ~ pairw.kw(.$y, .$species, conf = 0.95))) %>%
  # There is no broom::tidy method for objects of class pairw
  # but we can extract the summary ourselves
  mutate(summary = map(test, pluck, "summary")) %>%
  # Cast all the factor columns in the summary table to character, to
  # avoid a warning about converting factors to characters.
  mutate(summary = map(summary, mutate_if, is.factor, as.character)) %>%
  unnest(summary)
#> # A tibble: 6 x 9
#>   treated varname data    test   Diff   Lower Upper Decision `Adj. P-value`
#>   <chr>   <chr>   <list>  <list> <chr>  <chr> <chr> <chr>    <chr>         
#> 1 No      value1  <tibbl~ <S3: ~ 3.127~ -3.1~ 9.41~ FTR H0   0.329969      
#> 2 Yes     value1  <tibbl~ <S3: ~ -1.33~ -6.65 3.97~ FTR H0   0.622065      
#> 3 No      var1    <tibbl~ <S3: ~ -1.70~ -7.9~ 4.58~ FTR H0   0.595163      
#> 4 Yes     var1    <tibbl~ <S3: ~ -1.14~ -6.4~ 4.16~ FTR H0   0.672655      
#> 5 No      resp    <tibbl~ <S3: ~ 1.421~ -4.8~ 7.71~ FTR H0   0.657905      
#> 6 Yes     resp    <tibbl~ <S3: ~ 1.145~ -4.1~ 6.45~ FTR H0   0.672655

由reprex 包（v0.2.1）于 2019 年 3 月 4 日创建

如果您想灵活处理回复的数量和名称怎么办？然后指定响应列表：

responses <- c("value1", "var1", "resp")

并在gather语句中使用 tidy 评估如下：

example.df %>%
  # From wide format to narrow format, with tidy evaluation
  gather(varname, y, !!!rlang::syms(responses))
  # Rest of computation here.....
于 2019-03-04T10:50:14.503 回答