>
> 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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