The course of statistics takes a huge toll on your college career due to its difficulty. Especially when it comes to R programming, most students find it hard to wrap around. However, you can upgrade your R programming skills and your library with these top amazing language features. Learn what makes R programming one of the most effective and most-used languages by data scientists in today’s era.
- Open source: Any skilled programming assignment help expert will tell you that- R is one of the most popular open-source languages. Its open-source licence means that it is free of cost, and its main codebase can be modified according to the user’s requirement. One can add new libraries and develop frameworks around them.
- Strong Graphical Capabilities: R has built-in tools for making production-ready graphs and other graphics easily and has libraries that can draw interactive graphic visualisations. These libraries allow simple and easy data representation and visualisation. From simple XY charts, Pie charts to highly complex charts of stock markets, all lie within R’s capabilities.
- Highly Active Community: R’s open-source library has many growing users who contribute to it regularly. Its environments and libraries are constantly expanding, supported by its large user base.
- A Wide Selection of Packages: CRAN, also known as Comprehensive R Archive Network, contains approximately more than 10,000 libraries, packages and extensions that can provide solutions for all kinds of projects in data science. Production level graphics, website development, data analysis or even machine learning algorithms, there is an R package for each scenario. R has an ocean of libraries related to all kinds of study areas like biology, astronomy etc. It had initially had its application in academic fields but now has moved to industrial applications.
- Comprehensive Environment: 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 R Shiny package, which can be used to produce full-fledged web apps. R is used for highly interactive online data-driven storytelling combined with data analysis and visualisation. Wondering how to conquer your assignment this semester? Ask the experts of R studio assignment help for an effective learning method.
- Can Perform Complex Statistical Calculations: R Programming can execute simple and complex mathematical and statistics-based computations on different types of data objects.
- Distributed Computing: In distributed computing, various processes are divided between different processor cores to optimise processing duration and raise efficiency. R has in-built libraries (like DDR and multiplier) that can directly take advantage of distributed computing.
- Running Code Without a Compiler: As R is an interpreted language, it does not require a compiler to run. R can straight away interpret code into machine level calls.
- Interfacing with Databases: R has many libraries to interface with existing databases like Oracle and MySQL.
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