gap_table: Tabular summary of data and gaps in all groups

In LightLogR: Process Data from Wearable Light Loggers and Optical Radiation Dosimeters

Tabular summary of data and gaps in all groups

Description

gap_table() creates a gt::gt() with one row per group, summarizing key gap and gap-related information about the dataset. These include the available data, total duration, number of gaps, missing implicit and explicit data, and, optionally, irregular data.

Usage

gap_table(
  dataset,
  Variable.colname = MEDI,
  Variable.label = "melanopic EDI",
  title = "Summary of available and missing data",
  Datetime.colname = Datetime,
  epoch = "dominant.epoch",
  full.days = TRUE,
  include.implicit.gaps = TRUE,
  check.irregular = TRUE,
  get.df = FALSE
)

Arguments

dataset	A light logger dataset. Needs to be a dataframe.
Variable.colname	Column name of the variable to check for NA values. Expects a symbol.
Variable.label	Clear name of the variable. Expects a string
title	Title string for the table
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".
full.days	If TRUE, the gapless sequence will include the whole first and last day where there is data.
include.implicit.gaps	Logical. Whether to expand the datetime sequence and search for implicit gaps, or not. Default is TRUE. If no Variable.colname is provided, this argument will be ignored. If there are implicit gaps, gap calculation can be incorrect whenever there are missing explicit gaps flanking implicit gaps!
check.irregular	Logical on whether to include irregular data in the summary, i.e. data points that do not fall on the regular sequence.
get.df	Logical whether the dataframe should be returned instead of a gt::gt() table

Value

A gt table about data and gaps in the dataset

Examples

sample.data.environment |> dplyr::filter(MEDI <= 50000) |> gap_table()

LightLogR documentation built on June 10, 2025, 5:12 p.m.