Question

test <- data.frame('prod_id'= c("shoe", "shoe", "shoe", "shoe", "shoe", "shoe", "boat", "boat","boat","boat","boat","boat"), 
               'seller_id'= c("a", "b", "c", "d", "e", "f", "a","g", "h", "r", "q", "b"), 
               'Dich'= c(1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0),
               'price' = c(120, 20, 10, 4, 3, 4, 30, 43, 56, 88, 75, 44)
                )
test

       prod_id seller_id Dich price
 1     shoe         a    1   120
 2     shoe         b    0    20
 3     shoe         c    0    10
 4     shoe         d    0     4
 5     shoe         e    0     3
 6     shoe         f    0     4
 7     boat         a    0    30
 8     boat         g    0    43
 9     boat         h    1    56
10     boat         r    0    88
11     boat         q    0    75
12     boat         b    0    44

我想创建一个新列，该列基于Dich的值来获取价格列中观察值之间的差异，其中每个观察值与每个prod_id组中Dich == 1的观察值之间存在差异。这样做的语法如下。

test %>% 
group_by(prod_id) %>% 
mutate(diff_p = if(any(Dich ==1)) price - price[Dich == 1] else NA)

       prod_id seller_id Dich price diff_p
 1     shoe         a    1   120      0
 2     shoe         b    0    20     -100
 3     shoe         c    0    10     -110
 4     shoe         d    0     4     -116
 5     shoe         e    0     3     -117
 6     shoe         f    0     4     -116
 7     boat         a    0    30     -26
 8     boat         g    0    43     -13
 9     boat         h    1    56       0
10     boat         r    0    88      32
11     boat         q    0    75      19
12     boat         b    0    44     -12

现在，我想创建一个使用相同语法的函数，以便可以在新数据帧上使用该函数并获得相同结果。但是，当我尝试新创建的列仅具有NA值时。我在想通过在函数中使用mutate来解决问题？

trans <- function(e) {e %>%
         group_by(prod_id) %>% 
         mutate(diff_p = if(any(Dich ==1)) price -price[Dich == 1] else NA)
         }

Answer 1

一种选择是利用量化和评估（!!）

library(tidyverse)
trans <- function(dat, groupCol, valCol1, valCol2) {
  groupCol <- enquo(groupCol)
  valCol1 <- enquo(valCol1)
  valCol2 <- enquo(valCol2)
  dat %>%
     group_by(!! groupCol) %>% 
     mutate(diff_p = if(any((!! valCol1) ==1)) (!!valCol2) - 
                 (!!valCol2)[(!!valCol1) == 1] else NA)
     }
trans(test, prod_id, Dich, price)
# A tibble: 12 x 5
# Groups:   prod_id [2]
#   prod_id seller_id  Dich price diff_p
#   <fct>   <fct>     <dbl> <dbl>  <dbl>
# 1 shoe    a             1   120      0
# 2 shoe    b             0    20   -100
# 4 shoe    d             0     4   -116
# 5 shoe    e             0     3   -117
# 6 shoe    f             0     4   -116
# 7 boat    a             0    30    -26
# 8 boat    g             0    43    -13
# 9 boat    h             1    56      0
#10 boat    r             0    88     32
#11 boat    q             0    75     19
#12 boat    b             0    44    -12

注意：将列名作为参数应用到其他数据集可能更具有普遍性

使用dplyr中的mutate创建函数

1 个答案: