r - 在 dplyr 中按行分组以改变列

Question

我正在尝试创建按不同列分组的新列，但我不确定我这样做的方式是否是使用 group_by 的最佳方式。我想知道是否有一种方法可以让 group_by 排队？

我知道它可以使用 data.table 包来完成，其中语法是类型
DT[i,j,by]。

但由于这是使用 tidyverse 的较大代码中的一小部分，并且按原样运行良好，我只是不想偏离这一点。

## Creating Sample Data Frame
state <- rep(c("OH", "IL", "IN", "PA", "KY"),10) 
county <- sample(LETTERS[1:5], 50, replace = T) %>% str_c(state,sep = "-") 
customers <- sample.int(50:100,50) 
sales <- sample.int(500:5000,50)

df <- bind_cols(data.frame(state, county,customers,sales))

## workflow

df2 <- df %>%
  group_by(state) %>% 
  mutate(customerInState = sum(customers),
         saleInState = sum(sales)) %>% 
  ungroup %>% 
  group_by(county) %>% 
  mutate(customerInCounty = sum(customers),
         saleInCounty = sum(sales)) %>% 
  ungroup %>% 
  mutate(salePerCountyPercent  = saleInCounty/saleInState,
         customerPerCountyPercent = customerInCounty/customerInState) %>% 
  group_by(state) %>% 
  mutate(minSale = min(salePerCountyPercent)) %>%
  ungroup

我希望我的代码看起来像

df3 <- df %>%
  mutate(customerInState = sum(customers, by = state),
         saleInState = sum(sales, by = state),
         customerInCounty = sum(customers, by = county),
         saleInCounty = sum(sales, by = county),
         salePerCountyPercent  = saleInCounty/saleInState,
         customerPerCountyPercent = customerInCounty/customerInState,
         minSale = min(salePerCountyPercent, by = state))

它运行没有错误，但我知道输出不正确

我知道有可能在 mutates 周围玩杂耍，以使用较少的 group_bys 来获得我需要的东西。
但问题是，如果在 dplyr 中可以在 line group by 中做

Answer 1

你可以创建包装器来做你想做的事。如果您有一个分组变量，则此特定解决方案有效。祝你好运!

library(tidyverse)

mutate_by <- function(.data, group, ...) {

  group_by(.data, !!enquo(group)) %>%
    mutate(...) %>%
    ungroup

}

df1 <- df %>%
  mutate_by(state, 
            customerInState = sum(customers),
            saleInState = sum(sales)) %>%
  mutate_by(county,
            customerInCounty = sum(customers),
            saleInCounty = sum(sales)) %>%
  mutate(salePerCountyPercent  = saleInCounty/saleInState,
         customerPerCountyPercent = customerInCounty/customerInState) %>% 
  mutate_by(state,
            minSale = min(salePerCountyPercent))

identical(df2, df1)
[1] TRUE

编辑:或者，更简洁/类似于您的代码:

df %>%
  mutate_by(customerInState = sum(customers),
            saleInState = sum(sales), group = state) %>%
  mutate_by(customerInCounty = sum(customers),
            saleInCounty = sum(sales), group = county) %>%
  mutate(salePerCountyPercent  = saleInCounty/saleInState,
         customerPerCountyPercent = customerInCounty/customerInState) %>% 
  mutate_by(minSale = min(salePerCountyPercent), group = state)