7 really good reasons to partner SPSS analytics with R

Senior Writer and Content Strategist, IBM

The R language has become very popular with statisticians and data miners who use it to develop statistical software. In addition, R is widely used for advanced data analysis. R provides a wide variety of statistical and graphical techniques such as:

  • Linear and nonlinear modeling
  • Classical statistical tests
  • Time-series analysis
  • Classification
  • Clustering

Did you know that you can run R syntax from IBM SPSS Modeler and SPSS Statistics software? The combined strength of both helps address the needs of an organization that has few statistics or programming experts, but that wants to benefit from R. SPSS software complements R by extending R’s scalability: you can handle much larger data sets and distribute R packages to a wide range of users—even non-programmers.

Combining SPSS software and R truly gives you the best of both worlds. Still not convinced? Here are seven really good reasons why you should use SPSS and R together:

  1. Simple interface. The SPSS graphical user interface supports a variety of data preparation, statistical analysis and predictive modeling algorithms.
  2. puzzle pieces.jpgAlmost no learning curve. SPSS software can handle data, statistical analysis and modeling so you don’t have to.
  3. Easier data preparation. 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.
  4. More output options. 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 platforms.
  5. Superior performance. 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.
  6. Collaboration. When you use SPSS software and R together, you lose the “lone wolf” aspect of R. With custom dialogs, you can create new functions that anyone can use.
  7. Security. 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.

Extending the strengths of R with SPSS software makes sense. Learn more about how combining SPSS analytics with R is a great solution for your advanced data analysis in this new white paper.