Skip to main content

A Comprehensive Guide to Use R

1 Introduction

R is hot. Whether measured by more than 6,500+ add-on packages, the 2,000,000+ users or the 100,000+ R Meetup groups currently in existence, there can be little doubt that interest in the R statistics language, especially for data analysis, is soaring.

In this section, we’ll explore what R is, why it’s popular in the data science community, and what you need to get started with R.

1.1 What is R?

R is a programming language that is widely used in data analysis, statistics, and data science. It was developed by Ross Ihaka and Robert Gentleman in 1995, and is now maintained by the R Development Core Team. R is a free and open-source programming language that is widely used for statistical computing and graphics. It provides a wide range of statistical and graphical techniques, and has a large community of users who contribute packages and tools to extend its functionality. R can be used for data analysis, machine learning, statistical modeling, visualization, and more.

1.2 Why R?

R is a popular choice among data scientists and statisticians for several reasons. First, it’s free and open-source, so anyone can download and use it without cost. Second, it has a large and active community, which means that there are many resources and tools available for learning and using R. “You have a lot of prepackaged stuff that’s already available, so you’re standing on the shoulders of giants,” Third, R is highly flexible and customizable, allowing users to create their own functions and packages to extend its functionality. Finally, R has a wide range of packages available for data analysis, machine learning, and visualization, making it a versatile tool for data science projects.

1.3 Pre requisites

To get started with R, you will need a few things:

  • You need a desktop or laptop. Note: It runs on Windows, OS X and “a wide variety of Unix platforms,” but not yet on Android or iOS.
  • An internet Connection to download the softwares and packages, though you dont need to always be connected.
  • And most importantly the willingness for consistent learning. ** Consistency of the Power to the BRAIN ** And . . . you are ready to start working with R

“Despite our best efforts we always will” make errors, he notes. “The problem is that we often use tools and practices that make it difficult to find and correct our mistakes.”

1.4 Why NOT R?

Well, R can appear daunting at first. That’s often because R syntax is different from that of many other languages, not necessarily because it’s any more difficult than others.

“I have written software professionally in perhaps a dozen programming languages, and the hardest language for me to learn has been R,” writes consultant John D. Cook in a Web post about R programming for those coming from other languages

“The language is actually fairly simple, but it is unconventional.”