R is a comprehensive statistical programming language that iscooperatively developed on the Internet as an open source project. Itis often referred to as the “GNU S,” because it almostcompletely emulates the S programming language. It has packages to doregression, ANOVA, general linear models, hazard models andstructural equations.Graphical output can be created using a TeX plug-in to convert the standard ASCII-based output.
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Best Mac Os X Software
- R for macOS Developers This is the new home for experimental binaries and documentation related to R for macOS. To learn more about the R software or download released versions, please visit www.r-project.org. All software on this page is strictly experimental and subject to acceptance of the supplied R license agreement and the disclaimer at the end of the page.
- RStudio is an active member of the R community. We believe free and open source data analysis software is a foundation for innovative and important work in science, education, and industry. The many customers who value our professional software capabilities help us contribute to this community. Visit our Customer Stories page to learn more.
Shop H&R Block Premium & Business Tax Software Mac, Windows at Best Buy. Find low everyday prices and buy online for delivery or in-store pick-up. Price Match Guarantee. Does R run under my version of Windows? How do I update packages in my previous version of R? Should I run 32-bit or 64-bit R? Please see the R FAQ for general information about R and the R Windows FAQ for Windows-specific information. Patches to this release are incorporated in the r.
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 (CRAN). 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). 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. 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. See our free software page 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. It performed spectacularly. It's pretty easy to learn using online tutorials, in particular John Verzani's tutorial 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! Now that's how a program ought to work.
But the major advantages of R are that it is absolutely cross-platform (Linux, MacOS, Windows) and that it's open source. You've a good chance of accessing your data 10 years from now, which I wouldn't say with the commercial packages. The user base is large, active, and productive. The S language on which it's based is a well-accepted standard in statistics. R has stood the test of time and is likely to continue to do so.
There is one significant caveat: R is relentlessly command-line driven, and even the graphs cannot be edited with mouse clicks. It's trivial to take the PDF graphs into Illustrator, though, so this limitation hasn't been a problem for me.
Some resources include:
- The R project home page (with download links)
- This web page on R, S and S/Plus statistics systems, which provides a background on the software and summarizes available packages
- Using R for structural equation modeling
R has a massive range of tests and now has Matrix as a recommended package, a useKerning argument for PDF and PostScript output, a recursive argument for file.copy(), an unzip function to expand or list zip archives, and other changes.
There is a R for Mac Special Interest Group, called R-Sig-Mac. Thegroup is implemented as an e-mail list. You can subscribe to the list or see the archives going to its official web page:http://www.stat.math.ethz.ch/mailman/listinfo/r-sig-mac
S and R Programming Languages
Beginning in 1976, the Sprogramming language was developed at Bell Labs (whose statisticsdepartment employed John Tukey and Joseph Kruskal) by John Chambersand others. Version 1 required Honeywell mainframes, Version 2 (1980)added Unix support, Version 3 (1988) added functions and objects, andVersion 4 (1998) added full support for object-oriented design. In 1993, Bell Labs issued an exclusive license toStatSci (later MathSoft).S-Plus is Mathsoft’s commercial implementation of S, and the only waythe language is available outside Lucent.
R was begun by Robert Gentleman and Ross Ihaka of the Universityof Auckland. It is now an opensource project staffed by volunteers from around the world whose development is coordinated through the Comprehensive R Archivenetwork. Source code, binaries, and documentation areat the CRAN website.
Documentation that compares R and S include:
- The R and S discussion in CRAN’s FAQ.
- The online supplement to Venables and Ripley (1999).
- The published text of Venables and Ripley (2000), and its online errata.
Adapted from an August 2000 Academy of Management workshop on stat packages, we are showing how to use R for analyses common in management research:
Base package commands:
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- anova: analysis of variance
- glm: general linear model, including logit, probit and poisson models
- ls/lsfit: fit an OLS or WLS regression model
Built-in packages
- ts package:
- arima: ARIMA time series models
Contributed R packages and their capabilities:
- boot: bootstrapping and jacknifing
- coda: analysis and diagnostics for Markov Chain Monte Carlo simulation
- fracdiff: ARIMA time series models
- matrix: matrix math
- cmdscale: multi-dimensional scaling
- multiv: cluster analysis, correspondance analysis, principal component factor analysis
- pls: Partial Least Squares structural equation modeling
- survival5: survival analysis (hazard models)
Books by MacStats maintainer David Zatz • MacStats created in 1996 by Dr. Joel West; edited since 2005 by Dr. David Zatz of Toolpack Consulting. Copyright © 2005-2020 Zatz LLC. All rights reserved. Contact us.
This directory contains binaries for a base distribution and packages to run on Mac OS X (release 10.6 and above). Mac OS 8.6 to 9.2 (and Mac OS X 10.1) are no longer supported but you can find the last supported release of R for these systems (which is R 1.7.1) here. Releases for old Mac OS X systems (through Mac OS X 10.5) and PowerPC Macs can be found in the old directory.
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Note: CRAN does not have Mac OS X systems and cannot check these binaries for viruses.Although we take precautions when assembling binaries, please use the normal precautions with downloaded executables.
Package binaries for R versions older than 3.2.0 are only available from the CRAN archive so users of such versions should adjust the CRAN mirror setting (https://cran-archive.r-project.org) accordingly.
R 4.0.3 'Bunny-Wunnies Freak Out' released on 2020/10/10
Please check the MD5 checksum of the downloaded image to ensure that it has not been tampered with or corrupted during the mirroring process. For example type
openssl sha1 R-4.0.3.pkg
in the Terminal application to print the SHA1 checksum for the R-4.0.3.pkg image. On Mac OS X 10.7 and later you can also validate the signature using
pkgutil --check-signature R-4.0.3.pkg
Latest release:
| R-4.0.3.pkg (notarized and signed) SHA1-hash: 8402f586aef1fdb12c6e34c73b286f87318fb1be (ca. 85MB) | R 4.0.3 binary for macOS 10.13 (High Sierra) and higher, signed and notarized package. Contains R 4.0.3 framework, R.app GUI 1.73 in 64-bit for Intel Macs, Tcl/Tk 8.6.6 X11 libraries and Texinfo 6.7. The latter two components are optional and can be ommitted when choosing 'custom install', they are only needed if you want to use the tcltk R package or build package documentation from sources. Note: the use of X11 (including tcltk) requires XQuartz to be installed since it is no longer part of OS X. Always re-install XQuartz when upgrading your macOS to a new major version. Important: this release uses Xcode 10.1 and GNU Fortran 8.2. If you wish to compile R packages from sources, you will need to download and GNU Fortran 8.2 - see the tools directory. |
| NEWS (for Mac GUI) | News features and changes in the R.app Mac GUI |
| Mac-GUI-1.73.tar.gz SHA1-hash: 7f4b1d050757ce78545bdeb9d178a69d13046aa1 | Sources for the R.app GUI 1.73 for Mac OS X. This file is only needed if you want to join the development of the GUI, it is not intended for regular users. Read the INSTALL file for further instructions. |
Note: Previous R versions for El Capitan can be found in the el-capitan/base directory.Binaries for legacy OS X systems: | |
| R-3.6.3.nn.pkg (signed) SHA1-hash: c462c9b1f9b45d778f05b8d9aa25a9123b3557c4 (ca. 77MB) | R 3.6.3 binary for OS X 10.11 (El Capitan) and higher, signed package. Contains R 3.6.3 framework, R.app GUI 1.70 in 64-bit for Intel Macs, Tcl/Tk 8.6.6 X11 libraries and Texinfo 5.2. The latter two components are optional and can be ommitted when choosing 'custom install', they are only needed if you want to use the tcltk R package or build package documentation from sources. |
| R-3.3.3.pkg MD5-hash: 893ba010f303e666e19f86e4800f1fbf SHA1-hash: 5ae71b000b15805f95f38c08c45972d51ce3d027 (ca. 71MB) | R 3.3.3 binary for Mac OS X 10.9 (Mavericks) and higher, signed package. Contains R 3.3.3 framework, R.app GUI 1.69 in 64-bit for Intel Macs, Tcl/Tk 8.6.0 X11 libraries and Texinfo 5.2. The latter two components are optional and can be ommitted when choosing 'custom install', it is only needed if you want to use the tcltk R package or build package documentation from sources. Note: the use of X11 (including tcltk) requires XQuartz to be installed since it is no longer part of OS X. Always re-install XQuartz when upgrading your OS X to a new major version. |
| R-3.2.1-snowleopard.pkg MD5-hash: 58fe9d01314d9cb75ff80ccfb914fd65 SHA1-hash: be6e91db12bac22a324f0cb51c7efa9063ece0d0 (ca. 68MB) | R 3.2.1 legacy binary for Mac OS X 10.6 (Snow Leopard) - 10.8 (Mountain Lion), signed package. Contains R 3.2.1 framework, R.app GUI 1.66 in 64-bit for Intel Macs. This package contains the R framework, 64-bit GUI (R.app), Tcl/Tk 8.6.0 X11 libraries and Texinfop 5.2. GNU Fortran is NOT included (needed if you want to compile packages from sources that contain FORTRAN code) please see the tools directory. NOTE: the binary support for OS X before Mavericks is being phased out, we do not expect further releases! |
R Software For Mac Yosemite
The new R.app Cocoa GUI has been written by Simon Urbanek and Stefano Iacus with contributions from many developers and translators world-wide, see 'About R' in the GUI.Subdirectories:
| tools | Additional tools necessary for building R for Mac OS X: Universal GNU Fortran compiler for Mac OS X (see R for Mac tools page for details). |
| base | Binaries of R builds for macOS 10.13 or higher (High Sierra) |
| contrib | Binaries of package builds for macOS 10.13 or higher (High Sierra) |
| el-capitan | Binaries of package builds for OS X 10.11 or higher (El Capitan build) |
| mavericks | Binaries of package builds for Mac OS X 10.9 or higher (Mavericks build) |
| old | Previously released R versions for Mac OS X |
You may also want to read the R FAQ and R for Mac OS X FAQ. For discussion of Mac-related topics and reporting Mac-specific bugs, please use the R-SIG-Mac mailing list.
Information, tools and most recent daily builds of the R GUI, R-patched and R-devel can be found at http://mac.R-project.org/. Please visit that page especially during beta stages to help us test the Mac OS X binaries before final release!
Package maintainers should visit CRAN check summary page to see whether their package is compatible with the current build of R for Mac OS X.
Binary libraries for dependencies not present here are available from http://mac.R-project.org/libs and corresponding sources at http://mac.R-project.org/src.
Last modified: 2020/10/10, by Simon Urbanek