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.
Related
In my current table I have two columns. One represents the number of new cases on a given day. The other represents the number of days.
I want to create new variables representing individuals so I'd end up up with a new table.
For example:
On day 0, there was 1 patient infected so patient 1 is created. On day 1, 3 people are infected so there are 3 new patients created. I'm at a loss on even how to begin. I'm open to doing this in python if it isn't possible in SAS.
Just use an OUTPUT statement in a loop.
data want;
set have;
do i=1 to new_cases;
patient+1;
output;
end;
run;
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.
I'm trying to make a data step that creates a column in my table that has the sum of ten, fifteen, twenty and fortyfive lagged variables. What I have below works, but it is not practicle to write this code for the twenty and fortyfive summed lags. I'm new to SAS and can't find a good way to write the code. Any help would be greatly appreciated.
Here's what I have:
data averages;
set work.cuts;
sum_lag_ten = (lag10(col) + lag9(col) + lag8(col) + lag7(col) + lag6(col) + lag5(col) + lag4(col) + lag3(col) + lag2(col) + lag1(col));
run;
Proc EXPAND allows the easy calculation for moving statistics.
Technically it requires a time component, but if you don't have one you can make one up, just make sure it's consecutive. A row number would work.
Given this, I'm not sure it's less code, but it's easier to read and type. And if you're calculating for multiple variables it's much more scalable.
Transformout specifies the transformation, In this case a moving sum with a window of 10 periods. Trimleft/right can be used to ensure that only records with a full 10 days are included.
You may need to tweak these depending on what exactly you want. The third example under PROC EXPAND has examples.
Data have;
Set have;
RowNum = _n_;
Run;
Proc EXPAND data=have out=want;
ID rownum;
Convert col=col_lag10 / transformout=(MOVSUM 10 trimleft 9);
Run;
Documentation(SAS/STAT 14.1)
http://support.sas.com/documentation/cdl/en/etsug/68148/HTML/default/viewer.htm#etsug_expand_examples04.htm
If you must do this in the datastep (and if you do things like this regularly, SAS/ETS has better tools for sure), I would do it like this.
data want;
set sashelp.steel;
array lags[20];
retain lags1-lags20;
*move everything up one;
do _i = dim(lags) to 2 by -1;
lags[_i] = lags[_i-1];
end;
*assign the current record value;
lags[1] = steel;
*now calculate sums;
*if you want only earlier records and NOT this record, then use lags2-lags11, or do the sum before the move everything up one step;
lag_sum_10 = sum(of lags1-lags10);
lag_sum_15 = sum(of lags1-lags15); *etc.;
run;
Note - this is not the best solution (I think a hash table is better), but this is better for a more intermediate level programmer as it uses data step variables.
I don't use a temporary array because you need to use variable shortcuts to do the sum; with temporary array you don't get that, unfortunately (so no way to sum just 1-10, you have to sum [*] only).
Problem
I'm not a very experienced SAS user, but unfortunately the lab where I can access data is restricted to SAS. Also, I don't currently have access to the data since it is only available in the lab, so I've created simulated data for testing.
I need to create a macro that gets the values and dimensions from a PROC MEANS table and performs some tests that check whether or not the top two values from the data make up 90% of the results.
As an example, assume I have panel data that lists firms revenue, costs, and profits. I've created a table that lists n, sum, mean, median, and std. Now I need to check whether or not the top two firms make up 90% of the results and if so, flag if it's profit, revenue, or costs that makes up 90%.
I'm not sure how to get started
Here are the steps :
Read the data
Read the PROC MEAN table created, get dimensions, and variables.
Get top two firms in each variable and perform check
Create new table that lists variable, value from read table, largest and second largest, and flag.
Then print table
Simulated data :
https://www.dropbox.com/s/ypmri8s6i8irn8a/dataset.csv?dl=0
PROC MEANS Table
proc import datafile="/folders/myfolders/dataset.csv"
out=dt
dbms=csv
replace;
getnames=yes;
run;
TITLE "Macro Project Sample";
PROC MEANS n sum mean median std;
VAR V1 V2 V3;
RUN;
Desired Results :
Value Largest Sec. Largest Flag
V1 463138.09 9888.09 9847.13
V2 148.92 1.99 1.99
V3 11503375 9999900 1000000 Y
At the moment I can't open your simulated dataset but I can give you some advices, hope they will help.
You can add the n extreme values of given variables using the 'output out=' statement with the option IDGROUP.
Here an example using charity dataset ( run this to create it http://support.sas.com/documentation/cdl/en/proc/65145/HTML/default/viewer.htm#p1oii7oi6k9gfxn19hxiiszb70ms.htm)
proc means data=Charity;
var MoneyRaised HoursVolunteered;
output out=try sum=
IDGROUP ( MAX (Moneyraised HoursVolunteered) OUT[2] (moneyraised hoursvolunteered)=max1 max2);
run;
data var1 (keep=name1 _freq_ moneyraised max1_1 max1_2 rename=(moneyraised=value max1_1=largest max1_2=seclargest name1=name))
var2 (keep=name2 _freq_ HoursVolunteered max2_1 max2_2 rename=(HoursVolunteered=value max2_1=largest max2_2=seclargest name2=name));
length name1 name2 $4;
set try ;
name1='VAR1';
name2='VAR2';
run;
data finalmerge;
length flag $1;
set var1 var2;
if largest+seclargest > value*0.9 then flag='Y';
run;
in the proc means I choose to variables moneyraised and hoursvolunteered, you will choose your var1 var2 var3 and make your changes in all the program.
The IDgroup will output the max value for both variables, as you see in the parentheses, but with out[2], obviously largest and second largest.
You must rename them, I choose to rename max1 and max 2, then sas will add an _1 and _2 to the first and the second max values automatically.
All the output will be on the same line, so I do a datastep referencing 2 datasets in output (data var1 var2) keeping the variables needed and renaming them for the next merge, I also choose a naming system as you see.
Finally I'll merge the 2 datasets created and add the flag.
Here are some initial steps and pointers in a non macro approach which restructures the data in such a manner that no array processing is required. This approach should be good for teaching you a bit about manipulating data in SAS but will not be as fast a single pass approach (like the macros you originally posted) as it transposes and sorts the data.
First create some nice looking dummy data.
/* Create some dummy data with three variables to assess */
data have;
do firm = 1 to 3;
revenue = rand("uniform");
costs = rand("uniform");
profits = rand("uniform");
output;
end;
run;
Transpose the data so all the values are in one column (with the variable names in another).
/* Move from wide to deep table */
proc transpose
data = have
out = trans
name = Variable;
by firm;
var revenue costs profits;
run;
Sort the data so each variable is in a contiguous group of rows and the highest values are at the end of each Variable group.
/* Sort by Variable and then value
so the biggest values are at the end of each Variable group */
proc sort data = trans;
by Variable COL1;
run;
Because of the structure of this data, you could go down through each observation in turn, creating a running total, which when you get to the final observation in a Variable group would be the Variable total. In this observation you also have the largest value (the second largest was in the previous observation).
At this point you can create a data step that:
Is aware when it is in the first and last values of each variable group
by statement to make the data step aware of your groups
first.Variable temporary variable so you can initialise your total variable to 0
last.Variable temporary variable so you can output only the last line of each group
Sums up the values in each group
retain statement so SAS doesn't empty your total with each new observation
sum() function or + operator to create your total
Creates and populates new variables for the largest and second largest values in each group
lag() function or retain statement to keep the previous value (the second largest)
Creates your flag
Outputs your new variables at the end of each group
output statement to request an observation be stored
keep statement to select which variables you want
The macros you posted originally looked like they were meant to perform the analysis you are describing but with some extras (only positive values contributed to the Total, an arbitrary number of values could be included rather than just the top 2, the total was multiplied by another variable k1198, negative values where caught in the second largest, extra flags and values were calculated).
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;