gap_finder: Check for and output gaps in a dataset

Description

Quickly check for implicit missing Datetime data. Outputs a message with a short summary, and can optionally return the gaps as a tibble. Uses gap_handler() internally.

Usage

gap_finder(
  dataset,
  Datetime.colname = Datetime,
  epoch = "dominant.epoch",
  gap.data = FALSE,
  silent = FALSE,
  full.days = FALSE
)

Value

Prints message with a short summary of the gaps in the dataset. If gap.data = TRUE, returns a tibble of the gaps in the dataset.

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".
gap.data: Logical. If TRUE, returns a tibble of the gaps in the dataset. Default is FALSE.
silent: Logical. If TRUE, suppresses the message with the summary of the gaps in the dataset. Default is FALSE. Only used for unit tests.
full.days: If TRUE, the gapless sequence will include the whole first and last day where there is data.

Details

The gap_finder() function is a wrapper around gap_handler() with the behavior argument set to "gaps". The main difference is that gap_finder() returns a message with a short summary of the gaps in the dataset, and that the tibble with the gaps contains a column gap.id that indicates the gap number, which is useful to determine, e.g., the consecutive number of gaps between measurement data.

Examples

Run this code

dataset <-
tibble::tibble(Id = c("A", "A", "A", "B", "B", "B"),
              Datetime = lubridate::as_datetime(1) +
                         lubridate::days(c(0:2, 4, 6, 8)) +
                         lubridate::hours(c(0,12,rep(0,4)))) %>%
dplyr::group_by(Id)
dataset

#look for gaps assuming the epoch is the dominant epoch of each group
gap_finder(dataset)

#return the gaps as a tibble
gap_finder(dataset, gap.data = TRUE)

#assuming the epoch is 1 day, we have different gaps, and the datapoint at noon is now `irregular`
gap_finder(dataset, epoch = "1 day")