Counts the distinct PATTERNS of missingness across rows, rather than the
per-column rates profile_columns()
reports.
Value
A data frame of class bricklayer_missingness, one row per
pattern: pattern (a string of . for present and X
for missing, in column order), n_rows, pct_rows,
n_missing (columns missing in that pattern), and columns
(their names). Carries the column order as the "columns"
attribute.
Details
The distinction decides what to do about the gaps. Two columns each 30% missing at random need different handling from two columns 30% missing in THE SAME rows – the second is one structural gap (a join that failed, a form section nobody filled in) and often means those rows should be dropped or modelled separately, while the first does not. A per-column rate cannot tell the two apart; this can.
See also
profile_columns() for
per-column rates.
Examples
# Two columns missing in the SAME rows: one structural gap.
structural <- data.frame(
id = 1:10,
a = c(rep(NA, 3), 4:10),
b = c(rep(NA, 3), 4:10)
)
missingness_pattern(structural)
#> ── Missingness patterns ──────────────────────────────────────────
#> columns, in pattern order: id, a, b
#>
#> pattern n_rows pct_rows n_missing columns
#> ... 7 70.0 0
#> .XX 3 30.0 2 a, b
#> ──────────────────────────────────────────────────────────────────
# The same per-column rates, but missing independently.
scattered <- data.frame(
id = 1:10,
a = c(rep(NA, 3), 4:10),
b = c(1:7, rep(NA, 3))
)
missingness_pattern(scattered)
#> ── Missingness patterns ──────────────────────────────────────────
#> columns, in pattern order: id, a, b
#>
#> pattern n_rows pct_rows n_missing columns
#> ... 4 40.0 0
#> ..X 3 30.0 1 b
#> .X. 3 30.0 1 a
#> ──────────────────────────────────────────────────────────────────
# A complete frame has exactly one pattern.
missingness_pattern(data.frame(x = 1:3, y = 4:6))
#> ── Missingness patterns ──────────────────────────────────────────
#> columns, in pattern order: x, y
#>
#> pattern n_rows pct_rows n_missing columns
#> .. 3 100.0 0
#> ──────────────────────────────────────────────────────────────────