#SAS VS SPSS CODE#
More recently I have been excited about platforms where code can be written in different languages and integrated using literate programming (i.e., the weaving of the results of code with text to create reports). There are also many ways of interfacing with R using web-based tools like Rserve or, on Windows, the rcom interface to utilize COM and connect with, among other things, Word and Excel. A specially nice SAS macro to do this for those without the latest versions of SAS is %Proc_R, available here. Most commercial statistical packages, like SAS, SPSS and Statistica allow you to write R code to send to R and then get back the results. Packages in R allow communication out with general packages, like RSPython, RSPerl (both available at Omegahat) and rJava. There are now ways to communicate with R from other general programming languages like Java (through the rJava package and JNI), Perl ( Statistics::R, available in CPAN), Python ( rpy2, PypeR, available in PyPI). R is not the fastest nor most elegant of languages, but has by far the richest ecosystem of cutting-edge data analysis packages. I'll speak to the interfaces with R, which I'm most familiar with, but I'm sure that the community will point out other useful interfaces. There are now several interface packages available to talk between open-source languages. The trick is in finding the right "glue" that can string our workflow together. Each tool has its strengths and weaknesses, and often a mixture works best. Specially for data analysis, there is often no single tool that can do the end-to-end workflow well, however much we would like to believe that there is. We will often extract data from a SQL database, munge it using Perl or Python, and then do statistical analysis using R or SAS, reporting the results using Word or, increasingly, the web. The truth is, very few of us data geeks (data scientists, data analysts, statisticians, or what ever we call ourselves ) use only a single tool for all of our work. and, for data geeks, the SAS/SPSS/R/Matlab fight.This has led to well-contested rivalries and "fights" about which tool is better: Programmers have long been very proud and loyal with their tools, and often very vocal.