Derive Blocking Rules from Labeled Pairs — block_from

For each column, computes the fraction of true-match pairs that share the same value (recall). Helps identify which columns make effective blocking keys.

Usage

block_from_labels(.data, labels, columns = NULL, con = NULL)

Arguments

.data: A data frame or character table name.
labels: A data frame with unique_id_l, unique_id_r, and is_match.
columns: Character vector of column names to evaluate. NULL for all non-ID columns.
con: A DBI connection from DBI::dbConnect().

Value

A tibble::tibble() with columns column, recall (fraction of true matches caught), and n_matches_caught.

Examples

con <- DBI::dbConnect(duckdb::duckdb())
#> duckdb keeps downloaded extensions and secrets in a temporary directory:
#> ℹ /tmp/Rtmpye6umx/duckdb
#> This is removed when the R session ends.
#> • Extensions are re-downloaded each session.
#> • Secrets are lost.
#> ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
#> ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
#> ℹ See ?duckdb_storage for details and alternatives.
labels <- data.frame(
  unique_id_l = fake_1000_labels$unique_id_l,
  unique_id_r = fake_1000_labels$unique_id_r,
  is_match = as.integer(fake_1000_labels$clerical_match_score >= 0.5)
)
block_from_labels(fake_1000, labels, con = con)
#> # A tibble: 6 × 3
#>   column     recall n_matches_caught
#>   <chr>       <dbl>            <int>
#> 1 cluster     0.673             1367
#> 2 dob         0.403              819
#> 3 city        0.390              792
#> 4 email       0.359              730
#> 5 surname     0.258              525
#> 6 first_name  0.242              492
DBI::dbDisconnect(con, shutdown = TRUE)