将一个数据帧中的每一行与tidyverse中另一行的每一行进行比较

使用查看我的解决方案fuzzyjoin。基本上，它确实将左侧的每一行散布到右侧的每一行，因为我将max_dist设置为高（10），但是如果您无论如何都不想匹配不好，则可以降低它。然后，它使用group_by和top_n选择第一个数据帧中每个first_name和last_name的最佳匹配。

我添加了“不匹配”和“标签”条件（请参阅前两列）。您可以调整匹配功能选项（现在，它使用特定的stringdist方法Levenshtein比较指定的五列的字符串距离）。

同样，鲍勃·凯索（Bob Kelso）出现两次，因为最佳匹配被绑在2个（不良）匹配之间，因此该算法无法选择同样糟糕的匹配之一。

library(tidyverse); library(fuzzyjoin)

# Load data
df1 <- tibble::tribble(
             ~id, ~first_name,   ~last_name,                          ~account_nr, ~amount, ~currency, ~comment,
   "wW3A4QgpQQd",   "Lynnett",   "Labadini",      "ES46 2569 1625 6669 5490 4624", 9655.56,     "JPY",   "Z617",
   "LsoPIXEMOo5",    "Velvet",    "Ritelli",  "FR60 4478 1591 96PB SIMI FSTO L13", 6992.36,     "PHP",   "L841",
   "L2wBds77Pw8",       "orv",     "matfin", "LB61 6941 CQYE ONER G5T0 KNDU JU5H", 6184.38,     "CAD",   "o705",
   "ME4O9MKlOzO",      "ring",      "hecks",        "BG28 JYPB 4068 09NB FQ7I 6C", 4203.54,     "IDR",   "Y548",
   "d83N7Viwq8k",      "judd",    "Riddick",       "IL36 2200 2898 6944 4508 084", 3619.43,     "IDR",   "O762",
   "1l96680epEy",   "Edouard",   "Kapovski",   "IS73 1064 6186 1231 6178 3743 49", 5291.76,     "BRL",   "T397",
   "7JwvD23oMzC",      "Jake", "Rabinovich",           "KZ80 759G VOHS JHBY L5TY", 6994.26,     "NGN",   "Y784",
   "ZOcg2uprlN6",      "vere",   "gravener",      "SE39 1416 1830 7878 5026 6805", 5281.18,     "UAH",   "Z890",
   "AUrx3nYR2Ks",       "Bob",      "Kelso",           "VS41 5146 7748 1278 5362", 4324.12,     "USD",   "W312",
  "VrDS+DqRG4S1",     "Mitch",   "Mitchell",           "AT65 6306 7334 7478 1908", 4221.59,     "EUR",   "T352"
  )
df2 <- tibble::tribble(
                   ~id, ~first_name,  ~last_name, ~amount, ~currency, ~comment, ~recipient,
         "xGZx1tNE4oa",   "Lynnett",  "Labadini", 9655.56,     "JPY",   "Z617",         72,
         "nV7NtxiguPQ",    "Velvet",   "Ritelli", 6992.36,     "PHP",   "L841",        175,
         "Rto0EHOR17k",       "Orv",    "Matfin", 6184.38,     "CAD",   "O705",        412,
         "2VDMHTJnxcw",      "Ring",     "Hecks", 4203.54,     "IDR",   "Y548",         63,
         "VQI7I0EZf1q",      "Judd",   "Riddick", 3619.43,     "IDR",   "O163",         39,
         "w835JEfmJvZ",   "Edouard", "Avramovic", 5291.76,     "BRL",   "T397",        240,
         "of2FZZXFKY8",     "Ferdy", "Petracchi", 6994.26,     "NGN",   "Y784",        102,
         "XgUZFhKowB1",      "Vere",  "Gravener", 5281.18,     "IDR",   "U024",        111,
         "iGO9advyXP3",      "Temp", "McKeevers", 7364.49,     "TND",   "R404",        327,
         "5BCiYQVhfxM",     "Arnie",   "Ashdown", 4221.59,     "ZAR",   "N988",        262
         )

# Solution using fuzzyjoin
stringdist_left_join(df1, df2, by = c("first_name", "last_name", "amount", "currency", "comment"), 
                     max_dist = 10, ignore_case = TRUE, method = "lv", distance_col = "dist") %>%
  mutate(total.dist = first_name.dist + last_name.dist + amount.dist + currency.dist + comment.dist) %>%
  group_by(first_name.x, last_name.x) %>%
  top_n(-1, total.dist) %>%
  mutate(mismatch = (first_name.dist>0) + (last_name.dist>0) + (amount.dist>0) + (currency.dist>0) + (comment.dist>0),
         label = case_when(mismatch == 0 ~ "match",
                           mismatch == 1 ~ "high",
                           mismatch == 2 ~ "proposed",
                           mismatch > 2 ~ "none",
                           TRUE ~ "")) %>%
  select(label, mismatch, total.dist, everything())
#> # A tibble: 11 x 22
#> # Groups:   first_name.x, last_name.x [10]
#>    label mismatch total.dist id.x  first_name.x last_name.x account_nr
#>    <chr>    <int>      <dbl> <chr> <chr>        <chr>       <chr>     
#>  1 match        0          0 wW3A~ Lynnett      Labadini    ES46 2569~
#>  2 match        0          0 LsoP~ Velvet       Ritelli     FR60 4478~
#>  3 match        0          0 L2wB~ orv          matfin      LB61 6941~
#>  4 match        0          0 ME4O~ ring         hecks       BG28 JYPB~
#>  5 high         1          2 d83N~ judd         Riddick     IL36 2200~
#>  6 high         1          7 1l96~ Edouard      Kapovski    IS73 1064~
#>  7 prop~        2         14 7Jwv~ Jake         Rabinovich  KZ80 759G~
#>  8 prop~        2          7 ZOcg~ vere         gravener    SE39 1416~
#>  9 none         5         20 AUrx~ Bob          Kelso       VS41 5146~
#> 10 none         5         20 AUrx~ Bob          Kelso       VS41 5146~
#> 11 none         4         19 VrDS~ Mitch        Mitchell    AT65 6306~
#> # ... with 15 more variables: amount.x <dbl>, currency.x <chr>,
#> #   comment.x <chr>, id.y <chr>, first_name.y <chr>, last_name.y <chr>,
#> #   amount.y <dbl>, currency.y <chr>, comment.y <chr>, recipient <dbl>,
#> #   amount.dist <dbl>, comment.dist <dbl>, currency.dist <dbl>,
#> #   first_name.dist <dbl>, last_name.dist <dbl>

^{由reprex软件包（v0.2.1）创建于2019-03-17}