How can I match fuzzy match strings from two datasets?

Question

I've been working on a way to join two datasets based on a imperfect string, such as a name of a company. In the past I had to match two very dirty lists, one list had names and financial information, another list had names and address. Neither had unique IDs to match on! ASSUME THAT CLEANING HAS ALREADY BEEN APPLIED AND THERE MAYBE TYPOS AND INSERTIONS.

So far AGREP is the closest tool I've found that might work. I can use levenshtein distances in the AGREP package, which measure the number of deletions, insertions and substitutions between two strings. AGREP will return the string with the smallest distance (the most similar).

However, I've been having trouble turning this command from a single value to apply it to an entire data frame. I've crudely used a for loop to repeat the AGREP function, but there's gotta be an easier way.

See the following code:

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))

for (i in 1:6){
    a$x[i] = agrep(a$name[i], b$name, value = TRUE, max = list(del = 0.2, ins = 0.3, sub = 0.4))
    a$Y[i] = agrep(a$name[i], b$name, value = FALSE, max = list(del = 0.2, ins = 0.3, sub = 0.4))
}

Answer 1

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