One-to-Many Left Join / Merge in Data.Table in R

Question

If you know of an answer to this question elsewhere, please let me know, but I searched and read a large number of data.table-related join topics as well as the package\'s i

Answer 1

You did not indicate what you would like to see as the final result. You have three main options:

 A[B]
 B[A]
 merge(A, B, all=TRUE)

Note that the allow.cartesian error is something you will see if there is any duplicated keys in the joining table. In which case you can simply set the flag to TRUE or instead remove the duplicates

 B[unique(A)]
 # or
 B[A, allow=TRUE]

---

setkey(A, id, year)
setkey(B, id, year)


A[B]
#     id year var1 var2 id2 var3
#  1:  1 2010   10   20   1   20
#  2:  1 2010   10   20   2   30
#  3:  1 2011   70   20   1   20
#  4:  1 2011   70   20   2   60
#  5:  1 2012   90   30   2   10
#  6:  2 2011   50   60   3   40
#  7:  2 2013   90   80   2   80
#  8:  2 2013   90   80   3   70
#  9:  3 2010   50   50   3   50
# 10:  3 2012   90   30   1   30
# 11:  3 2012   90   30   3   30
# 12:  4 2013   NA   NA   1   50

B[A, allow=TRUE]
#     id year id2 var3 var1 var2
#  1:  1 2010   1   20   10   20
#  2:  1 2010   2   30   10   20
#  3:  1 2011   1   20   70   20
#  4:  1 2011   2   60   70   20
#  5:  1 2012   2   10   90   30
#  6:  1 2013  NA   NA   40   50
#  7:  2 2010  NA   NA   30   30
#  8:  2 2011   3   40   50   60
#  9:  2 2012  NA   NA   80   10
# 10:  2 2013   2   80   90   80
# 11:  2 2013   3   70   90   80
# 12:  3 2010   3   50   50   50
# 13:  3 2011  NA   NA   20   40
# 14:  3 2012   1   30   90   30
# 15:  3 2012   3   30   90   30
# 16:  3 2013  NA   NA   60   70

merge(A, B, all=TRUE, allow=TRUE)
#     id year var1 var2 id2 var3
#  1:  1 2010   10   20   1   20
#  2:  1 2010   10   20   2   30
#  3:  1 2011   70   20   1   20
#  4:  1 2011   70   20   2   60
#  5:  1 2012   90   30   2   10
#  6:  1 2013   40   50  NA   NA
#  7:  2 2010   30   30  NA   NA
#  8:  2 2011   50   60   3   40
#  9:  2 2012   80   10  NA   NA
# 10:  2 2013   90   80   2   80
# 11:  2 2013   90   80   3   70
# 12:  3 2010   50   50   3   50
# 13:  3 2011   20   40  NA   NA
# 14:  3 2012   90   30   1   30
# 15:  3 2012   90   30   3   30
# 16:  3 2013   60   70  NA   NA
# 17:  4 2013   NA   NA   1   50