Before we begin it is important to consider what R is. R is a programming language which is highly used in statistics. R is becoming more and more popular due to two major reasons:
1. R is open source.
2. R has most of the latest statistical methods.
R has a base language that allows a user to program almost anything they like. Of course to do this takes a lot of time and trial-and-error. This can be easily solved when you consider that there are also many user defined packages and functions.
In fact there are over 10,000 packages as of January 2017 and this number is growing exponentially.
Should you use R?
You may want to ask yourself these questions?
Do I need a tool to work with data?
Am I looking for something cost effective?
Do I want to learn to code in a language that gives me a great deal of freedom?
Would I like to be able to easily define my own procedures and functions?
If you answered Yes to any of those question than it may be worthwhile for you to start using R.
We will begin to layout a bit more framework on why so many statisticians choose to work with R over every other language.