Netezza Performance Server

nested-group-icon.png

DB2

Why should you choose R

By Nikita Nirbhavane posted 29 days ago

  

Why R?

The questions which arise in the mind of every R aspirant before starting to use R is – Why R? What are the reasons to use R for Data Science? So, let’s see why R is trending and getting important in the field of Data Science. R is the most popular language in the world of Data Science. It is heavily used in analyzing data that is both structured and unstructured. R allows various features that set it apart from other Data Science languages. So let us understand why you must use R and how it will benefit you in the domain of Data Science.

1. Why is R important?

R plays a very important role in Data Science. Many calculations are 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 is used in biology, genetics as well as in statistics. R is turning into a complete language where any type of task can perform.

2. Why is R good for 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, use of these visualizations have improved understanding of statistics and trends better, which indirectly helps to better and quick decision making for business.

3. Open-source

R is an open-source language. It is maintained by a community of active users and it is 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.

4. Popularity

R has become one of the three most popular programming languages for Data Science in the industries. Conventionally, R was mostly used in academics 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.

5. Visualization Library

R offers libraries like ggplot2, plotly that provide 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. Also there is much more happening in this area, as the R community creates and provides a lot of packages for visual analysis.

6. 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. There are several communities around the world that organize and solve R problems. e.g https://community.rstudio.com/

7. Statistics and Data Science

R was originally used for Statistics and now is popularly used for Data Science. R was developed for statistics, by statisticians. It was used by Statisticians way before it was used by Data Scientist. Statisticians and Data Scientists are most familiar with R than any other programming language. R facilitates various statistical operations through its thousands of packages.

8. R is used in almost everywhere

R is one of the most widely used programming languages in the world today. It is used in almost every industry from finance to banking to medicine and manufacturing. It is used for carrying out an analysis of business and market data. R is also used to implement various statistical measures to optimize industrial processes.

Summary

    • R is one of the most popular languages for Data Science.
    • R is an open-source that’s why you can run R anywhere any time and you have help from it's active community online.
    • R provides multiple packages for Data Analytics and Visualization
    • R is good for business as it offers aesthetic statistical data with its various packages.

Data Science has become a key factor in driving business in current times, R is beneficial to understand and resolve various Data Science problems.

 

0 comments
13 views

Permalink