sas programming

10月 172017
 

Would you like to format your macro variables in SAS? Good news. It's easy!  Just use the %FORMAT function, like this: %let x=1111; Log %put %format(&x,dollar11.); $1,111 %put %format(&x,roman.); MCXI %put %format(&x,worddate.); January 16, 1963   %let today=%sysfunc(today()); %put %format(&today,worddate.); October 13, 2017   %put %format(Macro,$3.); Mac What?!  You never [...]

The post How to format a macro variable appeared first on SAS Learning Post.

10月 062017
 

Sometimes life is just too busy, and I bet many of you feel the same way. If you’re like me, you’re playing a number of roles: employee, spouse, parent, community leader, coach and so on. With all the craziness, it’s likely we’re shortchanging another role that’s critically important to our [...]

The post Learning in the middle of the night - or whenever appeared first on SAS Learning Post.

9月 272017
 

In a large simulation study, it can be convenient to have a "control file" that contains the parameters for the study. My recent article about how to simulate multivariate normal clusters demonstrates a simple example of this technique. The simulation in that article uses an input data set that contains the parameters (mean, standard deviations, and correlations) for the simulation. A SAS procedure (PROC SIMNORMAL) simulates data based on the parameters in the input data set.

This is a powerful paradigm. Instead of hard-coding the parameters in the program (or as macro variables), the parameters are stored in a data set that is processed by the program. This is sometimes called data-driven programming. (Some people call it dynamic programming, but there is an optimization technique of the same name so I will use the term "data-driven.") In a data-driven program, when you want to run the program with new parameters, you merely modify the data set that contains the control parameters.

I have previously written about a different way to control a batch program by passing in parameters on the command line when you invoke the SAS program.

Static programming and hard-coded parameters

Before looking at data-driven programming, let's review the static approach. I will simulate clusters of univariate normal data as an example.

Suppose that you want to simulate normal data for three different groups. Each group has its own sample size (N), mean, and standard deviation. In my book Simulating Data with SAS (p. 206), I show how to simulate this sort of ANOVA design by using arrays, as follows.

/* Static simulation: Parameters embedded in the simulation program */
data AnovaStatic;
/* define parameters for three simulated group */
array N[3]       _temporary_ (50,   50,   50);   /* sample sizes */
array Mean[3]    _temporary_ (14.6, 42.6, 55.5); /* center for each group */
array StdDev[3]  _temporary_ ( 1.7,  4.7,  5.5); /* spread for each group */
 
call streaminit(12345);
do k = 1 to dim(N);              /* for each group */
   do i = 1 to N[k];             /* simulate N[k] observations */
      x = rand("Normal", Mean[k], StdDev[k]); /* from k_th normal distribution */
      output;
   end;
end;
run;

The DATA step contains two loops, one for the groups and the other for the observations within each group. The parameters for each group are stored in arrays. Notice that if you want to change the parameters (including the number of groups), you need to edit the program. I call this method "static programming" because the behavior of the program is determined at the time that the program is written. This is a perfectly acceptable method for most applications. It has the advantage that you know exactly what the program will do by looking at the program.

Data-driven programming: Put parameters in a file

An alternative is to put the parameters for each group into a file or data set. If the k_th row in the data set contains the parameters for the k_th group, then the implicit loop in the DATA step will iterate over all groups, regardless of the number of groups. The following DATA step creates the parameters for three groups, which are read and processed by the second DATA step. The parameter values are the same as for the static example, but are transposed and processed row-by-row instead of via arrays:

/* Data-driven simulation: Parameters in a data set, processed by the simulation program */
data params;                     /* define parameters for each simulated group */
input N Mean StdDev;
datalines; 
50 14.6 1.7
50 42.6 4.7
50 55.5 5.5
;
 
data AnovaDynamic;
call streaminit(12345);
set params;                      /* implicit loop over groups k=1,2,... */
do i = 1 to N;                   /* simulate N[k] observations */
   x = rand("Normal", Mean, StdDev); /* from k_th normal distribution */
   output;
end;
run;

Notice the difference between the static and dynamic techniques. The static technique simulates data from three groups whose parameters are specified in temporary arrays. The dynamic technique simulates data from an arbitrary number of groups. Currently, the PARAMS data specifies three groups, but if I change the PARAMS data set to represent 10 or 1000 groups, the AnovaDynamic DATA step will simulate data from the new design without any modification.

Generate the parameters from real data

The data-driven technique is useful when the parameters are themselves the results of an analysis. For example, a common simulation technique is to generate the moments of real data (mean, variance, skewness, and so forth) and to use those statistics in place of the population parameters that they estimate. (See Chapter 16, "Moment Matching," in Simulating Statistics with SAS.)

The following call to PROC MEANS generates the sample mean and standard deviation for real data and writes those values to a data set:

proc means data=sashelp.iris N Mean StdDev stackods;
   class Species;
   var PetalLength;
   ods output Summary=params;
run;

The output data set from PROC MEANS creates a PARAMS data set that contains the variables (N, MEAN, and STDDEV) that are read by the simulation program. Therefore, you can immediately run the AnovaDynamic DATA step to simulate normal data from the sample statistics. A visualization of the resulting simulated data is shown below.

You can run PROC MEANS on other data and other variables and the AnovaDynamic step will continue to work without any modification. The simulation is controlled entirely by the values in the "control file," which is the PARAMS data set.

You can generalize this technique by wrapping the program in a SAS macro in which the name of the parameter file and the name of the simulated data set are provided at run time. With a macro implementation, you can read from multiple input files and write to multiple output data sets. You could use such a macro, for example, to break up a large simulation study into smaller independent sub-simulations, each controlled by its own file of input parameters. In a gridded environment, each sub-simulation can be processed independently and in parallel, thus reducing the total time required to complete the study.

Although this article discusses control files in the context of statistical simulation, other applications are possible. Have you used a similar technique to control a program by using an input file that contains the parameters for the program? Leave a comment.

The post Data-driven simulation appeared first on The DO Loop.

9月 162017
 

Western Users of SAS Software 2017 logoThe Western Users of SAS Software 2017 conference is coming to Long Beach, CA, September 20-22.  I have been to a lot of SAS conferences, but WUSS is always my favorite because it is big enough for me to learn a lot, but small enough to be really friendly.

If you come I hope you will catch my presentations.  If you want a preview or if you can’t come, click the links below to download the papers.

On Wednesday, I will once again present SAS Essentials, a whirlwind introduction to SAS programming in just three hours specially designed for people who are new to SAS.

How SAS Thinks: SAS Basics I

Introduction to DATA Step Programming: SAS Basics II

Introduction to SAS Procedures: SAS Basics III

Then on Friday Lora Delwiche will present a Hands-On Workshop about SAS Studio, a new SAS interface that runs in a web browser.

SAS Studio: A New Way to Program in SAS

I hope to see you there!

 

 


9月 162017
 

A while back, I wrote about the proliferation of interfaces for writing SAS programs.  I am reposting that blog here (with a few changes) because a lot of SAS users still don’t understand that they have a choice.

These days SAS programmers have more choices than ever before about how to run SAS.  They can use the old SAS windowing enviroment (often called Display Manager because, face it, SAS windowing environment is way too vague), or SAS Enterprise Guide, or the new kid on the block: SAS StudioAll of these are included with Base SAS.

DisplayManager9-4window

Display Manager / SAS Windowing Environment

EG7-12window

SAS Enterprise Guide

SASStudio3-5window

SAS Studio

I recently asked a SAS user, “Which interface do you use for SAS programming?”

She replied, “Interface?  I just install SAS and use it.”

“You’re using Display Manager,” I explained, but she had no idea what I was talking about.

Trust me.  This person is an extremely sophisticated SAS user who does a lot of leading-edge mathematical programming, but she didn’t realize that Display Manager is not SAS.  It is just an interface to SAS.

This is where old timers like me have an advantage.  If you can remember running SAS in batch, then you know that Display Manager, SAS Enterprise Guide, and SAS Studio are just interfaces to SAS–wonderful, manna from heaven–but still just interfaces.  They are optional.  It is possible to write SAS programs in an editor such as Word or Notepad++, and copy-and-paste into one of the interfaces or submit them in batch.  In fact, here is a great blog by Leonid Batkhan describing how to use your web browser as a SAS code editor.

Each of these interfaces has advantages and disadvantages.  I’m not going to list them all here, because this is a blog not an encyclopedia, but the tweet would be

“DM is the simplest, EG has projects, SS runs in browsers.”

I have heard rumors that SAS Institute is trying to develop an interface that combines the best features of all three.  So someday maybe one of these will displace the others, but at least for the near future, all three of these interfaces will continue to be used.

So what’s your SAS interface?


9月 082017
 

It's time to share another tip about working with ZIP files in SAS. Since I first wrote about FILENAME ZIP to list and extract files from a ZIP archive, readers have been asking for more. Specifically, they want additional details about the files that are contained in a ZIP, including the original file datetime stamps, file size, and compressed size. Thanks to a feature that was quietly added into SAS 9.4 Maintenance 3, you can use the FINFO function to retrieve these details. In this article, I share a SAS macro program that does the job.

Here's an abridged example of the output. If you need to create something like this without the use of external ZIP tools like 7-Zip or WinZip (which are often unavailable in controlled environments), read on.

FILENAME ZIP output

You can download the full program from my public gist on GitHub: zipfiles_list_details.sas

ZIPpy details: a solution in three macros

Here's my basic approach to this problem:

  • First, create a list of all of the ZIP files in a directory and all of the file "members" that are compressed within. I've already shared this technique in a previous article. Like an efficient (or lazy) programmer, I'm just reusing that work. That's macro routine #1 (%listZipContents).
  • With this list in hand, iterate through each ZIP file member, "open" the file with FOPEN, and gather all of the available file attributes with FINFO. I've divided this into two macros for readability. %getZipMemberInfo (macro routine #2) retrieves all of the file details for a single member and stores them in a data set. %getZipDetails (macro routine #3) iterates through the list of ZIP file members, calls %getZipMemberInfo on each, and concatenates the results into a single output data set.

Here's a sample usage:

  %listzipcontents (targdir=C:\Projects\ZIPPED_Examples, outlist=work.zipfiles);
  %getZipDetails (inlist=work.zipfiles, outlist=work.zipdetails);

I tried to add decent comments to my program so that interested coders can study and adapt as needed. Here's a snippet of code that uses the FINFO function, which is really the important part for retrieving these file details.

/*
 Assumes an assignment like:
  FILENAME F ZIP "C:\ZIPPED_Examples\SudokuSolver_src.zip" member="src/AboutThisProject.txt";
*/
fId = fopen("&f","S");
if fID then
  do;
   infonum=foptnum(fid);
     do i=1 to infonum;
      infoname=foptname(fid,i);
      select (infoname);
       when ('Filename') filename=finfo(fid,infoname);
       when ('Member Name') membername=finfo(fid,infoname);
       when ('Size') filesize=input(finfo(fid,infoname),15.);
       when ('Compressed Size') compressedsize=input(finfo(fid,infoname),15.);
       when ('CRC-32') crc32=finfo(fid,infoname);
       when ('Date/Time') filetime=input(finfo(fid,infoname),anydtdtm.);
      end;    
   end;
 compressedratio = compressedsize / filesize;
 output;
 fId = fClose( fId );

The FINFO function in SAS provides access to file attributes and their values for a given file that you've accessed using the FOPEN function. The available file attributes can differ according to the type of file (FILENAME access method) that is used. ZIP files, as you can guess, have some attributes that are specific to them: "Compressed Size", "CRC-32", and others. This code checks for all of the available attributes and keeps those that we need for our detailed output. (And see the use of the SELECT/WHEN statement? So much more readable than a bunch of IF/THEN/ELSEs.)

Look, I'm not going to claim that my approach to this problem is the most elegant or most efficient -- but it works. If it can be improved, then I'm sure I'll hear from a few of you experts out there. Bring it on!

For more about ZIP files in SAS

The post Using FILENAME ZIP and FINFO to list the details in your ZIP files appeared first on The SAS Dummy.

8月 262017
 

News flash: My favorite SAS code editor is SAS Enterprise Guide. However, my favorite general purpose text editor is Notepad++, and I often find myself using that tool for viewing SAS log files and for making small modifications to SAS programs. Judging from the popularity of this SAS Support Communities discussion, I'm not alone. In this post, I'll share the steps for turning Notepad++ into a more useful home for SAS programs.

You can download Notepad++ for Windows from here -- you can use it for free, no cost. That's one reason that it's one of the first tools that I install on any new PC I get my hands on!

1. Associate SAS files with Notepad++

You accomplish this in the usual way with Windows. In Windows Explorer, right-click (or SHIFT+right-click depending on your setup) on a .SAS file (SAS program), and select Open with...

Open with menu

► You might see Notepad++ in the menu, but don't select it, Instead, select Choose another app.

Choose another app

► In this menu, select Notepad++ and check the "Always use this app" checkbox.

Repeat this step for SAS program logs (.LOG files) and listing output (.LST files) if you want.

2. Run a SAS program from Notepad++

You can add new program actions to the Run menu in Notepad++. Here's how to add a command to run a SAS program, if you have SAS for Windows installed. Note: These steps assume that the SAS program is open in Notepad++ and is saved in a file.

► Select Run... from the Run menu. In the program to run field, enter this command:

"C:\Program Files\SASHome\SASFoundation\9.4\sas.exe" -sysin "$(FULL_CURRENT_PATH)" -log "$(CURRENT_DIRECTORY)\$(NAME_PART).log" -print "$(CURRENT_DIRECTORY)\$(NAME_PART).lst"

all on one line. You might need to adjust the SAS.EXE path for your install. The command options use some Notepad++ environment variables to direct the SAS log and listing output to the same path as the SAS program file.

Click Save (not Run).

Click Save

Optionally, assign a shortcut key to the action, and name it "Run program file in SAS" (or whatever you want). This adds the command to your Run menu.

Run in SAS command

When you select it, Notepad++ will launch SAS, run your program in batch, and direct the output to the same folder where the program is stored.

3. Adding SAS syntax color coding to Notepad++

It's simple to "teach" Notepad++ to recognize the keywords from SAS and other languages. You can download new language definitions files from here -- follow the instructions on the page to have your Notepad++ recognize them. I've created an expanded definition file that includes more SAS keywords (many, many more!) -- you can grab that from my GitHub repo here.

Here's what SAS code looks like in my Notepad++:

my Notepad++ SAS look

What else?

I'm sure that some of you have spent more time than I have in creating a souped-up Notepad++ environment, or perhaps you've taken it to another level with other popular editors like Sublime or Vim or UltraEdit. If you have other tips to share, I'd love to hear from you in the comments.

The post Using Notepad++ as your SAS code editor appeared first on The SAS Dummy.

8月 192017
 

SAS programmers have high expectations for their coding environment, and why shouldn't they? Companies have a huge investment in their SAS code base, and it's important to have tools that help you understand that code and track changes over time. Few things are more satisfying as a SAS program that works as designed and delivers perfect results. (Oh, hyperbole you say? I don't think so.) But when your program isn't working the way it should, there are two features that can help you get back on track: a code debugger, and program revision history. Both of these capabilities are built into SAS Enterprise Guide. Program history was added in v7.1, and the debugger was added in v7.13.

I've written about the DATA step debugger before -- both as a teaching tool and as a productivity tool. In this article, I'm sharing a demo of the debugger's features, led by SAS developer Joe Flynn. Before joining the SAS Enterprise Guide development team, Joe worked in SAS Technical Support. He's very familiar with "bugs," and reported his share of them to SAS R&D. Now -- like every programmer -- Joe makes the bugs. But of course, he fixes most of them before they ever see the light of day. How does he do that? Debugging.

This video is only about 8 minutes long, but it's packed with good information. In the debugger demo, you'll learn how you can use standard debugging methods, such as breakpoints, step over and step through, watch variables, jump to, evaluate expression, and more. There is no better way to understand exactly what is causing your DATA step to misbehave.

Joe's debugger

In the program history demo (the second part of the video), you'll learn how team members can collaborate using standard source management tools (such as Git). If you establish a good practice of storing code in a central place with solid source management techniques, SAS Enterprise Guide can help you see who changed what, and when. SAS Enterprise Guide also offers a built-in code version comparison tool, which enhances your ability to find the breaking changes. You can also use the code comparison technique on its own, outside of the program history feature.

program history

Take a few minutes to watch the video, and then try out the features yourself. You don't need a Git installation to play with program history at the project level, though it helps when you want to extend that feature to support team collaboration.

See also

The post Code debugging and program history in SAS Enterprise Guide appeared first on The SAS Dummy.

8月 022017
 

In my prior posts (Data-driven SAS macro loops, Modifying variable attributes in all datasets of a SAS library, Automating the loading of multiple database tables into SAS tables), I presented various data-driven applications using SAS macro loops.

However, macro loops are not the only tools available in SAS for developing data-driven programs.

CALL EXECUTE is one of them. The CALL EXECUTE routine accepts a single argument that is a character string or character expression. The character expression is usually a concatenation of strings containing SAS code elements to be executed after they have been resolved. Components of the argument expression can be character constants, data step variables, macro variable reference, as well as macro references. CALL EXECUTE dynamically builds SAS code during DATA step iterations; that code executes after the DATA step’s completion outside its boundary. This makes a DATA step iterating through a driver table an effective SAS code generator similar to that of SAS macro loops.

However, the rather peculiar rules of the CALL EXECUTE argument resolution may make its usage somewhat confusing. Let’s straighten things out.

Argument string has no macro or macro variable reference

If an argument string to the CALL EXECUTE contains SAS code without any macro or macro variable references, that code is simply pushed out (of the current DATA step) and appended to a queue after the current DATA step. As the DATA step iterates, the code is appended to the queue as many times as there are iterations of the DATA step. After the DATA step completes, the code in the queue gets executed in the order of its creation (First In First Out).

The beauty of this process is that the argument string can be a concatenation of character constants (in single or double quotes) and SAS variables which get substituted with their values by CALL EXECUTE for each DATA step iteration. This will produce data-driven, dynamically generated SAS code just like an iterative SAS macro loop.

Let’s consider the following example. Say we need to load multiple Oracle tables into SAS tables.

Step 1. Creating a driver table

In order to make our process data-driven, let’s first create a driver table containing a list of the table names that needed to be extracted and loaded:

/* create a list of tables to extract & load */
libname parmdl '/sas/data/parmdata';
data parmdl.tablelist;
        length tname $8;
        input tname;
        datalines;
ADDRESS
ACCOUNT
BENEFIT
FINANCE
HOUSING
;

This program runs just once to create the driver table parmdl.tablelist.

Step 2. Loading multiple tables

Then, you can use the following data-driven program that runs each time you need to reload Oracle tables into SAS:

/* source ORACLE library */
libname oralib oracle path="xxx" schema="yyy" user="uuu"
 	PASSWORD="{SAS002}ABCDEFG12345678RTUR" access=readonly;
 
/* target SAS library */
libname sasdl '/sas/data/appdata';
 
/* driver table SAS library */
libname parmdl '/sas/data/parmdata';
 
data _null_;
   set parmdl.tablelist;
   call execute(cats(
      'data sasdl.',tname,';',
         'set oralib.',tname,';',
      'run;'));
run;

In order to concatenate the components of the CALL EXECUTE argument I used the cats() SAS function which returns a concatenated character string removing leading and trailing blanks.

When this program runs, the SAS log indicates that after the data _null_ step the following statements are added and executed:

NOTE: CALL EXECUTE generated line.
1   + data sasdl.ADDRESS;set oralib.ADDRESS;run;
2   + data sasdl.ACCOUNT;set oralib.ACCOUNT;run;
3   + data sasdl.BENEFIT;set oralib.BENEFIT;run;
4   + data sasdl.FINANCE;set oralib.FINANCE;run;
5   + data sasdl.HOUSING;set oralib.HOUSING;run;

In this example we use data _null_ step to loop through the list of tables (parmdl.tablelist) and for each value of the tname column a new data step gets generated and executed after the data _null_ step. The following diagram illustrates the process:

Diagram explaining CALL EXECUTE for SAS data-driven programming

Argument string has macro variable reference in double quotes

If an argument to the CALL EXECUTE has macro variable references in double quotes, they will be resolved by the SAS macro pre-processor during the DATA step compilation. Nothing unusual. For example, the following code will execute exactly as the above, and macro variable references &olib and &slib will be resolved to oralib and sasdl before CALL EXECUTE takes any actions:

%let olib = oralib;
%let slib = sasdl;
 
data _null_;
   set parmdl.tablelist;
   call execute (
      "data &slib.."!!strip(tname)!!';'
         "set &olib.."!!strip(tname)!!';'!!
      'run;'
   );
run;

Argument string has macro or macro variable reference in single quotes

Here comes the interesting part. If the argument to CALL EXECUTE has macro or macro variable references in single quotes, they still will be resolved before the code is pushed out of the DATA step, but not by the SAS macro pre-processor during the DATA step compilation as it was in the case of double quotation marks. Macro or macro variable references in single quotes will be resolved by CALL EXECUTE itself. For example, the following code will execute exactly as the above, but macro variable references &olib and &slib will be resolved by CALL EXECUTE:

%let olib = oralib;
%let slib = sasdl;
 
data _null_;
   set parmdl.tablelist;
   call execute('data &slib..'!!strip(tname)!!';'!!
                'set &olib..'!!strip(tname)!!';'!!
                'run;'
               );
run;

Timing considerations

CAUTION: If your macro contains some non-macro language constructs for assigning macro variables during run time, such as a CALL SYMPUT or SYMPUTX statement (in a DATA step) or an INTO clause (in PROC SQL), resolving those macro variable references by CALL EXECUTE will happen too soon, before your macro-generated code gets pushed out and executed. This will result in unresolved macro variables. Let’s run the following code that should extract Oracle tables into SAS tables as above, but also re-arrange column positions to be in alphabetical order:

%macro onetable (tblname);
   proc contents data=oralib.&tblname out=one(keep=name) noprint;
   run;
 
   proc sql noprint;
      select name into :varlist separated by ' ' from one;
   quit;
   %put &=varlist;
 
   data sasdl.&tblname;
      retain &varlist;
      set oralib.&tblname end=last nobs=n;
      if last then call symput('n',strip(put(n,best.)));
   run;
   %put Table &tblname has &n observations.;
%mend onetable;
 
data _null_;
   set parmdl.tablelist;
   call execute('%onetable('!!strip(tname)!!');');
run;

Predictably, the SAS log will show unresolved macro variable references, such as:

WARNING: Apparent symbolic reference VARLIST not resolved.
WARNING: Apparent symbolic reference N not resolved.
Table ADDRESS has &n observations.

SOLUTION: To avoid the timing issue when a macro reference gets resolved by CALL EXECUTE too soon, before macro variables have been assigned during macro-generated step execution, we can strip CALL EXECUTE of the macro resolution privilege. In order to do that, we can mask & and % characters using the %nrstr macro function, thus making CALL EXECUTE “macro-blind,” so it will push the macro code out without resolving it. In this case, macro resolution will happen after the DATA step where CALL EXECUTE resides. If an argument to CALL EXECUTE has a macro invocation, then including it in the %nrstr macro function is the way to go. The following code will run just fine:

data _null_;
   set parmdl.tablelist;
   call execute('%nrstr(%onetable('!!strip(tname)!!'));');
run;

When this DATA step runs, the SAS log indicates that the following statements are added and executed:

NOTE: CALL EXECUTE generated line.
1   + %onetable(ADDRESS);
2   + %onetable(ACCOUNT);
3   + %onetable(BENEFIT);
4   + %onetable(FINANCE);
5   + %onetable(HOUSING);

CALL EXECUTE argument is a SAS variable

The argument to CALL EXECUTE does not necessarily have to contain or be a character constant. It can be a SAS variable, a character variable to be precise. In this case, the behavior of CALL EXECUTE is the same as when the argument is a string in single quotes. It means that if a macro reference is part of the argument value it needs to be masked using the %nrstr() macro function in order to avoid the timing issue mentioned above.

In this case, the argument to the CALL EXECUTE may look like this:

arg = '%nrstr(%mymacro(parm1=VAL1,parm2=VAL2))';
call execute(arg);

Making CALL EXECUTE totally data-driven

In the examples above we used the tablelist driver table to retrieve values for a single macro parameter for each data step iteration. However, we can use a driver table not only to dynamically assign values to one or more macro parameters, but also to control which macro to execute in each data step iteration. The following diagram illustrates the process of totally data-driven SAS program:

Diagram explaining using CALL EXECUTE for SAS data-driven programming

Conclusion

CALL EXECUTE is a powerful tool for developing dynamic data-driven SAS applications. Hopefully, this blog post demonstrates its benefits and clearly explains how to avoid its pitfalls and use it efficiently to your advantage. I welcome your comments, and would love to hear your experiences with CALL EXECUTE.

CALL EXECUTE made easy for SAS data-driven programming was published on SAS Users.

7月 262017
 

Trivial Pursuit, Justin Bieber and Timbits. Some pretty great things have come from Canada, eh? Well, you can go ahead and add expert SAS programmers to that impressive list.

In this video, six Canadian SAS programmers, with more than 115 years of SAS programming experience between them, share some of their favorite, little-known SAS programming tips. You're sure to discover a new trick or two.

The video includes the following tips and more:

  • Standardizing and documenting your SAS program.
  • Creating parameter lookup tables.
  • Declaring your macro variables.
  • Using the Characterize Data task in SAS Enterprise Guide.
  • Data exploration best practices for SAS Enterprise Guide.

Looking for more great tips to help bring your SAS programming skills to the next level? Check out these great resources and learn even more from your SAS peers:

  • SAS Support Communities: Peer-to-peer support for SAS users about programming, data analysis, installation and deployment issues, tips and best practices and a whole lot more.
  • SAS Blogs: Connecting you to people, products and ideas from SAS with technical tips, support information and more.
  • SAS Newsletters: The latest news, tips, tricks and resources from SAS, plus advice and industry knowledge gleaned from top experts and other SAS users.

 

Programming tips from experienced SAS users was published on SAS Users.