5 Reasons Why R Programming is the best for Data Science Posted on : Jun 18 - 2018

Since its inception, the programming language R has been one of the leading preferences for Data scientists & researchers and statisticians. R is a GNU package which was appeared in late 1993; it is free software environment for statistical computing.  In recent years R’s popularity has increased exponentially due to advancements in Data analytics field.

As data science is evolving day by day, it is safe to assume that Data science is the future of business analytics. In this competitive environment you don’t want to lag behind your competitor hence one would not want to waste any time on the wrong tool. To be always one step ahead one should know which the best tool for the job is and here are some points proving R is the best programming language for data science.

1. R is Data Science for Non-computer Scientists

If you go and research for high-end data science tools you will find majorly only two options R or Python. Python is a programming language for software engineers with knowledge of math, stats, and Machine Learning but lacks in library support for important subjects in relation to subjects such as Econometrics and various communication tools such as reporting.

As mostly the people interested in Data science for business are from business background rather being from Technicalities of developing and programming, Learning Python is altogether a challenge for them which is coupled with no as such support for Econometrics., also most activities in a business & finances involves communication which is in form of infographics, reports or interactive applications. Clearly, support for these two is not provided as well by the python so we need to look to our other option that is R.

R is a statistical programming language with support libraries for ML, Stats, and data science. R is best fit for data science for business because it lends itself completely to its in-depth support for topic-specific packages and the infrastructure of its communication. Besides this R has support packages or libraries for Finance, Econometrics, etc. which is highly used for business analytics, it is interactive to use and is simple as compared to complexities of Python

2. Learning R is easy after the introduction of “Tidyverse”

In the beginning, R was considered as one of the most complex languages to learn and supposedly very inconsistent, as during that period structuring and formality was not the top priorities as it was in other programming languages. But this all changed when Tidyverse was introduced, it is a set of packages and tools which provide consistent structural programming interface. View More