R Programming Language

R has become a very popular programming language and development environment used for statistical computation and graphics. It started off as a GNU project that was similar to the language S but had many other features. It can be used to compute a wide variety of statistical tests that include the classic tests like Student’s Test and Correlation test. It has a powerful user base and strong community support. It is freely available to all.

What is the R programming Language?

The R programming language consists of a huge variety of statistical and graphical methods. It contains regression analysis algorithms, machine learning, time series and many more. Most of its packages or libraries are written in R.

To increase its efficiency, the procedures written in C, C++, FORTRAN, Python, and .NET can be integrated. The language has become popular among academic institutions as well as large corporations such as Uber, Google, and Facebook.

The core of the language is actually an interpreted programming language that supports modular programming, looping, and branching. It is heavily used in data analysis that is performed through the following steps:

• Programming
• Transforming
• Discovering
• Modelling
• Communicating the results

The R language programming environment is based on a command line interface.

History of R

The R programming language was developed at the University of Auckland, by Ross Ihaka and Robert Gentleman. It was named R based on the initials of the founders Ross and Robert. It is currently maintained and developed by the R Development Core Team. The project was started in 1992, and the initial version was launched in 1995. In the year 2000, the stable beta version 1.0.0 was launched for the everyone.

R is an implementation of S, another statistical programming language that was developed in Bell Labs, dating back to the 1970s. After the initial release of R, many people decided to join in and work on it in order to improve its features.

By 1995, the language had become open sourced and anyone could modify and enhance it. This was because Martin Mächler had convinced the creators of the language to use the GNU license to make R free for all.

Around the middle of 1997, a small team called the R Core Team was developed to modify the source code of R; which is operational until today.

Features of R language

The different features of the R programming language are as follows:

• It is simple and effective that contains conditional loops, recursive functions, and input/output facilities
• Its functions support procedural programming. Object-oriented programming is supported by the generic functions
• Matrix arithmetic operations are supported by the language
• Being an interpreted language, it can be operated through the command line
• Storage and data handling provisions are available
• It has many operations that can be used to work on arrays, lists, matrices, and vectors
• Features such as exporting data, handling database input, data viewing, variable labels are available
• It provides facilities for displaying the results of graphical analysis, as graphs either on screen or as a hardcopy
• R code can be run directly in the console without a compiler

Uses of R programming

The different applications of R are as follows:

• Primarily applied in the field of descriptive statistics, that is used for summarizing the important features of the data. It is used in the measurement of variability, skewness, central tendency, and finding kurtosis.
• It is used for working on continuous and discrete probability distributions. For example, the dbinom() function can be used for representing the binomial distribution.
• Exploratory data analysis is easier with R. The library package ggplot2 is very famous in this regard
• For validating statistical data models, R facilitates hypothesis testing
• R provides a package for developing interactive web applications called RShiny. Interactive visualizations can be created using this package that can be embedded in websites
• Using machine learning algorithms, R can be used for developing predictive data models
• It is used by e-commerce companies to target their customers and cross-sell their products. Product recommendations are also offered through R programming
• In social media, R is used for segmenting the users based on their preferences, and turn them into potential customers. It helps in analyzing the market and to generate more leads for companies selling their products through social media platforms.
• It is widely used in the healthcare sector, for analysis of drug safety data and genomic data. It is also used in epidemiology where doctors use statistical modelling for analysing the spread of diseases

Demand for R Programming at Present

R is currently the most popular analytic tool used by programmers around the world. It has around 2 million users across the globe. It has more number of blogs, discussion forums, and email lists than any other analytics tool including SAS. At present, all major corporations such as Google, Apple, Accenture, TechCrunch, Mozilla, Bing, The New York Times and Thomas Cook are adopting R as the primary language for data analytics.

Based on a study conducted recently, the R programming language is used mostly in the field of academics. This industry is followed by healthcare, government, consulting, insurance and energy. The language is very important if anyone wants to build a career in data science or data analytics.

The different job roles for candidates having R programming knowledge are:

• R Data Scientist
• Analyst Manager
• Business Analyst
• Senior Data Analyst
• Analyst Consultant

Companies Using R Programming Language

• Facebook - Behavior Analysis for status updates and profile pictures.
• Google - Advertising Effectiveness and Economic Forecasting.
• Twitter - Data Visualization & Semantic Clustering
• Microsoft - Use it for a variety of purposes.
• Uber - Statistical Analysis

Conclusion

The R programming language has a lot of promise and is expected to dominate the analytics and statistical modelling fields in the future. Having features like clustering, data reduction and correlation are easily performed through R. It does not have a very steep learning curve, so the programming community is expanding so quickly. Implementing artificial intelligence operations is efficient though R.