Objectives

1. basic graphics with R

2. elegant graphics with ggplot2

mpg dataframe

mpg contains observations collected by the US Environment Protection Agency on 38 models of car. You can see more details via ?mpg. Among the variables in mpg are:

1. displ, a car’s engine size, in litres.
2. hwy, a car's fuel efficiency on the highway, in miles per gallon (mpg). A car with a low fuel efficiency consumes more fuel than a car with a high fuel efficiency when they travel the same distance.
3. ...

Basic plots

Practice and look at the help document of plot.

library(ggplot2)
plot(mpg$displ, mpg$hwy)
abline(lm(hwy ~ displ, data = mpg))
title("Regression of MPG on engine size")

Histograms and density plots

hist(mpg$hwy)

d <- density(mpg$hwy)  ## returns the density data
plot(d)

Piechart

car.table <- table(mpg$manufacturer)
pie(car.table)

Boxplots

## Boxplot of MPG
boxplot(mpg$hwy, main = "Boxplot of MPG")
## Boxplot of MPG by Car Cylinders
boxplot(hwy ~ cyl, data = mpg, main = "Car Milage Data", xlab = "Number of Cylinders", ylab = "Miles Per Gallon")

Correlation plot

library(corrplot)
M <- cor(mtcars)
corrplot(M, addCoef.col = "grey")

Time series

plot(AirPassengers)

Multiple plots

par(mfrow = c(1, 2))
plot(AirPassengers)
boxplot(ggplot2::mpg$hwy, data = ggplot2::mpg, main = "Boxplot of MPG")

Creating a ggplot

library(ggplot2)
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy))

Creating a ggplot

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

1. With ggplot2, you begin a plot with the function ggplot(). ggplot() creates a coordinate system that you can add layers to.

2. You complete your graph by adding one or more layers to ggplot(). For example, geom_point() adds a layer of points to your plot, which creates a scatterplot. You can specify the color, size and shape of these points. Each geom function in ggplot2 takes a mapping argument.

Change the color, size and shape of points

ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy), color = 2, size = 3, shape = 18)

Facets

ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy)) + facet_wrap(~class, nrow = 2)

Facets

ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy)) + facet_grid(drv ~ cyl)

Pairwise plots

library(GGally)
ggpairs(subset(mtcars, select = c(1, 3, 4, 5, 6)))

Geometric objects

## left
p1 <- ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy))
## right
p2 <- ggplot(data = mpg) + geom_smooth(mapping = aes(x = displ, y = hwy))
## put the two plots in one row
gridExtra::grid.arrange(p1, p2, ncol = 2)

Geometric objects

Geometric objects

• A geom is the geometrical object that a plot uses to represent data.

• Bar charts use bar geoms, line charts use line geoms, boxplots use boxplot geoms, and so on.

• You can also add multiple geom functions.

ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy)) + geom_smooth(mapping = aes(x = displ, y = hwy))

Bar chart

ggplot(data = mpg) + geom_bar(mapping = aes(x = drv, fill = drv))

Histogram

ggplot(data = mpg) + geom_histogram(mapping = aes(x = hwy))

Boxplots

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + geom_boxplot()

Time series

library(ggplot2)
library(forecast)
autoplot(AirPassengers)

Summary

• We have seen basic R plots and plots from ggplot2.

• In this course, you will also use animated plots (e.g., gifs), interactive plots, etc.

References

R documentation of ggplot2.