SAS proc sql return duplicate group values ​​by / by variables

I have a fairly simple SQL that is supposed to provide 1 row per quarter for each asset1. Instead, I get multiple lines in each group.

Below is the SQL, SAS data step and some output. The number of duplicate rows (in the data below, 227708) is Num_borrowers, which is resource number 1.

proc sql outobs=max;

create table table1 as
select 
    case 
        when period_dt ='01DEC2003'd then '2003Q4'
        when period_dt ='01DEC2004'd then '2004Q4'
        when period_dt ='01DEC2005'd then '2005Q4'
        when period_dt ='01DEC2006'd then '2006Q4'
        when period_dt ='01DEC2007'd then '2007Q4' 
        when period_dt ='01DEC2008'd then '2008Q4'
        when period_dt ='01DEC2009'd then '2009Q4'
        when period_dt ='01DEC2010'd then '2010Q4'
        when period_dt ='01DEC2011'd then '2011Q4'
        when period_dt ='01DEC2012'd then '2012Q4'
        when period_dt ='01DEC2013'd then '2013Q4'
        when period_dt ='01JUN2014'd then '2014Q2'   
    end as QTR,
    case 
        when MM_ASSET in ('C&I', 'Foreign', 'Leasing','Scored-WF','Scored-WB')  THEN 'C&I'
        when MM_ASSET='Construction' THEN 'Construction RE'
        when MM_ASSET='Mortgage-IP' THEN 'Income Producing RE'
        when MM_ASSET='Mortgage-OO' THEN 'Owner Occupied RE'
        when MM_ASSET='Mortgage-SF' THEN 'Mortgage-SF'
        when MM_ASSET='Unknown' THEN 'Other'
    end as asset1,
    count (period_dt) as Num_Borrowers, 
    exposure,
    co_itd,
    MM_NINEQTR_LOSS,
    MM_LIFE_LOSS
  from td_prod.OBLIGOR_COMBINED
  where period_dt in ('01DEC2003'd,'01DEC2004'd,'01DEC2005'd,'01DEC2006'd,'01DEC2007'd,'01DEC2008'd, '01DEC2009'd,'01DEC2010'd,'01DEC2011'd,'01DEC2012'd,'01DEC2013'd,'01JUN2014'd)
  and mm_asset in ('C&I','Foreign','Leasing','Construction','Mortgage-IP','Scored-WF','Scored-WB'
               'Mortgage-OO','Mortgage-SF','Unknown')
  group by 1,2
  order by 1,2;

quit;



data table2; set table1;

  Total_Exposure = exposure/1000000;
  if total_exposure = 0 then total_exposure=.;
  Total_Charge_Offs =co_itd/1000000;
  Total_9Q_Losses = MM_NINEQTR_LOSS/1000000;
  Total_Life_Losses = MM_LIFE_LOSS/1000000;
  avg_borrower_exp = total_exposure/num_borrowers;
  co_rate = total_charge_offs/total_exposure;
  life_lossR = Total_life_losses/total_exposure;
  nineQtr_lossR = total_9q_losses/total_exposure;

run;



*** sample of output data set ***;
qtr             asset1      num_borrowers
2003Q4          C&I             227708
2003Q4          C&I             227708
2003Q4          C&I             227708
2003Q4          C&I             227708
2003Q4          C&I             227708
2003Q4          C&I             227708
2003Q4          C&I             227708
2003Q4          C&I             227708
2003Q4          C&I             227708
2003Q4          C&I             227708
2003Q4          C&I             227708
2003Q4          C&I             227708
2003Q4          C&I             227708
2003Q4          C&I             227708
2003Q4          C&I             227708
2003Q4          C&I             227708

      

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1 answer


Implemented my comment above is more like an answer.

In SAS SQL, in a query with a group by clause that includes extraneous columns in the select clause (that is, columns that are not part of a group, rather than derived from an aggregate function), SAS "rolls" the summary statistics back to the original data ( with a note on this). Most SQL queries just throw an error. Below is an example:

data have;
  input gender $ age score;
  cards;
M 10 100
M 20 200
F 30 300
F 40 400
;
run;

proc sql;
  select gender, mean(age) as AvgAge, SCore
    from have
    group by gender
  ;
quit;

      

returns:

 gender      AvgAge     score
 F               35       300
 F               35       400
 M               15       100
 M               15       200

      



In your excerpt code, co_itd, MM_NINEQTR_LOSS and MM_LIFE_LOSS are all extraneous columns causing SAS to crash.

Whenever an overflow occurs, you will see the following message in the SAS log:

NOTE. The request requires combining the summary of the statistician with the original data.

More on the data section of the SAS documentation on the final function

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