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Overview

Description

This R package is designed to block records for data deduplication and record linkage (also known as entity resolution) using approximate nearest neighbor algorithms (ANN) and graphs (via the igraph package).

It supports the following R packages that bind to specific ANN algorithms:

The package can be used with the reclin2 package via the blocking::pair_ann function.

Installation

Install the stable version from CRAN:

install.packages("blocking")

You can also install the development version from GitHub:

# install.packages("pak") # uncomment if needed
pak::pkg_install("ncn-foreigners/blocking")

Basic usage

Load packages for the examples:

library(blocking)
library(reclin2)
#> Loading required package: data.table

Generate simple data with three groups (df_example) and reference data (df_base):

df_example <- data.frame(txt = c(
  "jankowalski",
  "kowalskijan",
  "kowalskimjan",
  "kowaljan",
  "montypython",
  "pythonmonty",
  "cyrkmontypython",
  "monty"
))
df_base <- data.frame(txt = c("montypython", "kowalskijan", "other"))
df_example
#>               txt
#> 1     jankowalski
#> 2     kowalskijan
#> 3    kowalskimjan
#> 4        kowaljan
#> 5     montypython
#> 6     pythonmonty
#> 7 cyrkmontypython
#> 8           monty
df_base
#>           txt
#> 1 montypython
#> 2 kowalskijan
#> 3       other

Deduplication using the blocking function. Output contains information:

  • the method used (nnd refers to the NN descent algorithm),
  • number of blocks created (here 2 blocks),
  • number of columns used for blocking, i.e., how many shingles were created by the text2vec package (here 28),
  • reduction ratio, i.e., how large the reduction of comparison pairs is (here 0.5714, which means blocking reduces comparisons by over 57%).
blocking_result <- blocking(x = df_example$txt)
blocking_result
#> ========================================================
#> Blocking based on the nnd method.
#> Number of blocks: 2.
#> Number of columns used for blocking: 28.
#> Reduction ratio: 0.5714.
#> ========================================================
#> Distribution of the size of the blocks:
#> 4 
#> 2

Table with blocking results contains:

  • row numbers from the original data,
  • block number (integers),
  • distance (from the ANN algorithm).
blocking_result$result
#>        x     y block       dist
#>    <int> <int> <num>      <num>
#> 1:     1     2     1 0.10000002
#> 2:     2     3     1 0.14188367
#> 3:     2     4     1 0.28286284
#> 4:     5     6     2 0.08333331
#> 5:     5     7     2 0.13397455
#> 6:     5     8     2 0.27831215

Deduplication using the pair_ann function for integration with the reclin2 package. Use the pipeline with the reclin2 package:

pair_ann(x = df_example, on = "txt") |>
  compare_pairs(on = "txt", comparators = list(cmp_jarowinkler())) |>
  score_simple("score", on = "txt") |>
  select_threshold("threshold", score = "score", threshold = 0.55) |>
  link(selection = "threshold")
#>   Total number of pairs: 8 pairs
#> 
#> Key: <.y>
#>       .y    .x       txt.x           txt.y
#>    <int> <int>      <char>          <char>
#> 1:     2     1 jankowalski     kowalskijan
#> 2:     3     1 jankowalski    kowalskimjan
#> 3:     3     2 kowalskijan    kowalskimjan
#> 4:     4     1 jankowalski        kowaljan
#> 5:     4     2 kowalskijan        kowaljan
#> 6:     6     5 montypython     pythonmonty
#> 7:     7     5 montypython cyrkmontypython
#> 8:     8     5 montypython           monty

Linking records using the same function where df_base is the “register” and df_example is the reference data:

pair_ann(x = df_base, y = df_example, on = "txt", deduplication = FALSE) |>
  compare_pairs(on = "txt", comparators = list(cmp_jarowinkler())) |>
  score_simple("score", on = "txt") |>
  select_threshold("threshold", score = "score", threshold = 0.55) |>
  link(selection = "threshold")
#>   Total number of pairs: 8 pairs
#> 
#> Key: <.y>
#>       .y    .x       txt.x           txt.y
#>    <int> <int>      <char>          <char>
#> 1:     1     2 kowalskijan     jankowalski
#> 2:     2     2 kowalskijan     kowalskijan
#> 3:     3     2 kowalskijan    kowalskimjan
#> 4:     4     2 kowalskijan        kowaljan
#> 5:     5     1 montypython     montypython
#> 6:     6     1 montypython     pythonmonty
#> 7:     7     1 montypython cyrkmontypython
#> 8:     8     1 montypython           monty

See also

See section Data Integration (Statistical Matching and Record Linkage) in the Official Statistics Task View.

Packages that allow blocking:

  • klsh – k-means locality sensitive hashing,
  • reclin2 – pair_blocking, pair_minsim functions,
  • fastLink – blockData function.

Other:

  • clevr – evaluation of clustering, helper functions,
  • exchanger – Bayesian Entity Resolution with Exchangeable Random Partition Priors.

Funding

Work on this package is supported by the National Science Centre, OPUS 20 grant no. 2020/39/B/HS4/00941.