R on the Mac R is a comprehensive statistical programming language that is cooperatively developed on the Internet as an open source project. It is often referred to as the “GNU S,” because it almost completely emulates the S programming language. It has packages to do regression, ANOVA, general linear models, hazard models and structural equations. Graphical output can be created using a TeX plug-in to convert the standard ASCII-based output. R has a massive range of tests, PDF and PostScript output, a function to expand zip archives, and numerous other unexpected features. R programs and algorithms are distributed by the Comprehensive R Archive Network (). A simple graphic user interface is included for Mac users; R Commander can be installed using the built-in package installer, which can also install file import features (which aren't installed by default).

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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.

R Commander is an X11 program, which means it uses an alien interface and has odd open/save dialogues, but if you get past that it offers menu driven commands not dissimilar from, say, SPSS, just a lot more awkward to use, and without an output or data window. Like many open source projects, R is exceedingly capable but has a steep learning curve. Some believe this is for the best because people will get a deeper understanding of the statistics they generate with a program such as R, versus one which allows the rapid creation of scads of irrelevant statistics leading to incorrect conclusions. Best app to editing pdf files on mac for free.

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Those who expect even a basic graphical interface (e.g. SPSS 4) may be disappointed by the R community’s definition of a GUI. Most of this page is rather out of date.

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See our for more current but less detailed information. Ashish Ranpura wrote: Last week I finally put R through its paces on two recent experiments from our lab.

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It performed spectacularly. It's pretty easy to learn using online tutorials, in particular which is a course in introductory statistics using R. The highlight: figuring out the 15 or so commands to import, parse, slice and graph a 3-way comparison of control subjects using a scatterplot and a violin plot. Then using BBEdit to search and replace the word 'control' with my two experimental conditions, pasting that back into R, and generating a report with all 6 graphs in about 3 keystrokes!