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What Is R In Data Science?

The R Foundation is a non-profit that is focused on supporting the continual growth of R through the R Project The R Foundation describes R as "a language and environment for statistical computing and graphics." If you've heard of R as a data scientist, you'll realise it's much more than it sounds. R was invented in the early 1990s through Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand. In the early 1990s, the R language was modeled from the S language that was developed in Bell Laboratories by John Chambers as well as other employees. In the present, R is an open-source language, and is accessible as a free program that is compatible with various platforms and systems.


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Here are some crucial points to be aware of about R in data science:-


R is an open-source software.

R can be downloaded for free, and is scalable since it's an open source software. R's open interfaces permit it to be integrated with other software and systems. Open-source softwares meet an excellent quality because a variety of people are using them and they're constantly improving.


R is a programming language.

A programming language R offers operators, objects and functions that permit users to investigate the data, model it and display it.


R is used for data analysis.

The R language in data science is used to manage the storage and analysis of data. It is used to analyse data and for statistical modeling.


R is an environment for statistical analysis.

R offers a variety of statistical and graphic capabilities, including graphical and statistical. R Foundation notes that it has graphical and statistical capabilities. R Foundation notes that it is a tool for clustering, classification tests in statistical analysis, and for nonlinear and linear modeling.


R is a community.

R Project contributors include individuals who have proposed improvements, reported bugs, and made add-on software. There over 20 contributors who are official The R community also includes those who use the open-source software independently.


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