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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 is a programming language for statistical computing and data visualization. It has been adopted in the fields of data mining, bioinformatics, and data analysis. The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data. R software is open-source and free software.
Learn the R programming language in this tutorial course. This is a hands-on overview of the statistical programming language R, one of the most important to...
R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues.
The help() function and ? help operator in R provide access to the documentation pages for R functions, data sets, and other objects, both for packages in the standard R distribution and for contributed packages.
R is not just a programming language, but it is also an interactive ecosystem including a runtime, libraries, development environments, and extensions. All these features help you think about problems as a data scientist, while supporting fluent interaction between your brain and the computer.
R is an open-source programming language designed for data science and statistics. It’s a powerful tool for working with data, and its documentation and supportive community offer helpful resources for new programmers.
Welcome to the learn-r.org interactive R tutorial with Examples and Exercises. If you want to learn R for statistics, data science or business analytics, either you are new to programming or an experienced programmer this tutorial will help you to learn the R Programming language fast and efficient. R is a programming language used extensively ...
The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.
