LightLogR (version 0.9.2)

has_gaps: Does a dataset have implicit gaps

Description

Returns TRUE if there are implicit gaps in the dataset and FALSE if it is gapless. Gaps can make sense depending on the grouping structure, but the general sequence of Datetimes within a dataset should always be gapless.

Usage

has_gaps(dataset, Datetime.colname = Datetime, epoch = "dominant.epoch")

Value

logical

Arguments

dataset

A light logger dataset. Needs to be a dataframe.

Datetime.colname

The column that contains the datetime. Needs to be a POSIXct and part of the dataset.

epoch

The epoch to use for the gapless sequence. Can be either a lubridate::duration() or a string. If it is a string, it needs to be either '"dominant.epoch"' (the default) for a guess based on the data or a valid lubridate::duration() string, e.g., "1 day" or "10 sec".

See Also

Other regularize: dominant_epoch(), extract_gaps(), gap_finder(), gap_handler(), gapless_Datetimes(), has_irregulars()

Examples

#The sample dataset does not have gaps
sample.data.environment |> has_gaps()
#removing some of the data creates gaps
sample.data.environment |> dplyr::filter(MEDI <= 50000) |> has_gaps()

#having a grouped dataframe where the groups span multiple unconnected parts 
#is considered a gap, which can be relevant, e.g., when searching for clusters
sample.data.environment |>
  add_photoperiod(c(47.1, 10)) |>
  dplyr::group_by(photoperiod.state) |>
  has_gaps()

 #to avoid this, use `number_states()` for grouping
 sample.data.environment |>
  add_photoperiod(c(48.52, 9.06)) |>
  number_states(photoperiod.state) |>
  dplyr::group_by(photoperiod.state.count, .add = TRUE) |>
  has_gaps()