comparator implements similarity and distance measures for clustering and record linkage applications. It includes measures for comparing strings as well as numeric vectors. Where possible, measures are implemented in C/C to ensure fast performance.

• Levenshtein(): Levenshtein distance/similarity
• DamerauLevenshtein() Damerau-Levenshtein distance/similarity
• Hamming(): Hamming distance/similarity
• OSA(): Optimal String Alignment distance/similarity
• LCS(): Longest Common Subsequence distance/similarity
• Jaro(): Jaro distance/similarity
• JaroWinkler(): Jaro-Winkler distance/similarity

Not yet implemented.

• MongeElkan(): Monge-Elkan measure
• FuzzyTokenSet(): Fuzzy Token Set distance

• InVocabulary(): Compares strings using a reference vocabulary. Useful for comparing names.
• Lookup(): Retrieves distances/similarities from a lookup table
• BinaryComp(): Compares strings based on whether they agree/disagree exactly.

• Euclidean(): Euclidean (L-2) distance
• Manhattan(): Manhattan (L-1) distance
• Chebyshev(): Chebyshev (L-∞) distance
• Minkowski(): Minkowski (L-p) distance

You 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 measure can be instantiated by calling its constructor function. For instance, we can define a Levenshtein similarity measure that ignores differences in upper/lowercase characters as follows:

measure <- Levenshtein(similarity = TRUE, normalize = TRUE, ignore_case = TRUE)

A measure can be used to compare vectors element-wise as follows:

x <- c("John Doe", "Jane Doe")
y <- c("jonathon doe", "jane doe")
elementwise(measure, x, y)
#> [1] 0.6666667 1.0000000

# shorthand for above
measure(x, y)
#> [1] 0.6666667 1.0000000

Pairwise comparisons are also supported using the following syntax:

# compare each value in x with each value in y and return a similarity matrix
pairwise(measure, x, y, return_matrix = TRUE)
#>           [,1]      [,2]
#> [1,] 0.6666667 0.6842105
#> [2,] 0.5384615 1.0000000

# compare the values in x pairwise and return a similarity matrix
pairwise(measure, x, return_matrix = TRUE)
#>           [,1]      [,2]
#> [1,] 1.0000000 0.6842105
#> [2,] 0.6842105 1.0000000