Chapter 2 Overview and Setup

R is a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team.

The core of R is an interpreted computer language which allows branching and looping as well as modular programming using functions. R allows integration with the procedures written in the C, C++, .Net, Python or FORTRAN languages for efficiency.

R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac.

R is free software distributed under a GNU-style copy left, and an official part of the GNU project called GNU S.

2.1 Evolution of R

R was initially written by Ross Ihaka and Robert Gentleman at the Department of Statistics of the University of Auckland in Auckland, New Zealand. R made its first appearance in 1993.

  • A large group of individuals has contributed to R by sending code and bug reports.
  • Since mid-1997 there has been a core group (the “R Core Team”) who can modify the R source code archive.

2.2 Features of R

As stated earlier, R is a programming language and software environment for statistical analysis, graphics representation and reporting. The following are the important features of R

  • R is a well-developed, simple and effective programming language which includes conditionals, loops, user defined recursive functions and input and output facilities.

  • R has an effective data handling and storage facility,

  • R provides a suite of operators for calculations on arrays, lists, vectors and matrices.

  • R provides a large, coherent and integrated collection of tools for data analysis.

  • R provides graphical facilities for data analysis and display either directly at the computer or printing at the papers.

As a conclusion, R is world’s most widely used statistics programming language. It’s the # 1 choice of data scientists and supported by a vibrant and talented community of contributors. R is taught in universities and deployed in mission critical business applications. This tutorial will teach you R programming along with suitable examples in simple and easy steps.

2.3 Local Environment Setup

2.3.1 Windows Installation

You can download the Windows installer version of R for Windows (32/64 bit) and save it in a local directory.

As it is a Windows installer (.exe) with a name “R-version-win.exe”. You can just double click and run the installer accepting the default settings. If your Windows is 32-bit version, it installs the 32-bit version. But if your windows is 64-bit, then it installs both the 32-bit and 64-bit versions.

After installation you can locate the icon to run the Program in a directory structure “R3.2.2386.exe” under the Windows Program Files. Clicking this icon brings up the R-GUI which is the R console to do R Programming.

2.3.2 Linux Installation

R is available as a binary for many versions of Linux at the location R Binaries.

The instruction to install Linux varies from flavor to flavor. These steps are mentioned under each type of Linux version in the mentioned link. However, if you are in a hurry, then you can use yum command to install R as follows −

$ yum install R

Above command will install core functionality of R programming along with standard packages, still you need additional package, then you can launch R prompt as follows −

$ R
R version 3.2.0 (2015-04-16) -- "Full of  Ingredients"          
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-redhat-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many  contributors.                    
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.