I have a panel/longitudinal dataset in SAS.
One field indicates a class or type, another a point in time without breaks, another is the observed history and another is the log difference forecast for said history. I'd like to add a new field: the history field, advanced by the forecast field.
So if the time field is in the 'future', I want to recursively advance my goal variable with its own lag, multiplied by the exp of the log-difference forecast variable. A trivial operation it seems to me.
I've attempted to replicate the problem with a toy dataset below.
data in;
input class time hist forecast;
datalines;
1 1 100 .
1 2 . .1
1 3 . .15
1 4 . .17
2 1 100 .
2 2 . .18
2 3 . .12
2 4 . .05
run;
proc sort data=work.in;
by class time;
run;
data out;
set in;
by class time;
retain goal hist;
if time > 1 then goal= lag1(goal) * exp(forecast);
run;
JP:
You might want this:
data out;
set in;
by class time;
retain goal;
if first.class
then goal=hist;
else goal = goal * exp(forecast);
run;
Retaining a non data set variable can mostly be considered a lag1 type of stack. The initial goal needs to be reset at the start of each group.
Your first attempt is conditionally LAG1'ng a retained variable while BY group processing -- makes my head spin. LAG-n is tricky because the implicit LAG stack is updated only when processing flow goes through it. If a conditional bypasses the LAG function invocation there is no way the LAG stack can get updated. If you do see LAG in other SAS coding, it might appear in an unconditional place prior to any ifs.
NOTE: retaining data set variables (such as hist) is atypical because their values are overwritten when the SET statement is reached. The atypical case is when testing the retained data set variable prior to the SET statement has a functional purpose.
Related
I have been trying to replace bunch of data to different format.
For instance, the variable called Week starts from a value 1124 to a value 1175. I want to change this value starting from 1.
That is,
Week Week
1124 1
1125 2
If this was R, I would be using for-loop and store them back to week to replace them, but I am not sure how to formulate something similar in SAS.The only method I got was:
if Week = 1124 then Week = 1;
and so forth.
run;
This is very inefficient as I have to write 30+ times. Are there any efficient method to tackle this issue? In other words, is there something similar to for-loops?
SAS DATA step is an implicit loop -- every row in the data set is processed until there are no more rows. Use simple arithmetic to transform the value of the week variable.
data want;
set have;
week = week - 1123;
run;
I just start learning sas and would like some help with understanding the following chunk of code. The following program computes the annual payroll by department.
proc sort data = company.usa out=work.temp;
by dept;
run;
data company.budget(keep=dept payroll);
set work.temp;
by dept;
if wagecat ='S' then yearly = wagrate *12;
else if wagecat = 'H' then yearly = wagerate *2000;
if first.dept then payroll=0;
payroll+yearly;
if last.dept;
run;
Questions:
What does out = work.temp do in the first line of this code?
I understand the data step created 2 temporary variables for each by variable (first.varibale/last.variable) and the values are either 1 or 0, but what does first.dept and last.dept exactly do here in the code?
Why do we need payroll=0 after first.dept in the second to the last line?
This code takes the data for salaries and calculates the payroll amount for each department for a year, assuming salary is the same for all 12 months and that an hourly worker works 2000 hours.
It creates a copy of the data set which is sorted and stored in the work library. RTM.
From the docs
OUT= SAS-data-set
names the output data set. If SAS-data-set does not exist, then PROC SORT creates it.
CAUTION:
Use care when you use PROC SORT without OUT=.
Without the OUT= option, PROC SORT replaces the original data set with the sorted observations when the procedure executes without errors.
Default Without OUT=, PROC SORT overwrites the original data set.
Tips With in-database sorts, the output data set cannot refer to the input table on the DBMS.
You can use data set options with OUT=.
See SAS Data Set Options: Reference
Example Sorting by the Values of Multiple Variables
First.DEPT is an indicator variable that indicates the first observation of a specific BY group. So when you encounter the first record for a department it is identified. Last.DEPT is the last record for that specific department. It means the next record would the first record for a different department.
It sets PAYROLL to 0 at the first of each record. Since you have if last.dept; that means that only the last record for each department is outputted. This code is not intuitive - it's a manual way to sum the wages for people in each department. The common way would be to use a summary procedure, such as MEANS/SUMMARY but I assume they were trying to avoid having two passes of the data. Though if you're not sorting it may be just as fast anyways.
Again, RTM here. The SAS documentation is quite thorough on these beginner topics.
Here's an alternative method that should generate the exact same results but is more intuitive IMO.
data temp;
set company.usa;
if wagecat='S' then factor=12; *salary in months;
else if wagecat='H' then factor=2000; *salary in hours;
run;
proc means data=temp noprint NWAY;
class dept;
var wagerate;
weight factor;
output out=company.budget sum(wagerate)=payroll;
run;
(first time posting)
I have a data set where I need to create a new variable (in SAS), based on meeting a condition related to another variable. So, the data contains three variables from a survey: Site, IDnumb (person), and Date. There can be multiple responses from different people but at the same site (see person 1 and 3 from site A).
Site IDnumb Date
a 1 6/12
b 2 3/4
c 4 5/1
a 3 .
d 5 .
I want to create a new variable called Complete, but it can't contain duplicates. So, when I go to proc freq, I want site A to be counted once, using the 6/12 Date of the Completed Survey. So basically, if a site is represented twice and contains a Date in one, I want to only count that one and ignore the duplicate site without a date.
N %
Complete 3 75%
Last Month 1 25%
My question may be around the NODUP and NODUPKEY possibilities. If I do a Proc Sort (nodupkey) by Site and Date, would that eliminate obs "a 3 ."?
Any help would be greatly appreciated. Sorry for the jumbled "table", as this is my first post (hints on making that better are also welcomed).
You can do this a number of ways.
First off, you need a complete/not complete binary variable. If you're in the datastep anyway, might as well just do it all there.
proc sort data=yourdata;
by site date descending;
run;
data yourdata_want;
set yourdata;
by site date descending;
if first.site then do;
comp = ifn(date>0,1,0);
output;
end;
run;
proc freq data=yourdata_want;
tables comp;
run;
If you used NODUPKEY, you'd first sort it by SITE DATE DESCENDING, then by SITE with NODUPKEY. That way the latest date is up top. You also could format COMP to have the text labels you list rather than just 1/0.
You can also do it with a format on DATE, so you can skip the data step (still need the sort/sort nodupkey). Format all nonmissing values of DATE to "Complete" and missing value of date to "Last Month", then include the missing option in your proc freq.
Finally, you could do the table in SQL (though getting two rows like that is a bit harder, you have to UNION two queries together).
I'm very new to SAS and I'm trying to figure out some basic things available in other languages.
I have a table
ID Number
-- ------
1 2
2 5
3 6
4 1
I would like to create a new variable where I sum the value of one observation of Number to each other observations, like
Number2 = Number + Number[3]
ID Number Number2
-- ------ ------
1 2 8
2 5 11
3 6 12
4 1 7
How to I get the value of third observation of Number and add this to each observation of Number in a new variable?
There are several ways to do this; here is one using the SAS POINT= option:
data have;
input ID Number;
datalines;
1 2
2 5
3 6
4 1
run;
data want;
retain adder;
drop adder;
if _n_=1 then do;
adder = 3;
set have point=adder;
adder = number;
end;
set have;
number = number + adder;
run;
The RETAIN and DROP statements define a temp variable to hold the value you want to add. RETAIN means the value is not to be re-initialized to missing each time through the data step and DROP means you do not want to include that variable in the output data set.
The POINT= option allows one to read a specific observation from a SAS data set. The _n_=1 part is a control mechanism to only execute that bit of code once, assigning the variable adder to the value of the third observation.
The next section reads the data set one observation at a time and adds applies your change.
Note that the same data set is read twice; a handy SAS feature.
I'll start by suggesting that Base SAS doesn't really work this way, normally; it's not that it can't, but normally you can solve most problems without pointing to a specific row.
So while this answer will solve your explicit problem, it's probably not something useful in a real world scenario; usually in the real world you'd have a match key or some other element other than 'row number' to combine with, and if you did then you could do it much more efficiently. You also likely could rearrange your data structure in a way that made this operation more convenient.
That said, the specific example you give is trivial:
data have;
input ID Number;
datalines;
1 2
2 5
3 6
4 1
;;;;
run;
data want;
set have;
_t = 3;
set have(rename=number=number3 keep=number) point=_t ;
number2=number+number3;
run;
If you have SAS/IML (SAS's matrix language), which is somewhat similar to R, then this is a very different story both in your likelihood to perform this operation and in how you'd do it.
proc iml;
a= {1 2, 2 5, 3 6, 4 1}; *create initial matrix;
b = a[,2] + a[3,2]; *create a new matrix which is the 2nd column of a added
elementwise to the value in the third row second column;
c = a||b; *append new matrix to a - could be done in same step of course;
print b c;
quit;
To do this with the First observation, it's a lot easier.
data want;
set have;
retain _firstpoint; *prevents _firstpoint from being set to missing each iteration;
if _n_ = 1 then _firstpoint=number; *on the first iteration (usually first row) set to number's value;
number = number - _firstpoint; *now subtract that from number to get relative value;
run;
I'll elaborate a little more on this. SAS works on a record-by-record level, where each record is independently processed in the DATA step. (PROCs on the other hand may not behave this way, though many do at some level). SAS, like SQl and similar databases, doesn't truly acknowledge that any row is "first" or "second" or "nth"; however, unlike SQL, it does let you pretend that it is, based on the current sort. The POINT= random access method is one way to go about doing that.
Most of the time, though, you're going to be using something in the data to determine what you want to do rather than some related to the ordering of the data. Here's a way you could do the same thing as the POINT= method, but using the value of ID:
data want;
if n = 1 then set have(where=(ID=3) rename=number=number3);
set have;
number2=number+number3;
run;
That in the first iteration of the data step (_N_=1) takes the row from HAVE where Id=3, and then takes the lines from have in order (really it does this:)
*check to see if _n_=1; it is; so take row id=3;
*take first row (id=1);
*check to see if _n_=1; it is not;
*take second row (id=2);
... continue ...
Variables that are in a SET statement are automatically retained, so NUMBER3 is automatically retained (yay!) and not set to missing between iterations of the data step loop. As long as you don't modify the value, it will stay for each iteration.
While I've read quite a bit about conceptualizing the Program Data Vector when using a SAS data step, I still don't understand how the PDV works when there is by group processing. For example if I have the dataset olddata
GROUP VAL
A 10
A 5
B 20
And I call a datastep on it with a by statement, such as:
data newdata;
set olddata;
by group;
...
run;
then the compiler adds two temporary variables to the PDV: first.group and last.group. When you read any tutorial on the PDV it will tell you that on the first pass of the SET statement, the PDV will look like:
_N_ _ERROR_ FIRST.GROUP LAST.GROUP GROUP VAL
1 0 1 0 A 10
and that LAST.GROUP is zero because observation 1 is not the last observation in group A.
Herein lies my question: How does SAS know that this is not the last observation?
If SAS is processing olddata row-by-row, how is the PDV aware that the next row holds another group A observation instead of a new group? In other words, it seems like SAS must be using information from previous or future rows to update the FIRST and LAST variables, but I'm not sure how. Is there some trick in how the PDV retains values from row to row when the BY statement is called?
SAS actually looks ahead to the next record to see if it should set LAST.(var) or not. I haven't been able to find an article explaining that in any detail, unfortunately. I was a bit disappointed to see that even papers like http://www.wuss.org/proceedings09/09WUSSProceedings/papers/ess/ESS-Li1.pdf just gloss over how LAST is detemined.
SAS also looks ahead to see if the END= variable should be set, when specified, and a few other things. It's not just using metadata to determine those; you can remove or modify records without modifying the metadata, and it will still work - and SQL tables that don't have the usual SAS metadata will still allow you to perform normal BY group processing and such.
The FIRST variable doesn't need a look-behind, of course; it remembers where it was after all.
Edit: I crossposted this to SAS-L, and got the same answer - there doesn't seem to be any documentation of the subject, but it must read ahead. See http://listserv.uga.edu/cgi-bin/wa?A1=ind1303a&L=sas-l#8 for example.
Edit2: From SAS-L, Dan Nordlund linked to a paper that confirms this. http://support.sas.com/resources/papers/proceedings12/222-2012.pdf
The paper's logic that confirms the lookahead - look at the number of observations read from the data set.
DATA DS_Sample1;
Input Sum_Var
Product;
Cards;
100 3
100 2
100 1
;
*With BY statement - reads 3 observations even though it stops after 2.;
DATA DS_Sample2;
Set DS_Sample1;
by Sum_Var;
cnt+1; If CNT > 1 then stop;
Run;
*no BY statement - reads 2 observations as expected;
DATA DS_Sample2;
Set DS_Sample1;
cnt+1; If CNT > 1 then stop;
Run;
* END statement - again, a lookahead;
DATA DS_Sample2;
Set DS_Sample1 end=eof;
cnt+1; If CNT > 1 then stop;
Run;