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Getting the picture

> plot(infert)

However, the result can be really ugly. Ask R to plot a data frame and it does the best it can, spraying the data all over a matrix of scatter plots. To the experienced eye, this can be a rich source of information about the data, but it probably doesn't look like much to the beginner who just wants a simple plot. Let's start with a plot of the mean age by parity. First we'll have to get the group means, which we can obtain with the brkdn() function.

The brkdn() function returns not only the values of the means for each group defined by each value of parity, but also the variances and numbers of observations. In fact, it has been written to return an object like the one returned by dstats(). The returned object has been assigned the class dstat as well.

> agexeduc<-brkdn(age~educ,infert)
> agexeduc
           0-5yrs   6-11yrs   12+ yrs
Mean     35.25000  32.85000  29.72414
Variance 40.02273  28.66639  19.19280
n        12.00000 120.00000 116.00000
attr(,"class")
[1] "dstat"

Sending the means to the plot() function

> plot(agexeduc[1,])

gets a plot, but it's still pretty sparse and difficult for the reader to work out what's being illustrated. Try this next.

> plot(agexeduc[1,],main="Age by education",
+ xlab="Education",ylab="Age (years)",type="b")

Now it's starting to look like something. The xaxis ticks are not very good here. With R, of course, you can roll your own. First plot again without the x axis.

> plot(agexeduc[1,],main="Age by education",
+ xlab="Education (years)",ylab="Age (years)",type="b",xaxt="n")

Now, specify your own x axis as follows:

> axis(1,1:length(agexeduc[1,]),colnames(agexeduc))

Finally, a simple plot of means. Don't despair, it gets easier. Notice that the values used in most of the operations refer to the data being plotted. This means that as long as you are thoughtful in writing the function to produce the data for plotting, you will be able to automate most of the process by writing a function for producing point/line plots the way you want them. Now try to stick the separate commands in the preceding examples together to make a function called pointline.plot.

The first thing that will hit you in the eye is that the title and axis labels have been hard coded. Unless you want to spend the remainder of your career plotting age by education, this will have to be remedied. Fortunately, it's not too hard - see plot.dstat(). There are a number of functions in this file, as producing a "simple" plot is not all that simple.

This function is interactive, allowing the operator to specify the information that will change from plot to plot but is not included in the data passed to the function.

For more information, see An Introduction to R: High level plotting commands.

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