What Exactly is R in Data Science
The R Foundation, a nonprofit focused on helping the continued development of R through the R Project, explains R as “a language and environment for statistical computing and graphics.” But, if you’re familiar with data science R, you probably know it’s a lot more than that.
R was designed in the 1990s by Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand. The R programming language was modeled based on the S language developed at Bell Laboratories by John Chambers and other employees. Today, R is an open-source language; it’s accessible as a free software compatible with many systems and platforms.
Here are some important things to know about R in data science:
- R is open-source software. R is free and adaptable because it’s open-source software. R’s open interfaces allow it to integrate with other applications and systems.
- Open-source software has a high standard of quality since multiple people use and iterate on them.
- R is a programming language. As a programming language, R provides objects, operators, and functions that allow users to explore, model, and visualize data.
- R language is used for data analysis. R in data science is used to handle, store and analyze data. It can be used for data analysis and statistical modeling.
- R is an environment for statistical analysis. R has various statistical and graphical capabilities. The R Foundation notes that it can be used for classification, clustering, statistical tests and linear and nonlinear modeling.
- R is a community. R Project contributors include individuals who have suggested improvements, noted bugs and created add-on packages. While there are more than 20 official contributors, the R community extends to those using the open-source software on their own.
How Is R Used in Data Science?
R for data science focuses on the language’s statistical and graphical uses. When you learn R for data science, you’ll learn how to use the language to perform statistical analyses and develop data visualizations. R’s statistical functions also make it easy to clean, import and analyze data.
It may be equipped with an Integrated Development Environment (IDE). According to computer software company GitHub, the purpose of an IDE is to make writing and working with software packages easier. RStudio is an IDE for R that improves the accessibility of graphics and includes a syntax-highlighting editor that helps with code execution. This may be helpful as you begin to learn R for data science.
https://flipboard.com/@tarunsaini2020/technical-courses-bj6b5t7fy
