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Prepare, analyze, and visualize data with R
Arithmetic Artist
The R statistical programming language offers machine learning methods, dashboards, descriptive analyses, t -tests, cluster analyses, various regression methods, interactive graphs, and more. If you have ever wondered whether it would be worthwhile to immerse yourself in R, the following easy-to-grasp taster seeks to clarify who is likely to benefit.
Yesterday and Today
R [1] is based on the S programming language developed at Bell Laboratories in 1975-1976, which then split in the late 1980s and early 1990s into a commercial version named S-PLUS and the GNU project R. The name, which admittedly takes some getting used to, can be traced back to the first names of the developers, Ross Ihaka and Robert Gentleman, and alludes to the previous project name, as well.
R is often referred to as a programming environment, which is intended to emphasize the open package concept and to indicate that R differentiates itself from common monolithic statistical software. The basic functions are provided by eight packages included in the R source code. Additionally, many thousands of additional packages offer extensions.
In its early years, R was more of a niche player and was mainly used by statisticians and biometricians at universities. In the meantime, however, it has gained a firm place in the corporate world with the increasing entry of data science into many companies.
Syntax
An overview of the most important properties of its syntax facilitates any introduction to R. The R syntax is characterized by expressions and is case sensitive: An object named modelFit cannot be called as modelfit, for example. The assignment operator <- creates an object and points to the object that is assigned the content of an
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