About this blog
Rick Wicklin, PhD, is a distinguished researcher in computational statistics at SAS and is a principal developer of PROC IML and SAS/IML Studio. This blog focuses on statistical programming. It discusses statistical and computational algorithms, statistical graphics, simulation, efficiency, and data analysis. Rick is author of the books Statistical Programming with SAS/IML Software and Simulating Data with SAS.
Follow @RickWicklin on Twitter.
Tags9.3 9.4 9.22 12.1 12.3 13.1 13.2 14.1 14.2 Bootstrap and Resampling Ciphers Conferences Data Analysis Efficiency File Exchange Fractal Getting Started GTL Heat maps History IMLPlus Just for Fun Math Matrix Computations Missing Data Numerical Analysis Optimization Packages pi day R Reading and Writing Data SAS/IML Studio SAS Global Forum SAS Programming Simulation Spatial Data sports analytics Statistical Graphics Statistical Programming Statistical Thinking Strings Time series Tips and Techniques vectorization Video
Subscribe to this blog
Do you want to create customized SAS graphs by using PROC SGPLOT and the other ODS graphics procedures? An essential skill that you need to learn is how to merge, join, append, and concatenate SAS data sets that come from different sources. The SAS statistical graphics procedures (SG procedures) enable […]Post a Comment
If you obtain data from web sites, social media, or other unstandardized data sources, you might not know the form of dates in the data. For example, the US Independence Day might be represented as "04JUL1776", "07/04/1776", "Jul 4, 1776", or "July 4, 1776." Fortunately, the ANYDTDTE informat makes it […]Post a Comment
The WHERE clause in SAS is a powerful mechanism for selecting observations as you read or write a data set. The WHERE clause supports many operators, including the IN operator, which enables you to compactly specify multiple conditions for a categorical variable. A common use of the IN operator is […]Post a Comment
SAS formats are flexible, dynamic, and have many uses. For example, you can use formats to count missing values and to change the order of a categorical variable in a table or plot. Did you know that you can also use SAS formats to recode a variable or to bin […]Post a Comment
Every beginning SAS programmer learns the simple IF-THEN/ELSE statement for conditional processing in the SAS DATA step. The basic If-THEN statement handles two cases: if a condition is true, the program does one thing, otherwise the program does something else. Of course, you can handle more cases by using multiple […]Post a Comment
Last week I analyzed 12 million records of taxi cab transactions in New York City. As part of that analysis, I used a DATA step view to create a new variable, which was the ratio of the tip amount to the fare amount. A novice SAS programmer told me that […]Post a Comment
One of the first things SAS programmers learn is that SAS data sets can be specified in two ways. You can use a two-level name such as "sashelp.class" which uses a SAS libref (SASHELP) and a member name (CLASS) to specify the location of the data set. Alternatively, you can […]Post a Comment
A dummy variable (also known as indicator variable) is a numeric variable that indicates the presence or absence of some level of a categorical variable. The word "dummy" does not imply that these variables are not smart. Rather, dummy variables serve as a substitute or a proxy for a categorical […]Post a Comment
Novice SAS programmers quickly learn the advantages of using PROC SORT to sort data, followed by a BY-group analysis of the sorted data. A typical example is to analyze demographic data by state or by ZIP code. A BY statement enables you to produce multiple analyses from a single procedure […]Post a Comment