Sending the LOG and OUTPUT from PC SAS to a seperate file

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 procedure 

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Random Sample Selection

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 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83

;

run;

*Sort the records by increasing shuffling order;

proc sort data=randsamp; by random; run;

*CREATE MACRO VARIABLE FOR 9 (10% of 83 Subjects) RANDOM PATIENTS LIST;

proc sql;

select distinct(patno) into:random separated by "," from randsamp where monotonic() le 9;

quit;

%put &random;

Proc SQL Approach: 
In this approach also, the observations are shuffled using the RANUNI function which assigns a random number to each observation.

*RANDOMLY SELECTING 10% OF A LARGE DATASET using Proc SQl and RANUNI function.;

*The following Proc SQL code will create a table called rands consisting of approximately 10% of the records randomly selected from dataset randsamp;

DATA randsamp ;

input patno @@;

cards;

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83

;

run;

proc sql;

create table rands(where= (monotonic() eq 9)) as

select *, RANUNI(-1) as random from randsamp order by random ;

quit;

*Proc Surveyselect Approach;

*The following Proc Survey select code will create a table called sample consisting of approximately 10% of the records randomly selected from dataset randsamp;

DATA randsamp;

input patno @@;

cards;

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83

;

run;

PROC SURVEYSELECT DATA=randsamp2 OUT=sample SAMPSIZE=9 SEED=-1;

RUN;

Important Note:
The value generated by the RANUNI function depends on a seed. The seed should be a non-negative integer from 1 to 2,147,483,646 in order to replicate the results of the RANUNI function. That is, given the same seed, the function produces the same result. If no seed, zero, or negative integers are specified as the seed, the computer clock sets the seed and results are not replicable.
Source: SESUG-2000 (P-404).pdf on Random Sample Selection by Imelda C. Go, Richland County School District One, Columbia, SC

If you use a positive number as a seed then you can replicate the random sample records as long as you don’t change the seed number. If you use the negative number (as in the above programs) you can’t replicate the records. Every time you submit the program random samples generated will be different.
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WARNING: You may have unbalanced quotation marks.

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 (NOQUOTELENMAX) which will take care of the WARNING message.

Options noQuoteLenMax; *Just before the Data step;

Don’t forget to change back to Options QuoteLenMax; after the end of the Data step.

Options noQuoteLenMax;

CALL EXECUTE: Easy way to print or sort multiple files.

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.||'trim(memname)||';var _all_; run;");
run;

*Using Proc Contents and Call Execute;
proc contents data=libref._all_ out=contents(keep=memname);
run;

*Create a macro variable memname with list of all the datasets; 
proc sql;
select distinct memname into:memname from contents;
quit;
%put memname;

*Sorts all the datasets using Call Execute;
data _null_;
set dsn;
call execute ("proc sort data=libref.||'trim(memname)||';by usubjid; run;");
run;

*Prints all the datasets using Call Execute;
data _null_;
set dsn;
call execute ("proc print data=libref.||'trim(memname)||';var _all_; run;");
run;

*Using SASHELP Views and Call Execute to sort the dataset by usubjid;

*Sorts all the datasets using Call Execute;
data _null_;
set sashelp.vtable (where=(libname="LIBREF"));
call execute("proc sort data=libref.||'trim(memname)||';by usubjid;run;");
run;

*Prints all the datasets using Call Execute;
data _null_;
set sashelp.vtable (where=(libname="LIBREF"));
call execute("proc print data=libref.||'trim(memname)||';by _all_;run;");
run;

*If you are not printing/sorting all the datasets in the library here is code for that.The Following code only prints 4 datasets (AE, Conmed, Demog , Lab and Medhist);

data _null_;
do dsname='ae', 'conmed', 'demog', 'lab', 'medhist';
call execute("Proc print data=libref.||'trim(dsname))||'; var _all_; run;");
end;
run;

Write a Letter using SAS/ Emailing with SAS

SAS can do many things which most of us don’t have a clue. Here is one example….

Writing a letter:

filename formltr 'C:\Documents and Settings\sreddy\Desktop\formltr.rtf';

data address;
infile datalines;
input @ 1 stno
 @ 6 lane $12.
@19 aptno $7.
@27 city $9.
@37 state $2.
@40 zip ;

datalines;
2550 Augusta Blvd Apt#203 Fairfield OH 45014
;
run;

data _null_;
retain lm 5;
set address;
file formltr;* print notitles;
put _page_;
adr1 = trim(stno) ' ' trim(lane);
put @lm adr1;
adr2 = trim(aptno);
put @lm adr2;
adr3 = trim(city) ||', '|| trim(state) ||' '|| trim(zip);
put @lm adr3;
adr4 = trim('Dear')|| ' ' ||trim('SAS') || ' ' || trim('Users,');
put / @lm adr4;
put / @lm 'StudySAS Blog offers a lot of information regarding tips and tutorials on various topics ' ;
put @lm 'in SAS. It covers basics to get started to more in-depth topics like Macros and Proc SQL.';
put @lm 'It is a great site to browse to help broaden and deepen your SAS knowledge in a variety';
put @lm 'of areas.';
put / @lm 'Thanks for visiting StudySAS Blog. ';
put //// @lm 'Sarath Annapareddy';
run;

  

• lm: represents left margin
• / : forward slash symbol ( / ) skips a line.
• If you want to skip ‘N’ number of lines use ‘N’ number of flashes after the PUT statement.
• The trim function and concatenation operator (||) are important here because without these you will get extra spaces which we probably don't want see in our letter.

The Above SAS program will create a rtf in the specified location with the following information.

Emailing with SAS




How to send an email using SAS:

filename mymail email sastest@abc.com subject="Sending Email using SAS" from=abctest@gmail.com' attach="C:\Documents and Settings\sreddy\Desktop\formltr.rtf";


data _null_;
file mymail;
put 'Hello there, Please review the attached letter.';
put 'Thanks,';
put 'Sarath';
run;
quit;

Special Missing Values in SAS

Definition: Special missing value is a type of numeric missing value that enables you to represent different categories of missing data by using the letters A-Z or an underscore.

Ref: SAS 9.1.3 language reference: concepts page no: 102

The symbol usually used to represent a missing value for a numerical variable is the period or dot. Aside from the dot, there are 27 special missing values SAS can store in numerical variables. They are the dot-underscore (._), and dot-letter (.A thru .Z). Note that these special values are case insensitive. That is, .A=.a .B=.b .C=.c etc.

If you do not begin a special numeric missing value with a period, SAS identifies it as a variable name. Therefore, to use a special numeric missing value in a SAS expression or assignment statement, you must begin the value with a period, followed by the letter or underscore, as in the following example:

x=.d;

When SAS prints a special missing value, it prints only the letter. When data values contain characters in numeric fields that you want SAS to interpret as special missing values, use the MISSING statement to specify those characters.

Example:  Consider the following data step which contains a questionnaire data (three students, three questions, and three possible responses to each question 1, 2 and 3):

data test;

/* M = multiple, U = unreadable, .=Didn’t answer */

missing answer M U;

input student question answer;
datalines;

1 1 1
1 2 2

1 3 M

2 1 U

2 2 3

2 3 2

3 1 M

3 2 .

3 3 1

;

Proc print data=test; run;

The MISSING statement is needed here to keep special missing values for the numeric variable answer. In the above example, M is used to indicate multiple responses (not allowed) and U is used to indicate an unreadable response.

Order of Missing Values for Numeric Variables:

The numeric missing value (.) is sorted before the special numeric missing value .A, and both are sorted before the special missing value .Z. SAS does not distinguish between lowercase and uppercase letters when sorting special numeric missing values.

Checking for Missing Numeric Values:

Often the SAS programmer uses the following SAS code to check for a missing numeric value:

IF VALUE=. THEN PUT "*** Value is missing";

While in most instances the above code works as intended, there are occasions where it may not catch some missing values. The above statement assumes that only a dot is present, and none of the other 27 missing numeric values, are present in your data. In exhibit 1, it was shown that the dot-Z is the highest missing value. So, a better, more inclusive way to check for a missing numeric values is:

IF VALUE <=.Z THEN PUT "*** Value is missing";

Reference: http://analytics.ncsu.edu/sesug/2005/TU06_05.PDF

The latter IF statement checks for all 28 possible missing values.

For more details on Special Missing Values Please refer, Malachy J. Foley paper … MISSING VALUES: Everything You Ever Wanted to Know

The other thing you should know is... If the MISSING option is used in PROC FREQ, you'll get a breakdown for each type of missing value. For example, given (without MISSING):


*Without MISSING option:
proc freq data=test;
tables question*answer/ nopercent nocol norow;
run;

Output:

*With MISSING option:


proc freq data=test;
tables question*answer/ nopercent nocol norow missing;
run;

output;

How to create a macro variable containing a list of variables in a DATA set

Sometimes it is very handy to have a macro variable contanining the variables names of the dataset. Here are the 2 different ways you can create a macro variable with list of variables names ...

*Method1: Using Proc Contents and Proc SQL;



proc contents data=sashelp.class out=class;
run;

proc sql noprint;
select distinct(name) into:vars separated by " " from class;
quit;


%put &vars;


*Method2: Using SASHELP tables and Proc SQL;


data class;
set sashelp.vcolumn(where=(libname="SASHELP" and memname="CLASS"));
keep name;
run;


proc sql noprint;
select distinct(name) into:vars separated by " " from class;
quit;

%put &vars;