**Make your move to be the next data scientist by looking at the R features and know whether R is worth the attention or not.**

Look at everything around yourself and you will observe that you are surrounded by enormous **Data**!

And to work in the internet factory where the raw material is data the top skill in demand is to process these raw data into **business insights**.

The art of processing this data is called **Data Science** and the tool used for processing is a programming language.

*So have you started learning Data Science or want to start it?*

There is no special programming language dedicated to data science but looking at the exciting **features** of the R language you can make your mind.

R programming language is filled with such exciting and amazing features. In this article, we will see what makes R so popular, what are the advantages it has over other technologies, and what makes it an indispensable tool for data scientists all over the world.

Let’s look at the amazing features of R and how it can help you to learn data science and become a data scientist.

## Features of R Programming

There are many things R can do for data scientists and analysts. These key features are what set R apart from the crowd of statistical languages:

### 1. Open-source

R is an open-source software environment. It is **free** of cost and can be adjusted and adapted according to the user’s and the project’s requirements.

You can make improvements and **add packages** for additional functionalities.

R is freely available. You can learn how to install R, Download and start practicing it.

### 2. Strong Graphical Capabilities

R can produce static graphics with production quality visualizations and has extended libraries providing interactive graphic capabilities.

This makes **data visualization** and data representation very **easy**.

From concise charts to elaborate and interactive flow diagrams, all are well within R’s repertoire. Look at the attractive graphical visualizations in R.

### 3. Highly Active Community

R has an open-source library which is supported by its **growing** number of users.

The R environment is continuously growing. This growth is due to its **large user-base**.

### 4. A Wide Selection of Packages

**CRAN** or Comprehensive R Archive Network houses more than **10,000** different **packages** and extensions that help solve all sorts of problems in data science.

High-quality interactive graphics, web application development, quantitative analysis or machine learning procedures, there is a package for every scenario available.

**R contains a sea of packages** for all the forms of disciplines like astronomy, biology, etc. While R was originally used for academic purposes, it is now being used in industries as well.

### 5. Comprehensive Environment

R has a very comprehensive development environment meaning it helps in statistical computing as well as software development.

R is an **object-oriented programming language**. It also has a robust package called **Rshiny** which can be used to produce full-fledged web apps.

Combined with data analysis and data visualization, R can be used for highly interactive online data-driven storytelling.

### 6. Can Perform Complex Statistical Calculations

R can be used to perform simple and complex mathematical and statistical calculations on data objects of a wide variety. It can also perform such operations on large data sets.

### 7. Distributed Computing

In distributed computing, tasks are split between multiple processing nodes to reduce processing time and increase efficiency.

R has packages like **ddR** and **multiDplyr** that enable it to use distributed computing to process large data sets.

### 8. Running Code Without a Compiler

R is an **interpreted language** which means that it **does not need a compiler** to make a program from the code. R directly interprets provided code into lower-level calls and pre-compiled code.

### 9. Interfacing with Databases

R contains several packages that enable it to **interact with databases** like Roracle, Open Database Connectivity Protocol, RmySQL, etc.

### 10. Data Variety

R can handle a variety of structured and unstructured data. It also provides various data modeling and data operation facilities due to its interaction with databases.

### 11. Machine Learning

R can be used for machine learning as well. The best use of R when it comes to machine learning is in case of exploration or when building one-off models.

### 12. Data Wrangling

Data wrangling is the process of cleaning complex and inconsistent data sets to enable convenient computation and further analysis. This is a very time taking process.

R with its extensive library of tools can be used for **database manipulation** and wrangling.

### 13. Cross-platform Support

R is **machine-independent**. It supports the cross-platform operation. Therefore, it can be used on many different operating systems.

### 14. Compatible with Other Programming Languages

While most of its functions are written in R itself, C, C++ or FORTRAN can be used for computationally heavy tasks. Java, .NET, Python, C, C++, and FORTRAN can also be used to manipulate objects directly.

### 15. Data Handling and Storage

R is integrated with all the formats of data storage due to which data handling becomes easy.

### 16. Vector Arithmetic

Vectors are the most basic data structure in R, and most other data structures are derived from vectors.

R uses vectors and vector arithmetic and does not need a lot of looping to process a large set of values. This makes R much more efficient.

### 17. Compatibility with Other Data Processing Technologies

R can be easily paired with other data processing and **distributed computing technologies** like Hadoop and Spark. It is possible to remotely use a Spark cluster to process large datasets using R.

R and Hadoop can be paired as well to combine Hadoop’s large scale data processing and distributing computing capabilities with R’s statistical computing power.

### 18. Generates Report in any Desired Format

R’s markdown package is the only report generation package you will ever need when working with R. The markdown package can help produce **web pages**.

It can also generate reports in the form of word documents or PowerPoint presentations. All with your R code and results embedded into them

### Some Unique Features of R Programming

Due to a large number of packages available, there are many other handy features as well:

- Since R can perform operations directly on
**vectors**, it doesn’t require too much looping. - R can pull data from APIs, servers, SPSS files, and many other formats.
- R is useful for
**web scraping**. - It can perform multiple complex mathematical operations with a single command.
- Using R Markdown, it can create attractive reports that combine plain text with code and visualizations of the results.
- Due to a large number of researchers and statisticians using it, new ideas and technologies often appear in the R community first.

## Summary

In this article, we explored some of the important features of R. We learned why it is the **most preferred** language for statistical modeling. Its massive repository and sheer versatility make R the most **popular** statistical language.

**Don’t know what exactly R is?**

Read what is R and take your first step towards Data Science.

Still, have some doubts? **Ask us **and our **TechVidvan** experts will be happy to help you.

Keep Learning** 🙂**