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Structuring Wide Data

I have a really wide market research dataset.  Each respondents is a row and demos / question answers are the columns.  Which makes a very wide dataset - 6000 columns.  Data like so:

 

RespondentAgeGenderQ1Q2 . . . Q6000
125-30m12 . . . 1
230-35m23 . . . 2
325-30m13 . . . 5
. . .
400025-30f45 . . . 4

From this I need to create a table something like so:

   

AgeGenderRespondentsRespondents meeting criteria
25-30m800100
25-30f120050
30-35m1500300
30-35f50072

The dimensions for this table can be up to 10 of any of the 6000 columns chosen dynamically.

Using that table as 6000 columns by 4000 rows, I can create the app it is however a bit slow. 

I am thinking that if I transpose the data as such - then I can improve performance.  In this type of table I have no problem with one dimension, however, adding dimensions to get the result table above is escaping me.

My other thought is to break up the 6000 column table into logical use groups to enhance performance.

Any thoughts?

Thanks

  

1Age25-30
1Genderm
1Q11
1Q22
1 . . .   . . .
1Q60001
2Age30-35
2Genderm
2Q12
2Q23
2 . . . . . .
2Q60002
3Age25-30
3Genderm
3Q11
3Q23
3 . . . . . .
3Q60005
. . . . . . . . .
4000Age25-30
4000Genderf
4000Q14
4000Q25
4000 . . . . . .
4000Q60004
1 Reply
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Re: Structuring Wide Data

you could use a CrossTable load to get the data model you are looking for.

regards

Marco