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Introduction

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 being 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.

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What is 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 systems.
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 which makes it the hottest trend nowadays
It was designed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently being 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.

Why Use R?

Statistical Analysis: R is designed for analysis and It provides an extensive collection of graphical and statistical techniques, By making a preferred choice for statisticians and data analysts.
Open Source: R is an open – source software, which means it is freely available to anyone. It can be accessble by a vibrant community of users and developers.
Data Visulaization : R boasts an array of libraries like ggplot2 that enable the creation of high-quality, customizable data visualizations.
Data Manipulation : R offers tools that are for data manipulation and transformation. For example: IT simplifies the process of filtering , summarizing and transforming data.
Integration : R can be easily integrate with other programming languages and data sources. IT has connectors to various databases and can be used in conjunction with python, SQL and other tools.
Community and Packages: R has vast ecosystem of packages that extend its functionality. There are packages that can help you accomplish needs of analytics.

Features of R Programming Language

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.
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 distributions 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.

Basic R program

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!")

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 systems.
R programming is cross-platform and 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 on 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.

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