The R language is used to develop statistical software which has become very popular with statisticians and data miners. R-language is extensively used for advanced data analysis. It provides a broad array of statistical and graphical practices such as:
• Classical statistical tests
• Time-series analysis
• Linear and nonlinear modeling
It is an intelligent idea to combine SPSS software and R-syntax. It will benefit a lot. If you are not convinced, we have listed seven truly good reasons why you should use SPSS and R-language. Please have a look.
1. The interface is quite simple.
The SPSS graphical user interface supports a number of statistical analysis, data preparation, and analytical modeling algorithms.
2. There is no additional learning required.
SPSS software can easily handle statistical analysis, data, and modeling so you don’t have to learn any other thing.
3. Data preparation is easy.
SPSS software can read spreadsheets, text input, SAS files and more. It takes less time for extracting, manipulating and transforming data because wizards with pre-built connectors access data easily.
4. There are more options for output.
There is a one click accesses to SPSS presentation-ready charts and graphs. You can publish results to Word, PDF, PowerPoint, Excel and other formats. These results can be viewed on number of platforms.
5. The performance is superior.
The combination of SPSS with R-language works best for different environments such as SAP, Netezza Hana, and Oracle.
6. Group effort.
When you use SPSS software and R-syntax, you will lose the “lone work” aspect of R. With custom dialogs, you can generate new functions that anyone can use.
SPSS software provides an intelligent structure for securing, centralizing, and automating your analytical assets so that you can get assured that your environment is not at risk.
Combining the strengths of R with SPSS software makes logical sense.