The following excerpt from Joseph Adler's R in a Nutshell introduces the R Graphical User Interface. This is helpful if you would like to know what the R GUI looks like on other operating systems.
By default, R is installed into
(which is usually
C:\Program Files\R) and installed
into the Start menu under the group R. When you launch R in Windows,
you’ll see something like the user interface shown in Figure 2.1. Inside the R GUI window,
there is a menu bar, a toolbar, and the R console.
The default R installer will add an application called
R to your Applications folder that you can run like
any other application on your Mac. When you launch the R application on Mac OS X systems,
you’ll see something like the screen shown in Figure 2.2. Like the Windows system, there is a
menu bar, a toolbar with common functions, and an R console
On a Mac OS system, you can also run R from the terminal without using the GUI. To do this, first open a terminal window. (The terminal program is located in the Utilities folder inside the Applications folder.) Then enter the command “R” on the command line to start R.
Notice that it’s a capital “R”; filenames on Linux are case sensitive.
Unlike the default applications for Mac OS and Windows, this will start an interactive R session on the command line itself. If you prefer, you can launch R in an application window similar to the user interface on other platforms. To do this, use the following command:
R -g Tk &
This will launch R in the background running in its own window, as shown in Figure 2.3. Like the other platforms, there is a menu bar with some common function, but unlike the other platforms, there is no toolbar. The main window acts as the R console.
Learn more about this topic from R in a Nutshell.
R is rapidly becoming the standard for developing statistical software, and R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.