Chapter 2: Obtaining R, and Installation
The R Project for Statistical Computing can obtained free, at the homepage. Under the Download/Packages tab one can find a list of mirror sites where the respective download files are stored.
Any one of these mirrors will have R available for Linux, Mac OS X and Windows as source code and pre-compiled binaries. For the average user the pre-compiled binaries are the easy and smart choice. For reference the R environment used in this guide is Version 2.13 (Windows).
Regardless of which operating system you choose, all features are common across the platforms with minor aesthetic differences. It shouldn't be a problem following these examples on any platform or version.
A Note For Windows Users
Windows users have the option of 32-or/and 64-bit versions depending on their operating system's capabilities. It is possible to have both versions installed at one time; generally the advantages of the 64-bit version is access to more memory (>4Gb) and better/faster handling of larger objects. If is recommended “using the 'native' build, that is the 32-bit version on 32-bit Windows and the 64-bit version on 64-bit Windows”.
A Note About Languages
Important Note: R is available in a range of languages, but in Windows it will by default try to run in the language you run Windows in. For example if your Windows runs in German, R will run by default in this language despite the fact you may have downloaded and installed the English, Italian or Hebrew version!!
To change this behaviour: right click on the desktop shortcut, created during installation and select 'Properties'. Then in the tab named Target: scrolll to the end of the given path (“C:\Program Files\R-2.13.2\bin\x64\Rgui.exe” and add ´LANGUAGE = en´ after this path.
Updating and Add-On Packages
One of the great advantages of R over similar statistical software environments is the ease at which the user-community can access the tools and functionality they desire, and require for their work.
As mentioned earlier, R has a a dedicated group of thousands of users who invent, produce and share new functions, routines, and complete add-on packages with the rest of the community. These packages provide essential functionality, for example new methods of statistical analysis, production of graphics, or simpler tools to convert units and measurements. More advanced packages provide a whole new GUI interface to the R environment, or the ability to access certain data types etc.
In this course, we will actively introduce, and use some of the more common packages. A brief introduction to installing any package is presented below:
The easiest way to install packages is within the program itself. Clicking on the menu Packages and then Install Package/s. This will open a window where the nearest server to your location can be chosen, from here an alphabetical list of the available packages on CRAN TaskView is displayed.
Scroll through to choose your desired package, and click OK. The package (usually <1000kb) will be downloaded and 'installed'.
Important Note: To access the package during a working session one needs to load it. Type require(packagename) in the command line or alternatively use the Packages => Load Package menu.