data.frame
,
bdFrame
,
design
,
factor
,
formula
,
gam
,
glm
,
bdGlm
,
lm
,
bdLm
,
loess
,
preplot.gam
,
preplot.loess
,
profile
,
stl
,
surv.fit
,
times
,
tree
,
tree.sequence
.
plot(x, ...)
Generates plots summarizing the distribution of the variables on the current graphics device.
Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983).
Graphical Methods for Data Analysis.
Wadsworth, Belmont, California.
Cleveland, W. S. (1985).
The Elements of Graphing Data.
Wadsworth, Monterey, California.
The Cleveland reference is a clear and useful book with many hints
about how to create more effective graphics. Chambers,
et al.
details many of the techniques that are most useful for data
analysis.
plot(xlim = c(1955,1995), ylim = c(300,370)) # sets up plot axes without plotting points plot(corn.rain, corn.yield) # simple scatter plot plot(1:10, type="b", lty=2, pty=7) plot(wafer) # use plot.design to get a picture of the effects of factors plot.factor(wafer) # get several plots showing the effect of one factor population <- state.x77[,"Population"] area <- state.x77[,"Area"] plot(area, population, log="xy", xlab="Area in square miles", ylab="Population in thousands") states.lab <- c("Alaska", "California", "Florida", "Hawaii", "New Jersey", "New York", "Rhode Island", "Texas", "Wyoming") text(area[states.lab], population[states.lab], paste(" ", states.lab, sep=""), adj=0)