Chapter 5 Graphical Summaries

The graphical summary must match up with the type of variable(s).

Variable	Type of summary
1 Qualitative	(Single) Barplot
1 Quantitative	Histogram or (Single) Boxplot
2 Qualitative	Double Barplot, Mosaicplot
2 Quantitative	Scatterplot
1 Quantitative, 1 Qualitative	Double Boxplot

5.1 ggplot

There are two main methods with which to plot within R, base R and ggplot. This section will teach you how to be proficient in both styles!

All base R plots can be done in ggplot, allowing much greater customisation. Base R by default is installed, however ggplot must be installed before use.

• To use ggplot first, download the packageggplot into RStudio. This is a one off command.

install.packages("ggplot2")

• Each time you open RStudio, load the ggplot2 package

library(ggplot2) # Alternatively , use library(tidyverse) to load ggplot2, dplyr and many other useful packages

See the ggplot cheatsheet here

5.2 Barplot

5.2.1 Base R Method

A barplot is used for qualitative variables.

• Which variables are qualitative in mtcars?

• Produce a single barplot of the gears.

barplot(mtcars$gear) 

Notice this is not useful! This is because R has classified gear as a quantitative variable.

• Instead, first summarise gears into a table as follows.

# Produce frequency table
table(mtcars$gear)

# produce barplot (from frequency table)
counts = table(mtcars$gear)
barplot(counts)

• Now customise the barplot.

help(barplot)
barplot(counts, names.arg=c("3 Gears","4 Gears","5 Gears"),col="lightblue")

• Make the names of bars perpendicular to axis.

par(las=2) 
barplot(counts, names.arg=c("3 Gears","4 Gears","5 Gears"),col="lightblue")

• Now consider 2 qualitative variables: gear and cyl. Produce a double barplot by faceting or filtering the barplot of gear by cyl.

counts1 = table(mtcars$cyl, mtcars$gear)
barplot(counts1,names.arg=c("3 Gears","4 Gears","5 Gears"),col=c("lightblue","lightgreen","lightyellow"),legend = rownames(counts1))
barplot(counts1,names.arg=c("3 Gears","4 Gears","5 Gears"),col=c("lightblue","lightgreen","lightyellow"),legend = c("4 cyl","6 cyl","8 cyl"))
barplot(counts1, names.arg = c("3 Gears", "4 Gears", "5 Gears"), col = c("lightblue", 
    "lightgreen", "lightyellow"), legend = c("4 cyl", "6 cyl", "8 cyl"), beside = TRUE)

What do you learn?

5.2.2 ggplot Method

• Produce a single barplot.

# mtcars data
p = ggplot(mtcars, aes(x=factor(cyl)))  # Select the mtcars data, and focus on cyl as factor (qualitative) on x axis
p + geom_bar()  # Produce a barplot

# mpg data
p1 = ggplot(mpg, aes(class)) # Select the mpg data, and focus on class as x axis
p1 + geom_bar() # (1) Produce a barplot
p1 + geom_bar(aes(weight = displ)) # (2) Produce a barplot with counts from displacement variable

• Produce a double barplot.

p1 + geom_bar(aes(fill = drv)) # (3) Produce a (double) barplot divided by the drive variable
p1 +
 geom_bar(aes(fill = drv), position = position_stack(reverse = TRUE)) +
 coord_flip() +
 theme(legend.position = "top")  # (4) Customising (3)

5.3 Histogram

A histogram is used for quantitative variables.

• Which variables are quantitative in mtcars?

5.3.1 Base R Method

• Produce a histogram of the weights.

hist(mtcars$wt)

• Produce a probability histogram of the weights. What is the difference? Why do the 2 histograms have an identical shape here?

hist(mtcars$wt,freq=F)

• In this course, we will consider the probability histogram (2nd one) which means that the total area of the histogram is 1.

• What does the histogram tell us about weights of the cars?

• To see what customisations for hist are available, use help.

help(hist)

• Try this histogram of the weights.

hist(mtcars$wt, br=seq(0,6,by=0.5), freq=F, col="lightgreen",xlab="weight of cars (1000 lbs)",main="Histogram of Weights of Cars US 1973-74")

• Experiment with the customisations to see how they work.

• Try a histogram of the gross horsepower. What do you learn?

• Produce a histogram of mpg. What do you learn?

5.3.2 ggplot Method

Produce a histogram of the weights.

p = ggplot(data=mtcars, aes(x=wt))  # Select the mtcars data, and focus on wt (quantitative) on x axis
p + geom_histogram(aes(y=..density..),binwidth=0.5)  
+ xlab('Weight')+ylab('Density') # Produce a histogram with x and y axis labels

Using aes(y=..density..) turns a raw histogram into a probability histogram.

5.4 Boxplot

A boxplot is another summary for quantitative variables.

5.4.1 Base R Method

• Produce a single boxplot for the weights of cars.

boxplot(mtcars$wt)

• Produce a horizontal boxplot.

boxplot(mtcars$wt, horizontal = T)

Which orientation do you prefer?

Compare to the histogram of weights above: what different features are highlighted by a boxplot?

• Customise your boxplot.

help(boxplot)

• Now consider dividing the weights (qualitative) by cylinders (qualitative). Produce a double boxplot, by filtering or faceting wt by cyl.

boxplot(mtcars$wt~mtcars$cyl)
boxplot(mtcars$wt~mtcars$cyl, names=c("4 cyl", "6 cyl","8 cyl"),ylab="Weight of cars (1000 lbs)")

What do you learn about the weights of cars? Car Weight - see page 6

• Try faceting the weights by another qualitative variable.

5.4.2 ggplot Method

• Produce a single boxplot.

p = ggplot(data=mtcars, aes(x="", y=wt))  # Select the mtcars data, and focus on wt (quantitative) on y axis (with no filtering on x axis)
p + geom_boxplot() # Produce a boxplot

• Produce a double boxplot.

p = ggplot(data=mtcars, aes(x=factor(cyl),y=wt))  # Select the mtcars data, and focus on wt (quantitative) on y axis and cyl (qualitative) on x axis
p + geom_boxplot() # Produce a boxplot, of wt filtered by cyl

• geom_jitter plots the points with a small amount of random noise. We use it to investigate over-plotting in small data sets.

p = ggplot(data=mtcars, aes(x=factor(cyl),y=wt))
p + geom_boxplot() + geom_jitter()

What do the following customisations do?

p = ggplot(data=mtcars, aes(x=factor(cyl),y=wt))
p + geom_boxplot() + coord_flip()
p + geom_boxplot(notch = TRUE)
p + geom_boxplot(outlier.colour = "green", outlier.size = 3)

Now consider dividing the weights by another qualitative variable.

p + geom_boxplot(aes(fill = factor(cyl)))
p + geom_boxplot(aes(fill = factor(am)))

5.5 Mosaicplot

5.5.1 Base R Method

A mosaic plot visualises 2 qualitative variables.

counts2 = table(mtcars$gear, mtcars$am)  # Produces contingency table
plot(counts2) # Produces mosaic plot from contingency table

5.5.2 ggplot Method

To create mosaic plots in ggplot the library ggmosaic can be used. See here for further detail.

5.6 Scatterplot

A scatter plot considers the relationship between 2 quantitative variables.

5.6.1 Base R Method

• What does this plot tell us?

plot(mtcars$wt,mtcars$mpg)

• Customise your scatterplot. You can change the plotting symbols.

plot(mtcars$wt,mtcars$mpg, xlab="Car Weight", ylab="Miles per Gallon",col="darkred",pch=19)

• Add a line of best fit.

abline(lm(mtcars$mpg~mtcars$wt))

We’ll explore this further in Section 6.

• We can compare pairs of multiple quantitative variables using plot or pairs.

plot(mtcars)
pairs(~mpg+disp+drat+wt,data=mtcars)
pairs(mtcars)

Which variables seem to be related?

5.6.2 ggplot Method

• Produce a scatterplot.

p = ggplot(mtcars, aes(wt, mpg)) # Select the mtcars data, and focus on wt (quantitative) on x axis and mpg (quantiative) on y axis
p + geom_point()  # Produce a scatterplot of mpg vs wt

p + geom_point(aes(colour = factor(cyl)))  # Colour the points by cyl (qualitative)

• Notice what these customisations do.

p + geom_point(aes(shape = factor(cyl)))  # Shape the points by cyl (qualitative)
p + geom_point(aes(shape = factor(cyl))) + scale_shape(solid = FALSE) 
p + geom_point(aes(size = qsec))  # Size the points by qsec (qualitative)
p + geom_point(aes(colour = cyl)) + scale_colour_gradient(low = "blue")  # Colour the points by cyl (quantitative)

5.7 plotly **

plotly or plot.ly is included in the `ggplot2 package and allows you to have offline interactive tools.

• Produce this scatter plot. Notice what happens when you hover over the data points.

library("plotly")
p1 = plot_ly(mtcars, x = ~mpg, y = ~wt, type="scatter") 
p1

• Now add another variable.

p2 = plot_ly(mtcars, x = ~mpg, y = ~wt, symbol = ~cyl, type="scatter") 
p2

p3 = plot_ly(mtcars, x = ~mpg, y = ~wt, color = ~cyl, type="scatter") 
p3

p4 = plot_ly(mtcars, x = ~mpg, y = ~wt, color = ~factor(cyl), type="scatter") 
p4

• Try a boxplot.

library("plotly")
p5 = plot_ly(mtcars, x = ~mpg, y = ~factor(cyl), type='box') 

• Here’s the cheatsheet.
• More examples here.
• Guide to plotly.