Make your move to become the next data scientist by reviewing the R features and determining whether R is worth your time.
Examine your surroundings and you will notice that you are surrounded by massive Data!
And, in the internet factory, where the raw material is data, the most in-demand skill is the ability to transform raw data into business insights.
Data Science is the art of processing this data, and the tool used for processing is a programming language.
So, have you started or plan to start learning Data Science?
There is no special programming language dedicated to data science, but you can make up your mind by looking at the exciting features of the R language.
R programming language has so many exciting and amazing features. In this article, we'll look at what makes R so popular, what advantages it has over other technologies, and why it's a must-have tool for data scientists all over the world.
Let's take a look at some of R's amazing features and how they can help you learn data science and become a data scientist.
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R Programming Characteristics
1. Open-source software
R is a free and open-source programming environment. It is free and can be adjusted and adapted to meet the needs of the user and the project.
2. Excellent Graphical Skills
R can generate static graphics with high-quality visualizations and has extended libraries that enable interactive graphics.
This greatly simplifies data visualization and data representation.
3. Extremely Active Community
R has an open-source library that is used by a growing number of people.
The R environment is constantly expanding. This expansion is due to its large user base.
4. A Wide Range of Packages
CRAN, or the Comprehensive R Archive Network, contains over 10,000 different packages and extensions that help solve a wide range of data science problems.
There is a package for every scenario, whether it is high-quality interactive graphics, web application development, quantitative analysis, or machine learning procedures.
5. All-inclusive Environment
R has a very comprehensive development environment, which means it can be used for both statistical computing and software development.
R is a programming language that is object-oriented. It also includes a powerful package called Rshiny that can be used to create full-fledged web apps.
6. Capable of carrying out complex statistical calculations
R can be used to perform both simple and complex mathematical and statistical calculations on a wide range of data objects. Such operations can also be performed on large data sets.
7. Computing Distributed
Tasks are distributed across multiple processing nodes in distributed computing to reduce processing time and increase efficiency.
8. Execution of Code Without the Use of a Compiler
R is an interpreted language, which means that it does not require a compiler to generate code. R interprets provided code directly into lower-level calls and pre-compiled code.
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9. Database Interfacing
R includes a number of packages that allow it to interact with databases, including Roracle, Open Database Connectivity Protocol, RmySQL, and others.
10. Data Variety
R can work with both structured and unstructured data. Because of its interaction with databases, it also provides various data modelling and data operation facilities.