dplyr 过滤掉最大值（每组）低于前 3 个最大值（每组）的组

Question

所以我有这个数据框：

structure(list(id = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 
6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 
8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9), year = c("2017", "2018", 
"2019", "2020", "2021", "2022", "2023", "2024", "2025", "2026", 
"2017", "2018", "2019", "2020", "2021", "2022", "2023", "2024", 
"2025", "2026", "2017", "2018", "2019", "2020", "2021", "2022", 
"2023", "2024", "2025", "2026", "2017", "2018", "2019", "2020", 
"2021", "2022", "2023", "2024", "2025", "2026", "2017", "2018", 
"2019", "2020", "2021", "2022", "2023", "2024", "2025", "2026", 
"2017", "2018", "2019", "2020", "2021", "2022", "2023", "2024", 
"2025", "2026", "2017", "2018", "2019", "2020", "2021", "2022", 
"2023", "2024", "2025", "2026", "2017", "2018", "2019", "2020", 
"2021", "2022", "2023", "2024", "2025", "2026", "2017", "2018", 
"2019", "2020", "2021", "2022", "2023", "2024", "2025", "2026"
), volume = c(0.0013, 0.0013, 0.0012579, 0.0011895, 0.0011421, 
0.0010842, 0.0010211, 0.0010158, 0.00099474, 0.00092632, 0.07878, 
0.078791, 0.077295, 0.076638, 0.075538, 0.074468, 0.074776, 0.074051, 
0.071706, 0.068056, 0.023269, 0.023011, 0.022374, 0.021962, 0.021408, 
0.020949, 0.020811, 0.020354, 0.019309, 0.018042, 0.0004, 0.0004, 
0.00038421, 0.00035263, 0.00033158, 0.00032105, 0.00026842, 0.00028421, 
0.00026842, 0.00024211, 0.0002, 0.0001, 0.00011579, 0, 0, 0, 
0, 0, 0, 0, 0.028422, 0.028361, 0.027768, 0.027501, 0.027029, 
0.02651, 0.026588, 0.026209, 0.025094, 0.023391, 0.0001, 0.0001, 
0, 0, 0, 0, 0, 0, 0, 0, 0.0047, 0.0047158, 0.0048368, 0.0048316, 
0.0049263, 0.0049737, 0.0049947, 0.0051684, 0.0052526, 0.0051842, 
0.0106, 0.010389, 0.010279, 0.010005, 0.0098421, 0.0096368, 0.0094053, 
0.0093368, 0.0092526, 0.0089316)), class = c("tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -90L))

看起来像这样：

# A tibble: 6 × 3
     id year   volume
  <dbl> <chr>   <dbl>
1     1 2017  0.0013 
2     1 2018  0.0013 
3     1 2019  0.00126
4     1 2020  0.00119
5     1 2021  0.00114
6     1 2022  0.00108

Id 有 9 个不同的 ID，每个 ID 有 10 行。现在我想找到 volume 列的最大值，然后过滤掉突出显示在前 3 个最高音量值中的那些 ID 的组（或只制作一个额外的列，如 inTop3 ）。

这可能意味着最大的 3 个值在 ID = 2 的组内。但我真的只想将每个组的最大值与其他组的最大值进行比较。

获取每组的最大值很简单：

df %>% 
  group_by(id) %>% 
  mutate(
    m = max(volume)
  ) 

但后来我有点迷失了如何继续。特别是我想知道如何创建一个布尔列来指示一个组是否在前 3 名中。

Answer 1

另一种可能的解决方案：

library(dplyr)

df %>% 
  group_by(id) %>% 
  summarise(m = max(volume)) %>% 
  slice_max(m, n = 3)

#> # A tibble: 3 × 2
#>      id      m
#>   <dbl>  <dbl>
#> 1     2 0.0788
#> 2     6 0.0284
#> 3     3 0.0233

---

获取 3 个最大值中每一个的整个组：

library(tidyverse)

df %>% 
  group_by(id) %>% 
  summarise(m = max(volume)) %>% 
  slice_max(m, n = 3) %>% 
  group_split(id) %>% 
  map(~ inner_join(df, .x, by = "id"))

#> [[1]]
#> # A tibble: 10 × 4
#>       id year  volume      m
#>    <dbl> <chr>  <dbl>  <dbl>
#>  1     2 2017  0.0788 0.0788
#>  2     2 2018  0.0788 0.0788
#>  3     2 2019  0.0773 0.0788
#>  4     2 2020  0.0766 0.0788
#>  5     2 2021  0.0755 0.0788
#>  6     2 2022  0.0745 0.0788
#>  7     2 2023  0.0748 0.0788
#>  8     2 2024  0.0741 0.0788
#>  9     2 2025  0.0717 0.0788
#> 10     2 2026  0.0681 0.0788
#> 
#> [[2]]
#> # A tibble: 10 × 4
#>       id year  volume      m
#>    <dbl> <chr>  <dbl>  <dbl>
#>  1     3 2017  0.0233 0.0233
#>  2     3 2018  0.0230 0.0233
#>  3     3 2019  0.0224 0.0233
#>  4     3 2020  0.0220 0.0233
#>  5     3 2021  0.0214 0.0233
#>  6     3 2022  0.0209 0.0233
#>  7     3 2023  0.0208 0.0233
#>  8     3 2024  0.0204 0.0233
#>  9     3 2025  0.0193 0.0233
#> 10     3 2026  0.0180 0.0233
#> 
#> [[3]]
#> # A tibble: 10 × 4
#>       id year  volume      m
#>    <dbl> <chr>  <dbl>  <dbl>
#>  1     6 2017  0.0284 0.0284
#>  2     6 2018  0.0284 0.0284
#>  3     6 2019  0.0278 0.0284
#>  4     6 2020  0.0275 0.0284
#>  5     6 2021  0.0270 0.0284
#>  6     6 2022  0.0265 0.0284
#>  7     6 2023  0.0266 0.0284
#>  8     6 2024  0.0262 0.0284
#>  9     6 2025  0.0251 0.0284
#> 10     6 2026  0.0234 0.0284

Answer 2

您可以使用dplyr::top_n

df  %>%
  group_by(id) %>%
  arrange(id, desc(volume)) %>%
  top_n(3)

      id year   volume
   <dbl> <chr>   <dbl>
 1     1 2017  0.0013 
 2     1 2018  0.0013 
 3     1 2019  0.00126
 4     2 2018  0.0788 
 5     2 2017  0.0788 
 6     2 2019  0.0773 
 7     3 2017  0.0233 
 8     3 2018  0.0230 
 9     3 2019  0.0224 
10     4 2017  0.0004 
# … with 24 more rows

top3/nottop3

df  %>%
  group_by(id) %>%
  arrange(id, desc(volume)) %>%
  mutate(top3 = ifelse(row_number() %in% c(1,2,3), "top3", "nottop3"))

     id year    volume top3   
   <dbl> <chr>    <dbl> <chr>  
 1     1 2017  0.0013   top3   
 2     1 2018  0.0013   top3   
 3     1 2019  0.00126  top3   
 4     1 2020  0.00119  nottop3
 5     1 2021  0.00114  nottop3
 6     1 2022  0.00108  nottop3
 7     1 2023  0.00102  nottop3
 8     1 2024  0.00102  nottop3
 9     1 2025  0.000995 nottop3
10     1 2026  0.000926 nottop3