## Features of R programming

R is a domain-specific programming language which aims to do data analysis. It has some unique features which make it very powerful. The most important arguably being the notation of vectors. These vectors allow us to perform a complex operation on a set of values in a single command. There are the following features of R programming:

- It is a simple and effective programming language which has been well developed.
- It is data analysis software.
- It is a well-designed, easy, and effective language which has the concepts of user-defined, looping, conditional, and various I/O facilities.
- It has a consistent and incorporated set of tools which are used for data analysis.
- For different types of calculation on arrays, lists and vectors, R contains a suite of operators.
- It provides effective data handling and storage facility.
- It is an open-source, powerful, and highly extensible software.
- It provides highly extensible graphical techniques.
- It allows us to perform multiple calculations using vectors.
- R is an interpreted language.

## Why use R Programming?

There are several tools available in the market to perform data analysis. Learning new languages is time taken. The data scientist can use two excellent tools, i.e., R and Python. We may not have time to learn them both at the time when we get started to learn data science. Learning statistical modeling and algorithm is more important than to learn a programming language. A programming language is used to compute and communicate our discovery.

The important task in data science is the way we deal with the data: clean, feature engineering, feature selection, and import. It should be our primary focus. Data scientist job is to understand the data, manipulate it, and expose the best approach. For machine learning, the best algorithms can be implemented with R. **Keras** and **TensorFlow** allow us to create high-end machine learning techniques. R has a package to perform **Xgboost** . Xgboost is one of the best algorithms for **Kaggle competition** .

R communicate with the other languages and possibly calls Python, Java, C++. The big data world is also accessible to R. We can connect R with different databases like **Spark** or **Hadoop** .

In brief, R is a great tool to investigate and explore the data. The elaborate analysis such as clustering, correlation, and data reduction are done with R.