25 Jan 2019
R - A Powerful Statistical Language & Environment

R – A Powerful Statistical Language & Environment

In recent years, Data Science has emerged as one of the hottest professions and domain principles with the demand for data scientists, machine learning specialists, and data analysts. The epoch of Big Data has the field of Data Science constantly growing, enabling business organizations to become more data-driven with better visual percept and knowledge. Well, if you are looking for a tool that can be used for computational statistics, perception, and data science, then R is worth your consideration.

R is a language and environment for statistical computing, design and is profoundly extensible. It gives a wide variety of statistical, customary statistical tests, time-arrangement analysis, grouping, clustering, graphical techniques, and has brought revolutionary modifications in Big Data Analytics and other aspects of data analytics and data science. You don’t need to be a genius to work with R. It is a simple environment which has almost 5,000 packages (libraries of functions); huge portions of which are dedicated to specific applications.

R programming is more than a statistical package; it’s a programming language with which you can create your objects, functions, and packages. Well-designed publication-quality plots, of which mathematical symbols and formulae can be produced where needed. R has beautiful visualization capabilities, using packages such as lattice, ggplot2, ggbio and many more, which can be used in substances like econometrics, data mining, machine learning, spatial analysis, and bioinformatics. The analysis actions are explicitly recorded in R programs which make it easy to reproduce and update reports. Since it is free and platform–independent, it can be used anywhere and can be implemented in any organization without purchasing a license. Also being open-source, it allows anyone to examine the source code to understand exactly what it’s doing, fix bugs and add features. One of the strong features of R programming is that it allows integration with other languages such as C/C++, Java, Python, etc., and enable it to communicate with many data sources: OlabDBC-compliant databases (Excel, Access) and other statistical packages (SAS, Stata, SPSS, Matlab). R has the flexibility and power to create reproducible, excessive quality analysis. It can be used in any field you could think of.

Around the world, many companies like Facebook, Google, Ford Motor Company and millions of people from various fields are using R language for analysis, predicting economic activities, and many more. To make a mark in the analytics industry, R is the best skill an analyst can have.


Ashmitha Lopes

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