No, that’s not a typo. There’s really programming language that’s simply referred to as “R.”
Created by statistician and computer programming John Chambers while working for Bell Labs in the early 1990s, R is a programming language that’s closely aligned with the S language. What makes it different, however, is its use of lexical scoping semantics. Unlike dynamic scoping, lexical scoping (also referred to as static scoping) bases name resolution on the lexical context. This is defined by the place in which the variable or function is defined.
R Programming Uses
While it can be used for a wide range of tasks, R programming was designed primarily for use in statistical computing. It has become a primary tool among professional statisticians, data miners, data analysis workers, and Big Data scientists.
So, what makes the R language such a powerful tool for statisticians and similar professionals? Among other things, its libraries include statistical methods like linear modeling, nonlinear modeling, time-series analyses, clustering, etc. And being that it’s extensible using functions and extensions, R is incredibly versatile for use in statistics. It can even be used to create high-quality graphs containing mathematical symbols.
According to the website Inside-R.org, there are more than 2,000 packages with the R language available for free download — and more are being released every day. The website also claims that more than 2 million users have downloaded and used these packages, attesting to the language’s widespread popularity.
Here are some of R’s most notable features:
- Being an interpreted language, R is accessed using a command-line interpreter.
- Supports the use of matrix arithmetic — a visual representation in which data is displayed in rows and columns.
- Wide variety of data structures, including vectors, arrays, frames, lists, matrices and more.
- Supports the use of extensible objects for tasks such as time-series, geo-spatial coordinates, regression models, etc.
- Supports procedural programming.
- Generic functions that act differently based on the defined classes and arguments.
- Benchmark functions similar to that of GNU Octave.
How Much Does it Cost?
One of the many perks associated with R is its open-source license, meaning it’s completely free to download and use. Furthermore, you can view and edit the R source code if you’re feeling a little adventurous. Making R an open-source project was undoubtedly a smart decision as this increased the language’s exposure while attracting professional developers to improve and build upon it.
You can find download mirrors for the R language by visiting https://cran.r-project.org/mirrors.html.
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