I have monthly datasets in SAS Library for customers from Jan 2013 onwards with datasets name as CUST_JAN2013,CUST_FEB2013........CUST_OCT2017. These customers datasets have huge records of 2 million members for each month.This monthly datset has two columns (customer number and customer monthly expenses).
I have one input dataset Cust_Expense with customer number and month as columns. This Cust_Expense table has only 250,000 members and want to pull expense data for each member from SPECIFIC monthly SAS dataset by joining customer number.
Cust_Expense
------------
Customer_Number Month
111 FEB2014
987 APR2017
784 FEB2014
768 APR2017
.....
145 AUG2017
345 AUG2014
I have tried using call execute, but it tries to loop thru each 250,000 records of input dataset (Cust_Expense) and join with corresponding monthly SAS customer tables which takes too much of time.
Is there a way to read input tables (Cust_Expense) by month so that we read all customers for a specific month and then read the same monthly table ONCE to pull all the records from that month, so that it does not loop 250,000 times.
Depending on what you want the result to be, you can create one output per month by filtering on cust_expenses per month and joining with the corresponding monthly dataset
%macro want;
proc sql noprint;
select distinct month
into :months separated by ' '
from cust_expenses
;
quit;
proc sql;
%do i=1 %to %sysfunc(countw(&months));
%let month=%scan(&months,&i,%str( ));
create table want_&month. as
select *
from cust_expense(where=(month="&month.")) t1
inner join cust_&month. t2
on t1.customer_number=t2.customer_number
;
%end;
quit;
%mend;
%want;
Or you could have one output using one join by 'unioning' all those monthly datasets into one and dynamically adding a month column.
%macro want;
proc sql noprint;
select distinct month
into :months separated by ' '
from cust_expenses
;
quit;
proc sql;
create table want as
select *
from cust_expense t1
inner join (
%do i=1 %to %sysfunc(countw(&months));
%let month=%scan(&months,&i,%str( ));
%if &i>1 %then union;
select *, "&month." as month
from cust_&month
%end;
) t2
on t1.customer_number=t2.customer_number
and t1.month=t2.month
;
quit;
%mend;
%want;
In either case, I don't really see the point in joining those monthly datasets with the cust_expense dataset. The latter does not seem to hold any information that isn't already present in the monthly datasets.
Your first, best answer is to get rid of these monthly separate tables and make them into one large table with ID and month as key. Then you can simply join on this and go on your way. Having many separate tables like this where a data element determines what table they're in is never a good idea. Then index on month to make it faster.
If you can't do that, then try creating a view that is all of those tables unioned. It may be faster to do that; SAS might decide to materialize the view but maybe not (but if it's extremely slow, then look in your temp table space to see if that's what's happening).
Third option then is probably to make use of SAS formats. Turn the smaller table into a format, using the CNTLIN option. Then a single large datastep will allow you to perform the join.
data want;
set jan feb mar apr ... ;
where put(id,CUSTEXPF1.) = '1';
run;
That only makes one pass through the 250k table and one pass through the monthly tables, plus the very very fast format lookup which is undoubtedly zero cost in this data step (as the disk i/o will be slower).
I guess you could output your data in specific dataset like this example :
data test;
infile datalines dsd;
input ID : $2. MONTH $3. ;
datalines;
1,JAN
2,JAN
3,JAN
4,FEB
5,FEB
6,MAR
7,MAR
8,MAR
9,MAR
;
run;
data JAN FEB MAR;
set test;
if MONTH = "JAN" then output JAN;
if MONTH = "FEB" then output FEB;
if MONTH = "MAR" then output MAR;
run;
You will avoid to loop through all your ID (250000)
and you will use dataset statement from SAS
At the end you will get 12 DATASET containing the ID related.
If you case, FEB2014 , for example, you will use a substring fonction and the condition in your dataset will become :
...
set test;
...
if SUBSTR(MONTH,1,3)="FEB" then output FEB;
...
Regards
Related
I have 18 separate datasets that contain similar information: patient ID, number of 30-day equivalents, and total day supply of those 30-day equivalents. I've output these from a dataset that contains those 3 variables plus the medication class (VA_CLASS) and the quarter it was captured in (a total of 6 quarters).
Here's how I've created the 18 separate datasets from the snip of the dataset shown above:
%macro rx(class,num);
proc sql;
create table dm_sum&clas._qtr&num as select PatID,
sum(equiv_30) as equiv_30_&class._&num
from dm_qtrs
where va_class = "HS&class" and dm_qtr = &qtr
group by 1;
quit;
%mend;
%rx(500,1);
%rx(500,2);
%rx(500,3);
%rx(500,4);
%rx(500,5);
%rx(500,6);
%rx(501,1);
and so on...
I then need to merge all 18 datasets back together by PatID and what I'd like to do is iteratively add the next dataset created to the previous, as in, add dataset dm_sum_500_qtr3 to a file that already contains the results of dm_sum_500_qtr1 & dm_sum_500_qtr1.
Thanks for looking, Brian
In the macro append the created data set to it an accumulator data set. Be sure to delete it before starting so there is a fresh accumulation. If the process is run at different times (like weekly or monthly) you may want to incorporate a unique index to prevent repeated appendings. If you are stacking all these sums, the create table should also select va_class and dm_qtr
%macro (class, num, stack=perm.allClassNumSums);
proc sql; create table dm_sum&clas._qtr&num as … ;
proc append force base=perm.allClassNumSums data=dm_sum&clas._qtr#
run;
%mend;
proc sql;
drop table perm.allClassNumSums;
%rx(500,1)
%rx(500,2)
%rx(500,3)
%rx(500,4)
%rx(500,5)
…
A better approach might be a single query with an larger where, and leave the class and qtr as categorical variables. Your current approach is moving data (class and qtr) into metadata (column names). Such a transformation makes additional downstream processing more difficult.
Proc TABULATE or REPORT can be use a CLASS statement to assist the creation of output having category based columns. These procedures might even be able to work directly with the original data set and not require a preparatory SQL query.
proc sql;
create table want as
select
PatID, va_class, dm_qtr,
sum(equiv_30) as equiv_30_sum
from dm_qtrs
where catx(':', va_class, dm_sqt) in
(
'HS500:1'
'HS500:2'
'HS500:3'
…
'HS501:1'
)
group by PatID, va_class, dm_qtr;
quit;
I have a dataset where I have several variables with suffixes that correspond to given dates. I want to replace the suffixes with the dates to make my output tables more user friendly.
Here is a sample of my code
the fields in my sales dataset are
product number_of_sales_1 number_of_sales_2 number_of_sales_3 revenue_1 revenue_2 revenue_3 tax_1 tax_2 tax_3
The suffixes 1,2,3 correspond to dates which are held in a second dataset with the following format
dates
id date
1 01Apr
2 01May
3 01Jun
I want to bulk replace the suffixes with the dates so my fields in sales become
product number_of_sales_01Apr number_of_sales_01May number_of_sales_01Jun revenue_01Apr revenue_01May revenue_01Jun tax_01Apr tax_01May tax_01Jun
Both the number of dates and the numberof metrics in sales are dynamic so I can't just hardcode in the the code.
I assume your datasets look like below:
data sales;
product="abc";number_of_sales_1=1;number_of_sales_2=2;number_of_sales_3=3;
revenue_1=1000;revenue_2=2000;revenue_3=3000;tax_1=100;tax_2=200;tax_3=300;
run;
data dates;
id=1;date="01Apr";output;id=2;date="01May";output;id=3;date="01Jun";output;
run;
1st Step - Finding out the dates variables which needs to be renamed
proc contents data=sales out=sales_temp(keep=name) noprint; run;
data sales_temp1;
length check_date_vars $1. id 8.;
set sales_temp;
check_date_vars=compress(substr(name,length(name)));
temp=notdigit(check_date_vars);
if temp=0 then id=check_date_vars;
run;
2nd step - Merging the above dataset with the datset which contains the formats, to create a mapping between old names and new names and creating macro variables out of it
proc sort data=sales_temp1; by id; run;
proc sort data=dates; by id; run;
data sales_temp_date;
merge sales_temp1(in=a) dates(in=b);
by id;
if a and b;
new_name=substr(name,1,length(name)-1)||date;
run;
proc sql noprint;
select count(*) into :num_vars separated by " " from sales_temp_date;
quit;
proc sql noprint;
select name into:old_name1 - :old_name&num_vars. from sales_temp_date;
select new_name into:new_name1 - :new_name&num_vars. from sales_temp_date;
quit;
3rd Step - Renaming the variables
%macro rename();
proc datasets library=work nolist;
modify sales;
rename
%do i=1 %to &num_vars.;
&&old_name&i.= &&new_name&i.
%end;
;
run;
%mend;
%rename;
I have 2 variables and 3 records in a sas data set, and based on the date field in that data set, I need to read different monthly data sets.
For example,
I have
item no. Date
1 30Jun2015
2 31Jul2015
3 31Aug2015
When I read the first record, then based on the date field (30jun2015) here, it should merge another dataset suffixed with 30jun2015 with this current dataset.
How can I achieve that?
So as I'll hazard a guess what you're looking for I've left a bit of a gap where you'll have to specifiy the criteria for your own merge.
1) Read in base data
data MAIN_DATA;
infile cards;
input ITEM_NO DATE:date9.;
format DATE date9.;
cards;
1 30JUN2015
2 31JUL2015
3 31AUG2015
;
run;
2) Store all dates: into macro variables date1 to daten. Assuming ddmmyy6. is a good format for your table names
Data _null_;
Set Main_data;
Call symputx('date'||strip(_n_),put(DATE,ddmmyy6.));
Call symputx('daten', _n_);
Run;
3) Read in the variables and read the associated table - you haven't specified how to do the merge so I'll leave that up to you
%macro readin;
%do i = 1 %to &daten;
data NEW_TABLE_&&date&i..;
set TEST_&&date&i..; /*in this step you can merge on the original table however you intend to*/
run;
%end;
%mend readin;
%readin;
I have created a stored process in SAS that prompts the user to select a month/year combination which looks like this 2015_10. Then from the next box they can click on a calendar and select a startdate which is a timestamp and an enddate also a timestamp.
I would like to combine this into one step, where the user only selects the start and end date. However, my source table is in SQL Server, and the tables are partitioned by months, and the tables are named like this datatabel_2015_10 where the last two digits represent the month. Once the user selects the month, I have proc sql query that table, and then after that there is another query to pull only the rows which fall between the start date and end date, those are time1 and time2 stored as character strings in MS SQL Server which look like this 30JAN2015:19:52:29
How can I code this up so as to eliminate the month/year prompt and only have two selections, namely startdatetime and enddatetime, and still get the query.
Concatenating the monthly tables is not an option because they are huge and runs forever even if I use a pass through query.
Please help.
Thanks
LIBNAME SWAPPLI ODBC ACCESS=READONLY read_lock_type=nolock noprompt="driver=SQL server; server=XXX; Trusted Connection=yes; database=XXX" schema='dbo';
proc sql;
create table a as
select
startdate_time,
enddate_time
from SWAPPLI.SQL_DB_2015_10
quit;
proc sort data=a out=b;
by startdate_time, enddate_time
where enddate_time between "&startdate"dt and "&enddate"dt;
run;
You can delete a step by asking directly the timestamp begin and end.
Then you can deduce the YEAR and MONTH from the timestamp selected.
For &startdate = 30JAN2015:19:52:29, you can create 2 macro-variable by creating 2 substring following this :
%let startdate = 30JAN2015:19:52:29;
%let YEAR=%SUBSTR("&startdate",4,3);
%let MONTH=%SUBSTR("&startdate",7,4);
%PUT YEAR=&YEAR;
%PUT MONTH=&MONTH;
Result :
%PUT YEAR=&YEAR;
YEAR=JAN
%PUT MONTH=&MONTH;
MONTH=2015
Then you can create a %if condition to match JAN with 01, FEB with 02, ect...
Here MONTH will be 01
So you don't need to ask the information 2 times any more.
Then you can select your dataset by doing this :
proc sql;
create table a as
select
startdate_time,
enddate_time
from SWAPPLI.SQL_DB_&YEAR._&MONTH.
quit;
proc sort data=a out=b;
by startdate_time, enddate_time
where enddate_time between "&startdate"dt and "&enddate"dt;
run;
You should probably restrict the selection to a MONTH and not allow the selection through several month like start=01JAN and end=01MAR. Because the selection will be only on the dataset SQL_DB_2017_01 and not taking into account the end=01MAR.
Hi does anyone know how to calculate the standard deviation over the next four quarters for each quarter? Thanks :)
My attempt is below:
date1 is the sas date for the quarter in a year
Proc sql ; create table th.totalroll as
Select distinct permco, date1 ,
(select std(adjret) from th.returns1 where qtr between
intnx('quarter',qtr(date),0) and intnx('quarter', qtr(date),+3)) as
TOTALroll From th.returns1 group by permco ,date1;
QUIT;
It's hard to tell how close you are because I'm not entirely certain what your data looks like, but here's an example assuming you have more than one date in each quarter. Create sample data:
data have;
format date date9.;
do m = 1 to 128;
date = intnx('month','01JAN2008'd,m-1);
amount = round(ranuni(date)*10);
output;
end;
drop m;
run;
Using proc sql, create quarter variable (you might already have this variable?) and group by this variable. Use a having clause to restrict results to the first date of each quarter.
proc sql;
create table want as
select
yyq(year(t1.date),qtr(t1.date)) as quarter format=yyq.,
(select std(t2.amount)
from have t2
where t2.date >= yyq(year(t1.date),qtr(t1.date))
and t2.date < intnx('quarter',yyq(year(t1.date),qtr(t1.date)),4)) as stddev
from
have t1
group by
calculated quarter
having
t1.date = min(t1.date)
;
quit;
You should be able to adapt this to work for your data.
You can use proc expand if your dataset is already in quarterly. So something like this:
proc expand data=th.returns1
out=th.totalroll
from=quarter
to=quarter;
by permco date1;
id date;
convert adjret=TOTALroll / transformout=( MOVSTD 4 );
run;
Don't forget to sort you data first. And MOVSTD gives you backward moving standard deviation. You may need to shift the output stream back by 4 quarters if you want the forward moving STD.
Transformation Operations for proc expand:
http://support.sas.com/documentation/cdl/en/etsug/60372/HTML/default/viewer.htm#etsug_expand_sect026.htm