Date:         Thu, 19 Jun 2003 10:31:03 -0700
Reply-To:     Michelle Jellinghaus <michelle@EMODE.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Michelle Jellinghaus <michelle@EMODE.COM>
Subject:      Re: combine incomplete observations by var
Comments: To: Jack Hamilton <JackHamilton@firsthealth.com>
Content-Type: text/plain; charset="us-ascii"

This worked beautifully. Thank you very much for taking the time to
answer my post.

I am still trying to figure out *why* it worked, however ;) I hope you
won't mind a few questions.

This is what I have so far; will you let me know if I'm right?
        nway: this option removes an observation that contains a column
sum for all rows
        missing: I'm not sure what this does here, but it seems to work
:)
        sum= in the output statement: this seems to give a sum of all
values in a column by class instead of returning M, STD, etc.

I see the _type_ column, but I don't understand what it is.

Thanks again for your help.

M

-----Original Message-----
From: Jack Hamilton [mailto:JackHamilton@firsthealth.com]
Sent: Wednesday, June 18, 2003 6:11 PM
To: Michelle Jellinghaus; SAS-L@LISTSERV.UGA.EDU
Subject: Re: [SAS-L] combine incomplete observations by var

If those are all the variables in the data set, you could do something
like this (untested):

   proc summary nway missing data=in_dsn;
      class date;
      var p1 p2 p3 p4;
      output out=out_dsn (drop=_type_ _freq_)
             sum=;
   run;

You could use PROC REPORT instead if you also want a printout at the
same time.



--
JackHamilton@FirstHealth.com
Manager, Technical Development
Metrics Department, First Health
West Sacramento, California USA

>>> "Michelle Jellinghaus" <michelle@EMODE.COM> 06/18/2003 5:22 PM >>>
I have a data set that I would like to collapse based on date.

It looks something like this right now:

date       p1       p2       p3       p4
4/5/03    40        .          .          .
4/5/03      .        30        .          .
4/5/03      .         .        20         .
4/5/03      .          .          .       10
4/6/03    50        .          .          .
4/6/03      .        40        .          .
4/6/03      .         .        40         .
4/6/03      .          .          .       30

And I'd like it to look something like this:

date       p1       p2       p3       p4
4/5/03    40      30        20       10
4/6/03    50      40        40       30

This would provide me with one observation per date, with a complete
set of
values for each variable, ridding the data set of all missing values.

Any ideas would be very much appreciated.

Thank you,

Michelle