R is an open source statistics programming language, which is completely flexible and available for all mainstream operating systems at no cost. Presently, R has experienced an explosive growth in its usage globally. This explosive growth has partly resulted in the growth of user-contributed software in which most users provide code for implementing the latest statistical methods. R’s open source structure and its vast community of users have contributed in making R advanced. R is vital for professionals who want to become data scientists. The computer code on which techniques depend is critiqued and improved. The flexibility of R is seamless when compared to any other statistics program and its object-oriented programming language helps in the creation of functions that perform customised procedures and automation of tasks.

BASIC R COMMANDS

R is a completely the object-oriented language and environment where objects (single number, data set, or a model output) are stored in an R workspace. These objects are used to create other objects, which can then be used within functions. Usually, a function is an object itself. This expression is an assignment operator <− that assigns what is on the right to the object on the left and the expression −> assigns what is on the left to the object that is present on the right. The entry of all expressions into an R session is direct, and is usually denoted >. Of course, if you are in the IT profession with no knowledge of R, you needn’t fear. You can master the concepts of R through R certification training and become a pro in it.

Everything about R

In the 1990s, R was created by Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand as a statistical platform for students. R, an open source language, has been extended over decades by various user-created libraries.

What is R today?

R is very different from when it first started. It has many components:

R is data analysis software: Data scientists, analysts, and, statisticians—anyone who wants to make sense of data—can use R for data visualization, statistical analysis, and predictive modelling.

R is a programming language: R offers operators, objects, and functions that help users to explore, model, and evaluate data. Statisticians who create an object-oriented language can use R with ease.

R acts as an environment for statistical analysis: Standard statistical methods can be implemented using R, and much of the cutting-edge research in statistics along with predictive modelling is operated in R.

R is an open-source software project: R costs nothing and it is a result of years of study and tinkering by developers and users. This programming language has a great standard of numerical accuracy. The open interface of R helps it integrate with other systems and applications.

R is a community: The R project leadership has flourished to involve more than 20 leading statisticians along with computer scientists across the world. Many contributors have developed add-on packages that are extremely useful.

Various Data Science projects makes the most of R 

Once bound exclusively to academia, R now includes advocates and users across the private and public sectors. The programming software/ language environment has created inroads into social networking services, media outlets, and financial institutions. Understanding how some big names are using R guides you through its true potential.

Bank of America: Though most banking analysts have traditionally poured over Excel files, R is significantly being used for financial reshaping, especially as a visualization tool, according to the Vice President of the Bank of America, Niall O’Connor.

Facebook: Facebook’s Data Scientists use open-source R packages from Hadley Wickham, such as ggplot2, dplyr, plyr, and remodel to examine new data via custom visualizations.

New York Times: The Gray Lady uses R for interactive features like Election Forecast, data journalism and to evaluate data, whether from Mariano Rivera’s baseball career or the Facebook IPO.

Twitter: Twitter has recently developed the Breakout Detection package for R to track user experience on its network.

Wrapping Up:

R is known to be the standard language for doing statistical analysis. It has a low learning curve, but there are many data science areas to which it does not suit. While R is a bit hodgepodge of features, it is usually a fun language to operate.

Author Bio: 

Sanjay Kumar is a  Digital Marketer, living in India. My interests range from technology to design. I am also interested in web development, programming, and innovation.