Question

I’m trying to merge two data frames on a unique id and year. In SQL language I’m trying to do a left outer join, so in merge that is all.x=TRUE. Some elements of the y dataframe dont have all of the values (unique id,year combinations) in the x DF. In the case of a missing match I want to merge the row from the y data frame that has the same unique id as in the x data frame, but using the first year that I have prior to the missing one. Any suggestions on how to approach this merge? Thanks a lot!

Edit Wanted to make it more concrete

Dataframe x:

Id  year    var1 
1   2010    100
1   2011    105
1   2012    110
2   2010    100 
2   2011    105
2   2012    106

Dataframe y:

Id  year    var2    var3
1   2010    5       7
1   2011    10      8
2   2010    9       6

Desired merge:

Id  year    var1    var2    var3
1   2010    100     5       7
1   2011    105     10      8
1   2012    110     10      8
2   2010    100     9       6
2   2011    105     9       6
2   2012    106     9       6

Answer 1

I’d do this in two steps:

> out <- merge(x, y, all.x=T)
> out
  Id year var1 var2 var3
1  1 2010  100    5    7
2  1 2011  105   10    8
3  1 2012  110   NA   NA
4  2 2010  100    9    6
5  2 2011  105   NA   NA
6  2 2012  106   NA   NA

Then use na.locf from the zoo package:

library(zoo)

> apply(out, 2, na.locf)
     Id year var1 var2 var3
[1,]  1 2010  100    5    7
[2,]  1 2011  105   10    8
[3,]  1 2012  110   10    8
[4,]  2 2010  100    9    6
[5,]  2 2011  105    9    6
[6,]  2 2012  106    9    6

and this can be coerced to a data.frame easily enough.

> as.data.frame(apply(out, 2, na.locf))
  Id year var1 var2 var3
1  1 2010  100    5    7
2  1 2011  105   10    8
3  1 2012  110   10    8
4  2 2010  100    9    6
5  2 2011  105    9    6
6  2 2012  106    9    6