1月 212020

One great thing about being a SAS programmer is that you never run out of new things to learn. SAS often gives us a variety of methods to produce the same result. One good example of this is the DATA step and PROC SQL, both of which manipulate data. The DATA step is extremely powerful and flexible, but PROC SQL has its advantages too. Until recently, my knowledge of PROC SQL was pretty limited. But for the sixth edition of The Little SAS Book, we decided to move the discussion of PROC SQL from an appendix (who reads appendices?) to the body of the book. This gave me an opportunity to learn more about PROC SQL.

When developing my programs, I often find myself needing to calculate the mean (or sum, or median, or whatever) of a variable, and then merge that result back into my SAS data set. That would generally involve at least a couple PROC steps and a DATA step, but using PROC SQL I can achieve the same result all in one step.


Consider this example using the Cars data set in the SASHELP library. Among other things, the data set contains the 2004 MSRP for over 400 models of cars of various makes and car type. Suppose you want a data set which contains the make, model, type, and MSRP for the model, along with the median MSRP for all cars of the same make. In addition, you would like a variable that is the difference between the MSRP for that model, and the median MSRP for all models of the same make. Here is the PROC SQL code that will create a SAS data set, MedianMSRP, with the desired result:

*Create summary variable for median MSRP by Make;

   SELECT Make, Model, Type, MSRP,
          MEDIAN(MSRP) AS MedMSRP,
          (MSRP - MEDIAN(MSRP)) AS MSRP_VS_Median
   GROUP BY Make;


The CREATE TABLE clause simply names the SAS data set to create, while the FROM clause names the SAS data set to read. The SELECT clause lists the variables to keep from the old data set (Make, Model, Type, and MSRP) along with specifications for the new summary variables. The new variable, MedMSRP, is the median of the old MSRP variable, while the new variable MSRP_VS_Median is the MSRP minus the median MSRP. The GROUP BY clause tells SAS to do the calculations within each value of the variable Make. If you leave off the GROUP BY clause, then the calculations would be done over the entire data set. When you run this code, you will get the following message in your SAS log telling you it is doing exactly what you wanted it to do:

NOTE: The query requires remerging summary statistics back with the original data.

The following PROC PRINT produces a report showing just the observations for two makes – Porsche and Jeep.

  TITLE '2004 Car Prices';
  WHERE Make IN ('Porsche','Jeep');


Here are the results:

Now PROC SQL aficionados will tell you that if all you want is a report and you don’t need to create a SAS data set, then you can do it all in just the PROC SQL step. But that is the topic for another blog!


Expand Your SAS Knowledge by Learning PROC SQL was published on SAS Users.

1月 132020

Are you ready to get a jump start on the new year? If you’ve been wanting to brush up your SAS skills or learn something new, there’s no time like a new decade to start! SAS Press is releasing several new books in the upcoming months to help you stay on top of the latest trends and updates. Whether you are a beginner who is just starting to learn SAS or a seasoned professional, we have plenty of content to keep you at the top of your game.

Here is a sneak peek at what’s coming next from SAS Press.

For students and beginners

For beginners, we have Exercises and Projects for The Little SAS® Book: A Primer, Sixth Edition, the best-selling workbook companion to The Little SAS Book by Rebecca Ottesen, Lora Delwiche, and Susan Slaughter. Exercises and Projects for The Little SAS® Book, Sixth Edition will be updated to match the updates to the new The Little SAS® Book: A Primer, Sixth Edition. This hands-on workbook is designed to hone your SAS skills whether you are a student or a professional.



For data explorers of all levels

This free e-book explores the features of SAS® Visual Data Mining and Machine Learning, powered by SAS® Viya®. Users of all skill levels can visually explore data on their own while drawing on powerful in-memory technologies for faster analytic computations and discoveries. You can manually program with custom code or use the features in SAS® Studio, Model Studio, and SAS® Visual Analytics to automate your data manipulation and modeling. These programs offer a flexible, easy-to-use, self-service environment that can scale on an enterprise-wide level. This book introduces some of the many features of SAS Visual Data Mining and Machine Learning including: programming in the Python interface; new, advanced data mining and machine learning procedures; pipeline building in Model Studio, and model building and comparison in SAS® Visual Analytics



For health care data analytics professionals

If you work with real world health care data, you know that it is common and growing in use from sources like observational studies, pragmatic trials, patient registries, and databases. Real World Health Care Data Analysis: Causal Methods and Implementation in SAS® by Doug Faries et al. brings together best practices for causal-based comparative effectiveness analyses based on real world data in a single location. Example SAS code is provided to make the analyses relatively easy and efficient. The book also presents several emerging topics of interest, including algorithms for personalized medicine, methods that address the complexities of time varying confounding, extensions of propensity scoring to comparisons between more than two interventions, sensitivity analyses for unmeasured confounding, and implementation of model averaging.


For those at the cutting edge

Are you ready to take your understanding of IoT to the next level? Intelligence at the Edge: Using SAS® with the Internet of Things edited by Michael Harvey begins with a brief description of the Internet of Things, how it has evolved over time, and the importance of SAS’s role in the IoT space. The book will continue with a collection of chapters showcasing SAS’s expertise in IoT analytics. Topics include Using SAS Event Stream Processing to process real world events, connectivity, using the ESP Geofence window, applying analytics to streaming data, using SAS Event Stream Processing in a typical IoT reference architecture, the role of SAS Event Stream Manager in managing ESP deployments in an IoT ecosystem, how to use deep learning with Your IoT Digital, accounting for data quality variability in streaming GPS data for location-based analytics, and more!




Keep an eye out for these titles releasing in the next two months! We hope this list will help in your search for a SAS book that will get you to the next step in updating your SAS skills. To learn more about SAS Press, check out our up-and-coming titles, and to receive exclusive discounts make sure to subscribe to our newsletter.

Foresight is 2020! New books to take your skills to the next level was published on SAS Users.