Mutating a column of grouped data with a value from a column based on a specific timestamp from another column

I am looking for an efficient way to replicate a process below, for example using dplyr.

Suppose I have this df:

df <- data.frame(id = c("A", "A", "A", "A", "A", "A", "A", "A"),
                 date = as.Date(c("2022-01-01", "2022-01-01", "2022-01-01", "2022-01-01", "2022-01-02", "2022-01-02", "2022-01-02", "2022-01-02")),
                 timestamp = c("08:59:00", "09:00:00", "09:01:00", "09:02:00", "08:59:00", "09:00:00", "09:01:00", "09:02:00"),
                 result = c(55, 56, 54, 52, 58, 57, 60, 62))

> df
  id       date timestamp result
1  A 2022-01-01  08:59:00    55
2  A 2022-01-01  09:00:00    56
3  A 2022-01-01  09:01:00    54
4  A 2022-01-01  09:02:00    52
5  A 2022-01-02  08:59:00    58
6  A 2022-01-02  09:00:00    57
7  A 2022-01-02  09:01:00    60
8  A 2022-01-02  09:02:00    62

I would like to group by date and create a new column result1 based on the value of result at timestamp == "09:00:00" such that resulting data look like this:

> df
  id       date timestamp result result1
1  A 2022-01-01  08:59:00    55     56
2  A 2022-01-01  09:00:00    56     56
3  A 2022-01-01  09:01:00    54     56
4  A 2022-01-01  09:02:00    52     56
5  A 2022-01-02  08:59:00    58     57
6  A 2022-01-02  09:00:00    57     57
7  A 2022-01-02  09:01:00    60     57
8  A 2022-01-02  09:02:00    62     57

Solution

Something like this?

df %>% 
  group_by(date) %>% 
  mutate(result1 = result[timestamp=="09:00:00"])

# A tibble: 8 × 5
# Groups:   date [2]
  id    date       timestamp result result1
  <chr> <date>     <chr>      <dbl>   <dbl>
1 A     2022-01-01 08:59:00      55      56
2 A     2022-01-01 09:00:00      56      56
3 A     2022-01-01 09:01:00      54      56
4 A     2022-01-01 09:02:00      52      56
5 A     2022-01-02 08:59:00      58      57
6 A     2022-01-02 09:00:00      57      57
7 A     2022-01-02 09:01:00      60      57
8 A     2022-01-02 09:02:00      62      57