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 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 |
|
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 |
|
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
- 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.