Why R?

The questions which arise in the mind of every R aspirant before starting to learn R Programming is — Why Learn R? What are the reasons to learn R for Data Science? Here, in this tutorial, we will answer all these types of questions. And, I promise after completing this tutorial, you will not be having any confusion related to the importance of R Programming. So, let’s quickly start.

R is the most popular language in the world of Data Science. It is heavily used in analysing data that is both structured and unstructured. This has made R, the standard language for performing statistical operations. R allows various features that set it apart from other Data Science languages. In this article, we will explain why you must learn R and how it will benefit you in the domain of Data Science.

Why Learn R Programming?

There are various reasons to learn R, why learn R.

1. Why R is important for Data Science?

R plays a very important role in Data Science, you will be benefited with following operations in R.

• You can run your code without any compiler — R is an interpreted language. Hence we can run code without any compiler. R interprets the code and makes the development of code easier.
• Many calculations done with vectors — R is a vector language, so anyone can add functions to a single Vector without putting in a loop. Hence, R is powerful and faster than other languages.
• Statistical Language — R used in biology, genetics as well as in statistics. R is a turning complete language where any type of task can perform.

2. Why R is Good for Business?

R will just not help you in the technical fields, it will also be a great help in your business.

• Here, the major reason is that R is open-source, therefore it can be modified and redistributed as per the user’s need. It is great for visualization and has far more capabilities as compared to other tools.
• For data-driven businesses, lack of Data Scientists is a huge concern. Companies are using R programming as their core platform and are recruiting trained R programmers.

3. R is a gateway to Lucrative Career

R language is used extensively in Data Science. This field offers some of the highest-paying jobs in the world today. Data Scientists who are proficient in R make more than $117,000 on an average per year. If you want to enter the field of Data Science and earn a lucrative salary, then you must definitely learn R.

4. Open-source

R is an open-source language. It is maintained by a community of active users and you can avail R for free. You can modify various functions in R and make your own packages. Since R is issued under the General Public Licence (GNU), there are no restrictions on its usage.

5. Popularity

R has become one of the most popular programming languages in the industries. Conventionally, R was mostly used in academia but with the emergence of Data Science, the need for R in the industries became evident. R is used at Facebook for social network analysis. It is being used at Twitter for semantic analysis as well as visualizations.

6. Robust Visualization Library

R comprises of libraries like ggplot2, plotly that offer 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.

7. With R, you can develop amazing Web-Apps

R provides you with the ability to build aesthetic web-applications. Using the R Shiny package, you can develop interactive dashboards straight from the console of your R IDE. Using this, you can embed your visualizations and enhance the storytelling of your data analysis through aesthetic visualizations.

Any queries in why learn R article till now? Please comment below.

8. R enjoys a vast Community Support

R Programming is supported by a vast community that maintains and updates R. If you face any trouble with the code in R, you can avail the support of the community on places like Stack Overflow . There are several communities around the world that organize boot camps and R meetups.

9. A go-to language for Statistics and Data Science

R is the standard language for Statistics and Data Science. R was developed for statistics, by statisticians. It has been in use even before the word “Data Science” was coined. Statisticians and Data Scientists are most familiar with R than any other programming language. R facilitates various statistical operations through its thousands of packages.

10. R is being used in almost every industry

R is one of the most widely used programming languages in the world today. It is used in almost every industry, ranging from finance, banking to medicine and manufacturing. R is used for portfolio management, risk analytics in finance and banking industries. It is used for carrying out an analysis of drug discovery and genomic analysis in bioinformatics. R is also used to implement various statistical measures to optimize industrial processes.

Programming Features of R

R has various programming features which we will discuss below:

1. Data Inputs and Data Management

• Data inputs such as data type, importing data, keyboard typing.
• Data management such as data variables, operators.

2. Distributed Computing and R Packages

• Distributed Computing — Distributed computing is an open-source, high-performance platform for the R language. It splits tasks between multiple processing nodes to reduce execution time and analyse large datasets.
• R Packages — R packages are a collection of R functions, compiled code and sample data. By default, R installs a set of packages during installation.

Advantages and Disadvantages of R Programming

There are several benefits and some limitations of the R programming language. Let us discuss them one by one:

Pros of R Language

• R is the most comprehensive statistical analysis package, as new technology and ideas often appear first in R.
• R is an open-source that’s why you can run R anywhere any time, and even sell it under conditions of the license.
• It is cross-platform which runs on many operating systems. It’s best for GNU/Linux and Microsoft Windows.
• In R, everyone is welcomed to provide bug fixes, code enhancements, and new packages.

Cons of R Language

• The quality of some packages in R is less than perfect.
• There’s no customer support of R Language whom you can complain if something doesn’t work.
• R commands hardly concerns over memory management, and so R can consume all the available memory.

Additional Recourses

Where You can learn and be Master in R Programming Language

→ https://www.tutorialspoint.com/r/index.htm

→ https://www.youtube.com/watch?v=_V8eKsto3Ug

→ https://www.w3schools.com/r/

And here are some challenging and real-time Data Science projects

→ https://data-flair.training/blogs/data-science-projects-code/

Thanks.