R is a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac. This programming language was named R, based on the first letter of first name of the two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs Language S.

To start with R programming, the very basic program is to print "Hello, World!" You can use the print() function. Below is an example of R code to print "Hello, World!" −

Our R programming tutorial provides various examples to explain different concepts. We have provided Online R Compiler/Interpreter. You can Edit and Execute almost all the examples directly from your browser without the need to set up your development environment.

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Following are important characteristics of R Programming −

R is a well-developed, simple and effective programming language which includes conditionals, loops, user defined recursive functions and input and output facilities.

R has an effective data handling and storage facility.

R provides a suite of operators for calculations on arrays, lists, vectors and matrices.

R provides a large, coherent and integrated collection of tools for data analysis.

R provides graphical facilities for data analysis and display either directly at the computer or printing at the papers.

R is a programming language and software environment for statistical analysis, graphics representation and reporting. Following are applications of R programming language.

Statistical Analysis − R programming provides inbuilt support for descriptive statistics, inferential statistics, regression analysis, time series analysis and classical statistical tests.

Data Science − R is very important tool in data science workflow and supports data wrangling and preprocessing, data exploration and data import/export activities.

Data Visualization − Using R, we can easily create static graphics, interactive visualizations, and maps.

Machine Learning − R provides a rich environment for supervised learning, unsupervised learning, model evaluation, model selection and so on.

Acedemia and Research − R is a preferred language in many premier acedemic and research institutions for teaching statistics and data science and to publish researches.

R is used for it data analysis capabilities in many prominent companies like −

Google − to prepare advertizing models and to project economic forecasting.

Microsoft − Azure machine learning is having R programming language integrated with it.

Facebook − Uses R to perform social network analysis and to predict user interactions.

X (Twitter) − Uses R to visualize data and for semantic clustering.

ANZ Bank − R is used to model credit risks.

Ford − R is a used to analyze customer behaviors and market strategies.

New York Times − Uses R for visualizations and data journalism.

Practice R from the below given links−

If you know R nicely, then you have a great career ahead. Here are just a few of the career options where R is a key skill:

Game developer

Web designer

R developer

Full-stack developer

Machine learning engineer

Data scientist

Data analyst

Data engineer

DevOps engineer

Software engineer

Many more other roles

You can download R from its official website − https://cran.r-project.org/

This tutorial is designed for software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming. If you are trying to understand the R programming language as a beginner, this tutorial will give you enough understanding on almost all the concepts of the language from where you can take yourself to higher levels of expertise.

Before proceeding with this tutorial, you should have a basic understanding of Computer Programming terminologies. A basic understanding of any of the programming languages will help you in understanding the R programming concepts and move fast on the learning track.

You can explore a set of R Questions and Answers at R Questions − Answers.

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