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# Re: st: Aggregate different parts of data selectively

 From Chris Yang To statalist@hsphsun2.harvard.edu Subject Re: st: Aggregate different parts of data selectively Date Mon, 3 Feb 2014 15:39:58 -0500

```Thank you Nick. The example is just made-up data to illustrate the structure.

On Mon, Feb 3, 2014 at 2:33 PM, Nick Cox <njcoxstata@gmail.com> wrote:
> Your example is rather mysterious but you could
>
> keep if (group == 1 & no > 2) | (group == 2 & no > 5) | (group == 3 & no > 3)
>
> after which -collapse- would work on all specified observations.
>
> Nick
> njcoxstata@gmail.com
>
>
> On 3 February 2014 19:24, Chris Yang <bhpchg@gmail.com> wrote:
>> Dear All,
>>
>> I have a dataset of the following structure:
>>
>> group no var1 var2 var3
>> 1       1   3     2      1
>> 1       2   2     0      0
>> 1       3   3     0      1
>> 1       4   0     1      1
>> 1       5   1     1      2
>> ...
>> 2       1   3     2      1
>> 2       2   3     0      0
>> 2       3   1     1      1
>> 2       4   0     2      1
>> 2       5   0     0      2
>> ...
>> 3       1   1     1      1
>> 3       2   2     3      1
>> 3       3   2     1      1
>> ...
>>
>> I want to aggregate a subset of observations by group, e.g. aggregate
>> (using the mean) all the observations with `no` > 2 in group 1,
>> aggregate observations with `no` > 5 in group 2, aggregate
>> observations with `no` > 3 in group 3, and so on. And carry out
>> analysis using the aggregated data of different groups only.
>>
>> I know that --collapse-- can do the averaging job within each group,
>> however, it will also replace all existing data with the aggregated
>> data. In my case, it would mean that I lose the data for all the other
>> groups once I collapse/aggregate for any single group.
>>
>> A tedious way is to collapse and save respective groups individually,
>> and then merge the aggregated per-group data in the end. I wonder if
>> there is a more efficient way to achieve my goal here?
>>
>> Your thoughts and suggestions are appreciated. Thanks.
>>
>> Best regards,
>>
>> Chris
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```