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Beginning R: The Statistical Programming Language Mark Gardener
Publisher: Wrox Press, Inc.

In a world of big data and scientific approaches to Before beginning, you must know that R's assignment operator is <- and not =. Building R packages is not particular hard, but it can be a bit of a daunting endeavour at the beginning, particularly if you are more of a statistician than a computer scientist or programmer. Kick off your Memorial Day long weekend with a Polyglot Programmers meetup on Thursday, May 23. These analytics require scientific tools not commonly found within typical IC or DoD software baselines. Research activities will include designing and carrying out programs of data collection (in the field and in the lab), and analyzing those data using the statistical computing language R. Warning: what follows is a somewhat technical discussion of my love-hate relationship with the R statistical language, in which I somehow manage to waste 2,400 words talking about a single line of code. R and S-PLUS: R is a favourite in university stats departments. The R programming language has long been used for statistical analysis, though it tends not to be used directly within business intelligence (BI) applications, Bajuk-Yorgan said. Besides being freely available, it is also a powerful statistical programming language based on S. Ross Ihaka co-developed the R statistical programming language with Robert Gentleman. Beginning R: An Introduction to Statistical Programming is a hands-on book showing how to use the R language, write and save R scripts, build and import data files, and write your own custom statistical functions. For those unaware, R is effectively an open source implementation of S-PLUS, which in turn was based on S. R is a language for statistical computing.