Author

Jim Simon
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Principal Technical Training Consultant

Jim Simon is a principal instructor and course developer for SAS Education. Jim has a bachelor's degree from UCLA and a master's degree from California State University at Northridge. Prior to joining the SAS Irvine office in 1988, Jim was an instructor at Ventura College and a SAS programmer at The Medstat Group in Santa Barbara. Jim's areas of specialization include the DATA step, application development, web enablement, and the SAS macro language. A native of Southern California, Jim enjoys anything in the warm California sun. On weekends, Jim loves jumping in his Corvette, turning up the stereo, and cruising Pacific Coast Highway, top down, South to Laguna Beach or North to his old home town, Santa Barbara.

Learn SAS | Programming Tips
Jim Simon 6
How to format a macro variable

Would you like to format your macro variables? Easy!  Just use the %FORMAT function, like this: What?! You never heard of the %FORMAT function? Of course not, cuz it doesn't exist! No problem. Just create it, like this: %macro format(value,format); %if %datatyp(&value)=CHAR %then %sysfunc(putc(&value,&format)); %else %left(%qsysfunc(putn(&value,&format))); %mend format; The %FORMAT

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Jim Simon 5
Random Sampling: What's Efficient?

Suppose you wish to select a random sample from a large SAS dataset.  No problem. The PROC SURVEYSELECT step below randomly selects a 2 percent sample: proc surveyselect data=large out=sample method=srs /* simple random sample */ n=1000000; /* sample size */ run; Do you have a SAS/STAT license?   If not,

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Jim Simon 0
Reading Hierarchical Data - Part 3

This post is the third and final in a series that illustrates three different solutions to "flattening" hierarchical data.  Don't forget to catch up with Part 1 and Part 2. Solution 2, from my previous post, created one observation per header record, with detail data in a wide format, like

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Jim Simon 3
Reading hierarchical data - Part 2

This post is the second in a series that illustrates three different solutions to "flattening" hierarchical data. Solution 1, from my previous post, created one observation per header record, summarizing the detail data with a COUNT variable, like this: Summary Approach: One observation per header record   Obs Family Count

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