General Rweb interface

To run an Rweb example
Simply click on the radio button corresponding to the example you want to see (check out the code links, too), and hit the submit button.
To run Rweb using your own commands
type the R (or Splus) code you want to execute into the text window and then click on the submit button. A new html page is produced, with the text output of your code followed by the graphical output (if any) from your code (the short code snippet that occupies the text window at start-up is an example, in case you don't know R already).

Either

Choose an example file:

Or

Enter your code here (replacing the sample below)

x <- rnorm(100) # 100 random numbers from a normal(0,1) distribution y <- exp(x) + rnorm(100) # an exponential function with error result <- lsfit(x,y) # regress x on y and store the results ls.print(result) # print the regression results plot(x,y) # pretty obvious what this does abline(result) # add the regression line to the plot lines(lowess(x,y), col=2) # add a nonparametric regression line (a smoother) hist(result$residuals) # histogram of the residuals from the regression apropos("plot") # apropos will identify all commands like plot

You can use your own data!
The External Data Entry box below allows you to select a dataset on your computer. The dataset will be read in using read.table with header=T and stored in a dataframe called X. The dataframe, X, will then be attached so you can use the variable names in the commands you enter into the text area. Here's some example data, and some example commands for use with that data.

External Data Entry

Select a local file to submit:

The new R home page
In addition, here's a little help on R, and help on R functions (this is the help included in the R distribution).
The R mailing list archive
Ripley and Venebles have an R complement document to their book Modern Applied Statistics with S-Plus.
Rweb modules: these modules are designed as a point and click, forms-based interface to R for use in introductory statistics courses. Students do not need to know anything about writing programs in R. They choose a data set, the type of analysis, and the options for the analysis. The currently implemented analyses include Regression, Summary statistics and graphs, ANOVA, Two Way Tables, and a Probability calculator. The output includes printouts and graphs which can be cut and pasted into project writeups or printed out.
Why live with R rather than S-Plus?

See the Electronic Journal Article for more information on Rweb.

The computer time for Rweb is donated by the Department of Mathematics and Computer Science, Northern Kentucky University.

Page maintained by Andy Long. Comments appreciated.