For循环总结和dplyr的加入 [英] For-loop to summarize and joining by dplyr

问题描述

这是我的简化df：

GP_A <- c(rep("a",3),rep("b",2),rep("c",2))
GP_B <- c(rep("d",2),rep("e",4),rep("f",1))
GENDER <- c(rep("M",4),rep("F",3))
LOC <- c(rep("HK",2),rep("UK",3),rep("JP",2))
SCORE <- c(50,70,80,20,30,80,90)
df <- as.data.frame(cbind(GP_A,GP_B,GENDER,LOC,SCORE))

> df

GP_A GP_B GENDER LOC SCORE
1    a    d      M  HK    50
2    a    d      M  HK    70
3    a    e      M  UK    80
4    b    e      M  UK    20
5    b    e      F  UK    30
6    c    e      F  JP    80
7    c    f      F  JP    90

我想按GP_A，GP_B或本示例中未显示的其他分组列来汇总分数。由于分组列的数量可能多达50个，因此我决定使用for循环来汇总得分。

I want to summarize the score by GP_A, GP_B, or other grouping columns which are not showing in this example. As the count of grouping columns might up to 50, I decided to use for-loop to summarize the score.

原始方法是将1组的得分汇总为一个：

The original method is summarizing the score with 1 group one by one:

GP_A_SCORE <- df %>% group_by(GP_A,GENDER,LOC) %>% summarize(SCORE=mean(SCORE))
GP_B_SCORE <- df %>% group_by(GP_B,GENDER,LOC) %>% summarize(SCORE=mean(SCORE))
...

我想要的是使用这样的for循环（无法运行）：

What I want is using the for-loop like this (cannot run):

GP_list <- c("GP_A","GP_B",...)
LOC_list <- c("HK","UK","JP",...)
SCORE <- list()
for (i in GP_list){
    for (j in LOC_list){
SCORE[[paste0(i,j)]] <- df %>% group_by(i,j,GENDER) %>% summarize(SCORE=mean(SCORE))
}}

在 group_by（）中，变量被归类为字符，这是显示的错误：

As in "group_by()", the variables are classified as character and here is the error shown:

错误：列 I ， J 是u nknown

Error: Column I, J is unknown

是否有任何方法可以强制R识别变量？

Is there any method to force R to recognize the variable?

我在dplyr的left_join上面临着同样的问题。

I am facing the same problem on the left_join of dplyr.

我正在执行类似操作时显示错误： left_join（x，y，by = c（i = i））循环内。

Error is shown when I was doing something like: left_join(x,y,by=c(i=i)) inside a loop.

推荐答案

您可以获取长格式的数据，然后计算平均值

You could get the data in long format and then calculate the mean

library(dplyr)
library(tidyr)

df %>%
  pivot_longer(cols = starts_with('GP')) %>%
  group_by(GENDER ,LOC, name, value) %>%
  summarise(SCORE = mean(SCORE))

#   GENDER LOC   name  value SCORE
#   <fct>  <fct> <chr> <fct> <dbl>
# 1 F      JP    GP_A  c        85
# 2 F      JP    GP_B  e        80
# 3 F      JP    GP_B  f        90
# 4 F      UK    GP_A  b        30
# 5 F      UK    GP_B  e        30
# 6 M      HK    GP_A  a        60
# 7 M      HK    GP_B  d        60
# 8 M      UK    GP_A  a        80
# 9 M      UK    GP_A  b        20
#10 M      UK    GP_B  e        50

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