How to Perform Fuzzy Matching in R (With Example)

Often you may want to join together two datasets in R based on imperfectly matching strings. This is sometimes called fuzzy matching.

The easiest way to perform fuzzy matching in R is to use the stringdist_join() function from the fuzzyjoin package.

The following example shows how to use this function in practice.

Example: Fuzzy Matching in R

Suppose we have the following two data frames in R that contain information about various basketball teams:

```#create data frames
df1 <- data.frame(team=c('Mavericks', 'Nets', 'Warriors', 'Heat', 'Lakers'),
points=c(99, 90, 104, 117, 100))
df2 <- data.frame(team=c('Mavricks', 'Warrors', 'Heat', 'Netts', 'Kings', 'Lakes'),
assists=c(22, 29, 17, 40, 32, 30))

#view data frames
print(df1)

team points
1 Mavericks     99
2      Nets     90
3  Warriors    104
4      Heat    117
5    Lakers    100

print(df2)

team assists
1 Mavricks      22
2  Warrors      29
3     Heat      17
4    Netts      40
5    Kings      32
6    Lakes      30
```

Now suppose that we would like to perform a left join in which we keep all of the rows from the first data frame and simply merge them based on the team name that most closely matches in the second data frame.

We can use the following code to do so:

```library(fuzzyjoin)
library(dplyr)

#perform fuzzy matching left join
stringdist_join(df1, df2,
by='team', #match based on team
mode='left', #use left join
method = "jw", #use jw distance metric
max_dist=99,
distance_col='dist') %>%
group_by(team.x) %>%
slice_min(order_by=dist, n=1)

# A tibble: 5 x 5
# Groups:   team.x [5]
team.x    points team.y   assists   dist

1 Heat         117 Heat          17 0
2 Lakers       100 Lakes         30 0.0556
3 Mavericks     99 Mavricks      22 0.0370
4 Nets          90 Netts         40 0.0667
5 Warriors     104 Warrors       29 0.0417
```

The result is one data frame that contains each of the five original team names from the first data frame along with the team that most closely matches from the second data frame.

Note #1: We chose to use the jw distance metric for matching. This is short for the Jaro-Winkler distance, which is a metric that measures the difference between two strings.

Note #2: We used the slice_min() function from the dplyr package to only show the team name from the second data frame that most closely matched the team name from the first data frame.