时间戳

首页 > 解决方案 > 使用精确匹配和模糊匹配在 R 中加入两个大型数据集

r - 使用精确匹配和模糊匹配在 R 中加入两个大型数据集

问题描述

我正在尝试内部连接两个数据集：df150,000 个 obs 看起来像这样：

  Name              | Line.1           | Line.2     | Town       | County       | Postcode 
 -------------------|------------------|------------|------------|--------------|---------- 
  ACME Inc          | 63 Long Street   |            | Fakeington | Lincolnshire | PA4 8QU  
  BETA LTD          | 91a              | Main Drove | Cloud City | Something    | BN1 6LD  
  The Giga          | 344 Lorem Street |            | Ipsom      | Dolor        | G2 8LY

df2500,000 个 obs 看起来像这样：

  Name              | AddressLine1   | AddressLine2     | AddressLine3 | AddressLine4 | Postcode | RatingValue 
 -------------------|----------------|------------------|--------------|--------------|----------|------------- 
  ACME              |                | 63 Long Street   | Fakeington   | Lincolnshire | PA4 8QU  | 1           
  Random Company    |                | Rose Ave         | Fakeington   |              | AB2 51GL | 5           
  BETA Limited      | Business House | 91a Main Drove   | Something    |              | BN1 6LD  | 3           
  Giga Incorporated |                | 344 Lorem Street | Ipsum        | Dolor        | G2 8LY   | 5

我想得到类似的东西df_final。

  Name              | Postcode | RatingValue 
 -------------------|----------|------------- 
  ACME Inc          | PA4 8QU  | 1           
  BETA LTD          | BN1 6LD  | 3           
  Giga Incorporated | G2 8LY   | 5

这些是一对一的匹配，所有的值都df1应该存在于df2. Postcode是完全匹配，而地址被分成多行而没有常规模式，所以我认为我最好的选择是匹配Name.

我尝试了这个fuzzyjoin包，但我得到了一个Error: cannot allocate vector of size 120.6 Gb，所以我想我必须使用另一种适用于更大数据集的方法。

关于解决此问题的最佳方法的任何想法是什么？

df1 <- data.frame(
  stringsAsFactors = FALSE,
              Name = c("ACME Inc", "BETA LTD", "Giga Incorporated"),
            Line.1 = c("63 Long Street", "91a", "344 Lorem Street"),
            Line.2 = c(NA, "Main Drove", NA),
              Town = c("Fakeington", "Cloud City", "Ipsom"),
            County = c("Lincolnshire", "Something", "Dolor"),
          Postcode = c("PA4 8QU", "BN1 6LD", "G2 8LY")
)

df2 <- data.frame(
  stringsAsFactors = FALSE,
              Name = c("ACME", "Random Company","BETA Limited","Giga Incorporated"),
      AddressLine1 = c(NA, NA, "Business House", NA),
      AddressLine2 = c("63 Long Street", "Rose Ave","91a Main Drove","344 Lorem Street"),
      AddressLine3 = c("Fakeington", "Fakeington", "Something", "Ipsum"),
      AddressLine4 = c("Lincolnshire", NA, NA, "Dolor"),
          Postcode = c("PA4 8QU", "AB2 51GL", "BN1 6LD", "G2 8LY"),
       RatingValue = c(1L, 5L, 3L, 5L)
)

标签： rjoininner-joinfuzzyjoin

解决方案

也许像下面这样的东西会满足问题的要求。它使用包stringdist，而不是fuzzyjoin.

首先，mergeby Postcodeonly，因为匹配是精确的。然后得到Name's 之间的相似性。如果它们高于预定阈值，则保留这些行。

thresh <- 0.75

df_final <- merge(df2[c(1, 6:7)], df1[c(1, 6)], by = "Postcode", suffixes = c("",".y"))
i <- apply(df_final[c(2, 4)], 1, function(x) {stringdist::stringsim(x[1], x[2], method = 'jw')}) >= thresh

df_final <- df_final[i, c(2, 1, 3)]

df_final 
#               Name Postcode RatingValue
#1      BETA Limited  BN1 6LD           3
#2 Giga Incorporated   G2 8LY           5
#3              ACME  PA4 8QU           1

推荐阅读