Counts the Time differences (epochs) per group (in a grouped dataset)
Source:R/epochs.R
count_difftime.Rd
Counts the Time differences (epochs) per group (in a grouped dataset)
Arguments
- dataset
A light logger dataset. Expects a
dataframe
. If not imported by LightLogR, take care to choose a sensible variable for theDatetime.colname
.- Datetime.colname
column name that contains the datetime. Defaults to
"Datetime"
which is automatically correct for data imported with LightLogR. Expects asymbol
. Needs to be part of thedataset
.
Examples
#get a dataset with irregular intervals
filepath <- system.file("extdata/sample_data_LYS.csv", package = "LightLogR")
dataset <- import$LYS(filepath)
#>
#> Successfully read in 11'422 observations across 1 Ids from 1 LYS-file(s).
#> Timezone set is UTC.
#>
#> First Observation: 2023-06-21 00:00:12
#> Last Observation: 2023-06-22 23:59:48
#> Timespan: 2 days
#>
#> Observation intervals:
#> Id interval.time n pct
#> 1 sample_data_LYS 15s 10015 87.689%
#> 2 sample_data_LYS 16s 1367 11.969%
#> 3 sample_data_LYS 17s 23 0.201%
#> 4 sample_data_LYS 18s 16 0.140%
#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(dataset)
#> # A tibble: 4 × 3
#> # Groups: Id [1]
#> Id difftime n
#> <fct> <Duration> <int>
#> 1 sample_data_LYS 15s 10015
#> 2 sample_data_LYS 16s 1367
#> 3 sample_data_LYS 17s 23
#> 4 sample_data_LYS 18s 16
dominant_epoch(dataset)
#> # A tibble: 1 × 3
#> Id dominant.epoch group.indices
#> <fct> <Duration> <int>
#> 1 sample_data_LYS 15s 1
gap_finder(dataset)
#> Found 10758 gaps. 761 Datetimes fall into the regular sequence.
#irregular data can be regularized with `aggregate_Datetime`
dataset %>% aggregate_Datetime(unit = "15 secs") %>% count_difftime()
#> # A tibble: 2 × 3
#> # Groups: Id [1]
#> Id difftime n
#> <fct> <Duration> <int>
#> 1 sample_data_LYS 15s 11324
#> 2 sample_data_LYS 30s 97