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Rweb for Stat 3011 (Wild and Seber)

To run Rweb just type the R (or Splus) code you want to execute into the text window and then click on the submit button. You will get a new html page with the text output of your code followed by the graphical output (if any) from your code.

Below the submit button is a multiple choice menu of all of the datasets for the book Chance Encounters: A First Course in Data Analysis and Inference by Wild and Seber where you can choose a dataset for your analysis (if you want one). Click to highlight a dataset.

When you click "Submit", the dataset you have highlighted 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.

You can find out what the variable names are in one of two ways.

You can look at the file. Links to the files are on this page.
You can just run Rweb. It will report the variable names. For example, if you run Rweb after having chosen the first file on the menu (heart.txt) but without any R commands typed into the commands box, R will read in the data and report the variable names
```
Rweb:> names(X) 
 [1] "Id"      "EJEC"    "SYSVOL"  "DIAVOL"  "OCCLU"   "STEN"    "TIME"    
 [8] "OUTCOME" "AGE"     "SMOKE"   "BETA"    "CHOL"    "SURG"
```
(Note: Variable names in R are case sensitive, you must use Id, EJEC, and so forth exactly as they appear.)

If you use the back button on your browser to come back to this page you can modify your old code and then resubmit it, or you can clear the text area and type in all new code.

If you need a help with R functions you can use the HTML documentation for the version of R on this server.

Datasets from Wild and Seber

Example

Type

hist(length[gender == "female"], right=FALSE)

in the commands box, choose the file coyote.txt from the datasets menu, and submit. Rweb will draw the histogram in Figure 2.3.8 (p. 56) in the textbook (Wild and Seber).

Don't be intimidated by the fact that the command used to produce the histogram looks complicated. Part of the complication is from the fact that the histogram is only for part of the data, those individuals with gender == "female". The rest of the complication is from the fact that there are several ways to draw a histogram and the R defaults do not exactly match the histogram in the book, which is why we added right=FALSE to tell R to put values exactly on a bin boundary into the upper bin. Typically, we will use the simple command

hist(x)

to draw a histogram of all the values in a variable named x.

Modified for use at U of M: 10-Sep-2000 by Charles Geyer (charlie@stat.umn.edu)