R Statistical Software Mac Download

Jan 21, 2021 Statistical analysis is becoming increasingly important to the global economy with statistical software usage predicted to grow by at least 9% annually until 2025. Nowadays there’s some very powerful Mac statistical software for analyzing statistics and research on macOS, many of which are cheaper, easier and better to use than Windows options. R commander (Rcmdr) R provides a powerful and comprehensive system for analysing data and when used in conjunction with the R-commander (a graphical user interface, commonly known as Rcmdr) it also provides one that is easy and intuitive to use. Basically, R provides the engine that carri.

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.

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.

R Statistical Software Mac Download Windows 10

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Most of this page is rather out of date.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:

Video with photo booth mac app In the Photo Booth app on your Mac, if you see the View Photo button or the View Video button, click it to see the Take Photo button or Record Video button. If the Record Video button isn’t showing, at the bottom left, click the Record a movie clip button. Click the Record Video.

Base package commands:

  • anova: analysis of variance
  • glm: general linear model, including logit, probit and poisson models
  • ls/lsfit: fit an OLS or WLS regression model
Download

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 © 2005-2021 Zatz LLC. All rights reserved. Contact us.Terms/Privacy (end of page).

To Install R

R Statistical Software For Mac

  1. Open an internet browser and go to www.r-project.org.
  2. Click the 'download R' link in the middle of the page under 'Getting Started.'
  3. Select a CRAN location (a mirror site) and click the corresponding link.
  4. Click on the 'Download R for (Mac) OS X' link at the top of the page.
  5. Click on the file containing the latest version of R under 'Files.'
  6. Save the .pkg file, double-click it to open, and follow the installation instructions.
  7. Now that R is installed, you need to download and install RStudio.

To Install RStudio

  1. Go to www.rstudio.com and click on the 'Download RStudio' button.
  2. Click on 'Download RStudio Desktop.'
  3. Click on the version recommended for your system, or the latest Mac version, save the .dmg file on your computer, double-click it to open, and then drag and drop it to your applications folder.

To Install the SDSFoundations Package

Software
  1. Download SDSFoundations to your desktop (make sure it has the '.tgz' extension).
  2. Open RStudio.
  3. Click on the Packages tab in the bottom right window.
  4. Click 'Install.'
  5. Select install from 'Package Archive File.'
  6. Select the SDSFoundations package file from your desktop.
  7. Click install. You are done! You can now delete the SDSpackage file from your desktop.

To Install R:

  1. Open an internet browser and go to www.r-project.org.
  2. Click the 'download R' link in the middle of the page under 'Getting Started.'
  3. Select a CRAN location (a mirror site) and click the corresponding link.
  4. Click on the 'Download R for Windows' link at the top of the page.
  5. Click on the 'install R for the first time' link at the top of the page.
  6. Click 'Download R for Windows' and save the executable file somewhere on your computer. Run the .exe file and follow the installation instructions.
  7. Now that R is installed, you need to download and install RStudio.

To Install RStudio

R statistical software download

R Statistical Software Download

  1. Go to www.rstudio.com and click on the 'Download RStudio' button.
  2. Click on 'Download RStudio Desktop.'
  3. Click on the version recommended for your system, or the latest Windows version, and save the executable file. Run the .exe file and follow the installation instructions.

To Install the SDSFoundations Package

R Statistical Software Package

  1. Download SDSFoundationsto your desktop (make sure it has the '.zip' extension).
  2. Open RStudio.
  3. Click on the Packages tab in the bottom right window.
  4. Click 'Install.'
  5. Select install from 'Package Archive File.'
  6. Select the SDSFoundations package file from your desktop.
  7. Click install. You are done! You can now delete the SDSpackage file from your desktop.