Counts the Time differences (epochs) per group (in a grouped dataset)

Usage

count_difftime(dataset, Datetime.colname = Datetime)

Arguments

dataset: A light logger dataset. Expects a dataframe. If not imported by LightLogR, take care to choose a sensible variable for the Datetime.colname.
Datetime.colname: column name that contains the datetime. Defaults to "Datetime" which is automatically correct for data imported with LightLogR. Expects a symbol. Needs to be part of the dataset. Must be of type POSIXct.

Value

a tibble with the number of occurences of each time difference per group

Examples

#count_difftime returns the number of occurences of each time difference
#and is more comprehensive in terms of a summary than `gap_finder` or 
#`dominant_epoch`
count_difftime(sample.data.irregular)
#> # A tibble: 4 × 4
#> # Groups:   Id [1]
#>   Id    difftime       n group.indices
#>   <chr> <Duration> <int>         <int>
#> 1 P1    15s        10015             1
#> 2 P1    16s         1367             1
#> 3 P1    17s           23             1
#> 4 P1    18s           16             1
dominant_epoch(sample.data.irregular)
#> # A tibble: 1 × 3
#>   Id    dominant.epoch group.indices
#>   <chr> <Duration>             <int>
#> 1 P1    15s                        1
gap_finder(sample.data.irregular)
#> Found 10758 gaps. 761 Datetimes fall into the regular sequence.

#irregular data can be regularized with `aggregate_Datetime`
sample.data.irregular |> 
 aggregate_Datetime(unit = "15 secs") |> 
 count_difftime()
#> # A tibble: 2 × 4
#> # Groups:   Id [1]
#>   Id    difftime       n group.indices
#>   <chr> <Duration> <int>         <int>
#> 1 P1    15s        11324             1
#> 2 P1    30s           97             1