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In This Chapter

▶ Discovering the benefits of R

▶ Identifying some programming concepts that make R special

With an estimated worldwide user base of more than 2 million people, the R language has rapidly grown and extended since its origin as an academic demonstration language in the 1990s.

Some people would argue – and we think they’re right – that R is much more than a statistical programming language. It’s also

✔ A very powerful tool for all kinds of data processing and manipulation

✔ A community of programmers, users, academics, and practitioners

✔ A tool that makes all kinds of publication-quality graphics and data visualizations

✔ A collection of freely distributed add-on packages

✔ A versatile toolbox for extensive automation of your work

In this chapter, we fill you in on the benefits of R, as well as its unique features and quirks.

You can download R at www.r-project.org. This website also provides more information on R and links to the online manuals, mailing lists, conferences, and publications.

Tracing the history of R

Ross Ihaka and Robert Gentleman developed R as a free software environment for their teaching classes when they were colleagues at the University of Auckland in New Zealand. Because they were both familiar with S, a programming language for statistics, it seemed natural to use similar syntax in their own work. After Ihaka and Gentleman announced their software on the S-news mailing list, several people became interested and started to collaborate with them, notably Martin Mächler.

Currently, a group of 21 people has rights to modify the central archive of source code (http://www.r-project.org/contributors.html). This group is referred to as the R Core Team. In addition, many other people have contributed new code and bug fixes to the project.

Here are some milestone dates in the development of R:

✔ Early 1990s: The development of R began.

✔ August 1993: The software was announced on the S-news mailing list. Since then, a set of active R mailing lists has been created. The web page at www.r-project.org/mail.html provides descriptions of these lists 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.

It comes as free, open-source code

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.

Any freedom also has associated obligations. In the case of R, these obligations are described in the conditions of the license under which it is released: GNU General Public License (GPL), Version 2. The full text of the license is available at www.r-project.org/COPYING. It’s important to stress that the GPL does not pertain to your usage of R. There are no obligations for using the software – the obligations just apply to redistribution. In short, if you change and redistribute the R source code, you have to make those changes available for anybody else to use.

It runs anywhere

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.

It supports extensions

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.

It provides an engaged community

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.

It connects with other languages

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).

Initially, most of R was based on Fortran and C. Code from these two languages easily could be called from within R. As the community grew, C++, Java, Python, and other popular programming languages got more and more connected with R.

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 arguably being the notion of vectors. These vectors allow you to perform sometimes complex operations on a set of values in a single command.

Performing multiple calculations with vectors

R is a vector-based language. You can think of a vector as a row or column of numbers or text. The list of numbers {1,2,3,4,5}, for example, could be a vector. Unlike most other programming languages, R allows you to apply functions to the whole vector in a single operation without the need for an explicit loop.

It is time to illustrate vectors with some real R code. First, assign the values 1:5 to a vector called x:

> x <– 1:5

> x

[1] 1 2 3 4 5

Next, add the value 2 to each element in the vector x:

> x + 2

[1] 3 4 5 6 7

You can also add one vector to another. To add the values 6:10 element-wise to x, you do the following:

> x + 6:10

[1] 7 9 11 13 15

To do this in most other programming language would require an explicit loop to run through each value of x. However, R is designed to perform many operations in a single step. This functionality is one of the features that make R so useful – and powerful – for data analysis.

We introduce the concept of vectors in Chapter 2 and expand on vectors and vectorization in much more depth in Chapter 4.

Processing more than just statistics

R was developed by statisticians to make statistical data analysis easier. This heritage continues, making R a very powerful tool for performing virtually any statistical computation.

As R started to expand away from its origins in statistics, many people who would describe themselves as programmers rather than statisticians have become involved with R. The result is that R is now eminently suitable for a wide variety of nonstatistical tasks, including data processing, graphical visualization, and analysis of all sorts. R is being used in the fields of finance, natural language processing, genetics, biology, and market research, to name just a few.

R is Turing complete, which means that you can use R alone to program anything you want. (Not every task is easy to program in R, though.)

In this book, we assume that you want to find out about R programming, not statistics, although we provide an introduction to statistics with R in Part IV.

Running code without a compiler

R is an interpreted language, which means that – contrary to compiled languages like C and Java – you don’t need a compiler to first create a program from your code before you can use it. R interprets the code you provide directly and converts it into lower-level calls to pre-compiled code/functions.

In practice, it means that you simply write your code and send it to R, and the code runs, which makes the development cycle easy. This ease of development comes at the cost of speed of code execution, however. The downside of an interpreted language is that the code usually runs slower than the equivalent compiled code.

If you have experience in other languages, be aware that R is not C or Java. Although you can use R as a procedural language such as C or an object-oriented language such as Java, R is mostly based on the functional programming paradigm. As we discuss later in this book, especially in Part III, this characteristic requires a bit of a different mindset. Forget what you know about other languages, and prepare for something completely different.