Introduction to R Programming

Introduction to R

What is R

  • 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

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 6th in 2015 and 5th 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

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

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 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