This function calculates the total duration of data in each group of a dataset, based on a datetime column and a variable column. It uses the dominant epoch (interval) of each group to calculate the duration.
Usage
durations(
dataset,
Variable.colname = Datetime,
Datetime.colname = Datetime,
count.NA = FALSE,
show.missing = FALSE,
show.interval = FALSE,
FALSE.as.NA = FALSE
)Arguments
- dataset
A light logger dataset. Expects a dataframe. If not imported by LightLogR, take care to choose sensible variables for the Datetime.colname and Variable.colname.
- Variable.colname
Column name that contains the variable for which to calculate the duration. Expects a symbol. Needs to be part of the dataset.
- 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.
- count.NA
Logical. Should NA values in Variable.colname be counted as part of the duration? Defaults to FALSE.
- show.missing
Logical. Should the duration of NAs be provided in a separate column "Missing"? Defaults to FALSE.
- show.interval
Logical. Should the dominant epoch (interval) be shown in a column "interval"? Defaults to FALSE.
- FALSE.as.NA
Logical. Should FALSE values in the Variable.colname be treated as NA (i.e., missing)?
Value
A tibble with one row per group and a column "duration" containing
the duration of each group as a lubridate::duration(). If show.missing = TRUE, a column "missing" is added with the duration of NAs, and a column
"total" with the total duration. If show.interval = TRUE, a column
"interval" is added with the dominant epoch of each group.
Examples
# Calculate the duration of a dataset
durations(sample.data.environment)
#> # A tibble: 2 × 2
#> # Groups: Id [2]
#> Id duration
#> <fct> <Duration>
#> 1 Environment 518400s (~6 days)
#> 2 Participant 518400s (~6 days)
# create artificial gaps in the data
gapped_data <-
sample.data.environment |>
dplyr::filter(MEDI >= 10) |>
gap_handler(full.days = TRUE)
#by default, the Datetime column is selected for the `Variable.colname`,
#basically ignoring NA measurement values
gapped_data |>
durations(count.NA = TRUE)
#> # A tibble: 2 × 2
#> # Groups: Id [2]
#> Id duration
#> <fct> <Duration>
#> 1 Environment 518400s (~6 days)
#> 2 Participant 518400s (~6 days)
# Calculate the duration where MEDI are available
durations(gapped_data, MEDI)
#> # A tibble: 2 × 2
#> # Groups: Id [2]
#> Id duration
#> <fct> <Duration>
#> 1 Environment 307950s (~3.56 days)
#> 2 Participant 257470s (~2.98 days)
# Calculate the duration, show the duration of NAs separately
durations(gapped_data, MEDI, show.missing = TRUE)
#> # A tibble: 2 × 4
#> # Groups: Id [2]
#> Id duration missing total
#> <fct> <Duration> <Duration> <Duration>
#> 1 Environment 307950s (~3.56 days) 210450s (~2.44 days) 518400s (~6 days)
#> 2 Participant 257470s (~2.98 days) 260930s (~3.02 days) 518400s (~6 days)
# Calculate the duration, show the dominant epoch
durations(gapped_data, Variable.colname = MEDI, show.interval = TRUE)
#> # A tibble: 2 × 3
#> # Groups: Id [2]
#> Id duration interval
#> <fct> <Duration> <Duration>
#> 1 Environment 307950s (~3.56 days) 30s
#> 2 Participant 257470s (~2.98 days) 10s
# Calculate durations for day and night separately
gapped_data |>
add_photoperiod(coordinates = c(48.52, 9.06)) |>
dplyr::group_by(photoperiod.state, .add = TRUE) |>
durations(Variable.colname = MEDI, show.interval = TRUE, show.missing = TRUE)
#> # A tibble: 4 × 6
#> # Groups: Id, photoperiod.state [4]
#> Id photoperiod.state duration missing
#> <fct> <chr> <Duration> <Duration>
#> 1 Environment day 307950s (~3.56 days) 6270s (~1.74 hours)
#> 2 Environment night 0s 204180s (~2.36 days)
#> 3 Participant day 232550s (~2.69 days) 81680s (~22.69 hours)
#> 4 Participant night 24920s (~6.92 hours) 179250s (~2.07 days)
#> # ℹ 2 more variables: total <Duration>, interval <Duration>