The precision of the FED clock is in seconds.
It might happen that two events have the same exact timestamp.
This helper function is used to check for duplicated entries and deduplicate them using methods.
This function is internally called by read_fed() and the user is advised to first try several examples before modifying deduplicate_method in read_fed()
Usage
deduplicate_datetime(
data,
method = "offset",
offset = "0.1 sec",
reset_counts = FALSE,
reset_columns = c("Pellet_Count", "Left_Poke_Count", "Right_Poke_Count")
)Arguments
- data
FED data frame as read by
read_fed()- method
The method to deduplicate the identical timestamps (default is 'offset').
methodmust be one of 'keep_first', 'keep_last', 'remove', or 'offset', or 'interpolate'.- offset
The offset to be added to duplicate datetimes (e.g., '0.1 sec').
- reset_counts
whether to reset the
reset_columnsor not (default = FALSE).- reset_columns
The columns to be reset if
reset_counts = TRUE.
Examples
# data contains datetimes that will fail to parse and are duplicated
fed3::duplicate_test_data
#> # A tibble: 20 × 5
#> datetime Pellet_Count Left_Poke_Count Right_Poke_Count row_id
#> <chr> <dbl> <dbl> <dbl> <int>
#> 1 2023-06-10 08:00:00 1 1 10 1
#> 2 2023-06-10 08:01:00 2 2 9 2
#> 3 2023-06-10 08:02:00 3 3 8 3
#> 4 2023-06-10 08:03:00 4 4 7 4
#> 5 2023-06-10 08:04:00 5 5 6 5
#> 6 2023-06-10 08:05:00 6 6 5 6
#> 7 2023-06-10 08:06:00 7 7 4 7
#> 8 2023-06-10 08:06:00 8 8 3 8
#> 9 This will fail NA 9 2 9
#> 10 2023-06-10 08:07:00 10 10 1 10
#> 11 2023-06-10 08:08:00 1 1 10 11
#> 12 2023-06-10 08:09:00 2 2 9 12
#> 13 2023-06-10 08:10:00 3 3 8 13
#> 14 2023-06-10 08:11:00 4 4 7 14
#> 15 2023-06-10 08:11:00 5 5 6 15
#> 16 2023-06-10 08:12:00 6 6 5 16
#> 17 2023-06-10 08:13:00 7 7 4 17
#> 18 This will fail too NA 8 3 18
#> 19 2023-06-10 08:14:00 9 9 2 19
#> 20 2023-06-10 08:15:00 10 10 1 20
fed3:::deduplicate_datetime(duplicate_test_data, method = 'keep_first')
#> Warning: 2 failed to parse.
#> Warning: NA values found in `datetime` column after parsing.
#> Filling NAs with last observation carried forward.
#> # A tibble: 16 × 5
#> datetime Pellet_Count Left_Poke_Count Right_Poke_Count row_id
#> <dttm> <dbl> <dbl> <dbl> <int>
#> 1 2023-06-10 08:00:00 1 1 10 1
#> 2 2023-06-10 08:01:00 2 2 9 2
#> 3 2023-06-10 08:02:00 3 3 8 3
#> 4 2023-06-10 08:03:00 4 4 7 4
#> 5 2023-06-10 08:04:00 5 5 6 5
#> 6 2023-06-10 08:05:00 6 6 5 6
#> 7 2023-06-10 08:06:00 7 7 4 7
#> 8 2023-06-10 08:07:00 10 10 1 10
#> 9 2023-06-10 08:08:00 1 1 10 11
#> 10 2023-06-10 08:09:00 2 2 9 12
#> 11 2023-06-10 08:10:00 3 3 8 13
#> 12 2023-06-10 08:11:00 4 4 7 14
#> 13 2023-06-10 08:12:00 6 6 5 16
#> 14 2023-06-10 08:13:00 7 7 4 17
#> 15 2023-06-10 08:14:00 9 9 2 19
#> 16 2023-06-10 08:15:00 10 10 1 20
fed3:::deduplicate_datetime(duplicate_test_data, method = 'offset', offset = "1 sec")
#> Warning: 2 failed to parse.
#> Warning: NA values found in `datetime` column after parsing.
#> Filling NAs with last observation carried forward.
#> # A tibble: 20 × 5
#> datetime Pellet_Count Left_Poke_Count Right_Poke_Count row_id
#> <dttm> <dbl> <dbl> <dbl> <int>
#> 1 2023-06-10 08:00:00 1 1 10 1
#> 2 2023-06-10 08:01:00 2 2 9 2
#> 3 2023-06-10 08:02:00 3 3 8 3
#> 4 2023-06-10 08:03:00 4 4 7 4
#> 5 2023-06-10 08:04:00 5 5 6 5
#> 6 2023-06-10 08:05:00 6 6 5 6
#> 7 2023-06-10 08:06:00 7 7 4 7
#> 8 2023-06-10 08:06:01 8 8 3 8
#> 9 2023-06-10 08:06:02 NA 9 2 9
#> 10 2023-06-10 08:07:00 10 10 1 10
#> 11 2023-06-10 08:08:00 1 1 10 11
#> 12 2023-06-10 08:09:00 2 2 9 12
#> 13 2023-06-10 08:10:00 3 3 8 13
#> 14 2023-06-10 08:11:00 4 4 7 14
#> 15 2023-06-10 08:11:01 5 5 6 15
#> 16 2023-06-10 08:12:00 6 6 5 16
#> 17 2023-06-10 08:13:00 7 7 4 17
#> 18 2023-06-10 08:13:01 NA 8 3 18
#> 19 2023-06-10 08:14:00 9 9 2 19
#> 20 2023-06-10 08:15:00 10 10 1 20