如何按行汇总多列中的前 n 个值？

Question

在我的数据框中，我有多列包含学生成绩。我想对“测验”列进行求和（例如测验1、测验2）。但是，我只想对前 2 个值求和，而忽略其他值。我想创建一个包含总计（即前 2 个值的总和）的新列。

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一个问题是，有些学生的成绩与给定行中的前 2 名成绩并列。例如，Aaron 的得分很高，为 42，但随后有两个得分并列第二高（即 36）。

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数据

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df <- \n  structure(\n  list(\n    Student = c("Aaron", "James", "Charlotte", "Katie", "Olivia", \n                "Timothy", "Grant", "Chloe", "Judy", "Justin"),\n    ID = c(30016, 87311, 61755, 55323, 94839, 38209, 34096, \n           98432, 19487, 94029),\n    Quiz1 = c(31, 25, 41, 10, 35, 19, 27, 42, 15, 20),\n    Quiz2 = c(42, 33, 34, 22, 23, 38, 48, 49, 23, 30),\n    Quiz3 = c(36, 36, 34, 32, 43, 38, 44, 42, 42, 37),\n    Quiz4 = c(36, 43, 39, 46, 40, 38, 43, 35, 41, 41)\n  ),\n  row.names = c(NA, -10L),\n  class = c("tbl_df", "tbl", "data.frame")\n)\n

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我知道我可以pivot_longer这样做，这样我就可以按组排列，然后为每个学生取前 2 个值。这工作得很好，但我想要一种更有效的方式tidyverse，而不是来回转动。

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我尝试过的

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library(tidyverse)\n\ndf %>%\n  pivot_longer(-c(Student, ID)) %>%\n  group_by(Student, ID) %>%\n  arrange(desc(value), .by_group = TRUE) %>%\n  slice_head(n = 2) %>%\n  pivot_wider(names_from = name, values_from = value) %>%\n  ungroup() %>%\n  mutate(Total = rowSums(select(., starts_with("Quiz")), na.rm = TRUE))\n

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我还知道，如果我想对每行上的所有列求和，那么我可以使用rowSums，就像我在上面使用的那样。但是，我不确定如何rowSums仅处理 4 个测验列中的前 2 个值。

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预期输出

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# A tibble: 10 \xc3\x97 7\n   Student      ID Quiz2 Quiz3 Quiz1 Quiz4 Total\n   <chr>     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1 Aaron     30016    42    36    NA    NA    78\n 2 Charlotte 61755    NA    NA    41    39    80\n 3 Chloe     98432    49    NA    42    NA    91\n 4 Grant     34096    48    44    NA    NA    92\n 5 James     87311    NA    36    NA    43    79\n 6 Judy      19487    NA    42    NA    41    83\n 7 Justin    94029    NA    37    NA    41    78\n 8 Katie     55323    NA    32    NA    46    78\n 9 Olivia    94839    NA    43    NA    40    83\n10 Timothy   38209    38    38    NA    NA    76\n

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Answer 1

基于这个 StackOverflow 答案。

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library(tidyverse)\n\ndf <- \n  structure(\n    list(\n      Student = c("Aaron", "James", "Charlotte", "Katie", "Olivia", \n                  "Timothy", "Grant", "Chloe", "Judy", "Justin"),\n      ID = c(30016, 87311, 61755, 55323, 94839, 38209, 34096, \n             98432, 19487, 94029),\n      Quiz1 = c(31, 25, 41, 10, 35, 19, 27, 42, 15, 20),\n      Quiz2 = c(42, 33, 34, 22, 23, 38, 48, 49, 23, 30),\n      Quiz3 = c(36, 36, 34, 32, 43, 38, 44, 42, 42, 37),\n      Quiz4 = c(36, 43, 39, 46, 40, 38, 43, 35, 41, 41)\n    ),\n    row.names = c(NA, -10L),\n    class = c("tbl_df", "tbl", "data.frame")\n  )\n\ndf %>%\n  rowwise() %>% \n  mutate(Quiz_Total = sum(sort(c(Quiz1,Quiz2,Quiz3,Quiz4), decreasing = TRUE)[1:2])) %>% \n  ungroup()\n#> # A tibble: 10 \xc3\x97 7\n#>    Student      ID Quiz1 Quiz2 Quiz3 Quiz4 Quiz_Total\n#>    <chr>     <dbl> <dbl> <dbl> <dbl> <dbl>      <dbl>\n#>  1 Aaron     30016    31    42    36    36         78\n#>  2 James     87311    25    33    36    43         79\n#>  3 Charlotte 61755    41    34    34    39         80\n#>  4 Katie     55323    10    22    32    46         78\n#>  5 Olivia    94839    35    23    43    40         83\n#>  6 Timothy   38209    19    38    38    38         76\n#>  7 Grant     34096    27    48    44    43         92\n#>  8 Chloe     98432    42    49    42    35         91\n#>  9 Judy      19487    15    23    42    41         83\n#> 10 Justin    94029    20    30    37    41         78\n

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Answer 2

你不必这样做pivot_wider。请注意，较长的格式是整齐的格式。只要做pivot_longer并且left_join：

df %>% 
  left_join(pivot_longer(., -c(Student, ID)) %>%
  group_by(Student, ID) %>%
  summarise(Total = sum(sort(value, TRUE)[1:2]), .groups = 'drop'))

# A tibble: 10 x 7
   Student      ID Quiz1 Quiz2 Quiz3 Quiz4 Total
   <chr>     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 Aaron     30016    31    42    36    36    78
 2 James     87311    25    33    36    43    79
 3 Charlotte 61755    41    34    34    39    80
 4 Katie     55323    10    22    32    46    78
 5 Olivia    94839    35    23    43    40    83
 6 Timothy   38209    19    38    38    38    76
 7 Grant     34096    27    48    44    43    92
 8 Chloe     98432    42    49    42    35    91
 9 Judy      19487    15    23    42    41    83
10 Justin    94029    20    30    37    41    78