Introduction to Stata 8

10.7. Reshaping data
collapse [R] collapse
You want to create an aggregated data set, not with the characteristics of each individual, but
of groups of individuals. One situation might be to characterize physicians by number of
patient contacts, another to make a reduced data set for Poisson regression (see section 13):
. // gen.stcollaps.cancer2.do
. use c:\dokumenter\proj1\stsplit.cancer2.dta , clear
. collapse (sum) risktime died , by(agegr drug)
. save c:\dokumenter\proj1\stcollaps.cancer2.dta
. summarize
Variable | Obs Mean Std. Dev. Min Max
-------------+-----------------------------------------------------
drug | 15 2 .8451543 1 3
agegr | 15 55 7.319251 45 65
risktime | 15 4.133334 3.01067 .3581161 10.88906
died | 15 2.066667 2.374467 0 8
reshape [R] reshape
E.g. with repeated measurements some analyses require a 'wide', some a 'long' data structure:
Long structure Wide structure
id time sbp
1 1 140
1 2 120
1 3 130
1 4 135
2 1 155
etc.
id sbp1 sbp2 sbp3 sbp4
1 140 120 130 135
2 155 etc.
Switch between structures by:
reshape wide sbp , i(id) j(time) // Restructures long to wide
reshape long sbp , i(id) j(time) // Restructures wide to long
xpose [R] xpose
You may transpose observations and variables, i.e. let observations become variables and
variables become observations. This may be useful e.g. for restructuring data for a graph:
xpose , clear // clear is not optional
expand [R] expand, contract
You may duplicate observations according to a weighting variable. Look at the example in
section 7 about weighting observations. You can obtain the same result by expanding the four
observations to 130:
expand pop
tabulate expos case , chi
contract does the opposite of expand.
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