R is ideal for machine learning operations such as regression and classification. It’s well suited for Machine Learning.As a result, programmers prefer it over other languages for statistical tool development. Statistics are a big thing today, and R shines in this regard. For example, the R language has more than 10,000 packages stored in the CRAN repository, and the number is continuously increasing. This independence is yet another reason why R is cost-effective!
R runs on all operating systems, so developers only need to create one program that can work on competing systems. No fees or licenses are needed, so it’s a low-risk venture if you’re developing a new program. Here is a list of some of its major strong points: The R programming language has a lot going for it. It is often referred to as a different implementation of the S language and environment and is considered highly extensible. R is a universal programming language compatible with the Windows, Macintosh, UNIX, and Linux platforms. R was developed in 1993 by Ross Ihaka and Robert Gentleman and includes linear regression, machine learning algorithms, statistical inference, time series, and more. Keywords, reserved words that have a special meaning for the compiler.
A vast, easily understandable, integrated assortment of intermediate tools dedicated to data analysis.A suite of operators for array calculations, mainly matrices.A high-performance data storage and handling facility.The R environment consists of an integrated suite of software facilities designed for data manipulation, calculation, and graphical display. What better place to find a good definition of the language than the R Foundation’s website? According to, R is “… a language and environment for statistical computing and graphics.” It’s an open-source programming language often used as a data analysis and statistical software tool.
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