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If you want to view SAS dataset in SPSS you can use GET SAS command of SPSS. Here is the syntax; get sas data= 'C:\data\class.sas7bdat' . For conversion of SAS to SPSS we need to see if any formats assigned to variables in the dataset or not. If there are no formats then we just follow following steps to convert SAS dataset to SPSS. **STEP1: Creating .xpt file of a SAS dataset using Proc COPY.** libname SAS 'c:\sas\data\ '; libname SPSS xport 'c:\sas\data\class.xpt' ; proc copy in=sas out =spss; select class; run ; **STEP2: Use SPSS command to convert the transport format SAS file to SPSS;** You should use following commands to convert transport format file to SPSS data. get sas data='c:\sas\data\class.xpt'. execute. *******************************************************************************************; If there are formats then we need to convert the formats catalog to a SAS data set before converting the SAS dataset...

/* Sample data set */ data missing; input n1 n2 n3 n4 n5 n6 n7 n8 c1 $ c2 $ c3 $ c4 $; datalines ; 1 . 1 . 1 . 1 4 a . c . 1 1 . . 2 . . 5 e . g h 1 . 1 . 3 . . 6 . . k i 1 . . . . . . . . . . . 1 . . . . . . . c . . . . . . . . . . . . . . . ; run; *If you want to delete observation  if the data for every variable is missing then use the following code; *Approach 1: Using the coalesce option inside the datastep; data drop_misobs; set missing; if missing(coalesce(of _numeric_)) and missing(coalesce(of _character_)) then delete ; run ;   Pros: *Simple code Cons; *This code doesn't work if we want to delete observation based on specific variables and not all of them. *Approach 2:Using N/NMISS option inside the datastep; data drop_missing; set missing; *Checks the Non missing values using ; if n(n1, n2, n3, n4, n5, n6, n7, n8, c1, c2, c3, c4)=0 then delete ; run; data drop_missing; set missing; *Checks the missing values us...

Folkes,  Here is the answer from Andrew Karp..... Direct link ****************************************************************************************************; Allow me to weigh in on this topic. It comes up alot when I give SAS training classes. First, RUN and QUIT are both " explicit step boundaries " in the SAS Programming Language. PROC and DATA are "implied step boundaries." Example 1: Two explicit step boundaries. DATA NEW; SET OLD: C = A + B; RUN ; PROC PRINT DATA=NEW; RUN ; In this example, both the data and the proc steps are explicitly "ended" by their respective RUN statements. Example 2: No explicit step boundaries. DATA NEW; SET OLD; C = A + B; PROC PRINT DATA=NEW; In this example, the data step is implicitly terminated by the PROC statement. But, there is no step boundary for the PROC PRINT step/task, so it will not terminate unless/until the SAS supervisor "receives" a step boundary. Some PROCS support what is c...

Here is how to direct the SAS LOG file and or SAS Output  to a seperate file. Approach 1: Using Display Manager Statements; filename log ' C:\temp\logfile.log '; filename out ' C:\temp\output.lst '; *Select only male students and age less than 16; proc sql; create table males as select age, height, weight from sashelp.class where sex=' M ' and age lt 16  order by age; quit; *Get the descriptive statistics for height variable by age; proc means data =males ; by age; var height; output out =htstats mean =mean n =n std =sd median =med min =min max =max; run; DM ' OUT;FILE OUT REP; '; DM ' LOG;FILE LOG REP; '; Information about Display Manager Commands: DEXPORT and DIMPORT: DISPLAY MANAGER commands used to IMPORT and EXPORT the Tab delimited (Excel and .CSV) files; SAS Display Manager Commands Approach 2: Using Proc PRINTTO procedure; Refer:  How to save the log file or what is PROC PRINTTO procedur...

Last week my manager asked me to randomly pick 10%observations from a large data set and then create a listing so that the Data management programmers can QC the data. I want to share some thoughts here … how easy and simple to do random sampling. Approach 1: Data step Approach: In this approach, the observations are shuffled using the RANUNI function which assigns a random number to each observation. Step1 : Generating the Random Vector (shuffling) using the RANUNI function; The RANUNI function generates a random number from a continuous uniform distribution (the interval (0, 1). Step2 : After assigning a random number to each record, the records can then be sorted in ascending or descending order of the random numbers.; data randsamp ; input patno @@; random= RANUNI ( -1 ); * RANUNI function to assign a random number to each record.; * Here the seed is negative integer (-1) so the results are not replicable.; cards; 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 2...

SAS can allow the strings up to 32,767 characters long but some times SAS will write a Warning message ‘ WARNING: The quoted string currently being processed has become more than 262 characters long. You may have unbalanced quotation marks. ’ , when you try to keep a character string longer than 262 characters to a variable.  It is hard to look back at the SAS code to search for unbalanced quotes. To make it more clearly I am going to show an example. I want to add a 263 characters long name to a variable (longvar) and to do that I will simply use a data step… and when I do that I will see the WARNING message in Log. data TEST; x=" (SEE DOCTOR'S LETTER)3RD ADMINISTRATION OF MTX WAS DELAYED BY 14 DAYS AND WAS REDUCED TO 1G/M2 INSTEAD OF 5G/M2, PROBLEMS, E.COLI SEPSIS WITH HEART INSUFFICIENCY WITH SINUS TACHYCARDY, PARALYTIC ILEUS, TACHYPNEA , PATIENT DIED ON 21.04.98 FROM MULTIORGAN FAILURE. "; y=length(x); put x; run ; LOG FILE: There is a SAS option (NOQUO...

When printing multiple files, or sorting multiple datasets, the traditional method is to write multiple steps as below. Proc print data =libref.ae; var _all_; run; Proc print data =libref.conmed; var _all_; run; Proc print data =libref.demog; var _all_; run; Proc print data =libref.lab; var _all_; run; Proc print data =libref.medhist; var _all_; run; If you are like me who likes to simplify the traditional SAS code here is the tip. CALL EXECUTE comes to rescue here. *Using Disctionary Tables and Call Execute; proc sql ; create table dsn as select distinct memname from dictionary.tables where libname=" LIBREF " and memtype=" DATA "; quit ; *Sorts all the datasets using Call Execute; data _null_ ; set dsn; call execute (" proc sort data=final .||'memname||'; by usubjid; run ;"); run ; *Prints all the datasets using Call Execute; data _null_ ; set dsn; call execute (" proc print data=final .||...