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What is R? - An Introduction to The Statistical Computing . . . R is a statistical programming tool that’s uniquely equipped to handle data, and lots of it Wrangling mass amounts of information and producing publication-ready graphics and visualizations is easy with R So are all sorts of data analysis, mining, and modeling tasks
R: Documentation Browsable HTML versions of the manuals, help pages and NEWS for the developing versions of R “ R-patched ” and “ R-devel ”, updated daily CRAN has a growing list of contributed documentation in a variety of languages The Journal of Statistical Software features R-related articles like package guides on a regular basis
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
R Tutorial - W3Schools R is a programming language R is often used for statistical computing and graphical presentation to analyze and visualize data With our "Try it Yourself" editor, you can edit R code and view the result How to output some text, and how to do a simple calculation in R: "Hello World!" Result: [1] "Hello World!"
RStudio Education 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
What Is R Programming? Use Cases and FAQ - Coursera R is a free, open-source programming language, meaning anyone can use, modify, and distribute it It was initially written by Ross Ihaka and Robert Gentleman (also known as R R) of the University of Auckland’s Statistics Department
R Programming Language Explained (Careers, Use Cases . . . 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