How to replace `cur_data()` with `pick()` when adding a row to each group in a dataframe

I see that in dplyr the function cur_data() has been deprecated in favor of pick(). However, I’m confused about how to use pick() when trying to add rows to each group in a grouped dataframe. I’m cleaning some funky data and need to insert rows into each group that propagate the grouping variable’s value while inserting a specific value into an accompanying column.

Here’s example code that uses cur_data() to produce my desired output:

df <- tibble::tribble(
  ~id,   ~val,
  "A",   95,
  "A",   20,
  "A",   45,
  "B",   10,
  "B",   50,
  "C",   80
)

df_new_rows <- df |>
  dplyr::group_by(id) |>
  dplyr::reframe(tibble::add_row(dplyr::cur_data(), val = 100)) |>
  dplyr::ungroup()

Here’s the desired output – the rows with 100 in the val column have been inserted:

# A tibble: 9 × 2
  id      val
  <chr> <dbl>
1 A        95
2 A        20
3 A        45
4 A       100
5 B        10
6 B        50
7 B       100
8 C        80
9 C       100

How can I use pick() or another function to do this? For development reasons I’d like to use either base R or tidyverse functions, but if there is no way to do that I’m open to other suggestions. Thanks in advance for any help!

>Solution :

You can use the group_modify() to applies a function to each group in a grouped tibble, and returns a grouped tibble.

df <- tibble::tibble(
 id = c("A", "A", "A", "B", "B", "C"),
 val = c(95, 20, 45, 10, 50, 80)
)

add_row_func <- function(df) {
 tibble::add_row(df, id = df$id[1], val = 100)
}

df_new_rows <- df |>
 dplyr::group_by(id) |>
 dplyr::group_modify(add_row_func) |>
 dplyr::ungroup()

print(df_new_rows)