Skip to contents

Fuzzy join backend using 'data.table' + 'C++' row binding

Source: R/dt_fuzzy_join_cpp.R
fuzzystring_join_backend.Rd

Low-level engine used by fuzzystring_join and the 'C++'-optimized fuzzy join helpers. It builds the match index with R 'data.table' and then assembles the result using a compiled 'C++' binder for speed.

Usage

fuzzystring_join_backend(
  x,
  y,
  by = NULL,
  match_fun = NULL,
  multi_by = NULL,
  multi_match_fun = NULL,
  index_match_fun = NULL,
  mode = "inner",
  ...
)

Arguments

x

A data.frame or data.table.

y

A data.frame or data.table.

by

Columns by which to join the two tables. See fuzzystring_join.

match_fun

A function used to match values. It must return a logical vector (or a data.frame/data.table whose first column is logical) indicating which pairs match. For multi-column joins, you may pass a list of functions (one per column).

multi_by

A character vector of column names used for multi-column matching when multi_match_fun is supplied.

multi_match_fun

A function that receives matrices of unique values for x and y (rows correspond to unique combinations of multi_by). It must return a logical vector (or a data.frame/data.table whose first column is logical) indicating which rows match.

index_match_fun

A function that receives the joined columns from x and y and returns a table with integer columns x and y (1-based row indices).

mode

One of "inner", "left", "right", "full", "semi", or "anti".

...

Additional arguments passed to the matching function(s).

Value

A joined table (same container type as x). See fuzzystring_join.

Details

This function works like fuzzystring_join, but replaces the R-based row binding with a 'C++' implementation. This provides better performance, especially for large joins with many matches. It is intended as a backend and does not compute distances itself; use fuzzystring_join for string-distance based matching.

The C++ implementation handles:

  • Efficient subsetting by row indices

  • Proper handling of NA values in outer joins

  • Type-safe column operations for all common R types

  • Preservation of factor levels and attributes

  • Column name conflicts with .x/.y suffixes