Features of R Programming that will make you obsessed with R Language!

Whenever we tend to learn something new, we’re curious to know about its features. The same condition applies while learning R programming. We’re very eager to know about its features. R provides a wide variety of features that makes it stand alone from the crowd. These features of R programming are very easy to use and are universally acceptable.

In this article, we will see how R has emerged as one of the most popular languages. This emergence is mainly contributed to the features that R possesses. These features have made R a quintessential language for statistics. It is due to these features that R has evolved into the lingua franca of Data Science.

Take a deep dive into core concepts of R from our R Programming Tutorial Series

Latest Features of R Programming

R comes under the most popular programming languages for learning Data Science. It has advanced features that have enabled R to become one of the most widely used programming languages. R has several features which have contributed towards its overall popularity. In this article, we will have a look at some of the unique features of R:

1. Comprehensive Language

R is a comprehensive programming language, meaning that it provides services for statistical modeling as well as for software development. It is the primary language for Data Science as well as for developing web applications through its robust package RShiny. R is also an object-oriented programming language which is an addition to its procedure programming feature.

Learn everything about Object-Oriented Programming in R

2. Provides a Wide Array of Packages

R is most widely used because of its wide availability of libraries. R has CRAN, which is a repository holding more than 10,0000 packages. These packages appeal to every functionality and different fields that deal with data. Based on user requirements and preferences, these packages provide different features to their users.

3. Possesses a Number of Graphical Libraries

The most important feature of R that sets it apart from other programming languages of Data Science is its massive collection of graphical libraries like ggplot2, plotly, etc. that are capable of making aesthetic and quality visualizations.

4. Open-source

R is an open-source programming language. This means that it is free of cost and requires no license. Furthermore, you can contribute towards the development of R, customize its packages and add more features.

5. Cross-Platform Compatibility

R supports cross-platform compatibility. It can be run on any OS in any software environment. It can also be run on any hardware configuration without any extra workarounds.

6. Facilities for Various Industries

Almost every industry that makes use of data, utilizes the R language. While only the academic areas made use of R in the past, it is now being heavily used in industries that require to mine insights from the data.

The health industry makes use of R for drug design and analyzing genomic strands. Manufacturing industries like Ford use it in their optimization procedures. Furthermore, Airbnb and social media companies like Twitter use it to analyze its users.

Want to know about more applications of R? Then you must have a look at the R Applications Tutorial

7. Interactive Data Analysis Environment

Another standout feature of R is its interactive data analysis environment. With tools like RStudio, R provides a seamless interface for exploratory data analysis and iterative development.

This interactive environment allows users to execute code snippets, visualize data, and immediately see results, enhancing productivity and facilitating rapid prototyping in data science workflows. Additionally, the integration of R with Jupyter notebooks further enhances its usability, offering a versatile platform for collaborative analysis and documentation.

8. Integration with Databases

One unique feature of R language is its seamless integration with databases. R provides various extensions and packages that allow users to connect with different databases such as MySQL, PostgreSQL, and MongoDB. This capability enables data analysts and scientists to directly query, retrieve, and manipulate data from databases within their R environment, streamlining the data analysis process and enhancing workflow efficiency.

Furthermore, R’s integration with databases facilitates the handling of large volumes of structured and unstructured data, empowering users to perform complex analyses and derive valuable insights from diverse data sources without the need for extensive data preprocessing.

7. No Need for a Compiler

R language is interpreted instead of compiled. Therefore, it does not need a compiler to compile code into an executable program. The R code is interpreted one step at a time and directly converted into machine level calls. This makes running an R script much less time-consuming.

8. Performs Fast Calculations

Through R, you can perform a wide variety of complex operations on vectors, arrays, data frames and other data objects of varying sizes. Furthermore, all these operations operate at a lightning speed. It provides various suites of operators to perform these miscellaneous calculations.

9. Can Handle all Sorts of Data

R provides excellent data handling and storage facilities.

In conjunction with data platforms like Hadoop, R facilitates the handling of structured as well as unstructured data that imparts a comprehensive data capability. Furthermore, R provides various data modeling and data operation facilities that are often a result of active interaction with the storage facility. It also provides extensions for SQL and Big Data.

10. Integration with Other Technologies

R can be integrated with a number of different technologies, frameworks, software packages, and programming languages. It can be paired with Hadoop to use its distributed computing ability. It can also be integrated with programs in other programming languages like C, C++, Java, Python, and FORTRAN.

11. R has an Active Community

R is continuously evolving. The contribution is fuelled by the growing number of users who are using R on a daily basis. As mentioned above, R is an open-source library that is supported and maintained by a large user-base. Not only this, R has an engaging community that organizes seminars, boot camps and other training sessions of R. Once you start your journey in R, you will never feel alone.

12. Machine Learning with R

Earlier R had different packages for different machine learning algorithms. This may be considered inefficient and, therefore, the MLR package which stands for Machine Learning in R has become highly popular. This package is useful for all machine learning algorithms and provides other tools that help with machine learning as well.

Summary

In this article, we went through the important features of the R programming language. We understood as to why R is the most preferred language for statistical modeling in the world. Due to these quintessential features, R has become widely popular. R has set its foothold in the Data Science industry and owing to its massive repository, it has become the most sought after programming language in the world.

Curious to know, how R is important for learning Data Science? Then, check out this tutorial on R for Data Science