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 pre-compiled 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 s-news 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 open-source software, under GPL2 license.
1997	R core group formed.
1997	CRAN founded (by Kurt Jornik and Fritz Leisch).
1999	The R website developed, r-project.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 domain-specific 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
Twitter	Monitor user experience
New York Times	Info-graphics, 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
Google	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 object-oriented 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 high-performance 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

1. 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.
2. 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.
3. 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.
4. Machine Learning: R provides many applications in predictive analytics and machine learning. It has many packages for common machine learning tasks like linear and non-linear regression, decision trees, linear and non-linear classification etc. Using R researchers can implement machine algorithms in fields like finance, genetics research, retail, marketing and health care.

Reference: https://www.javatpoint.com/r-introduction https://www.guru99.com/r-programming-introduction-basics.html