dplyr 使用 if 语句根据订单条件进行汇总

Question

按组 (group_by(id))，我试图根据 types 的选择对变量求和。但是，这些types 有一个优先顺序。示例：

library(tidyverse)
df <- data.frame(id = c(rep(1, 6), 2, 2, 2, rep(3, 4), 4, 5),
                 types = c("1a", "1a", "2a", "3b", "4c", "7d",
                          "4c", "7d", "7d","4c", "5d", "6d", "6d","5d","7d"),
                 x = c(10, 15, 20, 15, 30, 40,
                       10, 10, 15, 10, 10, 10, 10, 10, 10),
                 y = c(1:15),
                 z = c(1:15)
)
df
#    id types  x  y  z
# 1   1    1a 10  1  1
# 2   1    1a 15  2  2
# 3   1    2a 20  3  3
# 4   1    3b 15  4  4
# 5   1    4c 30  5  5
# 6   1    7d 40  6  6
# 7   2    4c 10  7  7
# 8   2    7d 10  8  8
# 9   2    7d 15  9  9
# 10  3    4c 10 10 10
# 11  3    5d 10 11 11
# 12  3    6d 10 12 12
# 13  3    6d 10 13 13
# 14  4    5d 10 14 14
# 15  5    7d 10 15 15

我想根据types的偏好按此顺序sum(x)：

preference_1st = c("1a", "2a", "3b")
preference_2nd = c("7d")
preference_3rd = c("4c", "5d", "6d")

所以这意味着如果id 包含preference_1st 中的任何类型，我们将它们相加并忽略其他类型，如果preference_1st 中没有，我们将所有preference_2nd 相加并忽略其余类型。最后，如果preference_3rd 中只有types，我们将这些相加。所以对于id=1，我们要忽略类型4c 和7d。 （我还想要更直接地计算其他变量，在本例中为z 和y）。

期望的输出：

desired
  id sumtest ymean zmean
1  1      60   3.5   3.5
2  2      25   8.0   8.0
3  3      40  11.5  11.5
4  4      10  14.0  14.0
5  5      10  15.0  15.0

我认为一种可能的选择是使用mutate 和case_when 创建某种顺序变量，但我认为使用if 语句时应该有更好的选择？以下内容很接近，但无法正确区分偏好：

df %>%
  group_by(id) %>%
  summarise(sumtest = if (any(types %in% preference_1st)) {
    sum(x)
  } else if (any(!types %in% preference_1st) & any(types %in% preference_2nd)) {
    sum(x)
  } else {
    sum(x)
  },
            ymean = mean(y),
            zmean = mean(z))
#      id sumtest ymean zmean
#   <dbl>   <dbl> <dbl> <dbl>
# 1     1     130   3.5   3.5
# 2     2      35   8     8  
# 3     3      40  11.5  11.5
# 4     4      10  14    14  
# 5     5      10  15    15  

也对其他方法持开放态度？有什么建议吗？

谢谢

Answer 1

这是一个 dplyr 解决方案：

df %>% 
  group_by(id) %>%
  mutate(ymean = mean(y), zmean = mean(z), 
         pref = 3 * types %in% preference_3rd + 
                2 * types %in% preference_2nd +
                1 * types %in% preference_1st ) %>%
  filter(pref == min(pref)) %>%
  summarise(sumtest = sum(x), ymean = first(ymean), zmean = first(zmean))
#> # A tibble: 5 x 4
#>      id sumtest ymean zmean
#>   <dbl>   <dbl> <dbl> <dbl>
#> 1     1      60   3.5   3.5
#> 2     2      25   8     8  
#> 3     3      40  11.5  11.5
#> 4     4      10  14    14  
#> 5     5      10  15    15 

Answer 2

使用reduce 和anti_join 迭代过滤数据。

pref <- list(c("1a", "2a", "3b"), c("7d"), c("4c", "5d", "6d"))

pref %>%
  map(~ df %>% filter(types %in% .x)) %>%
  reduce(~ anti_join(.y, .x, by = "id") %>% bind_rows(.x, .)) %>%
  group_by(id) %>%
  summarise(sumtest = sum(x)) %>%
  left_join(df %>% group_by(id) %>% summarise(ymean = mean(y), zmean = mean(z)))

# # A tibble: 5 x 4
#      id sumtest ymean zmean
#   <dbl>   <dbl> <dbl> <dbl>
# 1     1      60   3.5   3.5
# 2     2      25   8     8  
# 3     3      40  11.5  11.5
# 4     4      10  14    14  
# 5     5      10  15    15   

Answer 3

虽然我更喜欢上述解决方案，但我在最初尝试 if 语句时忘记将 sum(x) 设置为子集

df %>%
  group_by(id) %>%
  summarise(sumtest = if (any(types %in% preference_1st)) {
    sum(x[types %in% preference_1st])

  } else if (any(!types %in% preference_1st) & any(types %in% preference_2nd)) {
    sum(x[types %in% preference_2nd])

  } else {
    sum(x[types %in% preference_3rd])

  },
  ymean = mean(y),
  zmean = mean(z))
#      id sumtest ymean zmean
#   <dbl>   <dbl> <dbl> <dbl>
# 1     1      60   3.5   3.5
# 2     2      25   8     8  
# 3     3      40  11.5  11.5
# 4     4      10  14    14  
# 5     5      10  15    15