and instructions for subscribing. (For more information, turn to “It provides an engaged community,” later in this chapter.)
✔ June 1995: After some persuasive arguments by Martin Mächler (among others) to make the code available as “free software,” the code was made available under the Free Software Foundation’s GNU General Public License (GPL), Version 2.
✔ Mid-1997: The initial R Development Core Team was formed (although, at the time, it was simply known as the core group).
✔ February 2000: The first version of R, version 1.0.0, was released.
✔ October 2004: Release of R version 2.0.0.
✔ April 2013: Release of R version 3.0.0.
✔ April 2015: Release of R-3.2.0 (the version used in this book).
Ross Ihaka wrote a comprehensive overview of the development of R. The web page http://cran.r-project.org/doc/html/interface98-paper/paper.html provides a fascinating history.
Recognizing the Benefits of Using R
Of the many attractive benefits of R, a few stand out: It’s actively maintained, it has good connectivity to various types of data and other systems, and it’s versatile enough to solve problems in many domains. Possibly best of all, it’s available for free, in more than one sense of the word.
R is available under an open-source license, which means that anyone can download and modify the code. This freedom is often referred to as “free as in speech.” R is also available free of charge – a second kind of freedom, sometimes referred to as “free as in beer.” In practical terms, this means that you can download and use R free of charge.
As a result of this freedom, many excellent programmers have contributed improvements and fixes to the R code. For this reason, R is very stable and reliable.
The R Core Team has put a lot of effort into making R available for different types of hardware and software. This means that R is available for Windows, Unix systems (such as Linux), and the Mac.
R itself is a powerful language that performs a wide variety of functions, such as data manipulation, statistical modeling, and graphics. One really big advantage of R, however, is its extensibility. Developers can easily write their own software and distribute it in the form of add-on packages. Because of the relative ease of creating and using these packages, literally thousands of packages exist. In fact, many new (and not-so-new) statistical methods are published with an R package attached.
The R user base keeps growing. Many people who use R eventually start helping new users and advocating the use of R in their workplaces and professional circles. Sometimes they also become active on
✔ The R mailing lists (http://www.r-project.org/mail.html
✔ Question-and-answer (Q&A) websites, such as
● StackOverflow, a programming Q&A website (www.stackoverflow.com/questions/tagged/r)
● CrossValidated, a statistics Q&A website (http://stats.stackexchange.com/questions/tagged/r)
In addition to these mailing lists and Q&A websites, R users may
✔ Blog actively (www.r-bloggers.com).
✔ Participate in social networks such as Twitter (www.twitter.com/search/rstats).
✔ Attend regional and international R conferences.
See Chapter 11 for more information on R communities.
As more and more people moved to R for their analyses, they started trying to incorporate R in their previous workflows. This led to a whole set of packages for linking R to file systems, databases, and other applications. Many of these packages have since been incorporated into the base installation of R.
For example, the R package foreign (http://cran.r-project.org/web/packages/foreign/index.html) forms part of the recommended packages of R and enables you to read data from the statistical packages SPSS, SAS, Stata, and others (see Chapter 12).
Several add-on packages exist to connect R to database systems, such as
✔ RODBC, to read from databases using the Open Database Connectivity protocol (ODBC) (http://cran.r-project.org/web/packages/RODBC/index.html)
✔ ROracle, to read Oracle data bases (http://cran.r-project.org/web/packages/ROracle/index.html).
As more data analysts started using R, the developers of commercial data software no longer could ignore the new kid on the block. Many of the big commercial packages have add-ons to connect with R. Notably, both IBM’s SPSS and SAS Institute’s SAS allow you to move data and graphics between the two packages, and also call R functions directly from within these packages.
Other third-party developers also have contributed to better connectivity between different data analysis tools. For example, Statconn developed RExcel, an Excel add-on that allows users to work with R from within Excel (http://www.statconn.com/products.html).
Looking At Some of the Unique Features of R
R is more than just a domain-specific programming language aimed at data analysis. It has some unique features that make it very powerful, the most important one
