Adding NA for Age at the start and end of the data collected when grouped by S.NO in R

I have a dataset that looks like this and I am trying to add NA at the beginning and end of the age data collected for each S.NO. as shown in the expected dataset in R.
Would appreciate any help. Thank you.

Expected Dataset

Using tibble::add_row, dplyr::group_split and purrr::map_dfr you could do:

library(dplyr, warn = FALSE)
library(tibble)
library(purrr)

pad_rows <- function(x) {
  x |> 
    tibble::add_row(S.NO = first(x$S.NO), AGE_2 = first(x$AGE_1), .before = 1) |> 
    tibble::add_row(S.NO = last(x$S.NO), AGE_1 = last(x$AGE_2), .after = Inf)
}

dat |> 
  group_split(S.NO) |> 
  purrr::map_dfr(pad_rows)
#> # A tibble: 15 × 3
#>     S.NO AGE_1 AGE_2
#>    <int> <int> <int>
#>  1   123    NA    19
#>  2   123    19    24
#>  3   123    24    NA
#>  4   124    NA    18
#>  5   124    18    21
#>  6   124    21    28
#>  7   124    28    35
#>  8   124    35    NA
#>  9   125    NA    13
#> 10   125    13    19
#> 11   125    19    23
#> 12   125    23    NA
#> 13   126    NA    19
#> 14   126    19    21
#> 15   126    21    NA

DATA

dat <- data.frame(
        S.NO = c(123L, 124L, 124L, 124L, 125L, 125L, 126L),
       AGE_1 = c(19L, 18L, 21L, 28L, 13L, 19L, 19L),
       AGE_2 = c(24L, 21L, 28L, 35L, 19L, 23L, 21L)
)

Using group_modify

library(dplyr)
library(tibble)
df %>% 
  group_by(S.NO) %>% 
  group_modify(~ .x %>% 
                add_row(AGE_2= first(.x$AGE_1), .before = 1) %>% 
                add_row(AGE_1 = last(.x$AGE_2))) %>% 
  ungroup

-output

# A tibble: 15 × 3
    S.NO AGE_1 AGE_2
   <int> <int> <int>
 1   123    NA    19
 2   123    19    24
 3   123    24    NA
 4   124    NA    18
 5   124    18    21
 6   124    21    28
 7   124    28    35
 8   124    35    NA
 9   125    NA    13
10   125    13    19
11   125    19    23
12   125    23    NA
13   126    NA    19
14   126    19    21
15   126    21    NA

An approach using nest_by

library(dplyr)
library(tidyr) # unnest

df %>% 
  nest_by(S.NO) %>% 
  mutate(data = list(unique(unlist(data)))) %>% 
  unnest(data) %>% 
  group_by(S.NO) %>% 
  reframe(AGE_1 = c(NA, data), AGE_2 = c(data, NA))
# A tibble: 15 × 3
    S.NO AGE_1 AGE_2
   <int> <int> <int>
 1   123    NA    19
 2   123    19    24
 3   123    24    NA
 4   124    NA    18
 5   124    18    21
 6   124    21    28
 7   124    28    35
 8   124    35    NA
 9   125    NA    13
10   125    13    19
11   125    19    23
12   125    23    NA
13   126    NA    19
14   126    19    21
15   126    21    NA

Data

df <- structure(list(S.NO = c(123L, 124L, 124L, 124L, 125L, 125L, 126L
), AGE_1 = c(19L, 18L, 21L, 28L, 13L, 19L, 19L), AGE_2 = c(24L, 
21L, 28L, 35L, 19L, 23L, 21L)), class = "data.frame", row.names = c(NA, 
-7L))