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denis
Asked: 2024-10-23 18:12:15 +0800 CST

如何避免使用pivot_wider将重复项转置到列表中？

  • 772

前 3 列中有重复项，我想在pivot_wider转置后保留这些重复项，但不要在列表格式中保留。
该怎么做？

有重复的初始数据：

dat0 <-
structure(list(id = c("P1", "P1", "P1", "P1", "P1", "P1", "P1", 
"P1", "P1", "P1", "P2", "P2", "P2", "P2", "P2", "P2"), analyte = c("A", 
"A", "B", "B", "B", "B", "C", "C", "D", "D", "B", "B", "B", "B", 
"D", "D"), analyzer = c("X", "Y", "X", "Y", "X", "Y", "X", "Y", 
"X", "Y", "X", "Y", "X", "Y", "X", "Y"), result = c(0.7, 0.9, 
1.26, 1.23, 1.24, 1.22, 5.7, 5.3, 4.1, 4.2, 1.22, 1.23, 1.21, 
1.22, 4.4, 4.5)), row.names = c(NA, -16L), class = c("tbl_df", 
"tbl", "data.frame"))

在此处输入图片描述

pivot_wider运行后生成以下消息：

dat1 <- dat0 %>% 
  pivot_wider(names_from = analyzer, values_from = result)

在此处输入图片描述

Values from `result` are not uniquely identified; output will contain list-cols.
• Use `values_fn = list` to suppress this warning.
• Use `values_fn = {summary_fun}` to summarise duplicates.
• Use the following dplyr code to identify duplicates.
  {data} |>
  dplyr::summarise(n = dplyr::n(), .by = c(id, analyte, analyzer)) |>
  dplyr::filter(n > 1L)

期望输出重复项：

在此处输入图片描述

感谢帮助

1 个回答

  1. Best Answer

    您可以row_number()在分组数据中使用来区分重复项，例如，

    dat0 %>%
    mutate(grp = row_number(), .by = c(id, analyte, analyzer)) %>%
    pivot_wider(names_from = analyzer, values_from = result) %>%
    select(-grp)

    由此得出

    # A tibble: 8 × 4
  id    analyte     X     Y
  <chr> <chr>   <dbl> <dbl>
1 P1    A        0.7   0.9
2 P1    B        1.26  1.23
3 P1    B        1.24  1.22
4 P1    C        5.7   5.3
5 P1    D        4.1   4.2
6 P2    B        1.22  1.23
7 P2    B        1.21  1.22

    另一个选择是利用unnest你已经取得的成果

    dat0 %>%
    pivot_wider(names_from = analyzer, values_from = result, values_fn = list) %>%
    unnest(cols = c("X", "Y"))

    这也给出了所需的输出

    # A tibble: 8 × 4
  id    analyte     X     Y
  <chr> <chr>   <dbl> <dbl>
1 P1    A        0.7   0.9
2 P1    B        1.26  1.23
3 P1    B        1.24  1.22
4 P1    C        5.7   5.3
5 P1    D        4.1   4.2
6 P2    B        1.22  1.23
7 P2    B        1.21  1.22
8 P2    D        4.4   4.5
    • 2

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