This is a slightly modified version of Martin Morgan’s original vignette.
Colors should reflect the nature of the data and be carefully chosen to convey equivalent information to all viewers. The RColorBrewer package provides an easy way to choose colors; see also the colorbrewer2 web site.
library(RColorBrewer)
display.brewer.all()
We’ll use a color scheme from the ‘qualitative’ series, to represent different levels of factors and for choice of colors. We’ll get the first four colors.
palette <- brewer.pal(4, "Dark2")
We’ll illustrate ‘base’ graphics using the built-in mtcars
data set
data(mtcars) # load the data set
head(mtcars) # show header and top 6 lines
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
The basic model is to plot data, e.g., the relationshiop between miles per gallon and horsepower. The relationship is symbolized by ~
.
plot(mpg ~ hp, mtcars)
Figure 1: Plotting example
Within the default plot each point is represented by an open circle.
The appearance can be influenced by arguments, see ?plot
then ?plot.default
and par
.
pch
represents the plot character (or symbol.)cex
modifies the default character sizecol
allows to chose the color, here from the palette we defined previously.For example, the code below is modified to change to a green filled circle, larger than the default size.
plot(mpg ~ hp, mtcars, pch=20, cex=2, col=palette[1])
Figure 2: Modified plot example
More complicated plots can be composed via a series of commands, e.g., to plot a linear regression: we first make the plot, and then add the regression line using abline()
.
The line is first computer with the lm()
linear model function.
We can also define the line width (lwd
) and its color (col
.)
# Make the default plot
plot(mpg ~ hp, mtcars)
# Compute regression line
fit <- lm(mpg ~ hp, mtcars)
# Add line to the plot
abline(fit, col=palette[1], lwd=3)