R Programming for Beginners
An Introduction to Learn R Programming with Tutorials and Hands-On Examples
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Table of Contents
1. Introduction
R is an object oriented scripting language that provides an environment for statistical computing. While R can also be used as a general purpose programming language, the features that it provides are well suited and are very useful for statistical computing.
In 1976, a programming language called S was developed by John Chambers at Bell Laboratories . R is an implementation of S programming language developed by Ross Ihaka and Robert Gentleman at the University of Auckland , New Zealand . At present, R is developed and maintained by R Development Core Team . R being an implementation of S programming language, most of S code should run in R environment.
R is completely open source and is released with a GNU General Public License . As an end user, you do not have to build R using its source code as pre-compiled binaries are available for Windows, Linux and MAC.
When learning R, some previous programming experience will definitely help but is not mandatory. However, you should be comfortable with using your system, be it Windows, Linux or MAC; especially be comfortable with using the Command Prompt on Windows and Terminal/Shell on MAC/Linux. Apart from general purpose programming, this book also covers statistical computing. Hence, some knowledge of statistics is needed to understand relevant sections.
2. Scope of R
R is mostly used for statistical computing, data analytics, data visualization and in relevant fields but in general, R can more or less do all the basic things that any other scripting language such as Python, Perl, Ruby, etc. can do. By basic I mean perform logical /arithmetic operations, interact with the user, access files on the system, etc. Having said that, R cannot do things like lower level system operations such as memory manipulation; there is no native framework to build a GUI or work with web projects. Despite these drawbacks what makes R special is that it can easily be extended using packages. It is also possible to interact with R code using other programming languages such as C/C++, Java, Python, etc. This feature opens up a whole new possibility of using R almost everywhere. For example, consider a user has some data and wants to plot a histogram. You can write code in R to accept data and plot the histogram in the backend and build a GUI front end using any framework of your choice (that is supported by R) such as VB.NET/C# .NET, Qt C++, etc. The user will provide data through the GUI (which is not built using R), the GUI will send the data to the R script where the histogram will be plotted and sent back to the GUI for the user to see. Alternatively, you can build a nice website using HTML/CSS/JS which will let a user to upload data, receive this data using Python CGI script, call the R code via Python, get the histogram, sent it back to the webpage. The possibilities of R integration with other languages/frameworks is endless! You can look at R as a feature rich computing environment.
Databases such as MySQL, Oracle, PostgreSQL, etc. can be accessed using R with the help of the relevant drivers. With this feature and powerful data analytics, data visualization and statistical tools, R is a very powerful programming language for Data Science and Big Data.
In this book, you will learn about basics of R and some basics of statistical computing using R. Integration of R with other languages and databases is not covered as it would also need good knowledge of other programming languages and databases in question.
3. Getting Started
R is an interpreted language and hence R interpreter is needed for programs to work. The interpreter is available via a command line interface but there also exists several integrated development environments (IDEs) such as RStudio which provides many more tools that can be used for purposes other than just writing and executing scripts. We will only use the R interpreter for executing our programs. A R program/script can be written using any text editor such as Notepad . I recommend using Notepad++ (https://notepad-plus-plus.org/ ). Simple R programs/script files have the extensions of .r or .R . There are other extensions such as . RData, .rds and .rda about which we will learn in the appropriate sections.
3.1 Install R Environment
The R environment is available for Windows, Linux and MAC. We will be using a Windows PC to write and execute R programs. R script written on one OS shall work seamlessly on other OS as long as there is no OS specific code. In order to install R environment, download the latest installation file from https://www.r-project.org/ under the Download section. At the time of writing this book (July 2019), the latest R release is 3.6.1 and the downloads are hosted by CRAN mirror servers. Once you have the installation file, log into your system with an account that has administrative privileges, execute the file, select language if asked and you will be presented with the following window:
Click Next and you will see a window which will let you set the installation location of R:
