>
> Color me puzzled. Can you express the run more clearly in Boolean logic?
>
> Its a bit tedious to explain in Boolean logic..

Suppose the data is subsetted according to two distinct 'clm'
variables  (i.e 1 set consisting of only "General" & other only of "Life")

*  General.dat*
*  * id loc     clm
1  A1  B1 General
2  A2  B2 General
3  A3  B3 General
4  A4  B4 General
5  A5  B5 General
6  A3  B1 General
7 A3  B3 General
8 A3  B3 General
9 A4  B4 General

*Life*.*dat*
   id loc     clm
1  A2  B2    Life
2  A3  B3    Life
3  A4  B4    Life
4  A5  B5    Life

 Basically, the records in General.dat & Life.dat with same color (matching
pairs) are created in one data set.
 The remaing records form other data set. (Although row 7 & 8 in General.dat
are matched with row 2 in Life.dat, these are duplicates of row 3 in
General.dat that require further attention. Similarly
 for row 9 in General.dat)


> If someone has five policies: 3 Life and 2 General ...  is he in or out?
>
> He is in with 1 life policy as long as the policies are identical (i.e same
'id' & 'loc' values).



> Applying the alternate strategy to that data set I get:
> out <- tapply( dat$clm, dat$uid, paste ,collapse=",")
> >
> > out
>                         A1.B1                          A2.B2
>            A3.B1
>                     "General"                 "General,Life"
>        "General"
>                         A3.B3                          A4.B4
>            A5.B5
> "General,Life,General,General"         "General,Life,General"
>   "General,Life"
>
> Please explain why you want A3.B3.
>
> A3.B3 (2 records) & A4.B4 (1 record) are required to examine
matching (between 'General' & 'Life' with identical 'ID' and 'loc')
duplicated records.


> --
> David.
>
>
>

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