Skip to contents

Check for and output gaps in a dataset

Source: R/gaps.R
gap_finder.Rd

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
)

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.

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.

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.

See also

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

Examples

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
#> # A tibble: 6 × 2
#> # Groups:   Id [2]
#>   Id    Datetime           
#>   <chr> <dttm>             
#> 1 A     1970-01-01 00:00:01
#> 2 A     1970-01-02 12:00:01
#> 3 A     1970-01-03 00:00:01
#> 4 B     1970-01-05 00:00:01
#> 5 B     1970-01-07 00:00:01
#> 6 B     1970-01-09 00:00:01

#look for gaps assuming the epoch is the dominant epoch of each group
gap_finder(dataset)
#> Found 2 gaps. 6 Datetimes fall into the regular sequence.

#return the gaps as a tibble
gap_finder(dataset, gap.data = TRUE)
#> Found 2 gaps. 6 Datetimes fall into the regular sequence.
#> # A tibble: 2 × 3
#> # Groups:   Id [1]
#>   gap.id Datetime            Id   
#>    <int> <dttm>              <chr>
#> 1      1 1970-01-01 12:00:01 A    
#> 2      1 1970-01-02 00:00:01 A    

#assuming the epoch is 1 day, we have different gaps, and the datapoint at noon is now `irregular`
gap_finder(dataset, epoch = "1 day")
#> Found 3 gaps. 5 Datetimes fall into the regular sequence.