dplyr：在group_by（）之后在summary（）中使用自定义函数 [英] dplyr: Use a custom function in summarize() after group_by()

问题描述

在 group_by（）之后如何使用自定义函数？
我检查了类似的帖子（ 1 ，2 和 3 ），但我当前的代码对所有组均返回相同的值。

How can we use a custom function after group_by()? I checked similar posts (1, 2, and 3), but my current code returns the same values for all groups.

> data
   village     A     Z      Y 
     <chr> <int> <int>   <dbl> 
 1       a     1     1   500     
 2       a     1     1   400     
 3       a     1     0   800  
 4       b     1     0   300  
 5       b     1     1   700  

z <- 1
data %>%
    group_by(village) %>%
    summarize(Y_village = Y_hat_village(., z))

Y_hat_village <- function(data_village, z){
    # Calculate the mean for a specific z in a village
    data_z <- data_village %>% filter(Z==get("z"))
    return(mean(data_z$Y))
}

I希望对村庄 a有（500 + 400）/ 2 = 450，对村庄 b有700。

I want to have (500 + 400)/2 = 450 for village "a" and 700 for village "b".

推荐答案

如果您从不带任何附加功能的情况下开始编写，则更容易理解。在那种情况下，应该是：

It's easier to understand if you start by writing it without an extra function. In that case it would be:

df %>%
  group_by(village) %>%
  summarize(Y_village = mean(Y[Z == z]))

## A tibble: 2 x 2
#  village Y_village
#  <fct>       <dbl>
#1 a            450.
#2 b            700.

因此，您函数应该类似于

Hence, your function should be something like

Y_hat_village <- function(Ycol, Zcol, z){
  mean(Ycol[Zcol == z])
}

然后使用它：

df %>%
  group_by(village) %>%
  summarize(Y_village = Y_hat_village(Y, Z, z))

请注意，我编写的函数仅处理可从内部直接提供的原子向量摘要。您不需要在其中提供整个data.frame。

Note that the function I wrote only deals with atomic vectors which you can supply directly from within summarise. You don't need to supply the whole data.frame into it.

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