The SAS Jedi Mark Jordan presents his SAS Global Forum 2018 paper "Working with Big Data in SAS"
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The SAS Jedi Mark Jordan presents his SAS Global Forum 2018 paper "Working with Big Data in SAS"
The post Jedi @ SASGF: Working with Big Data in SAS appeared first on SAS Learning Post.
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If you are a SAS programmer, you may wonder why you should read this. After all isn’t SAS Enterprise Guide just for folks who don’t want to be bothered writing SAS code? SAS Enterprise Guide is just point, click, and get results, right? Well there is a...
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Here's a Proc Print trick for grouped data. Suppose your data is divided into groups, such as males and females. You could sort by the grouping variable before printing, like this: Suppose you want to better emphasize the groups. You could add a BY sta...
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A ghoulish Halloween Boo to all my readers! Hope my costume freaks you out, but even if it doesn't, I’m positive PROC FREQ will in a few amazing ways! Today’s Programming 2: Data Manipulation Techniques class asked about the power of PROC FREQ. Since I stopped to explain some of it's benefits to
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Suppose you want a list of car manufacturers from the CARS dataset. Easy! Call the %CHARLIST macro from a %PUT statement, like this: The CHARLIST macro generates a list of unique values of a selected variable from a selected dataset. So does PROC FREQ. But, if you don't need statistics, the CHARLIST
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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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In this post I describe the important tasks of data preparation, exploration and binning.These three steps enable you to know your data well and build accurate predictive models. First you need to clean your data. Cleaning includes eliminating variable...
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Datasets are rarely ready for analysis, and one of the most prevalent problems is missing data. This post is the first in a short series focusing on how to think about missingness, how JMP13 can help us determine the scope of missing data in a given ta...
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