Brute force method for cleaning dataset B of spurious missing
observations:
If you do this
reshape wide awardamt, i(id year semester) j(granttype)
foreach i in 1 2 {
preserve
tempfile file`i'
keep id year semester granttype`i'
drop if granttype`i'==.
sort id year semester
save "`file`i''", replace
restore
}
*preserve
clear
use "`file1'"
merge id year semester using "`file2'"
tab _merge
Then you will get the "squished" file you're looking for. From tab
_merge you should see see whether all your observations in that file
have two non-missing awardamt's each. There's no guarantee that they
should, right?
Gabi
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Jennifer
Delaney
Sent: Tuesday, January 31, 2006 4:08 PM
To: [email protected]
Subject: st: merging data unique identification problem
I have two datasets that I am trying to merge. One dataset (A) is
uniquely identified by three variables (ID YEAR SEMESTER). The other
dataset is uniquely identified by four variables (ID YEAR SEMESTER
GRANTTPYE). In the second dataset (B) students can get more than one
GRANTTYPE in a given semester so it is important to keep this factor.
What I've been trying to do is to collapse the second dataset (B) so
that it is identified by only three variables then I can merge it with
dataset A.
I think it's clearest if I can illustrate with a simple example...
Current form of dataset B:
ID YEAR SEMESTER GRANTTYPE AWARDAMT
1 1990 Fall 1 500
1 1990 Fall 0 200
Form that I am attempting to convert dataset B into:
ID YEAR SEMESTER GRANTTYPE1awardamt GRANTTYPE2awardamt
1 1990 Fall 500 200
The only form that I have been able to achieve so far:
ID YEAR SEMESTER GRANTTYPE1awardamt GRANTTYPE2awardamt
1 1990 Fall 500 .
1 1990 Fall . 200
How do I "squish" the data so that I end up with one entry per kid per
year per term?
Thanks,
Jennifer
--
Jennifer Delaney
PhD candidate in Higher Education
Stanford University
[email protected]
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