comparator implements comparison functions for clustering and record linkage applications. It includes functions for comparing strings, sequences and numeric vectors. Where possible, comparators are implemented in C/C++ to ensure fast performance.
Levenshtein(): Levenshtein distance/similarityDamerauLevenshtein() Damerau-Levenshtein
distance/similarityHamming(): Hamming distance/similarityOSA(): Optimal String Alignment
distance/similarityLCS(): Longest Common Subsequence
distance/similarityJaro(): Jaro distance/similarityJaroWinkler(): Jaro-Winkler distance/similarityNot yet implemented.
MongeElkan(): Monge-Elkan similarityFuzzyTokenSet(): Fuzzy Token Set distanceInVocabulary(): Compares strings using a reference
vocabulary. Useful for comparing names.Lookup(): Retrieves distances/similarities from a
lookup tableBinaryComp(): Compares strings based on whether they
agree/disagree exactly.Euclidean(): Euclidean (L-2) distanceManhattan(): Manhattan (L-1) distanceChebyshev(): Chebyshev (L-∞) distanceMinkowski(): Minkowski (L-p) distanceYou can install the latest release from CRAN by entering:
install.packages("comparator")The development version can be installed from GitHub using
devtools:
# install.packages("devtools")
devtools::install_github("ngmarchant/comparator")A comparator is instantiated by calling its constructor function. For example, we can instantiate a Levenshtein similarity comparator that ignores differences in upper/lowercase characters as follows:
comparator <- Levenshtein(similarity = TRUE, normalize = TRUE, ignore_case = TRUE)We can apply the comparator to character vectors element-wise as follows:
x <- c("John Doe", "Jane Doe")
y <- c("jonathon doe", "jane doe")
elementwise(comparator, x, y)
#> [1] 0.6666667 1.0000000
# shorthand for above
comparator(x, y)
#> [1] 0.6666667 1.0000000This comparator is also defined on sequences:
x_seq <- list(c(1, 2, 1, 1), c(1, 2, 3, 4))
y_seq <- list(c(4, 3, 2, 1), c(1, 2, 3, 1))
elementwise(comparator, x_seq, y_seq)
#> [1] 0.4545455 0.7777778
# shorthand for above
comparator(x_seq, y_seq)
#> [1] 0.4545455 0.7777778Pairwise comparisons are also supported using the following syntax:
# compare each string in x with each string in y and return a similarity matrix
pairwise(comparator, x, y, return_matrix = TRUE)
#> [,1] [,2]
#> [1,] 0.6666667 0.6842105
#> [2,] 0.5384615 1.0000000
# compare the strings in x pairwise and return a similarity matrix
pairwise(comparator, x, return_matrix = TRUE)
#> [,1] [,2]
#> [1,] 1.0000000 0.6842105
#> [2,] 0.6842105 1.0000000