如何使用purrr的map函数执行逐行的prop.test并将结果添加到数据框？ [英] How to use purrr's map function to perform row-wise prop.tests and add results to the dataframe?

问题描述

我正在尝试解决R中的以下问题：我有一个包含两个变量（成功次数和总试验次数）的数据框。

I'm trying to solve the following problem in R: I have a dataframe with two variables (number of successes, and number of total trials).

# A tibble: 4 x 2
 Success     N
    <dbl> <dbl>
1     28.   40.
2     12.   40.
3     22.   40.
4      8.   40.

我想在每一行上执行prop.test或binom.test并将结果列表添加到数据框（或其中的某些元素，例如p-

I would like to perform a prop.test or binom.test on each row and add the resulting list to the dataframe (or certain elements of it, like the p-value and CIs).

理想情况下，我想添加第三列，其中包含p值和CI范围。到目前为止，我的尝试都没有成功。这是一个最小的编码示例：

Ideally, I would like to add a third column with the p-values and the CI-range. My attempts so far were painly unsuccessful. Here is a minimal coding example:

Success <- c( 38, 12, 27, 9)
N <- c( 50, 50, 50, 50)
df <- as.tibble( cbind(Success, N))


df %>%
  map( ~ prop.test, x = .$Success, n = .$N)

没有给出期望的结果。

Doesn't give the desired result. Any help would be much appreciated.

干杯，

Luise

推荐答案

如果您想要一个新列，可以使用@akrun的方法，但要撒一些 dplyr 和扫帚在 purrr

If you want a new column, you'd use @akrun's approach but sprinkle in a little dplyr and broom amongst the purrr

library(tidyverse) # for dplyr, purrr, tidyr & co.
library(broom)

analysis <- df %>%
  set_names(c("x","n")) %>% 
  mutate(result = pmap(., prop.test)) %>% 
  mutate(result = map(result, tidy)) 

从那里开始，您可以得到整齐的嵌套小标题的结果。如果您只想将其限制为某些变量，只需遵循 mutate / map 将函数应用于

From there that gives you the results in a tidy nested tibble. If you want to just limit that to certain variables, you'd just follow the mutate/map applying functions to the nested frame, then unnest().

analysis %>% 
  mutate(result = map(result, ~select(.x, p.value, conf.low, conf.high))) %>% 
  unnest()

# A tibble: 4 x 5
      x     n   p.value conf.low conf.high
  <dbl> <dbl>     <dbl>    <dbl>     <dbl>
1 38.0   50.0 0.000407    0.615      0.865
2 12.0   50.0 0.000407    0.135      0.385
3 27.0   50.0 0.671       0.395      0.679
4  9.00  50.0 0.0000116   0.0905     0.319

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