dplyr: inner_join with a partial string match
The fuzzyjoin
library has two functions regex_inner_join
and fuzzy_inner_join
that allow you to match partial strings:
x <- data.frame(idX=1:3, string=c("Motorcycle", "TractorTrailer", "Sailboat"))
y <- data.frame(idY=letters[1:3], seed=c("ractor", "otorcy", "irplan"))
x$string = as.character(x$string)
y$seed = as.character(y$seed)
library(fuzzyjoin)
x %>% regex_inner_join(y, by = c(string = "seed"))
idX string idY seed
1 1 Motorcycle b otorcy
2 2 TractorTrailer a ractor
library(stringr)
x %>% fuzzy_inner_join(y, by = c("string" = "seed"), match_fun = str_detect)
idX string idY seed
1 1 Motorcycle b otorcy
2 2 TractorTrailer a ractor
You can also use base-r with this function (slightly adapted from this answer here: https://stackoverflow.com/a/34723496/3048453, it uses dplyr to bind the columns together, use cbind
if you don't want to use dplyr):
partial_join <- function(x, y, by_x, pattern_y)
idx_x <- sapply(y[[pattern_y]], grep, x[[by_x]])
idx_y <- sapply(seq_along(idx_x), function(i) rep(i, length(idx_x[[i]])))
df <- dplyr::bind_cols(x[unlist(idx_x), , drop = F],
y[unlist(idx_y), , drop = F])
return(df)
}
With your example
x <- data.frame(idX=1:3, string=c("Motorcycle", "TractorTrailer", "Sailboat"))
y <- data_frame(idY=letters[1:3], seed=c("ractor", "otorcy", "irplan"))
df_merged <- partial_join(x, y, by_x = "string", pattern_y = "seed")
df_merged
# # A tibble: 2 × 4
# idX string idY seed
# <int> <chr> <chr> <chr>
# 1 1 Motorcycle b otorcy
# 2 2 TractorTrailer a ractor
Speed Benchmarks:
Functions
library(dplyr)
x <- data_frame(idX=1:3, string=c("Motorcycle", "TractorTrailer", "Sailboat"))
y <- data_frame(idY=letters[1:3], seed=c("ractor", "otorcy", "irplan"))
partial_join <- function(x, y, by_x, pattern_y) {
idx_x <- sapply(y[[pattern_y]], grep, x[[by_x]])
idx_y <- sapply(seq_along(idx_x), function(i) rep(i, length(idx_x[[i]])))
df <- dplyr::bind_cols(x[unlist(idx_x), , drop = F],
y[unlist(idx_y), , drop = F])
return(df)
}
partial_join(x, y, by_x = "string", pattern_y = "seed")
#> # A tibble: 2 × 4
#> idX string idY seed
#> <int> <chr> <chr> <chr>
#> 1 1 Motorcycle b otorcy
#> 2 2 TractorTrailer a ractor
joran <- function(x, y, by_x, pattern_y) {
library(dplyr)
my_db <- src_sqlite(path = tempfile(), create= TRUE)
x_tbl <- copy_to(dest = my_db, df = x)
y_tbl <- copy_to(dest = my_db, df = y)
result <- tbl(my_db,
sql(sprintf("select * from x, y where x.%s like '%%' || y.%s || '%%'", by_x, pattern_y)))
collect(result, n = Inf)
}
joran(x, y, "string", "seed")
#> # A tibble: 2 × 4
#> idX string idY seed
#> <int> <chr> <chr> <chr>
#> 1 1 Motorcycle b otorcy
#> 2 2 TractorTrailer a ractor
stephen <- function(x, y, by_x, pattern_y) {
library(dplyr)
d <- full_join(mutate(x, i=1),
mutate(y, i=1), by = "i")
# quoting issue here, defaulting to base-r
d$take <- stringr::str_detect(d[[by_x]], d[[pattern_y]])
d %>%
filter(take == T) %>%
select(-i, -take)
}
stephen(x, y, "string", "seed")
#> # A tibble: 2 × 4
#> idX string idY seed
#> <int> <chr> <chr> <chr>
#> 1 1 Motorcycle b otorcy
#> 2 2 TractorTrailer a ractor
feng <- function(x, y, by_x, pattern_y) {
library(fuzzyjoin)
by_string <- pattern_y
names(by_string) <- by_x
regex_inner_join(x, y, by = by_string)
}
feng(x, y, "string", "seed")
#> # A tibble: 2 × 4
#> idX string idY seed
#> <int> <chr> <chr> <chr>
#> 1 1 Motorcycle b otorcy
#> 2 2 TractorTrailer a ractor
Benchmark
library(microbenchmark)
res <- microbenchmark(
joran(x, y, "string", "seed"),
stephen(x, y, "string", "seed"),
feng(x, y, "string", "seed"),
partial_join(x, y, "string", "seed")
)
res
#> Unit: microseconds
#> expr min lq mean
#> joran(x, y, "string", "seed") 18953.008 20099.0540 21641.6646
#> stephen(x, y, "string", "seed") 1320.161 1456.9415 1704.9218
#> feng(x, y, "string", "seed") 5187.366 5625.8825 6926.2336
#> partial_join(x, y, "string", "seed") 190.264 222.0055 257.7906
#> median uq max neval cld
#> 20675.5855 21827.764 70707.324 100 c
#> 1579.8925 1670.719 9676.176 100 a
#> 5842.8150 6065.530 107961.805 100 b
#> 242.0735 283.870 523.649 100 a
set.seed(123123)
x_large <- x %>% sample_n(1000, replace = T)
y_large <- y %>% sample_n(1000, replace = T)
res_large <- microbenchmark(
joran(x_large, y_large, "string", "seed"),
# stephen(x_large, y_large, "string", "seed"),
feng(x_large, y_large, "string", "seed"),
partial_join(x_large, y_large, "string", "seed")
)
res_large
#> Unit: milliseconds
#> expr min lq mean median uq max neval cld
#> joran(x_large, y_large, "string", "seed") 321.03631 324.49262 334.2760 329.13991 335.30185 368.1153 10 c
#> feng(x_large, y_large, "string", "seed") 88.00369 89.85744 103.8686 93.84477 97.69121 200.0473 10 a
#> partial_join(x_large, y_large, "string", "seed") 286.01533 286.78024 290.6295 288.89405 291.79887 303.4524 10 b