R is hot. Whether measured by more than 10,000 add-on packages, the 95,000+ members of LinkedIn's R group or the more than 400 R Meetup groups currently in existence, there can be little doubt that interest in the R statistics language, especially for data analysis, is soaring.

Why R? It's free, open source, powerful and highly extensible. 'You have a lot of prepackaged stuff that's already available, so you're standing on the shoulders of giants,' Google's chief economist told The New York Times back in 2009.

JASP is an open-source statistics program that is free, friendly, and flexible. Armed with an easy-to-use GUI, JASP allows both classical and Bayesian analyses. MacStats - a listing of Macintosh statistical software with usage notes, graphing software, time series and SEM, and reviews of key Mac statistics packages.

Because it's a programmable environment that uses command-line scripting, you can store a series of complex make errors, he notes. 'The problem is that we often use tools and practices that make it difficult to find and correct our mistakes.'

Sure, you can easily examine complex formulas on a spreadsheet. But it's not nearly as easy to run multiple data sets through spreadsheet formulas to check results as it is to put several data sets through a script, he explains.

Indeed, the mantra of 'Make sure your work is reproducible!' is a common theme among R enthusiasts.

Why not R? Well, R can appear daunting at first. That's often because R syntax is different from that of many other languages, not necessarily because it's any more difficult than others.

'I have written software professionally in perhaps a dozen programming languages, and the hardest language for me to learn has been R,' writes consultant John D. Cook in a Web post about R programming for those coming from other languages. 'The language is actually fairly simple, but it is unconventional.'

And so, this guide. Our aim here isn't R mastery, but giving you a path to start using R for basic data work: Extracting key statistics out of a data set, exploring a data set with basic graphics and reshaping data to make it easier to analyze.

Introduction to R

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

The R environment

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes

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• an effective data handling and storage facility,
• a suite of operators for calculations on arrays, in particular matrices,
• a large, coherent, integrated collection of intermediate tools for data analysis,
• graphical facilities for data analysis and display either on-screen or on hardcopy, and
• a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.

R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.

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Many users think of R as a statistics system. We prefer to think of it as an environment within which statistical techniques are implemented. R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics.

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R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hardcopy.