Tabular summary of data and gaps in all groups

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()


  Summary of available and missing data
    
Variable: melanopic EDI
    

      
        Data
      
      
        Missing
      
    

      
        Regular
      
      
        Irregular
      
      
        Range
      
      
        Interval
      
      
        Gaps
      
      
        Implicit
      
      
        Explicit
      
    
Time
      %
      n¹
      n^2,1
      Time
      n¹
      Time
      N
      ø
      øn¹
      Time
      %
      n¹
      Time
      %
      n¹
      Time
      %
      n¹
    
Overall
1w 4d 6h 42m 50s
94.0%³
67,003
0
1w 5d
69,120
30; 10
200
7m 8s
15
17h 17m 10s
6.0%³
2,117
17h 17m 10s
6.0%³
2,117
0s
0.0%³
0
Environment
    

5d 6h 53m 30s
88.1%
15,227
0
6d
17,280
30s
148
6m 56s
14
17h 6m 30s
11.9%
2,053
17h 6m 30s
11.9%
2,053
0s
0.0%
0
Participant
    

5d 23h 49m 20s
99.9%
51,776
0
6d
51,840
10s
52
12s
1
10m 40s
0.1%
64
10m 40s
0.1%
64
0s
0.0%
0
¹ Number of (missing or actual) observations
    
² If n > 0: it is possible that the other summary statistics are affected, as they are calculated based on the most prominent interval.
    
³ Based on times, not necessarily number of observations

	Data							Missing
Summary of available and missing data
Variable: melanopic EDI
	Regular			Irregular	Range		Interval	Gaps						Implicit			Explicit
	Time	%	n¹	n^2,1	Time	n¹	Time	N	ø	øn¹	Time	%	n¹	Time	%	n¹	Time	%	n¹
Overall	1w 4d 6h 42m 50s	94.0%³	67,003	0	1w 5d	69,120	30; 10	200	7m 8s	15	17h 17m 10s	6.0%³	2,117	17h 17m 10s	6.0%³	2,117	0s	0.0%³	0
Environment
	5d 6h 53m 30s	88.1%	15,227	0	6d	17,280	30s	148	6m 56s	14	17h 6m 30s	11.9%	2,053	17h 6m 30s	11.9%	2,053	0s	0.0%	0
Participant
	5d 23h 49m 20s	99.9%	51,776	0	6d	51,840	10s	52	12s	1	10m 40s	0.1%	64	10m 40s	0.1%	64	0s	0.0%	0
¹ Number of (missing or actual) observations
² If n > 0: it is possible that the other summary statistics are affected, as they are calculated based on the most prominent interval.
³ Based on times, not necessarily number of observations