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R is technically a language and environment that provides a lot of easy but robust functionalities in the realm of advanced/general statistical computing and it also provides a very useful platform to generate graphs or graphical representations of various analysis as well. It was actually developed by the research scientists (John Chambers and other team members) of the Bell labs. R was conceptualised in terms of a different implementation of the S (another language), though there are quite a few differences in the overall framework.
R is quite flexible in terms of its implementation of various statistical and graphical methods and this framework is also getting aligned to state-of-the-art scalable environments (like H2O etc.). In a nutshell, R provides a gateway or an open-source route to implement numerous different functionalities in it.
One great highlight of R’s capabilities would be the kind of charts or graphical representations of the data, statistical tests and many more functionalities that can be created using R. This is an evolving space and almost every day new capabilities are getting added in R. It also gives the user the full control to come-up newer approaches to experiment with as well.
R is an integrated suite of software facilities for data manipulation, statistical analysis, and graphical display. Some of the key highlights about what R combines:
R is not typically a fast language. This is purely an outcome of choice!. R was purposely designed to make data analysis and statistics easier for you to do. It was not designed to make life easier for your computer. While R is slow compared to other programming languages, for most purposes, it’s fast enough.
R is an extremely dynamic programming language. Almost anything can be modified after it is implemented. To give just a few examples:
The only thing you can’t change are objects in sealed namespaces, which are created when you load a package.
The advantage of dynamism is that you need minimal upfront planning. You can always do a course correction, iterating your way to a solution without having to start afresh. On the other hand, the cons of such dynamism are that it is difficult to predict what’s going to happen, the easier it is for an interpreter or compiler to make optimizations. (If you’d like more details, Charles Nutter expands on this idea at On Languages, VMs, Optimization, and the Way of the World.) If an interpreter can’t predict what’s going to happen, it has to consider many options before it finds the right one.
There are quite a few exciting new implementations of R that are available. While they all try to stick as closely as possible to the existing language definition, they also improve speed by using ideas from modern interpreter design. The four most mature open-source projects are:
R was actually built by statisticians, for statisticians – and most developers can tell that by looking at its particular syntax. One major reason of popularity for R is also related to the recent developments in the realm of machine learning and deep learning. Since the mathematical computations involved in machine learning are derived from statistics, R comes in handy to those who want to gain a better understanding of the underlying details and build something truly innovative.
Let’s discuss some pros and cons of R as a go-to language for ML/DL tasks:
Given its wide range of usage, it is quite important to learn the key nuggets of this language. In this tutorial, we will delve deep further and learn about various nuances of the R language.