- The R Project for Statistical Computing
R is a free software environment for statistical computing and graphics It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS To download R, please choose your preferred CRAN mirror
- R-4. 5. 1 for Windows - The Comprehensive R Archive Network
Download R-4 5 1 for Windows The R-project for statistical computing This build requires UCRT, which is part of Windows since Windows 10 and Windows Server 2016 On older systems, UCRT has to be installed manually from here
- R Programming Language - Introduction - GeeksforGeeks
R is a programming language and software environment that has become the first choice for statistical computing and data analysis Developed in the early 1990s by Ross Ihaka and Robert Gentleman, R was built to simplify complex data manipulation and create clear, customizable visualizations
- An Introduction to R
This is an introduction to R (“GNU S”), a language and environment for statistical computing and graphics R is similar to the award-winning 1 S system, which was developed at Bell Laboratories by John Chambers et al
- CS50s Introduction to Programming with R
An introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains Learn to use RStudio, a popular integrated development environment (IDE)
- Home - RDocumentation
Easily search the documentation for every version of every R package on CRAN and Bioconductor
- R Programming Language Explained (Careers, Use Cases, Difficulty)
R is a free, open-source programming language built for statistical analysis, data mining, visualization, and machine learning Its strong visualization capabilities make it a favorite among data scientists and analysts who need to share insights with technical and non-technical audiences alike
- R | STAT ONLINE
"R is a language and environment for statistical computing and graphics " "R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ) and graphical techniques, and is highly extensible "
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