This is my current issue:
I have 53 variable headers in a SAS data set that need to be changed, for example:
Current_Week_0 TS | Current_Week_1 TS | Current_Week_2 TS -- etc.
I need it to change such that Current_Week_# TS = Current_Week_# -- dropping the TS
Is there a way to automate this such as looping it like:
i = 0,53
Current_week_i TS = Current_Week_i ?
I just don't understand the proper syntax.
Edit: Thank you for editing my formats Sergiu, appreciate it! :)
Edit:
I used the following code, but I get the following error:
Missing numeric suffix on a numbered variable list (TS-Current_Week_53)
DATA True_Start_8;
SET True_Start_7;
ARRAY oldnames (53) Current_Week_1 TS-Current_Week_53 TS;
ARRAY newnames (53) Current_Week_1-Current_Week_53;
DO i = 1 TO 53;
newnames(i) = oldnames(i) ;
END;
RUN;
#Joe EDIT
Here's what the data looks like before and after the "denorm" / transpose
BEFORE
Product ID CurrentWeek Market TS
X 75av2kz Current_Week_0 Z 1
Y 7sav2kz Current_Week_0 Z 1
X 752v2kz Current_Week_1 Z 1
Y 255v2kz Current_Week_1 Z 1
Product ID Market Current_Week_0_TS Current_Week_1_TS
X 75av2kz Z 1 0
Y 7sav2kz Z 1 1
X 752v2kz Z 1 1
Y 255v2kz Z 1 0
This isn't too hard. I assume these are variable labels.
proc sql;
select cats('%relabel_nots(',name,')') into :relabellist separated by ' '
from dictionary.columns
where libname='WORK' and memname='True_Start_7'
and name like '%TS'; *you may need to upper case the dataset name (memname) depending on your OS;
quit;
%macro relabel_nots(name);
label &name.= substr(vlabel(&name.),1,length(vlabel(&name.))-3);
%mend relabel_nots;
data want;
set True_Start_7;
&relabellist.;
run;
Basically the PROC SQL grabs the different names that qualify for the relabelling, and generates a large macro variable with all of the rename macro calls. The relabel_nots macro generates the new labels. You may need to change the logic behind the WHERE in the PROC SQL if the variable names don't also contain the TS.
Another option is to do this in the transpose. Your example data either doesn't match the example desired output, or there is something in logic not explained, but this does the simple transpose; if there is a logical reason that the current_week_0/1 are different in yours than in the below, explain why.
data have;
format currentWeek $20.;
input Product $ ID $ CurrentWeek $ Market $ TS;
datalines;
X 75av2kz Current_Week_0 Z 1
Y 7sav2kz Current_Week_0 Z 1
X 752v2kz Current_Week_1 Z 1
Y 255v2kz Current_Week_1 Z 1
;;;;
run;
proc sort data=have;
by market id product;
run;
proc transpose data=have out=want;
by market id product ;
id currentWeek;
var TS;
run;
Related
I want to assign a new variable from existing highest n variable.
So if we have a table that has increasing number of columns -
data have;
input uid $ var1 $ var2 $ var3 $;
datalines;
1111 1 0 1
2222 1 0 0
3333 0 0 0
4444 1 1 1
5555 0 0 0
6666 1 1 1
;
I want derive the variable var3 as final_code.
data want;
set have;
final_code = max(of var1-var3);
run;
Above doesn't make sense here as I want only var3 column to remain. Similarly, if var4 is there, I wish to have var4 only.
Does somebody want to help me here ?
If I understand you right, you don't want max of the values but the value from the highest-numbered-variable.
Lots of ways to do this, which way depends on how the variables are named. Here's the easiest, if they're actually named as you say.
data want;
set have;
array var[*] var:;
final_code = var[dim(var)];
run;
Here we make an array out of var: and then choose the last element in the array using dim (to say the size of the array).
I think this is what you are looking for is:
%let n=3
data want;
set have;
var&n = max(of var1-var&n);
drop var1-var%eval(&n-1);
run;
The macro variable &n holds the value of n. This acts as a substitution during the compilation phase of the code.
The DROP statement tells the data step to drop those variable.
The %eval() macro function performs integer math on macro values. So we are dropping 1 through N-1.
For this example, i do not mind if it's solved using SAS, or any SQL language
What I want to do, is productionise model equations so that I can calculate a prediction on the fly
I have a lookup, that stores all model equations
For this example we can assume the formula is always intercept + coef1*var1 + coef2*var2 etc
model coef variable
churn 0.8 intercept
churn 0.5 var1
churn -0.2 var2
churn 0.2 var3
I then have a denormalised table of variables for each customer i.e.
customer_id var1 var2 var3
a 3 2 4
b 5 1 2
c 2 4 5
The output I want is the prediction from the formula for each customer
I.e. for customer a:
The output would be the result of 0.8 + 0.5*3 + -0.2*2 + 0.2*4
What is the best way to have this model information stored in a database, and for me to calculations on the fly, Do I need to normalise the customer table so that I can directly join on each variable?
Normalizing the customer data is certainly going to be the easier way if you're doing this in SQL within SAS. If you're doing it in SQL Server, you could probably use pivot to get things in the same direction.
In base SAS, I would do the opposite. Two options: PROC SCORE or a simple data step.
PROC SCORE is really intended for this purpose. Either way you need to PROC TRANSPOSE the normalized model data into one row, if you're doing the data step version make sure to add a prefix using the prefix option (say model_var1 etc.), then do one of the following:
data model;
input model $ coef variable :$10.;
datalines;
churn 0.8 intercept
churn 0.5 var1
churn -0.2 var2
churn 0.2 var3
;;;;
run;
data have;
input customer_id $ var1 var2 var3;
datalines;
a 3 2 4
b 5 1 2
c 2 4 5
;;;;
run;
proc transpose data=model out=model_score;
id variable;
by model;
run;
data model_score_fin;
set model_score;
drop _name_;
rename
model = _model_;
_type_ = 'PARMS';
run;
proc score data=have out=want_score score=model_score_fin type=parms predict nostd;
var var1 var2 var3;
run;
or
proc transpose data=model out=model_t prefix=model_;
id variable;
where model="churn";
run;
data want;
if _n_=1 then set model_t;
set have;
array models model_var:;
array vars var:;
result = model_intercept;
do _i = 1 to dim(vars);
result = sum(result,models[_i]*vars[_i]);
end;
run;
Finally, if you have SAS IML, you can easily do this by transposing (like above or with IML transpose operator) and then use matrix multiplication.
I have 90 variables in the data, I want to do the following in SAS.
Here is my SAS code:
data test;
length id class sex $ 30;
input id $ 1 class $ 4-6 sex $ 8 survial $ 10;
cards;
1 3rd F Y
2 2nd F Y
3 2nd F N
4 1st M N
5 3rd F N
6 2nd M Y
;
run;
data items2;
set test;
length tid 8;
length item $8;
tid = _n_;
item = class;
output;
item = sex;
output;
item = survial;
output;
keep tid item;
run;
What if I have 90 variables to input the data like this? There should be a very long list. I want to simplify it.
You could use an ARRAY or alternately a PROC TRANSPOSE.
The following is untested, because you haven't provided an exxample of your input dataset.
DATA ITEMS;
ARRAY VARS {*} VAR1-VAR90;
SET REPLACE;
DO I = LBOUND(VARS) TO HBOUUND(VARS);
ITEM = VARS{I};
OUTPUT;
END;
RUN;
OR
PROC TRANSPOSE DATA = TEST OUT = WANT;
BY ID;
VAR CLASS -- SURVIAL;
RUN;
In the future it would be best is you could supply your input and desired output.
I don't seem to be able to add another comment to the above answer, as such I am adding one here.
You need to extend the VAR statement to include all variables that you want transposed.
CLASS -- SURVIAL means all variables between CLASS and SURVIVAL inclusive.
Post your code and the error so that I can help you better.
For example, the dataset a is
id x
1 15
2 25
3 35
4 45
I want to add a column y to dataset a, y being the average of x excluding the current id.
so y_1 = (x_2+x_3+x_4)/3 = (25+35+45)/3.
Easiest way to do it without SQL is to add the mean and the n to each row (use PROC MEANS, then merge on the values), and then use math to remove the current value. IE, if x_mean=(15+25+35+45)/4 = 30, and x=15, then
x_mean_others = ((30*4)-15)/(4-1) = 105/3 = 35
Alternateively, in SQL, you can calculate it on the fly with the same idea.
proc sql;
create table want as
select x, (mean(x)*n(x) - x)/(n(x)-1) as y
from have H
;
quit;
This takes advantage of SAS's automatic remerging, in something like SQL Server you'd need a WITH clause to make this work I imagine.
I am trying to create SAS variable names based on data contained within other variables. For example, I could start with
Obs Var1 Var2
1 abc X
2 def X
3 ghi Y
4 jkl X
and I would like to end up with
Obs Var1 Var2 X Y
1 abc X abc
2 def X def
3 ghi Y ghi
4 jkl X jkl
I do have one way of doing this but it requires somewhat ugly macros to first create the variables needed (using a length statement) and then creating a whole series of numbered macro variables (1 per observation) that are later called inside a data step loop. It works but is complicated and I don't think will scale well to the real data, which contain multiple variables for creation per row, and a few thousand rows.
I've also tried something with arrays - saving variables names in a macro var, using it to generate an array statement, and trying to keep track of which array index is needed for each new variable, but it is also complicated.
What would really help would be something analogous to
vvaluex(var2)=var1
except vvaluex can't be on the left-hand side of an equals. Any thoughts or ideas?
PROC TRANSPOSE is a handy way to do the example in the question.
data have;
input Obs Var1 $ Var2 $;
datalines;
1 abc X
2 def X
3 ghi Y
4 jkl X
;;;;
run;
proc transpose data=have out=want;
by obs;
id var2;
var var1;
copy var1 var2;
run;
Another option is probably similar to what you've tried before, using arrays and VNAME:
proc sql;
select var2 into :var2list separated by ' ' from have;
quit;
data want;
set have;
array newvars $ &var2list;
do _t = 1 to dim(newvars);
if vname(newvars[_t]) = Var2 then do;
newvars[_t] = var1;
leave;
end;
end;
run;
PROC TRANSPOSE should be faster and is probably more flexible, but this might work better for some purposes.