15 More Base Graphics

R comes with many functions that let us produce a wide variety of graphics, plots, diagrams, charts, maps, … you name it.

In this chapter we describe the behavior of the plot() method and it can be used to produce a large number of graphics.

15.1 The `plot()` function

plot() is the most important high-level function in traditional graphics

The first argument to plot() provides the data to plot
The provided data can take different forms: e.g. vectors, factors, matrices, data frames.
To be more precise, plot() is a generic function
You can create your own plot() method function

In its basic form, we can use plot() to make graphics of:

one single variable
two variables
multiple variables

15.2 One variable graphics

High-level graphics of a single variable

Function	Data	Graphic
`plot()`	numeric	scatterplot
`plot()`	factor	barplot
`plot()`	1-D table	barplot

A numeric object can be either a vector or a 1-D array (e.g. row or column from a matrix)

Figure 15.1: One-variable objects can take multiple forms.

Here’s an example:

# plot numeric vector
num_vec <- (c(1:10))^2
plot(num_vec)

# plot factor
set.seed(4)
abc <- factor(sample(c('A', 'B', 'C'), 20, replace = TRUE))
plot(abc)

# plot 1D-table
abc_table <- table(abc)
plot(abc_table)

15.2.1 More high-level graphics of a single variable

Function	Data	Graphic
`barplot()`	numeric	barchart
`pie()`	numeric	piechart
`dotchart()`	numeric	dotplot

`boxplot()`	numeric	boxplot
`hist()`	numeric	histogram
`stripchart()`	numeric	1-D scatterplot
`stem()`	numeric	stem-and-leaf plot

Examples: one signle variable plots

# barplot numeric vector
barplot(num_vec)

# pie chart
pie(1:3)

# dot plot
dotchart(num_vec)

Examples: one single variable plots

# barplot numeric vector
boxplot(num_vec)

# pie chart
hist(num_vec)

# dot plot
stripchart(num_vec)

15.3 Plots of Two Variables

Function	Data	Graphic
`plot()`	numeric	scatterplot
`plot()`	numeric	stripcharts
`plot()`	factor	boxplots
`plot()`	factor	spineplot
`plot()`	2-column numeric matrix	scatterplot
`plot()`	2-column numeric data.frame	scatterplot
`plot()`	2-D table	mosaicplot

A numeric object can be either a vector or a 1-D array (e.g. row or column from a matrix)

Figure 15.2: One-variable objects can take multiple forms.

Here’s an example:

# plot numeric, numeric
plot(iris$Petal.Length, iris$Sepal.Length)

# plot numeric, factor
plot(iris$Petal.Length, iris$Species)

# plot factor, numeric
plot(iris$Species, iris$Petal.Length)

# plot factor, factor
plot(iris$Species, iris$Species)

15.3.1 Example: Plots of two variables

# some fake data
set.seed(1)

# hair color
hair <- factor(
  sample(c('blond', 'black', 'brown'), 100, replace = TRUE))

# eye color
eye <- factor(
  sample(c('blue', 'brown', 'green'), 100, replace = TRUE))

# plot factor, factor
plot(hair, eye)

15.3.2 More high-level graphics of two variables

Function	Data	Graphic
`sunflowerplot()`	numeric, numeric	sunflower scatterplot
`smoothScatter()`	numeric, numeric	smooth scatterplot
`boxplot()`	list of numeric	boxplots
`barplot()`	matrix	stacked barplot
`dotchart()`	matrix	dotplot
`stripchart()`	list of numeric	stripcharts
`spineplot()`	numeric, factor	spinogram
`cdplot()`	numeric, factor	conditional density plot
`fourfoldplot()`	2x2 table	fourfold display
`assocplot()`	2-D table	association plot
`mosaicplot()`	2-D table	mosaicplot

Sunflower plot

# sunflower plot (numeric, numeric)
sunflowerplot(iris$Petal.Length, iris$Sepal.Length)

Smooth scatter plot

# smooth scatter plot (numeric, numeric)
smoothScatter(iris$Petal.Length, iris$Sepal.Length)

Boxplots

# boxplots (numeric, numeric)
boxplot(iris$Petal.Length, iris$Sepal.Length)

Barplot

m <- matrix(1:8, 4, 2)
# barplot (numeric matrix)
barplot(m, beside = TRUE)

Conditional Density Plot

# conditional density plot (numeric, factor)
cdplot(iris$Petal.Length, iris$Species)