This R Programming Tutorial is a comprehensive guide on how to get started with R programming, why you should learn it and how you can learn it.
What is R Programming?
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 is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes
• 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.
Why R Programming?
The R programming language is an important tool for development in the numeric analysis and machine learning spaces. R is designed for statistical computing, with thousands of packages that contain the implementations of almost every available statistical methods, making it meet the first characteristic perfectly. In fact, it’s perhaps the only tool that does so in such a comprehensive fashion.
Think of R packages as Lego pieces. When a problem requires a different method, just plug in a different R package. It’s that simple. In addition, R can handle more data points than Excel by default. You can even write R code in a parallel fashion and use inexpensive commodity computers in the cloud to analyze large datasets, like Amazon Web Services (AWS) instances. Finally, you can easily integrate R with web technologies to make analytic web apps.
How Difficult is it to Learn “R”?
If you are already using Visual Basic, sophisticated macros, or solver templates in Excel, you’ll probably find R easy to pick up. However, R is definitely not for the faint of heart. There’s a learning curve involved and it can be steep depending on your statistics and programming background. However, the reward is also huge, and like anything else, the more R code you write, the better you’ll get.
Around the world, millions of analysts and data researchers use R Programming to take care of their most difficult issues in the fields running from computational science to extensive marketing. R Programming, or R, has turned into the most prevalent language for data science and a fundamental tool for Finance and analytics-driven organizations, for example, Google, Facebook, and LinkedIn.