I have the following data, where some of the quantities are missing. I want to replace them by a rolling 3 month average of previous 3 months. I've tried to explain what I'm trying to achieve here-
ID | Month | Qty | Rolling average | Rolling average value |
1 | Jan | 2 | - | - |
1 | Feb | 3 | 2 | 2 |
1 | Mar | 2 | average(3,2) | 2.5 |
1 | Apr | 6 | average(2,3,2) | 2.333333333 |
1 | May | 4 | average(6,2,3) | 3.666666667 |
1 | Jun | 5 | AVERAGE(4,6,2) | 4 |
1 | Jul | . | average(5,4,6) | 5 |
1 | Aug | . | average(5,5,4) | 4.666666667 |
1 | Sep | . | average(4.666667,5,5) | 4.888888889 |
1 | Oct | . | average(4.8888889,4.666667,5) | 4.851851852 |
1 | Nov | . | average(4.8518519, 4.888889, 4.666667) | 4.802469136 |
1 | Dec | . | average(4.8024691, 4.8518519, 4.8888889) | 4.847736626 |
2 | Jan | 5 | ||
2 | Feb | 1 | ||
2 | Mar | 4 | ||
2 | Apr | 3 | ||
2 | May | 4 | ||
2 | Jun | 6 | ||
2 | Jul | . | ||
2 | Aug | . | ||
2 | Sep | . | ||
2 | Oct | . | ||
2 | Nov | . | ||
2 | Dec | . |
Can anyone please help on how to do this in SAS?
Try this and replace as you like.
data have;
input ID Month $ Qty;
datalines;
1 Jan 2
1 Feb 3
1 Mar 2
1 Apr 6
1 May 4
1 Jun 5
1 Jul .
1 Aug .
1 Sep .
1 Oct .
1 Nov .
1 Dec .
2 Jan 5
2 Feb 1
2 Mar 4
2 Apr 3
2 May 4
2 Jun 6
2 Jul .
2 Aug .
2 Sep .
2 Oct .
2 Nov .
2 Dec .
;
data want;
array lag[0:2] _temporary_;
call missing(of lag[*]);
do _N_ = 1 by 1 until (last.id);
set have;
by id;
output;
lag[mod(_N_, 3)] = coalesce(Qty, avg);
avg = mean(of lag[*]);
end;
run;
Result:
ID Month Qty avg 1 Jan 2 . 1 Feb 3 2 1 Mar 2 2.5 1 Apr 6 2.3333333333 1 May 4 3.6666666667 1 Jun 5 4 1 Jul . 5 1 Aug . 4.6666666667 1 Sep . 4.8888888889 1 Oct . 4.8518518519 1 Nov . 4.8024691358 1 Dec . 4.8477366255 2 Jan 5 . 2 Feb 1 5 2 Mar 4 3 2 Apr 3 3.3333333333 2 May 4 2.6666666667 2 Jun 6 3.6666666667 2 Jul . 4.3333333333 2 Aug . 4.7777777778 2 Sep . 5.037037037 2 Oct . 4.7160493827 2 Nov . 4.8436213992 2 Dec . 4.865569273
Try this and replace as you like.
data have;
input ID Month $ Qty;
datalines;
1 Jan 2
1 Feb 3
1 Mar 2
1 Apr 6
1 May 4
1 Jun 5
1 Jul .
1 Aug .
1 Sep .
1 Oct .
1 Nov .
1 Dec .
2 Jan 5
2 Feb 1
2 Mar 4
2 Apr 3
2 May 4
2 Jun 6
2 Jul .
2 Aug .
2 Sep .
2 Oct .
2 Nov .
2 Dec .
;
data want;
array lag[0:2] _temporary_;
call missing(of lag[*]);
do _N_ = 1 by 1 until (last.id);
set have;
by id;
output;
lag[mod(_N_, 3)] = coalesce(Qty, avg);
avg = mean(of lag[*]);
end;
run;
Result:
ID Month Qty avg 1 Jan 2 . 1 Feb 3 2 1 Mar 2 2.5 1 Apr 6 2.3333333333 1 May 4 3.6666666667 1 Jun 5 4 1 Jul . 5 1 Aug . 4.6666666667 1 Sep . 4.8888888889 1 Oct . 4.8518518519 1 Nov . 4.8024691358 1 Dec . 4.8477366255 2 Jan 5 . 2 Feb 1 5 2 Mar 4 3 2 Apr 3 3.3333333333 2 May 4 2.6666666667 2 Jun 6 3.6666666667 2 Jul . 4.3333333333 2 Aug . 4.7777777778 2 Sep . 5.037037037 2 Oct . 4.7160493827 2 Nov . 4.8436213992 2 Dec . 4.865569273
Depending on your real data this discussion might provide all the answers you need.
Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 25. Read more here about why you should contribute and what is in it for you!
Learn how use the CAT functions in SAS to join values from multiple variables into a single value.
Find more tutorials on the SAS Users YouTube channel.