Maarten is right. If data are like this
id female likes_cats likes_dogs
-- ------ ---------- ----------
1 0 0 0
2 1 1 0
3 0 1 0
...
in which each person is represented by only one
observation (record), then it's easy to count
how many people satisfy two (or indeed more)
different conditions.
e.g. -count if female & likes_cats & likes_dogs-
Nor are indicators (dummy, logical, Boolean
variables) essential as we can always use explicit
true or false conditions instead.
This kind of structure is I think also
assumed by Carlo Lazzaro in his posting in
this thread.
However,
This is not the structure Joseph
has and it would be unnatural to force
his dataset into a different structure
given the irregularity of dates that
he presumably has.
Hence Kit's proposal is closer to, indeed
on, the mark.
What's more, this is essentially the same
problem as that posted by Paul O'Brien
just the same day and already replied to
with code
<http://www.hsph.harvard.edu/cgi-bin/lwgate/STATALIST/archives/statalist.0710/date/article-161.html>
The class of problem is this:
1. There is some kind of grouping, most obviously into panel
or longitudinal data. For concreteness, we'll talk "panels" and
remember that the idea is more general. (Indeed, no
kind of time basis, regular or irregular, is essential here.)
2. Hence, multiple observations for each panel
are likely.
3. Some question arises about panels that requires
comparison of different observations.
4. For each observation, we can say whether
it satisfies some condition. That is a true-or-false
calculation.
5. We need to summarise that true-or-false result
over all observations in each panel. This can be done with
-egen, by(<panelid>)- or -by <panelid>: egen- or
-by <panelid>: gen-.
6. Then we need to combine information on different
conditions using logical operators such as &, | and !.
7. Finally, we must count panels, not individuals.
Nick
[email protected]
Kit Baum
---------------------------------------------
I think this should work, without the necessity of reshaping:
bysort id: gen early = inrange(age, 17, 25)
by id: gen late = age > 30
by id: gen both = cond(_n==_N, (sum(early) & sum(late)) , .)
count if both == 1
To test,
set obs 1000
g id=mod(_n,100)+1
g age=40*uniform()
Maarten Buis
--------------------------------------------
This kind of problem usually becomes a lot easier when you first use
-reshape- to put the data into wide format.
Joseph Wagner
--------------------------------------------
> I have a dataset of x-ray records with multiple records per
> patient. The records consist of id, age, and sex and I need
> to know how many persons had an x-ray when they were between
> the age of 17 and 25 AND when when they were over 30.
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