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Business Intelligence & Analytics for Digital Transformation

Why R for Data Scientist?

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If you are struggling to decide on choosing the language for your dream to be a Data Scientist and a never-ending war between R and Python is confusing you. While each of these have their own pros and cons and are equally competent. Here we discuss the advantages of R in Data Science and why R proves to be a fine choice in this space. Before we take a look at the advantages of R, let’s understand

What R is?

R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.

It provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible.

It is one of the most popular languages used by statisticians, data analysts, researchers and marketers to retrieve, clean, analyse, visualize and present data.

Due to its expressive syntax and easy-to-use interface, it has grown in popularity in recent years.

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

Here are the top reasons for choosing R as your Data Science language.

  1. Open Source:

R is an Open Source language, which makes it very cost effective for a project of any size. It provides the user a platform to modify and create new dynamic functions. Developments in R is happening at a rapid scale and the community of developers is huge. All of this, along with a tremendous amount of learning resources makes R programming a perfect choice to begin learning R programming for data science.

  1. Data Wrangling

Data Wrangling is the process of cleaning the raw data in a form that can be consumed for further analysis and generate insights. R provides a vast range of extensive inbuilt libraries that can be utilized for Data Manipulation and Data Wrangling.

  1. Data Visualization

R provides a large base of packages for creating beautiful and Interactive visuals that can be used by the business to generate insights. Libraries like ggplot2, plotly offers aesthetic graphical plots to its users. R is most widely recognized for its stunning visualizations which gives it an edge over other Data Science programming languages.

  1. Popularity

Conventionally, R was mostly used in Academia by the researchers and Scholars for experimenting in Data Science. With the advancement in this field and the need of Data Analysis, R gained its popularity across the space.

  1. Advance Modelling

R provides the biggest and most powerful code base for data analysis in the world. The richness and depth of available statistical models is unparalleled and growing by the day, thanks to the huge community of open source package developers and contributors.

  1. Community Support

R enjoys a vast Community support, Professionals working as Data Scientists round the globe keeps maintaining and updating R. Making R the finest choice for Data Analytics and Data Science.

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