Compare or match proper names from different sources, accounting for potential variation in format, name order, and spelling (e.g. “Beyoncé Knowles” vs. “KNOWLES-CARTER, Beyonce”). Specifically, uses string-standardization to account for variation in punctuation, accents, and character case, token-permutation to account for variation in name order, and fuzzy matching to handle alternate spellings.
Installation
Install from GitHub with:
# install.packages("remotes")
remotes::install_github("epicentre-msf/nmatch")Example usage
library(nmatch)
library(dplyr)
library(purrr)
library(tidyr)
# example data with names to compare
data("names_ex")
# compare with default settings
names_ex %>%
mutate(is_match = nmatch(name_source1, name_source2))
#> name_source1 name_source2 is_match
#> 1 Beyoncé Knowles Beyonce Knowles-Carter TRUE
#> 2 Frédéric François Chopin CHOPIN, Fryderyk F. FALSE
#> 3 Kendrick Lamar Duckworth LAMAR, Kendrik TRUE
#> 4 Calvin Cordozar Broadus Jr. Snoop Dogg FALSE
#> 5 Céline Marie Claudette Dion DION, Céline TRUE
#> 6 Aubrey Drake Graham Drake FALSE
# return full match details
names_ex %>%
mutate(match_df = purrr::map2(name_source1, name_source2, nmatch, return_full = TRUE)) %>%
tidyr::unnest("match_df")
#> # A tibble: 6 × 11
#> name_source1 name_source2 is_match id k_x k_y k_align n_match dist_total freq_score align
#> <chr> <chr> <lgl> <int> <int> <int> <int> <int> <int> <chr> <list>
#> 1 Beyoncé Knowles Beyonce Knowles… TRUE 1 2 3 2 2 0 <NA> <tibble>
#> 2 Frédéric François Chopin CHOPIN, Frydery… FALSE 1 3 2 2 1 3 <NA> <tibble>
#> 3 Kendrick Lamar Duckworth LAMAR, Kendrik TRUE 1 3 2 2 2 1 <NA> <tibble>
#> 4 Calvin Cordozar Broadus Jr. Snoop Dogg FALSE 1 4 2 2 0 10 <NA> <tibble>
#> 5 Céline Marie Claudette Dion DION, Céline TRUE 1 4 2 2 2 0 <NA> <tibble>
#> 6 Aubrey Drake Graham Drake FALSE 1 3 1 1 1 0 <NA> <tibble>
# use a custom function to classify matches, based on any of the vars returned
# when return_full = TRUE (i.e. k_x, k_y, k_align, n_match, dist_total)
# note that fn must include argument `...` even if not used
classify_matches <- function(k_align, dist_total, ...) {
k_align >= 1 & dist_total <= 3
}
names_ex %>%
mutate(is_match = nmatch(name_source1, name_source2, eval_fn = classify_matches))
#> name_source1 name_source2 is_match
#> 1 Beyoncé Knowles Beyonce Knowles-Carter TRUE
#> 2 Frédéric François Chopin CHOPIN, Fryderyk F. TRUE
#> 3 Kendrick Lamar Duckworth LAMAR, Kendrik TRUE
#> 4 Calvin Cordozar Broadus Jr. Snoop Dogg FALSE
#> 5 Céline Marie Claudette Dion DION, Céline TRUE
#> 6 Aubrey Drake Graham Drake TRUEUsing with fuzzyjoin
library(fuzzyjoin)
# example hospital datasets (IPD and ICU) with patients names in different
# formats (note these names are just random combinations of common French names)
data("dat_ipd")
data("dat_icu")
# join IPD and ICU datasets by name variables, using fuzzy matching with nmatch()
fuzzyjoin::fuzzy_join(
dat_ipd,
dat_icu,
by = c("name_ipd" = "name_icu"),
match_fun = nmatch::nmatch,
mode = "left"
)
#> # A tibble: 10 × 4
#> name_ipd date_ipd name_icu date_icu
#> <chr> <date> <chr> <date>
#> 1 COLLET, André Daniel 2020-07-17 André D. Colet 2020-07-19
#> 2 LÈFEVRE, Françoise Sylvie 2020-06-03 Francoise Lefevre 2020-06-05
#> 3 DUBOIS, Monique Léa 2020-06-10 Monique Léa Dubois 2020-06-14
#> 4 GUÉRIN, Jacqueline Hélène 2020-07-09 Jacqueline Hélène Guérin 2020-07-09
#> 5 MARTIN, Philippe Arnaud 2020-06-11 <NA> NA
#> 6 DUMONT, René Stéphane 2020-07-26 Renae Dumont 2020-07-29
#> 7 LÉVEILLÉ, Anne-Charlotte Catherine 2020-06-01 <NA> NA
#> 8 MARCHAND, Louis Enzo 2020-07-17 <NA> NA
#> 9 SANCHEZ, Isabelle Suzanne 2020-07-03 <NA> NA
#> 10 ROUX, Nathalie Elisabeth 2020-07-06 Natalia Roux 2020-07-06Note that matching large datasets using fuzzy_join() and nmatch() may be very slow. The nmatch package will eventually contain stand-alone join functions that are optimized for name-matching.