Creates comparison levels based on the best-matching pair of values
across two array columns. For each record pair, every element of the
left array is compared against every element of the right array. The
best score (maximum similarity for 'jaro_winkler', minimum distance
for 'levenshtein') is then tested against each threshold.
Usage
cl_array_min_distance(fn = c("jaro_winkler", "levenshtein"), ...)Arguments
- fn
Distance function:
'jaro_winkler'(default) or'levenshtein'. For'jaro_winkler', thresholds are similarity scores (0–1, descending, strictest first). For'levenshtein', thresholds are edit distances (non-negative integers, ascending, strictest first).- ...
Numeric thresholds, from strictest to most lenient.
Value
A comparison-level object for use in il_compare().
Details
On DuckDB the pairwise comparison runs in SQL via an UNNEST cross-join scalar subquery. On SQLite it falls back to an R-side nested apply.
Examples
# Jaro-Winkler: best pairwise similarity >= 0.9 or >= 0.7
il_spec() |>
il_compare(aliases, cl_array_min_distance('jaro_winkler', 0.9, 0.7))
#> Linkage Specification
#> Comparisons (1):
#> aliases : array_min_distance
#> Blocking rules: (none)
# Levenshtein: best pairwise edit distance <= 1 or <= 2
il_spec() |>
il_compare(aliases, cl_array_min_distance('levenshtein', 1, 2))
#> Linkage Specification
#> Comparisons (1):
#> aliases : array_min_distance
#> Blocking rules: (none)