• ggplot2 is an R package for visualizing data.
• It is part of the tidyverse
• It is based on a concept called The Grammar of Graphics by Leland Wilkinson
• It has been programmed and implemented as an R package by Hadley Wickham.
• There is a book available on ggplot2
• Which you find at https://ggplot2-book.org/ as an online book.

A ggplot graphic has at least three key components:

1. data
2. A set of aesthetic mappings between variables in the data and visual properties (x and y axis, colour, dotsize etc.)
3. At least one geometries layer which describes how to render each observation (lines, point, bars etc.). Layers are usually created with a geom function

• Copy and execute these lines:
• displ displacement by hwy highway miles per gallon

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

library(tidyverse)
ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point()

• Copy and execute these lines:
• Axis: displ displacement and hwy highway miles per gallon
• Colour: drv f = front-wheel drive, r = rear wheel drive, 4 = 4wd
• Size: cyl number of cylinders

ggplot(mpg, aes(x = displ, y = hwy, color = drv, size = cyl)) + 
  geom_point()

ggplot(mpg, aes(x = displ, y = hwy, color = drv, size = cyl)) + 
  geom_point()

The main function is ggplot(). It takes two arguments:

1. data : A data frame
2. mapping : Aesthetic mappings provided with the aes() function.

Additional layers are added with a + sign.

ggplot(mpg, aes(x = displ, y = hwy, color = drv, size = cyl)) + 
  geom_point()

• Take the mpg data frame.
• Plot a graph with …
  • cty and hwy displayed on the axis and
  • the colour is mapped on the variable class and
  • the shape (shape = drv) is mapped on the variable drv.
• Use the geom_point() layer.

ggplot(mpg, aes(cty, hwy, colour = class, shape = drv))  + 
  geom_point()

• Aestetics can also be provided in the geom function.
• Here they are not mapped to variables but fixed.

ggplot(diamonds, aes(carat, price)) + 
  geom_point(colour ="red", shape = "+", size = 1)

• geom_point() : Dots for each data point.
• geom_line() : Lines connecting each x-axis data point
• geom_bar() : Bars
• geom_text() : Text at x and y positions
• geom_smooth() : Smoothed conditional means

• Take the economics data frame
• Create a lineplot for date and unemployment. (geom_line())
• Add a second layer for red dots of size = 1 (geom_point())

ggplot(economics, aes(date, unemploy)) + 
  geom_line() + 
  geom_point(color = "red", size = 1)

• geom_bar() draws bars
• By default, it counts the numbers of entities of categories provided as the x variable

# Number of cars in each class:
ggplot(mpg, aes(class)) +
  geom_bar()

• Take the mpg data frame.
• Create a barplot with the counts of categories for the drv variable.
• Colour the bars red with the fill argument.
• Set the argument width = 0.8 to resize the bar width.

ggplot(mpg, aes(drv)) +
  geom_bar(fill = "red", width = 0.8)

• With the argument stat = "identity", bar heights and bar categories are taken from the x and y variables:

df <- data.frame(
  type = c("A", "B", "C"), 
  mean = c(2.5, 4.4, 6.3)
)
ggplot(df, aes(x = type, y = mean)) +
  geom_bar(stat = "identity")

• Take the starwars database.
• Calculate the bmi. (mutate(bmi = mass / (height / 100)^2))
• Summar ise the median of the bmi grouped by species. (summarise(mean_bmi = median(bmi, na.rm = TRUE))
• Create a fitting barplot.

starwars %>% 
  mutate(bmi = mass / (height / 100)^2) %>% 
  group_by(species) %>% 
  summarise(
    mean_bmi = median(bmi, na.rm = TRUE)
  ) %>%
  ggplot(aes(species, mean_bmi)) +
    geom_bar(stat = "identity")

• Take the economics data frame.
• Create a scatterplot with number of unemployed unemploy by population pop.
• Add a geom_smooth layer.

economics %>%
  ggplot(aes(pop, unemploy)) + 
    geom_point() + 
    geom_smooth()

• Install and activate the library dslabs.
• Take the dataframe gapminder.
• Group the data by year and continent. (group_by(year, continent))
• Use the summarize() function to calculate the mean of infant_mortality.
• Create a line and dot plot with year on x-axis, mean of infant_mortality on y-axis, and continent as line/dot colours.
• Add a smooth layer.

When you have multiple values ordered in a categorical variable. Simple plots become messy:

ggplot(mpg,aes(drv, hwy)) +
  geom_point()

Solutions

• geom_jitter() : Adds a litle random jitter to each datapoint
• geom_boxplot() : Draws a boxplot
• geom_violin() : Draws a violine plot

• Take the mgp dataset
• Create the following plots for the variables x = drv and y = hwy
  • geom_jitter()
  • geom_boxplot()
  • geom_violin()

ggplot(mpg,aes(drv, hwy)) +geom_jitter(width = 0.2)
ggplot(mpg,aes(drv, hwy)) +geom_boxplot()
ggplot(mpg,aes(drv, hwy)) +geom_violin()

• Add a title: ggtitle() ( e.g. ggtitle(“My first plot”) )
• Change axis labels: labs(x = NULL, y = NULL) (e.g. labs(x = “Categories”, y = “Mean”))
• Change axis scales: ylim(min = 0, max = 10) ; xlim(min = 0, max = 10)