This section will serve as a critical evaluation and summary of the R language's ability to process very large, out-of memory data and its connectivity with a variety of existing Big Data platforms and tools.
We began the book with a brief revision of the most common techniques used to analyze data with the R language (Chapter 2, Introduction to R Programming Language and Statistical Environment). We guided you from importing the data into R, through data management and processing methods, cross-tabulations, aggregations, hypothesis testing, and visualizations. We then explained major limitations of the R language in terms of its requirement of memory resources for data storage and its speed of processing. We said that the data must fit within the available RAM installed on your computer if you were to use only a single machine for data processing in the R language. However, as a system runs other processes...
