2 
library(tidyverse)
library(fuzzyjoin)
df1 <- tibble(col1 = c("Apple Shipping", "Banana Shipping", "FedEX USA Ground",
                       "FedEx USA Commercial", "FedEx International"),
              col2 = 1:5)
#> # A tibble: 5 x 2
#>   col1                  col2
#>   <chr>                <int>
#> 1 Apple Shipping           1
#> 2 Banana Shipping          2
#> 3 FedEX USA Ground         3
#> 4 FedEx USA Commercial     4
#> 5 FedEx International      5

df2 <- tibble(col3 = c("Banana", "FedEX USA"), col4 = c(700, 900))
#> # A tibble: 2 x 2
#>   col3       col4
#>   <chr>     <dbl>
#> 1 Banana      700
#> 2 FedEX USA   900

我正在使用的两个数据框如上所示。我想模糊加入他们col1并col3想出类似于下面直接显示的内容。基本上，规则是，“如果所有文本col3都在任何col1一个匹配中”。

#> # A tibble: 3 x 4
#>   col1                  col2  col3      col4
#>   <chr>                <int>  <chr>    <int>
#> 1 Banana Shipping          2  Banana     700
#> 2 FedEX USA Ground         3  FedEx USA  900
#> 3 FedEx USA Commercial     4  FedEx USA  900

这个较旧的 SO question 似乎提供了解决方案，但在这种情况下它似乎并不完全有效，我得到如下所示的错误：

df1 %>% regex_inner_join(df2, by = c(string = "col3"))
#> Error: All columns in a tibble must be 1d or 2d objects:
#> * Column `col` is NULL
#> Run `rlang::last_error()` to see where the error occurred.

library(stringr)
df1 %>% fuzzy_inner_join(df2, by = c("string" = "col3"), match_fun = str_detect)
#> Error: All columns in a tibble must be 1d or 2d objects:
#> * Column `col` is NULL
#> Run `rlang::last_error()` to see where the error occurred.

如何使用 R 执行这种模糊连接？

4

1 回答 1

5

也许这就是你要找的？

library(dplyr)
library(fuzzyjoin)
library(stringr)
df1 %>% fuzzy_inner_join(df2,by=c("col1" = "col3"),match_fun = str_detect)
## A tibble: 2 x 4
#  col1              col2 col3       col4
#  <chr>            <int> <chr>     <dbl>
#1 Banana Shipping      2 Banana      700
#2 FedEX USA Ground     3 FedEX USA   900

如果你想忽略大小写，你可以定义你自己的str_detect.

my_str_detect <- function(x,y){str_detect(x,regex(y, ignore_case = TRUE))}
df1 %>% fuzzy_inner_join(df2,by=c("col1" = "col3"),match_fun = my_str_detect)
## A tibble: 3 x 4
#  col1                  col2 col3       col4
#  <chr>                <int> <chr>     <dbl>
#1 Banana Shipping          2 Banana      700
#2 FedEX USA Ground         3 FedEX USA   900
#3 FedEx USA Commercial     4 FedEX USA   900

您可以agrepl从这个问题中使用奖励积分。

您可以修改max.distance =参数并可能添加cost =. 查看help(agrepl)更多。

my_match_fun <- Vectorize(function(x,y) agrepl(x, y, ignore.case=TRUE, max.distance = 0.7, useBytes = TRUE))
df1 %>% fuzzy_inner_join(df2,by=c("col1" = "col3"),match_fun = my_match_fun)
## A tibble: 4 x 4
#  col1                  col2 col3       col4
#  <chr>                <int> <chr>     <dbl>
#1 Banana Shipping          2 Banana      700
#2 FedEX USA Ground         3 FedEX USA   900
#3 FedEx USA Commercial     4 FedEX USA   900
#4 FedEx International      5 FedEX USA   900
于 2020-04-02T14:48:59.160 回答