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dplyr：部分的に文字列が一致するinner_join

データフレームseedのy列がstringのx列の部分一致である場合、2つのデータフレームを結合したいと思います。この例は、次のことを示しています。

# What I have
x <- data.frame(idX=1:3, string=c("Motorcycle", "TractorTrailer", "Sailboat"))
y <- data_frame(idY=letters[1:3], seed=c("ractor", "otorcy", "irplan"))


x

  idX         string
1   1     Motorcycle
2   2 TractorTrailer
3   3       Sailboat

y

Source: local data frame [3 x 2]

    idY   seed
  (chr)  (chr)
1     a ractor
2     b otorcy
3     c irplan


# What I want
want <- data.frame(idX=c(1,2), idY=c("b", "a"), string=c("Motorcycle", "TractorTrailer"), seed=c("otorcy", "ractor"))

want

  idX idY         string   seed
1   1   b     Motorcycle otorcy
2   2   a TractorTrailer ractor

つまり、

inner_join(x, y, by=stringr::str_detect(x$string, y$seed))
rstringjoindplyrstringr
20
Stephen Turner

fuzzyjoinライブラリには2つの関数regex_inner_joinおよびfuzzy_inner_joinを使用すると、部分的な文字列を照合できます。

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
20
Feng Mai

また、この関数でbase-rを使用することもできます（ここでのこの回答からわずかに変更： https://stackoverflow.com/a/34723496/304845 、それはdplyrを使用して列をバインドし、cbindを使用しますdplyrを使用したくない場合）：

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)
}

あなたの例で

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

速度のベンチマーク：

関数


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

基準

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
6
David

これがより大きなデータに対してどのように機能するかはわかりませんが、それ（またはその変形）は試してみる価値があります。

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"))

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("select * from x,y where x.string like '%' || y.seed || '%'"))
> collect(result)

Source: local data frame [2 x 4]

    idX         string   idY   seed
  (int)          (chr) (chr)  (chr)
1     1     Motorcycle     b otorcy
2     2 TractorTrailer     a ractor

このパフォーマンスがDB間でどのように異なるかについても説明できません。この種のクエリでは、postgresまたはmysqlの方が良い場合と悪い場合があります。

4
joran

これは機能しますが、巨大なデータセットでは信じられないほど遅くなります。

x <- data.frame(idX=1:3, string=c("Motorcycle", "TractorTrailer", "Sailboat"))
y <- data_frame(idY=letters[1:3], seed=c("ractor", "otorcy", "irplan"))

library(dplyr)
full_join(mutate(x, i=1), 
          mutate(y, i=1)) %>% 
  select(-i) %>% 
  filter(str_detect(string, seed))
1
Stephen Turner