Why Should You Learn R for Data Science?

by November 26, 2019

Data Science is the most popular field of study in today’s world that leverages scientific methods, processes, algorithms and systems to convert structured and unstructured data into meaningful insights. Since it is mostly comprised of statistics, R is the bridging language of this field. It is a popular programming language and software environment and an important tool for data science.

R is one that enables statistical computing which is used widely by the data miners and statisticians for data analysis. It has various programming features, including data inputs and data management; and distributed computing and R packages – a collection of R functions, with code and sample data.

R carries a collection of more than 10,000 packages in its CRAN repository, where these packages appeal to several statistical applications. This language, for beginners, may offer a steep learning curve, but its syntax may be easy to understand. It is an expressive tool that is used to implement statistical learning.


Why Choose R?

Since the world of data science is evolving at a rapid pace, there is a pressing need to assess and create insights from the raw data. Thus, it requires various significant tools to churn that data and R is one of the programming languages that offer an intensive environment for anyone who wants to learns it to examine, process, transform and visualize information.

As R was originally practiced for academic purposes, it is now being leveraged in industries as well. It also provides support for operations on arrays, matrices, and vectors. It is famous for its graphical libraries that allow a user to outline aesthetic graphs and make them intractable for others. We have listed here the major points of why to learn R.

A Complete Language – R provides the ability to run your code without any compiler, add functions to a single vector without putting in a loop and perform any type of task in statistical language. It also contains several features of an Object-Oriented Programming language.

Open-Source – R is an open-source language and maintained by a community of active users. A data science enthusiast can avail R for free. He/she can easily adapt various functions in R and make their own packages. Since R is issued under the General Public Licence (GNU), there are no limitations on its usage.

Robust Visualization Library – R has a range of visualization libraries like ggplot2, plotly and Tidyr that provide aesthetic graphical plots to its users. R is most widely recognized for its remarkable visualizations which gives it an edge over other Data Science programming languages.

Easy to Develop Web-Apps – With R, a developer can construct incredible web applications. By leveraging its robust packages, you can develop interactive dashboards straight from the console of your R programming IDE. Using this, you can also implant your visualizations and improve the storytelling of data analysis through aesthetic visualizations.

Extensive Community Support – R Programming has an active community that maintains and updates R. If a user faces any trouble with the code in this language, he/she can smoothly avail the support of the community on places like Stack Overflow. R provides several global boot camps and workshops and R meetups. This makes R an ideal choice for many.

So, considering the aforementioned qualities, we can say that R is vital in data science. It is usually used in the data science field when the task requires a distinct analysis of data for standalone or distributed computing.