Statisticians and data miners use the R language to develop statistical software. It’s free and open source, so there’s lots to like about it, especially if programming is your thing. R provides a wide variety of statistical and graphical techniques such as:
Linear and nonlinear modeling
Classical statistical tests
If you’re someone who thinks you can benefit from R but are daunted by the idea of learning a programming language, you now have another option. You can run R syntax from IBM SPSS software. Running R from SPSS Modeler or SPSS Statistics augments the powerful data manipulation, statistical analysis and predictive algorithms already in SPSS software with custom R code for additional power and flexibility. You can conduct custom analysis, create and work with output, and integrate new insights into your analysis plans. So, combining SPSS software and R gives you the best of both worlds
Still not convinced? Here are some of the other benefits of using SPSS and R together.
R does not have a modern graphical user interface, which makes it difficult for those who are not R programmers to use it. By contrast, the graphical interface offered by SPSS software supports a variety of data preparation, statistical analysis and predictive modeling algorithms.
Almost no learning curve
R is not easy to learn for everyone. Not everyone is a programmer, statistician or data modeler. SPSS software can handle data, statistical analysis and modeling so you don’t have to.
Easier data preparation
With R, accessing the data needed for analysis requires a great deal of time and effort. You must write volumes of code, implement packages and even employ Java. SPSS software can read text input, spreadsheets, SAS files and more. Wizards with prebuilt connectors access data so extracting, manipulating and transforming it takes less time.
More output options
With R, production of publish-ready output is difficult. With SPSS software, one click accesses to SPSS presentation-ready charts and graphs. You can publish results to PDF, Word, PowerPoint, Excel and other formats and view those outputs on most device platform.
R can very quickly consume almost all available memory. IBM SPSS analytics scales R in database for environments such as SAP Hana, Netezza and Oracle. SPSS can also scale R in-Hadoop with IBM® SPSS® Analytic Server.
R is a language that is designed for the individual. However, most analytics work is a collaborative effort with a number of people contributing to models and statistical analysis. When you use SPSS software and R together, you lose the lone wolf aspect of R. With custom dialogs, you make new functions that just about anyone can use.
R lacks a formal release process, so you are most likely using it at your own risk. SPSS software provides a framework for centralizing, securing and automating your analytical assets so you can rest assured that your environment is not at risk.
Want more information?
Extending the strengths of R with SPSS software makes sense. Click here to learn more about combining SPSS analytics with R in this white paper.