进行“模糊连接". (和non-fuzzyjoin)结合data.table中的合并 [英] Doing a "fuzzyjoin" (and non-fuzzyjoin) in combination with a merge in data.table

问题描述

我正在使用多个数据库.对于这些数据库中的每一个，我都创建了一个名为matchcode的密钥. matchcode是国家/地区代码和年份的组合.通常，当我合并这些数据集时，我只是这样做:

I am using multiple databases. For each of these databases I have created a key called matchcode. This matchcode is a combination of a country code and a year. Mostly when I merge these datasets I simply do:

dfA<- merge(dfA, dfB, by= "matchcode", all.x = TRUE, allow.cartesian=FALSE)

问题是有时年份不完全匹配:

The problem is that sometimes the years do not completely match:

   dfA <- read.table(
  text = "A   B   C   D   E   F   G   iso   year   matchcode
  1   0   1   1   1   0   1   0   NLD   2010   NLD2010
  2   1   0   0   0   1   0   1   NLD   2014   NLD2014
  3   0   0   0   1   1   0   0   AUS   2010   AUS2010
  4   1   0   1   0   0   1   0   AUS   2006   AUS2006
  5   0   1   0   1   0   1   1   USA   2008   USA2008
  6   0   0   1   0   0   0   1   USA   2010   USA2010
  7   0   1   0   1   0   0   0   USA   2012   USA2012
  8   1   0   1   0   0   1   0   BLG   2008   BLG2008
  9   0   1   0   1   1   0   1   BEL   2008   BEL2008
  10   1   0   1   0   0   1   0  BEL   2010   BEL2010",
  header = TRUE
)

   dfB <- read.table(
  text = "K   L   M   N   O   P   Q   iso   year   matchcode
  1   0   1   1   1   0   1   0   NLD   2009   NLD2009
  2   1   0   0   0   1   0   1   NLD   2014   NLD2014
  3   0   0   0   1   1   0   0   AUS   2011   AUS2011
  4   1   0   1   0   0   1   0   AUS   2007   AUS2007
  5   0   1   0   1   0   1   1   USA   2007   USA2007
  6   0   0   1   0   0   0   1   USA   2011   USA2010
  7   0   1   0   1   0   0   0   USA   2013   USA2013
  8   1   0   1   0   0   1   0   BLG   2007   BLG2007
  9   0   1   0   1   1   0   1   BEL   2009   BEL2009
  10   1   0   1   0   0   1   0  BEL   2012   BEL2012",
  header = TRUE
)

我正在寻找一种data.table解决方案，该解决方案的功能类似于以下 answer由Jaap :

I am looking for a data.table solution which does something similar to the following answer by Jaap :

library(data.table)
setDT(dfA)
setDT(dfB)

dfA[dfB
       , on = .(iso, year)
       , roll = "nearest"
       , .(ID, year.x = i.year, year.y = x.year, value, delta = abs(i.year- x.year))]

但是，除了这种解决方案之外，我还希望:

I would however, in addition to this solution like to:

1. 将两个数据库的所有列都保存在新的data.table中.
2. 将纸卷限制为+1 | -1.但是，当我输入此内容时，它似乎无法正确应用.

1. Have all columns of both databases in the new data.table.
2. Have the roll limited to +1 | -1. However when I enter this, it does not seem to apply this correctly.

对于1.我显然需要使用mget，

For 1. I apparently need to use mget, again thanks to Jaap:

dfA[dfB, on = .(iso, year), names(dfB)[1:10] := 
        mget(paste0("i.", names(dfB)[1:10]))]

但是我似乎无法成功地将其组合在一起.我试过了:

I however cannot seem to be able to put this together successfully. I tried:

dfA[dfB, on = .(iso, year), roll = "nearest", names(dfB)[1:10] := 
   mget(paste0("i.", names(dfB)[1:10])),
   .(matchcode, year.x = i.year, year.y = x.year, delta = abs(i.year - x.year))]

但这给出了:

Error in eval(bysub, xss, parent.frame()) : object 'i.year' not found.

有效的方法如下:

 dfA[dfB
     , on = .(iso, year)
     , roll = "nearest"
     , .(matchcode, year.x = i.year, year.y = x.year, delta = abs(i.year - x.year))]
    matchcode year.x year.y delta
 1:   NLD2010   2009   2010     1
 2:   NLD2014   2014   2014     0
 3:   AUS2010   2011   2010     1
 4:   AUS2006   2007   2006     1
 5:   USA2008   2007   2008     1
 6:   USA2010   2011   2010     1
 7:   USA2012   2013   2012     1
 8:   BLG2008   2007   2008     1
 9:   BEL2008   2009   2008     1
10:   BEL2010   2012   2010     2

关于如何进行的任何建议?

Any suggestions on how to proceed?

推荐答案

希望这对您有用:

dfA[, yearA := year]

res1 <- dfA[dfB, on = .(iso, year), roll = 1, nomatch = 0]
res2 <- dfA[dfB, on = .(iso, year), roll = -1, nomatch = 0]
res <- rbind(res1, res2[yearA > year])
setnames(res, c('year', 'matchcode', 'i.matchcode'), c('yearB', 'matchcodeA', 'matchcodeB'))

#    A B C D E F G iso yearB matchcodeA yearA K L M N O P Q matchcodeB
# 1: 1 0 0 0 1 0 1 NLD  2014    NLD2014  2014 1 0 0 0 1 0 1    NLD2014
# 2: 0 0 0 1 1 0 0 AUS  2011    AUS2010  2010 0 0 0 1 1 0 0    AUS2011
# 3: 1 0 1 0 0 1 0 AUS  2007    AUS2006  2006 1 0 1 0 0 1 0    AUS2007
# 4: 0 0 1 0 0 0 1 USA  2011    USA2010  2010 0 0 1 0 0 0 1    USA2010
# 5: 0 1 0 1 0 0 0 USA  2013    USA2012  2012 0 1 0 1 0 0 0    USA2013
# 6: 0 1 0 1 1 0 1 BEL  2009    BEL2008  2008 0 1 0 1 1 0 1    BEL2009
# 7: 0 1 1 1 0 1 0 NLD  2009    NLD2010  2010 0 1 1 1 0 1 0    NLD2009
# 8: 0 1 0 1 0 1 1 USA  2007    USA2008  2008 0 1 0 1 0 1 1    USA2007
# 9: 0 1 0 1 0 0 0 USA  2011    USA2012  2012 0 0 1 0 0 0 1    USA2010
# 10: 1 0 1 0 0 1 0 BLG  2007    BLG2008  2008 1 0 1 0 0 1 0    BLG2007
# 11: 1 0 1 0 0 1 0 BEL  2009    BEL2010  2010 0 1 0 1 1 0 1    BEL2009

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