Introduction to R Programming
What is R Programming?
 R is a programming language which provides an environment for software used for statistical analysis, graphics representation and reporting.
 It possesses an extensive catalog of graphical and statistical methods. It includes machine learning algorithm, time series, linear regression, statistical interference, etc.
 R was developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team.
 R libraries are written in R programming language, but for a heavy computational task, C/C++ and FORTRAN codes are preferred.
 R is freely available under GNU General Public License, and various precompiled binary versions are provided for various Operating systems like Windows, Mac, and Linux.
Evolution of R Programming
The First public announcement about R was posted by Ross Ihaka and Robert Gentleman to the snews mailing at the University of Auckland, New Zealand, on August 4, 1993.
R is named partly after the first letter of their names of its authors.
A brief history of R project:
Year 
Description 
1993 
R development begins as a research project in Auckland, NZ by Ross Ihaka and Robert Gentleman. 
1994 
First binary versions of R published at Statlib. 
1995 
R was first distributed as opensource software, under GPL2 license. 
1997 
R core group formed. 
1997 
CRAN founded (by Kurt Jornik and Fritz Leisch). 
1999 
The R website developed, rproject.org, founded. 
2000 
R 1.0.0 released (February 29). 
2000 
John Chambers, recipient of the 1998 ACM Software Systems got Award for the S language, joins R Core. 
2001 
R News developed (later to become the R Journal). 
2003 
R Foundation founded. 
2004 
First UseR! Conference (in Vienna). 
2004 
R 2.0.0 released. 
2009 
First edition of the R Journal. 
2013 
R 3.0.0 released. 
2015 
R Consortium founded, with R Foundation participation. 
2016 
New R logo adopted. 
Why use R for Statistical Computing and Graphics?
 R is open Source and Free
R is freely available as it is licensed under the terms of GNU General Public license. You can look at the source to see the actual working. Many R packages and libraries are available under the same license so, you can use them in your commercial applications.
 R is popular and increasing in popularity
There is a list of most popular programming languages publishes each year. The rank of R was 6^{th} in 2015 and 5^{th} in 2016. It is a big deal for domainspecific language like R to be more popular and useful than a general purpose language like C#. This increment is not only in R as a programming language but also of the fields like data science and machine learning where it is commonly used.
 R runs on all platforms
R is a programming language that can run on all popular platforms like Windows, Linux, and Mac. R codes written in one platform can easily be ported to another without any issue.
 Learning R will also increase your chances of getting a job
According to data science, a salary of data scientists by O’Reilly Media in 2014 was a median of $98,000 worldwide. The figure is higher in the US which is around $144,000. Even if you are applying for the post of software developer, R programming experience can make you stand out from the crowd.
 R is used by the biggest tech giants
R is the right mix of simplicity and power.
Here are some companies using R:
Company 
Application/Contribution 
Ford 
Analyze social media to support design decisions for their cars 

Monitor user experience 
New York Times 
Infographics, data journalism 
Microsoft 
Founded Microsoft R open, an enhanced R distribution and Microsoft R server after acquiring Revolution Analytics in 2015 
Human Rights Data Analysis Group 
Measure the import of war 

Created the R style guide for the R user community inside Google 
Features of R Programming
The following are the features of the R programming language:
 R is a simple programming language.
 R is free and open source software.
 R is a procedural programming language with functions and objectoriented programming (OOP) with generic function. Procedural programming language includes procedures, records, modules and procedure calls while OOP includes class, objects and functions.
 R programming also supports packages which are useful in collecting sets of R functions into a single unit.
 R provides an effective data handling and storage facility.
 R is an interpreted language. Hence we use it through a command line argument.
 R supports database input, exporting data, viewing data, variable labels, missing data, etc.
 R also supports matrix arithmetic.
 R provides a large pool of operators for performing operations on arrays and matrices.
 R provides the facility to print the reports for the analysis performed in the form of graphs either on screen or on hardcopy.
 R supports distributed computing. Where distributed computing is an open source highperformance platform for the R language. It divides the tasks between multiple processing nodes to reduce execution time and analyze large datasets.
Applications of R Programming in the Real World
 Finance : Many research programmers and data analyst use R because R is the most prevalent language. Therefore, we use R as a fundamental tool for finance.
 Data Science: R is the favorite tool of data scientist’s where data scientists is a statistician with an extra asset like computer programming skills.
 Statistical computing: R is the most famous programming language among statisticians. In fact, it was initially developed by statisticians for statisticians. It provides more than 9100 packages with every statistical function you can imagine. R also supports charting capabilities, which means you can plot your data and create interesting visualizations from any dataset.
 Machine Learning: R provides many applications in predictive analytics and machine learning. It has many packages for common machine learning tasks like linear and nonlinear regression, decision trees, linear and nonlinear classification etc. Using R researchers can implement machine algorithms in fields like finance, genetics research, retail, marketing and health care.