Check the Timing of Missing Values

Finds the runs of consecutive missing values (NA) in an aniframe and returns them as a compact table - one row per gap, not per frame. Where the gaps fall (at the start, the end, scattered, or in long bursts) is often more telling than how many there are; this check exposes that timing, and plot.check_na_timing() draws it as a missingness strip.

Usage

check_na_timing(data, ...)

# Default S3 method
check_na_timing(data, ...)

# S3 method for class 'aniframe'
check_na_timing(data, variable = "x", ...)

Arguments

data

An aniframe object.

...

Additional arguments (currently unused).

variable

Name(s) of the column(s) whose missingness to track. A frame counts as missing when any named column is NA there. Defaults to "x".

Value

A data frame of class check_na_timing with one row per missing run: the aniframe's grouping columns (every variables_what and non-time variables_when column), the run's start and stop time, and its length in frames. Per-group totals (frame and missing counts, time range), the checked variable(s), the time unit, and the typical time step are stored as attributes for the summary(), print(), and plotting methods. Use summary() for a per-group overview.

Details

Returning gaps rather than per-frame rows keeps the object (and any plot built from it) tiny even for million-frame recordings: its size scales with the number of gaps, not the length of the data.

This is the data-generating half of the check. The plotting method (plot.check_na_timing()) lives in anivis, mirroring the performance / see split in easystats: check_*() computes a classed object with summary() / print() methods, and a plot.*() method in the companion package draws it. (check_*() functions are destined for the anicheck package; they are kept here for now for convenience.)

See also

plot.check_na_timing(), summary.check_na_timing()

Examples

af <- aniframe::as_aniframe(data.frame(
  keypoint = rep(c("head", "tail"), each = 6),
  time = rep(1:6, 2),
  x = c(1, NA, NA, 4, 5, 6, 1, 2, 3, 4, 5, 6)
))
check_na_timing(af)
#> 
#> ── Check: timing of missing values 
#> Tracking x across 12 frames - 2 missing (16.7%) in 1 gap.
#> By group (keypoint):
#> • head: 33.3% missing in 1 gap (longest 2)
#> • tail: 0% missing in 0 gaps (longest 0)
summary(check_na_timing(af))
#>   keypoint n_frames n_missing pct_missing n_gaps longest_gap
#> 1     head        6         2        33.3      1           2
#> 2     tail        6         0         0.0      0           0