Here is a solution using the fuzzyjoin
package. It uses dplyr
-like syntax and stringdist
as one of the possible types of fuzzy matching.
As suggested by @C8H10N4O2, the stringdist
method=”jw” creates the best matches for your example.
As suggested by @dgrtwo, the developer of fuzzyjoin
, I used a large max_dist
and then used dplyr::group_by
and dplyr::slice_min
to get only the best match with minimum distance. (slice_min
replaces the older top_n
and if the original order is important and not alphabetical, use mutate(rank = row_number(dist)) %>% filter(rank == 1)
)
a <- data.frame(name = c('Ace Co', 'Bayes', 'asd', 'Bcy', 'Baes', 'Bays'),
price = c(10, 13, 2, 1, 15, 1))
b <- data.frame(name = c('Ace Co.', 'Bayes Inc.', 'asdf'),
qty = c(9, 99, 10))
library(fuzzyjoin); library(dplyr);
stringdist_join(a, b,
by = "name",
mode = "left",
ignore_case = FALSE,
method = "jw",
max_dist = 99,
distance_col = "dist") %>%
group_by(name.x) %>%
slice_min(order_by = dist, n = 1)
#> # A tibble: 6 x 5
#> # Groups: name.x [6]
#> name.x price name.y qty dist
#> <fctr> <dbl> <fctr> <dbl> <dbl>
#> 1 Ace Co 10 Ace Co. 9 0.04761905
#> 2 Bayes 13 Bayes Inc. 99 0.16666667
#> 3 asd 2 asdf 10 0.08333333
#> 4 Bcy 1 Bayes Inc. 99 0.37777778
#> 5 Baes 15 Bayes Inc. 99 0.20000000
#> 6 Bays 1 Bayes Inc. 99 0.20000000