5 Reasons Why R Programming is the best for Data Science

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.

Also Read: Top 5 Data Science and Machine Learning Courses for Programmers

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.

Also Read: Best Data Sources For Building Data Science Models

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.

After the arrival of tools like “dplyr” and “ggplot2” learning curve complexities were reduced even further. As with time R kept on evolving like any other programming interface it achieved more and more structural and consistent, Tidyverse came to be much more efficient, which included support packages for manipulation, visualization, iteration, modeling, and communication, which all made R an easy language to learn.

3. R is majorly for Business:

The major advantage of R in comparison to any other programming language is that it is capable of producing business ready reports and infographics, and ML powered web applications. None other tools in the market are as effective as R. Two qualities we are pressurizing on are namely “RMARKDOWN” & “shiny”.

“RMARKDOWN” is a framework which creates reconstructable reports which has come a long way to building blogs, even presentations, websites, books journals and much more. This tool not only sounds cool but really is, many top management firms use this as their way to prepare a report to analyses business for their firms and even commercialize what they achieve through this awesome framework.

“Shiny” is a framework which is capable of creating interactive web applications which are powered by R. This is a widely used framework as almost all projects require a website where results are shown hence shiny is a very handy tool.

Also Read: 3 Best Programming Languages for Data Mining/Analytics

4. R is the best Allrounder

Terming R as just powerful is actually an understatement for the power it possesses. From businesses perspective, R is basically Excel on Steroids and lots of them. R is not just powerful but it is smart and has a powerful infrastructure. It implements many algorithms including high-end Machine learning package (H2O), TensorFlow deep learning packages, xgboost the top Kaggle algorithm and many more.

Tidyverse the infrastructure we have already talked a lot about it but this infrastructure is the major strengths of language R as this enables the ecosystem of application to be developed using a more appropriate and structural approach which is consistent. It comes with libraries such as “dpylr”, ”tidyr”, ”stringr”, ”lubridate”, “forcats” and many more which simplifies the developing process even further.
Thus it won’t be wrong to say R is an all-rounder.

5. Community Support

For any Programming language or interface to excel its community support needs to be top notch, even if the product is best but with no community support it is likely to be not used as there won’t be any helping hand neither would there be the referrers. Like any other top languages, R has a huge community support. It’s a group of tech enthusiast peoples with a lot of eager to learn and deliver the learning, community is always maintained in a fun environment and every question is answered peacefully and rapidly, providing a helping hand to the beginners, all things a newbie might need are already present there and that’s the coolest part of having this huge community.

All these features make R stand out when it comes to business analytics by data science, as this tech received a limelight in last few years learning this right now might be good for newbies and even for preexisting developers or person belonging from non-programming background.