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.
There are many things R can do for data scientists and analysts.
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 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.
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.
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.
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.
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.
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.
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
R contains several packages that enable it to interact with databases like Roracle, Open Database Connectivity Protocol, RmySQL, etc.
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.
