You can also use if (cond) then {something} if you don't like ifelse()
function. See ?Control.

David

2009/6/23 Peter Flom <peterflomconsulting@mindspring.com>

> >Dieter Menne wrote:
> >>
> >>>> IF TYPE='TRUCK' and count=12 THEN VEHICLES=TRUCK+((CAR+BIKE)/2.2);
> >>> vehicles <- ifelse(TYPE=='TRUCK' & count=12, TRUCK+((CAR+BIKE)/2.2),
> NA)
> >>>
> >>>
> >>
> >> Read both versions to an audience, and you will have to admit that this
> is
> >> one of the cases where SAS is superior.
>
> And Peter Dalgaard replied
>
> >That's not entirely clear. For instance, SAS is not being clear about
> >what happens when the condition is FALSE.
> >
>
> Well, OK, but then it is easy to add an ELSE, and I do find a structure
> such as
>
>  IF X = 1 THEN ....
> ELSE IF X = 2 THEN ...
> ELSE X =
>
> quite a lot easier to both read and write than the equivalent in R.  I also
> find the DO loops
> in SAS clear.
>
>
> >SAS is also not distinguishing comparison and assignment, but then again
> >you don't accidentally do count=12 when you mean count==12....
> >
> >As a generic matter, SAS (the DATA step) is generally good at things
> >that are done by sequential sweeps through a data file. The shortcomings
> >come in when you do things that can't be done sequentially, or require
> >substantial rearranging of data first. x - ave(x,g,median) is an example
> >which IIRC requires
> >
> >proc sort
> >proc means by group (saving medians to data set)
> >data step (merge and subtract)
> >
>
> True.  But two points:
> 1) Speaking only for me, but I use an IF THEN ELSE type structure a lot
> more often then I have to do something like the second example.
>
> 2) Again, speaking for me, the second sort of thing seems like it OUGHT to
> be hard, while IF THEN seems like it ought to be easy.  This may be because
> I learned SAS before R
>
>
> Peter
>
> Peter L. Flom, PhD
> Statistical Consultant
> www DOT peterflomconsulting DOT com
>
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