Differences between two columns having missing values in one

This is my database example in longitudinal way containing:

1. id = Individuals
2. grup_int = categorical variable of group
3. gen = variables to be measured
4. time = point in time for id and gene
5. value

        id grup_int gen   time   value
     <dbl>    <dbl> <chr> <chr>  <dbl>
1 60801001        1 adrb2 2     2.11  
2 60801001        1 ccl2  1     0.941 
3 60801001        1 ccl2  2     0.248 
4 60801001        1 ccl3  1     5.65  
5 60801001        1 ccl3  2     NA

What I want to do in my whole database is substracting variables from time = 2 minus time = 1 grouped by id and grup_int --> something like this:

df %>% dplyr::group_by(id, gen) %>%  dplyr::mutate(d_post_pre =  value [time == 2] - value [time == 1])

`d_post_pre` must be size 1, not 0.
ℹ The error occurred in group 1: id = 60801001, gen = "adrb2".

As you can see for the adrb2 gen, there is no even entry for time == 1, and because of that is throwing this error. The possibilities to be found in value are:

• Missing values in any time point as in "adrb2"
• NAs in any time point as in "ccl3" , this shouldn't matter

Can you suggest any option to perform the line to be shielded from missing values or just paste some text or string to be filtered out?

Thanks in advance!

df <- structure(list(id = structure(c(60801001, 60801001, 60801001, 
60801001, 60801001), label = "Identificador", format.spss = "F9.0", display_width = 14L), 
    grup_int = structure(c(1, 1, 1, 1, 1), format.spss = "F2.0"), 
    gen = c("adrb2", "ccl2", "ccl2", "ccl3", "ccl3"), time = c("2", 
    "1", "2", "1", "2"), value = c(2.1098254, 0.94088, 0.24778089, 
    5.6529145, 0.06939283)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -5L))

What about something like this? Use pivot_wider to define value columns for time == 1 and time == 2, and then you can simply subtract the columns. Then you can convert to long format again.

df |> 
  tidyr::pivot_wider(
    id_cols = c("id", "grup_int", "gen"),
    names_from = "time",
    names_prefix = "time_",
    values_from = "value"
  ) |> 
  dplyr::mutate(d_post_pre = time_2 - time_1) |> 
  tidyr::pivot_longer(
    cols = c("time_1", "time_2"),
    names_to = "time",
    names_prefix = "time_"
  ) |>
  dplyr::filter(!is.na(value)) |> 
  dplyr::select(id, grup_int, gen, time, value, d_post_pre, everything())

# # A tibble: 6 × 6
#         id grup_int gen   time    value d_post_pre
#      <dbl>    <dbl> <chr> <chr>   <dbl>      <dbl>
# 1 60801001        1 adrb2 2      2.11       NA    
# 2 60801001        1 ccl2  1      0.941      -0.693
# 3 60801001        1 ccl2  2      0.248      -0.693
# 4 60801001        1 ccl3  1      5.65       -5.58 
# 5 60801001        1 ccl3  2      0.0694     -5.58