Given the nature of the task, I would vote for the datastep approach to this problem. Both approaches have room for coding error, thus have to be carefully checked to ensure that you are doing what you intend to do, but the datastep approach, in this case, will run in almost half the time.
Similarly, there are many cases where proc summary can be used to transpose data and do it with much less code and quite a bit quicker than proc transpose.
On the other hand, as the complexity of the task increases, yes, proc transpose or proc summary can definitely pose less risk than hard coding the solution.
Art
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> Hello everyone,
>
> I have a dataset that I want to transform into a
> panel data set. I usually do it in 2 ways: (1)
> built-in Proc Transpose, and (2) array in a Data
> step. For example,
>
> 1st way:
> proc sort data=mydata; by isin; run;
>
> proc transpose data=mydata out=myPaneldata
> (rename=(col1=myvar));
> by isin;
> var var1-var 500;
> run;
>
>
> 2nd way:
> data myPaneldata;
> set mydata;
> array vars{*} _NUMERIC_;
> do k=1 to dim(vars);
> myvar=vars{k}; output;
> end;
> drop k var1-var500;
> run;
>
> My question:
> - What is the strength and weakness of each method?
> - From what I observed repeatedly in several large
> datasets I run, it seems that using ARRAY in a data
> set is faster? How come? I argue that PROC TRANSPOSE
> is a built-in function and for that reason, experts
> must have chosen the most efficient, fastest way of
> transposing a dataset, and so anything that is not
> built-in is 99% of the time slower or at equally fast
> but less safe.
>
> Can anyone please advise?
