# 11 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.

## 11.1 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

## 11.2 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)

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)

### 11.2.1 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)

## 11.3 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)

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)

### 11.3.1 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)

### 11.3.2 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)