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R Programming Language – Introduction

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R is an open-source programming language that is widely used as a statistical software and data analysis tool. R generally comes with the Command-line interface. R is available across widely used platforms like Windows, Linux, and macOS. Also, the R programming language is the latest cutting-edge tool.

It was designed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R programming language is an implementation of the S programming language. It also combines with lexical scoping semantics inspired by Scheme. Moreover, the project conceives in 1992, with an initial version released in 1995 and a stable beta version in 2000.

Why R Programming Language?

  • R programming is used as a leading tool for machine learning, statistics, and data analysis. Objects, functions, and packages can easily be created by R.
  • It’s a platform-independent language. This means it can be applied to all operating system.
  • It’s an open-source free language. That means anyone can install it in any organization without purchasing a license.
  • R programming language is not only a statistic package but also allows us to integrate with other languages (C, C++). Thus, you can easily interact with many data sources and statistical packages.
  • The R programming language has a vast community of users and it’s growing day by day.
  • R is currently one of the most requested programming languages in the Data Science job market that makes it the hottest trend nowadays.

Features of R Programming Language

Statistical Features of R: 

  • Basic Statistics: The most common basic statistics terms are the mean, mode, and median. These are all known as “Measures of Central Tendency.” So using the R language we can measure central tendency very easily.
  • Static graphics: R is rich with facilities for creating and developing interesting static graphics. R contains functionality for many plot types including graphic maps, mosaic plots, biplots, and the list goes on.
  • Probability distributions: Probability distributions play a vital role in statistics and by using R we can easily handle various types of probability distribution such as Binomial Distribution, Normal Distribution, Chi-squared Distribution and many more.
  • Data analysis: It provides a large, coherent and integrated collection of tools for data analysis.

Programming Features of R:  

  • R Packages: One of the major features of R is it has a wide availability of libraries. R has CRAN(Comprehensive R Archive Network), which is a repository holding more than 10, 0000 packages.
  • Distributed Computing: Distributed computing is a model in which components of a software system are shared among multiple computers to improve efficiency and performance. Two new packages ddR and multidplyr used for distributed programming in R were released in November 2015.

Programming in R:

Since R is much similar to other widely used languages syntactically, it is easier to code and learn in R. Programs can be written in R in any of the widely used IDE like R Studio, Rattle, Tinn-R, etc. After writing the program save the file with the extension .r. To run the program use the following command on the command line:  

R file_name.r



# R program to print Welcome to GFG!
# Below line will print "Welcome to GFG!"
cat("Welcome to GFG!")


Welcome to GFG!

Advantages of R:  

  • R is the most comprehensive statistical analysis package. As new technology and concepts often appear first in R.
  • As R programming language is an open source. Thus, you can run R anywhere and at any time.
  • R programming language is suitable for GNU/Linux and Windows operating system.
  • R programming is cross-platform which runs on any operating system.
  • In R, everyone is welcome to provide new packages, bug fixes, and code enhancements.

Disadvantages of R:  

  • In the R programming language, the standard of some packages is less than perfect.
  • Although, R commands give little pressure to memory management. So R programming language may consume all available memory.
  • In R basically, nobody to complain if something doesn’t work.
  • R programming language is much slower than other programming languages such as Python and MATLAB.

Applications of R:  

  • We use R for Data Science. It gives us a broad variety of libraries related to statistics. It also provides the environment for statistical computing and design.
  • R is used by many quantitative analysts as its programming tool. Thus, it helps in data importing and cleaning.
  • R is the most prevalent language. So many data analysts and research programmers use it. Hence, it is used as a fundamental tool for finance.
  • Tech giants like Google, Facebook, bing, Twitter, Accenture, Wipro and many more using R nowadays.

R and Python both play a major role in data science. It becomes confusing for any newbie to choose the better or the most suitable one among the two, R and Python. So take a look at R vs Python for Data Science to choose which language is more suitable for data science.

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Last Updated : 15 Aug, 2021
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