R 如何使用我的数据框架（多组/结果，多个定量变量）进行大量t.测试？

通过在R中运行多个t.test（根据多个定量变量比较多个组），我想在R中得到如下结果：

我尝试并修改了此处提供的解决方案：

我面临多个问题：

• （1） 我有两条警告消息，我不知道这两条消息是如何解决的

• （2） 如何获得每组/结果的一行
• （3） 如何通过组名更改“1”（第二行的第一个字符）（在本例中为“vs”和“am”）
• （4） 如何将此输出导出为数据帧（如我开始的示例中所示）

非常感谢你的帮助

库（tidyverse）
library(tidyverse)

vars_to_test <- c("disp","hp","drat","wt","qsec")
iv <- c("vs", "am")

expand.grid(vars_to_test, iv, stringsAsFactors = F) %>%              # create pairs of variables
  rowwise() %>%                                                      # for each pair
  mutate(p_val = t.test(mtcars[,Var1] ~ mtcars[,Var2])$p.value) %>%  # get p value from t.test
  spread(Var1, p_val)                                                # reshape output

# # A tibble: 2 x 6
#   Var2        disp       drat         hp       qsec         wt
#   <chr>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>
# 1 am    0.000230   0.00000527 0.221      0.209      0.00000627
# 2 vs    0.00000248 0.0129     0.00000182 0.00000352 0.000728  

每对的vars_至_测试%#
变异（p_val=t.test（mtcars[，Var1]~mtcars[，Var2]）$p.value）%>%#从t.test中获取p值
扩展（Var1，p#val）#重塑输出
##A tibble:2 x 6
#Var2显示图纸hp qsec wt
#                                 
#1 am 0.000230 0.0000527 0.221 0.209 0.00000627
#2对0.00000248 0.0129 0.00000182 0.00000352 0.000728

使用

map\u-dfr对
iv
中的每个变量应用
总结

vars_to_test <- c("disp","hp","drat","wt","qsec")
iv <- c("vs", "am")
#map_dfr use these names as id
names(iv) <- iv

library(dplyr)
library(purrr)
map_dfr(iv,  function(x) mtcars %>%
  summarise_at(vars_to_test,
    list( 
       #!! and parse_expr will transfer the string "output of sprintf" into epression
     ~!!(parse_expr(sprintf("stats::t.test(.[%s == 0], .[%s == 1])$p.value", x, x)))
    )), .id = "group")

  group         disp           hp         drat           wt         qsec
1    vs 2.476526e-06 1.819806e-06 1.285342e-02 7.281397e-04 3.522404e-06
2    am 2.300413e-04 2.209796e-01 5.266742e-06 6.272020e-06 2.093498e-01

vars\u-to\u测试
Please use summarise_if(), summarise_at(), or summarise_all() instead: 

  - To map `funs` over all variables, use summarise_all()
  - To map `funs` over a selection of variables, use summarise_at()
This warning is displayed once per session. 
2: funs_() is deprecated. 
Please use list() instead

library(tidyverse)

vars_to_test <- c("disp","hp","drat","wt","qsec")
iv <- c("vs", "am")

expand.grid(vars_to_test, iv, stringsAsFactors = F) %>%              # create pairs of variables
  rowwise() %>%                                                      # for each pair
  mutate(p_val = t.test(mtcars[,Var1] ~ mtcars[,Var2])$p.value) %>%  # get p value from t.test
  spread(Var1, p_val)                                                # reshape output

# # A tibble: 2 x 6
#   Var2        disp       drat         hp       qsec         wt
#   <chr>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>
# 1 am    0.000230   0.00000527 0.221      0.209      0.00000627
# 2 vs    0.00000248 0.0129     0.00000182 0.00000352 0.000728  

vars_to_test <- c("disp","hp","drat","wt","qsec")
iv <- c("vs", "am")
#map_dfr use these names as id
names(iv) <- iv

library(dplyr)
library(purrr)
map_dfr(iv,  function(x) mtcars %>%
  summarise_at(vars_to_test,
    list( 
       #!! and parse_expr will transfer the string "output of sprintf" into epression
     ~!!(parse_expr(sprintf("stats::t.test(.[%s == 0], .[%s == 1])$p.value", x, x)))
    )), .id = "group")

  group         disp           hp         drat           wt         qsec
1    vs 2.476526e-06 1.819806e-06 1.285342e-02 7.281397e-04 3.522404e-06
2    am 2.300413e-04 2.209796e-01 5.266742e-06 6.272020e-06 2.093498e-01