R is an open source programming language that is widely used as statistical software and data analysis tool. R is usually shipped with a command line interface. R is available on widely used platforms such as Windows, Linux, and macOS. In addition, the R programming language is the latest innovative tool.

It was designed by Ross Ihaka and Robert Gentleman of the University of Auckland, New Zealand and is currently being developed by the R core development team. The R programming language is an implementation of the S programming language. It is also combined with the lexical semantics of schema-inspired areas. In addition, the project was conceived in 1992, with the first version released in 1995 and a stable beta version released in 2000.

Why R Programming Language? 

• R programming is employed as a number one 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 statistical package, but also allows us to integrate with other languages ??(C, C++). It allows you to easily interact with multiple 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 statistical terms are mean, mode, and mean. They are all referred to as "central trend measures". With R language, we can measure the central trend very easily.
• Static graphics: R offers many opportunities to create and develop attractive static graphics. R contains functions for various types of scenes including graphics cards, mosaics, biplots, and the list goes on.
• Probability distributions: Probability distribution plays an important role in statistics and with the help of R we can easily deal with different types of probability distributions, such as: B. binomial distribution, normal distribution, chi-square distribution and many others.
• Data analysis: It provides a large, coherent and integrated collection of tools for data analysis.

Programming Features of R:  

• R Packages: One of the main features of R is its widely used library. R has CRAN (Comprehensive R Archive Network), a repository with more than 10,000 packages.
• Distributed Computing: Distributed computing is a model in which the components of a software system are shared by multiple computers to increase efficiency and performance. In November 2015, two new ddR and multidplyr packages for distributed R programming were introduced.

R is an open-source programming language used for statistical computing and graphics. It was developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand in the 1990s. Today, it has become one of the most popular programming languages used by statisticians, data scientists, and researchers in various fields.

R is designed to handle large datasets, and has a vast library of statistical and graphical functions that can be used to perform complex analyses. It also offers a wide range of data visualization tools, including histograms, box plots, and scatterplots, that can help users to interpret and communicate their results effectively.

One of the most significant advantages of R is that it is open-source, meaning that it is free to use and can be modified by users to suit their needs. It also has a large community of developers who contribute to its development and maintenance.

R can be used for a variety of applications, including data cleaning, data analysis, machine learning, and visualization. Its popularity and versatility have led to its widespread adoption in academia, industry, and government.

Learning R requires some programming knowledge, but it is considered relatively easy to learn, especially for those who are already familiar with programming concepts. Many resources are available online, including tutorials, documentation, and forums, making it easier to get started with R.