Fuzzy join with 2 large data frames

Question

Here is my example:

id <- 1:5
names_1 <- c("hannah", "marcus", "fred", "joe", "lara")
df_1 <- data.frame(id, names_1)
df_1$phonenumberFound <- NA


names_2 <- c("hannah", "markus", "fredd", "joey", "paul", "mary", "olivia")
phone <- c(123, 234, 345, 456, 567, 678, 789)
df_2 <- data.frame(names_2, phone)

What I want to achieve is:

If one of the names in df_2 (at least approximately) matches a name in df_1, then I want to add the corresponding phone number in df_1.

Basically, it's some kind of a fuzzy left join but I have not succeeded to do it.

In fact, my true df_1 has 30.000 rows and my true df_2 has 500.000 rows. Is there a fast way to do this?

Thank you!

EDIT:

I need to change and clarify my example as I'm running into memory issues using the answers provided so far. (I'm using a Windows notebook with 16 GB RAM.)

id_1 <- 1:30000
names_1 <- sample(c("hannah", "marcus", "fred", "joe", "lara"), 30000, replace = TRUE, prob = c(0.2, 0.2, 0.2, 0.2, 0.2))
df_1 <- data.frame(id_1, names_1)
df_1$numberFound <- NA

id_2 <- 1:500000
names_2 <- sample(c("hannah", "markus", "paul", "mary", "olivia"), 500000, replace = TRUE, prob = c(0.2, 0.2, 0.2, 0.2, 0.2))
anyNumber <- sample(c(123, 234, 345, 456, 567), 500000, replace = TRUE, prob = c(0.2, 0.2, 0.2, 0.2, 0.2))
df_2 <- data.frame(id_2, names_2, anyNumber)

Any helpful comments and answers are highly appreciated.

Answer 1

Here is one option with fuzzyjoin

library(fuzzyjoin)
stringdist_right_join((df_2, df_1, by = c("names_2" = "names_1")) %>%
      select(names(df_1), phone)
#  id names_1 phone
#1  1  hannah   123
#2  2  marcus   234
#3  3    fred   345
#4  4     joe   456
#5  5    lara   678

---

Or create a matrix with stringdistmatrix from stringdist package

library(stringdist)
df_2$phone[max.col(-stringdistmatrix(df_1$names_1, df_2$names_2), 'first')]

Answer 2

We can use adist which computes string distance between character vectors.

adist(df_1$names_1, df_2$names_2)
#     [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#[1,]    0    5    6    6    5    5    6
#[2,]    5    1    5    6    4    3    6
#[3,]    6    5    1    3    4    4    6
#[4,]    6    6    4    1    4    4    6
#[5,]    4    4    5    4    3    2    4

Define some suitable threshold which can be allowed and assign the corresponding phone column.

thresh <- 1
mat <- adist(df_1$names_1, df_2$names_2) <= thresh
inds <- max.col(mat) * (rowSums(mat) > 0)
df_1$phone <- df_2$phone[replace(inds, inds == 0, NA)]

df_1
#  id names_1 phone
#1  1  hannah   123
#2  2  marcus   234
#3  3    fred   345
#4  4     joe   456
#5  5    lara    NA

However, since this generates a m by n matrix it might not be the most efficient method.