# to read the commands from a source file directly and to save the output named 'example1.txt' as a text file Here are a couple of other handy commands that you can use in R: # to read the commands from a source file directly and to output it in the R console instead of doing it line by line or copying the source file, in the command line envoke: One nice feature of the step-by-step command lines in R is that you may scroll through previous commands using the Up and Down arrow keys. In Windows, the pathname is C:/Users/Username/Documents/.On a Mac, your pathname is shown at the bottom of your Finder window, (/Users/Username/Documents/.The command setwd('/pathname') sets the R working directory. The command getwd() will print your working directory to your screen. It is often useful to set a working directory so that file names without a pathname will refer to files in that directory on your system. You may also source this program from where it is saved on your computer as shown below. This program can either be copied and pasted into the R command line, line by line or as an entire program. This text is not read by the R application. The # symbol indicates a programmer's comment. # Change pathname to wherever you saved example1.datĮxample1 = scan('/Users/Shared/WD/Rdirectory/example1.dat') Here is an example program: # Read data file into R as a vector Here is an example data set you may save on your computer: You may also save R programs as simple text files to open in a separate window so that you can enter multiple lines of code at once and save your commands. In R you can enter each line of code at the prompt in a step-by-step approach. The idea is to find the location geographically closest to you.

The website will require you to choose a 'CRAN Mirror'. It runs on a wide variety of platforms including UNIX, Windows and MacOS.ĭownload a copy of the most recent version of this application from their site: The R - Project for Statistical Computing R is free software - see the R site above for the terms of use. 'One of R's strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.' ) and graphical techniques, and is highly extensible.' 'R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering. 'R is a language and environment for statistical computing and graphics.' According to their site The R - Project for Statistical Computing:

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