Introduction to ggplot2

The gg in ggplot2 stands for grammar of graphics. The idea is that every graph is built from the same basic components:

In ggplot2, the components are combined using the + operator.

ggplot(data, mapping = aes(x = ..., y = ..., color = ...)) + geom_point() + coord_cartesian()

Each geom_X uses a stat_Y function to calculate what is visualizes. For example, geom_bar uses stat_count to create a bar chart by counting how often each value appears in the data (see ? geom_bar). geom_point just uses the stat "identity" to display the points using the coordinates as they are.

RStudio’s Data Visualization Cheat Sheet offers a comprehensive overview of available components. A good introduction can be found in the Chapter on Data Visualization of the free book R for Data Science.

library(ggplot2)

1. Plot a sin(x)/x.

Hint: Trigonometric functions in R use angles in radians (see ? sin). 2 times pi is 360 degrees.

Create the data

x <- seq(-6*pi, 6*pi, length.out = 100)

ggplot2 needs the data to be in a data.frame or tibble with all the data.

dat <- data.frame(x = x, y = sin(x)/x)
head(dat)
##           x             y
## 1 -18.84956 -3.898172e-17
## 2 -18.46876 -2.012385e-02
## 3 -18.08796 -3.815130e-02
## 4 -17.70716 -5.137086e-02
## 5 -17.32636 -5.765016e-02
## 6 -16.94556 -5.576687e-02

We map x in the data.frame to the x-axis and y to the y-axis and then add a geom that produces a line representation. The Cartesian coordinate system is the default.

ggplot(data = dat, mapping = aes(x = x, y = y)) + 
  geom_line()

2. The ‘cars’ data set gives the speed of cars and the distances taken to stop. Note that the data were recorded in the 1920s. Plot the ‘cars’ data set as a scatter plot. Plot all data points with distances taken to stop greater than 80 in red.

data(cars)
head(cars)
##   speed dist
## 1     4    2
## 2     4   10
## 3     7    4
## 4     7   22
## 5     8   16
## 6     9   10

basic version with points

ggplot(cars, mapping = aes(x = speed, y = dist)) + 
  geom_point()

Add color as a mapping that is only used by geom_points. Mappings defined in ggplot are used by all geoms of the graph.

ggplot(cars, mapping = aes(x = speed, y = dist)) + 
  geom_point(mapping = aes(color = dist > 80))

define the colors using a manual color scale

ggplot(cars, mapping = aes(x = speed, y = dist)) + 
  geom_point(mapping = aes(color = dist > 80)) + 
  scale_color_manual(values = c("black", "red"))

add a second geom that produces a smoothed line (default is a local polynomial regression).

ggplot(cars, mapping = aes(x = speed, y = dist)) + 
  geom_point(mapping = aes(color = dist > 80)) + 
  scale_color_manual(values = c("black", "red")) +
  geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

3. Plot histograms for speed and dist in ‘cars’.

ggplot(cars, aes(x = speed)) + 
  geom_histogram(bins = 10)