R And R Commander For Mac

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This document directly describes the use of the R Commander under the Windows version of R. There are small di erences in the appearance and use of the R Commander under Mac OS X and on Linux and Unix systems. Information about installing the R Commander on these platforms is available by following. How to Install R and the ‘R Commander’ The ‘R Commander’ is a basic-statistics graphical user interface (GUI) for R, a free interactive programming language and environment. Therefore, in order to be able to use the R Commander, you must install both R and the R Commander.

  1. Commander One Mac
  2. Download R For Mac
This is a beginner guide that is designed to save yourself a headache and valuable time if you decide to install R yourself.

R is one of the main languages used for data science today. As such, it is natural that any beginner may want to know how to get started with this powerful language regardless of the operating system running on a computer. Thus, this tutorial will address this by covering the installation process of R on Windows 10, Mac OSX, and Ubuntu Linux.

Furthermore, it will also go over the installation of RStudio, which is an IDE (Integrated Development Environment) that makes R easier to use as well as how to install R packages such as dplyr or ggplot2.

Installing R on Windows 10

Installing R on Windows 10 is very straightforward. The easiest way is to install it through CRAN, which stands for The Comprehensive R Archive Network. Just visit the CRAN downloads page and follow the links as shown in the video below:

Once the download is finished, you will obtain a file named 'R-3.6.3-win.exe' or similar depending on the version of R that you download. The links shown in the video above will take you to the most recent version. To finish installing R on your computer, all that is left to do is to run the .exe file. Most of the time, you will likely want to go with the defaults, so click the button 'Next' until the process is complete, as shown in the video below. Note that, even though I do not do so, you can add desktop or quick start shortcuts during the process.

Installing RStudio

Once R is installed, you can proceed to install the RStudio IDE to have a much-improved environment to work in your R scripts. It includes a console that supports direct code execution and tools for plotting and keeping track of your variables in the workspace, among other features. The installation process is very straightforward, as well. Simply go to the RStudio downloads page and follow the video below:

Once the download is complete, you will get a file named 'RStudio-1.2.5033.exe' or similar. Again this will be dependent on the version. To complete the installation, it is as easy as before. Just run the previously mentioned .exe file with the default settings by clicking 'Next', and wait until the installation finishes. Bear in mind that RStudio requires that R is installed beforehand.

Installing Packages in R

Now you have base R installed on your system and a nice IDE to begin your R programming journey. However, base R is rather limited in the things that it can do, which is why we have R packages such as dplyr for enhanced,'ggplot2'))

The second is shown in the video below. It is an easy-to-use graphical interface built into RStudio from which you can search and download any R package available on CRAN.

Installing R on Mac OSX

Installing R on Mac OS is similar to Windows. Once again, The easiest way is to install it through CRAN by going to the CRAN downloads page and following the links as shown in the video below:

The next step is to click on the 'R-3.6.2.pkg' (or newer version) file to begin the installation. You can leave the default options as is just like for Windows.

Installing RStudio and R packages

This process is essentially the same as in Windows. To download RStudio, go to the RStudio downloads page and get the .dmg for Mac OS, as shown in the image below. Remember to keep default installation options.

Once you open RStudio, installing packages is the same as with Windows. You can use either install.packages(c('dplyr','ggplot2')) in the console or go ahead and use the graphical interface shown in the video under the installing packages in R subsection of this tutorial.

Installing R on Ubuntu 19.04/18.04/16.04

Installing R on Ubuntu maybe a little bit more tricky for those unused to working in the command line. However, it is perhaps just as easy as with Windows or Mac OS. Before you start, make sure to have root access in order to use sudo.

As it is common, prior to installing R, let us update the system package index and upgrade all our installed packages using the following two commands:

sudo apt update

sudo apt -y upgrade

After that, all that you have to do is run the following in the command line to install base R.

sudo apt -y install r-base

Installing RStudio and R Packages

Once base R is installed, you can go ahead and install RStudio. For that we are going to head over again to the RStudio downloads page and download the .deb file for our Ubuntu version as shown in the image below:

Once you have the .deb file, all that is left is to navigate to your downloads folder using cd Downloads in the command line and then run the following command to begin the installation process:

sudo dpkg -i rstudio-1.2.5033-amd64.deb

You may encounter some dependency problems that may cause your first try to install RStudio to fail, but this has an easy fix. Just run the following command and try again:

sudo apt -f install

When the process finishes, you will have an RStudio shortcut in your Ubuntu app list, but you will also be able to start RStudio by typing rstudio in the command line.

Once you open RStudio, installing packages can be done in the exact same manner as with Windows or Mac OS. Either by typing install.packages(c('dplyr','ggplot2')) in the console or using the graphical interface shown in the video under the installing packages in R subsection of this tutorial


I hope that this tutorial will help those of you eager to dive into the world of R programming regardless of your operating system choice. If you are looking to start learning R as such after installing it, please refer to the Introduction to R course, which will guide you through the basics of R programming. Keep learning; the sky is the limit.

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 carries out the analyses and Rcmdr provides a convenient way for users to input commands. The Rcmdr program enables analysts to access a selection of commonly-used R commands using a simple interface that should be familiar to most computer users. It also serves the important role of helping users to implement R commands and develop their knowledge and expertise in using the command line --- an important skill for those wishing to exploit the full power of the program.

Information about installing R can be found on the web at the R homepage http://www.r-project.org/ which provides lots of information about the R project and also directs users to one of the CRAN sites (the Comprehensive R Archive Network) that have been set up on many servers across the world in order for users to download the software. CRAN provides all files necessary to install R on a number of different computing platforms (Linux, MacOS X and Windows) along with detailed information about installation and also offers manuals and contributed documentation in a number of langauges and for a number of specific disciplines.

Definitive information about the Rcmdr can be found at it's author's (John Fox) webpage:

R commander Plugins (RcmdrPlugin)

RcmdrPlugin.BCARcmdr Plug-In for Business and Customer Analytics
RcmdrPlugin.coinRcmdr Coin Plug-In
RcmdrPlugin.depthToolsR commander Depth Tools Plug-In
RcmdrPlugin.doByRcmdr doBy Plug-In
RcmdrPlugin.DoER Commander Plugin for (industrial) Design of Experiments
RcmdrPlugin.doexRcmdr plugin for Stat 4309 course
RcmdrPlugin.EACSPIRPlugin de R-Commander para el manual EACSPIR
RcmdrPlugin.EBMRcmdr Evidence Based Medicine Plug-In package
RcmdrPlugin.epackRcmdr plugin for time series
RcmdrPlugin.EZRR Commander Plug-in for the EZR (Easy R) Package
RcmdrPlugin.HHRcmdr support for the HH package
RcmdrPlugin.IPSURAn IPSUR Plugin for the R Commander
RcmdrPlugin.KMggplot2Rcmdr Plug-In for Kaplan-Meier Plots and Other Plots Using the ggplot2 Package
RcmdrPlugin.mosaicAdds menu items to produce mosaic plots and assoc plots to Rcmdr
RcmdrPlugin.MPAStatsR Commander Plug-in for MPA Statistics
RcmdrPlugin.orlocaorloca Rcmdr Plug-in
RcmdrPlugin.plotByGroupRcmdr plots by group using lattice
RcmdrPlugin.qccRcmdr qcc Plug-In
RcmdrPlugin.qualRcmdr plugin for quality control course
RcmdrPlugin.SCDARcmdr plugin for designing and analyzing single-case experiments
RcmdrPlugin.seegRcmdr Plugin for seeg
RcmdrPlugin.SLCSLC Rcmdr Plug-in
RcmdrPlugin.SMRcmdr Sport Management Plug-In
RcmdrPlugin.StatisticalURVStatistical URV Rcmdr Plug-In
RcmdrPlugin.steepnessSteepness Rcmdr Plug-in
RcmdrPlugin.survivalR Commander Plug-in for the survival Package
RcmdrPlugin.TeachingDemosRcmdr Teaching Demos Plug-In
RcmdrPlugin.temisGraphical user interface providing an integrated text mining solution
RcmdrPlugin.UCAUCA Rcmdr Plug-in

A number of plugins are available that provide direct access to R packages through the Rcmdr interface. These plugins are installed in the same way as for other R packages (for information about installation see www.UsingRcmdr.com) and can be loaded via the R-console or by using the Rcmdr menus `Tools, Load Rcmdr plugin(s)...'. There are currently 29 Plugins that provide support for specific analyses, graphics, books and teaching. Full information about the Plugins can be obtained by following the links provided in the table...

Commander One Mac

Using the Rcmdr in conjunction with the Rstudio.

Download R For Mac

Rcmdr integrates with Rstudio


Further information and resources.

Forthcoming book (due out in 2013): Data Analysis using R and the R commander. Hutcheson, G. D. Sage Pulications.