String Similarity Computation Using 'RapidFuzz'.
RapidFuzz 
Provides a high-performance interface for calculating string similarities and distances, leveraging the efficient C++ library RapidFuzz developed by Max Bachmann and Adam Cohen. This package integrates the C++ implementation, allowing R users to access cutting-edge algorithms for fuzzy matching and text analysis.
Installation
You can install directly from CRAN or the development version of pikchr from GitHub with:
# install.packages("pak")
pak::pak("StrategicProjects/RapidFuzz")
library(RapidFuzz)
Overview
The RapidFuzz package is an R wrapper around the highly efficient RapidFuzz C++ library. It provides implementations of multiple string comparison and similarity metrics, such as Levenshtein, Jaro-Winkler, and Damerau-Levenshtein distances. This package is particularly useful for applications like record linkage, approximate string matching, and fuzzy text processing.
String comparison algorithms calculate distances and similarities between two sequences of characters. These distances help to quantify how similar two strings are. For example, the Levenshtein distance measures the minimum number of single-character edits required to transform one string into another.
RapidFuzz leverages advanced algorithms to ensure high performance while maintaining accuracy. The original library is open-source and can be accessed on RapidFuzz GitHub Repository.
Functions
Process String Function
processString(): Process a string with options to trim, convert to lowercase, and transliterate to ASCII.
Opcode Functions
opcodes_apply_str(): Apply Opcodes to transform a string.opcodes_apply_vec(): Apply Opcodes to transform a string into a character vector.
Edit Operation Utilities
get_editops(): Retrieve Edit Operations between two strings.
Edit Operations Functions
editops_apply_str(): Apply Edit Operations to transform a string.editops_apply_vec(): Apply Edit Operations to transform a string into a character vector.
Damerau-Levenshtein Functions
damerau_levenshtein_distance(): Calculate the Damerau-Levenshtein Distance.damerau_levenshtein_normalized_distance(): Calculate the Normalized Damerau-Levenshtein Distance.damerau_levenshtein_normalized_similarity(): Calculate the Normalized Damerau-Levenshtein Similarity.damerau_levenshtein_similarity(): Calculate the Damerau-Levenshtein Similarity.
Fuzz Ratio Functions
fuzz_QRatio(): Perform a Quick Ratio Calculation.fuzz_WRatio(): Perform a Weighted Ratio Calculation.fuzz_partial_ratio(): Calculate Partial Ratio.fuzz_ratio(): Calculate a Simple Ratio.fuzz_token_ratio(): Calculate Combined Token Ratio.fuzz_token_set_ratio(): Perform Token Set Ratio Calculation.fuzz_token_sort_ratio(): Perform Token Sort Ratio Calculation.
Hamming Functions
hamming_distance(): Calculate Hamming Distance.hamming_normalized_distance(): Calculate Normalized Hamming Distance.hamming_normalized_similarity(): Calculate Normalized Hamming Similarity.hamming_similarity(): Calculate Hamming Similarity.
Indel Functions
indel_distance(): Calculate Indel Distance.indel_normalized_distance(): Calculate Normalized Indel Distance.indel_normalized_similarity(): Calculate Normalized Indel Similarity.indel_similarity(): Calculate Indel Similarity.
Jaro Functions
jaro_distance(): Calculate Jaro Distance.jaro_normalized_distance(): Calculate Normalized Jaro Distance.jaro_normalized_similarity(): Calculate Normalized Jaro Similarity.jaro_similarity(): Calculate Jaro Similarity.
Jaro-Winkler Functions
jaro_winkler_distance(): Calculate Jaro-Winkler Distance.jaro_winkler_normalized_distance(): Calculate Normalized Jaro-Winkler Distance.jaro_winkler_normalized_similarity(): Calculate Normalized Jaro-Winkler Similarity.jaro_winkler_similarity(): Calculate Jaro-Winkler Similarity.
Longest Common Subsequence (LCSseq) Functions
lcs_seq_distance(): Calculate LCSseq Distance.lcs_seq_editops(): Retrieve LCSseq Edit Operations.lcs_seq_normalized_distance(): Calculate Normalized LCSseq Distance.lcs_seq_normalized_similarity(): Calculate Normalized LCSseq Similarity.lcs_seq_similarity(): Calculate LCSseq Similarity.
Levenshtein Functions
levenshtein_distance(): Calculate Levenshtein Distance.levenshtein_normalized_distance(): Calculate Normalized Levenshtein Distance.levenshtein_normalized_similarity(): Calculate Normalized Levenshtein Similarity.levenshtein_similarity(): Calculate Levenshtein Similarity.
Optimal String Alignment (OSA) Functions
osa_distance(): Calculate Distance Using OSA.osa_editops(): Retrieve Edit Operations Using OSA.osa_normalized_distance(): Calculate Normalized Distance Using OSA.osa_normalized_similarity(): Calculate Normalized Similarity Using OSA.osa_similarity(): Calculate Similarity Using OSA.
Prefix Functions
prefix_distance(): Calculate the Prefix Distance between two strings.prefix_normalized_distance(): Calculate the Normalized Prefix Distance between two strings.prefix_normalized_similarity(): Calculate the Normalized Prefix Similarity between two strings.prefix_similarity(): Calculate the Prefix Similarity between two strings.
Example Usage
Prefix Functions
prefix_distance("abcdef", "abcxyz")
# Output: 3
prefix_normalized_similarity("abcdef", "abcxyz", score_cutoff = 0.0)
# Output: 0.5
Postfix Functions
postfix_distance("abcdef", "xyzdef")
# Output: 3
Damerau-Levenshtein Functions
damerau_levenshtein_distance("abcdef", "abcfed")
# Output: 2
Extract Matches
# Example data
query <- "new york jets"
choices <- c("Atlanta Falcons", "New York Jets", "New York Giants", "Dallas Cowboys")
score_cutoff <- 0.0
# Find the best match
extract_matches(query, choices, score_cutoff, scorer = "PartialRatio")
# Output:
# choice score
# 1 New York Jets 100.00000
# 2 New York Giants 81.81818
# 3 Atlanta Falcons 33.33333
Original Library
The RapidFuzz package is a wrapper of the RapidFuzz C++ library, developed by Max Bachmann and Adam Cohen. The library implements efficient algorithms for approximate string matching and comparison.
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