{"id":73626,"date":"2019-12-06T15:52:49","date_gmt":"2019-12-06T10:22:49","guid":{"rendered":"https:\/\/techvidvan.com\/tutorials\/?p=73626"},"modified":"2019-12-06T15:52:49","modified_gmt":"2019-12-06T10:22:49","slug":"r-features","status":"publish","type":"post","link":"https:\/\/techvidvan.com\/tutorials\/r-features\/","title":{"rendered":"15 Features of R Programming you can\u2019t afford to overlook"},"content":{"rendered":"

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.<\/strong><\/p>\n

Look at everything around yourself and you will observe that you are surrounded by enormous Data<\/strong>!<\/p>\n

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<\/strong>.<\/p>\n

The art of processing this data is called Data Science<\/strong> and the tool used for processing is a programming language.<\/p>\n

So have you started learning Data Science or want to start it?<\/em><\/strong><\/p>\n

There is no special programming language dedicated to data science but looking at the exciting features<\/strong> of the R language you can make your mind.<\/p>\n

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.<\/p>\n

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

Features of R Programming<\/h2>\n

\"R<\/a><\/p>\n

There are many things R can do for data scientists and analysts. These key f<\/span>eatures are what set R apart from the crowd of statistical languages:<\/span><\/p>\n

1. Open-source<\/h3>\n

R is an open-source software environment. It is free<\/strong> of cost and can be adjusted and adapted according to the user\u2019s and the project\u2019s requirements.<\/p>\n

You can make improvements and add packages<\/strong> for additional functionalities.<\/span><\/p>\n

R is freely available. You can learn how to install R, Download and start practicing it.<\/span><\/p>\n

2. Strong Graphical Capabilities<\/h3>\n

R can produce static graphics with production quality visualizations and has extended libraries providing interactive graphic capabilities.<\/p>\n

This makes data visualization<\/strong> and data representation very easy<\/strong>.<\/p>\n

From concise charts to elaborate and interactive flow diagrams, all are well within R\u2019s repertoire. Look at the attractive graphical visualizations in R.<\/p>\n

\"R<\/a><\/p>\n

3. Highly Active Community<\/h3>\n

R has an open-source library which is supported by its growing<\/strong> number of users.<\/p>\n

The R environment is continuously growing. This growth is due to its <\/span>large user-base<\/strong>.<\/span><\/p>\n

4. A Wide Selection of Packages<\/h3>\n

CRAN<\/strong> or Comprehensive R Archive Network houses more than 10,000<\/strong> different packages<\/strong> and extensions that help solve all sorts of problems in data science.<\/p>\n

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

R contains a sea of packages<\/strong><\/span> 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.<\/p>\n

5. Comprehensive Environment<\/h3>\n

R has a very comprehensive development environment meaning it helps in statistical computing as well as software development.<\/p>\n

R is an object-oriented programming language<\/strong>. It also has a robust package called Rshiny<\/strong> which can be used to produce full-fledged web apps.<\/p>\n

Combined with data analysis and data visualization, R can be used for highly interactive online data-driven storytelling.<\/p>\n

6. Can Perform Complex Statistical Calculations<\/h3>\n

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.<\/p>\n

7. Distributed Computing<\/h3>\n

In distributed computing, tasks are split between multiple processing nodes to reduce processing time and increase efficiency.<\/p>\n

R has packages like ddR<\/strong> and multiDplyr<\/strong> that enable it to use distributed computing to process large data sets<\/a>.<\/p>\n

8. Running Code Without a Compiler<\/h3>\n

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

9. Interfacing with Databases<\/h3>\n

R contains several packages that enable it to interact with databases<\/strong> like Roracle, Open Database Connectivity Protocol, RmySQL, etc.<\/p>\n

10. Data Variety<\/h3>\n

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.<\/p>\n

11. Machine Learning<\/h3>\n

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.<\/p>\n

12. Data Wrangling<\/h3>\n

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.<\/p>\n

R with its extensive library of tools can be used for database manipulation<\/strong> and wrangling.<\/p>\n

13. Cross-platform Support<\/h3>\n

\"Cross<\/a><\/p>\n

R is machine-independent<\/strong>. It supports the cross-platform operation. Therefore, it can be used on many different operating systems.<\/p>\n

14. Compatible with Other Programming Languages<\/h3>\n

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.<\/p>\n

15. Data Handling and Storage<\/h3>\n

R is integrated with all the formats of data storage due to which data handling becomes easy.<\/p>\n

16. Vector Arithmetic<\/h3>\n

Vectors are the most basic data structure in R, and most other data structures are derived from vectors.<\/p>\n

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.<\/p>\n

17. Compatibility with Other Data Processing Technologies<\/h3>\n

R can be easily paired with other data processing and distributed computing technologies<\/strong> like Hadoop and Spark. It is possible to remotely use a Spark cluster to process large datasets using R.<\/p>\n

R and Hadoop can be paired as well to combine Hadoop\u2019s large scale data processing and distributing computing capabilities with R\u2019s statistical computing power.<\/p>\n

18. Generates Report in any Desired Format<\/h3>\n

R\u2019s markdown package is the only report generation package you will ever need when working with R. The markdown package can help produce web pages<\/strong>.<\/p>\n

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

Some Unique Features of R Programming<\/h3>\n

Due to a large number of packages available, there are many other handy features as well:<\/p>\n