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Diagnostics

Three complementary questions about the same missingness, plus the per-row view:

missingpatterns — Unique missingness patterns

Shows which combinations of columns are missing together — the same diagnostic as R's mice::md.pattern(). Patterns are sorted most-frequent first.

missingpatterns(tbl)
missingpatterns(tbl; max_patterns=10, min_pct=5.0)
missingpatterns(tbl; color_cells=true, emphasis=:missing)
missingpatterns(tbl; show_bar=false)           # hide frequency bar

missingsummary — Per-column missing summary

Shows each column's type, missing count, percentage, and a distribution sparkline.

missingsummary(tbl)
missingsummary(tbl; sortby=:missing)           # sort by missing count (default)
missingsummary(tbl; sortby=:name)
missingsummary(tbl; sortby=:none)
missingsummary(tbl; bins=5)                    # group by bins of N rows
missingsummary(tbl; color=:always)

missingcooccurrence — Pairwise correlation of missingness

Computes ϕ (phi) coefficient or Jaccard index between every pair of columns based on their missingness masks. Positive values indicate columns tend to be missing together.

missingcooccurrence(tbl)
missingcooccurrence(tbl; method=:jaccard)       # Jaccard instead of ϕ
missingcooccurrence(tbl; max_cols=10)            # cap displayed columns
missingcooccurrence(tbl; color=:always)

missingrows — Per-row completeness

The transposed view: not which columns are missing, but how many values are missing in each row. The 0 line is the complete-case count — everything below it is what dropmissing would discard.

missingrows(tbl)
missingrows(tbl; sortby=:rows)                 # most common shape first
missingrows(tbl; bar_width=50)
missingrows(tbl; color=:always)
 missing/row  rows        %  distribution
 0               3   37.50%  ██████████████████████████████
 1               3   37.50%  ██████████████████████████████
 2               2   25.00%  ████████████████████
 3 complete rows (37.50%) ┊ 5 with ≥1 missing (62.50%) ┊ 3 distinct counts across 3 columns

missingdrop — What dropping a column buys

missingrows prices listwise deletion for the table as it stands. The question that follows is the one you act on: which column is buying that cost, and what does complete-case analysis look like without it. A single sparse column often accounts for most of the loss.

missingdrop walks the greedy path — at each step removing the column that turns the most rows complete — and reports what survives dropmissing after each one.

missingdrop(tbl)
missingdrop(tbl; bar_width=50)
missingdrop(tbl; color=:always)
 drop    cols  complete        %  distribution
 —          5       626   62.60%  ███████████████████
 lab        4       940   94.00%  ████████████████████████████  ◀ most complete-case cells
 income     3       980   98.00%  █████████████████████████████
 age        2      1000  100.00%  ██████████████████████████████
 626 of 1000 rows complete as given (62.60%) ┊ dropping 1 column leaves 940 complete across 4 columns (94.00%)

Read the flagged row: dropping lab alone takes complete-case analysis from 626 rows to 940, at the price of one variable. The flag marks the step that maximizes complete × columns left — the size of the surviving complete-case block — since past that point each drop costs more in columns than it returns in rows. Whether that trade is worth making is a modeling judgment the package does not make for you; it only prices it.

The walk stops once every row is complete or one column is left. Use missingdropstats for the same numbers as data.