rdplyrduplicatespivot-table

How to avoid transposition of duplicates into lists with pivot_wider?

I have duplicates on the first 3 columns that I would like to keep after pivot_wider transposition, but not in list format.
How to do it?

Initial data with duplicates:

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"))

enter image description here

What pivot_wider produces after running, with the following message:

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

enter image description here

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)

Desired output with duplicates:

enter image description here

Thanks for help

Solution

  • You can use row_number() within grouped data to distinguish the duplicates, e.g.,

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

    which gives

    # 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

    Another option is using unnest on top of what you have achieved

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

    which gives the desired output as well

    # 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