R has numerous plotting functions in the base package graphics

?plot
?boxplot
?hist

Go to the bottom of each help page and copy a few lines

• ggplot2 gives much more flexibility and power
• Has unique syntax and approach
• We add layers of plotting information

library(ggplot2)

The main function is ggplot()

• In this first stage we set the plotting aesthetics using aes()
• Defines what is plotted on which axis, what defines the colour/shape etc.

ggplot(data, aes(x = weight, y = height))

No data will be plotted. We get the plot area only…

After defining the plot aesthetics, we add the geometry using various geom_...() functions

We tell the first line that "more is to come" by adding a + symbol at the end

ggplot(data, aes(x = weight, y = height)) +
  geom_point()

There are numerous aesthetics available for geom_point()

?geom_point

ggplot(data, aes(x = weight, y = height, colour = transport)) +
  geom_point()

ggplot(data, aes(x = weight, y = height, 
                 colour = transport, shape = gender)) +
  geom_point()

We can put the general aesthetics in ggplot(), with the geom_point() specific ones in that line

ggplot(data, aes(x = weight, y = height)) +
  geom_point(aes(colour = transport, shape = gender))

Aesthetics set outside of aes() are general across all points

ggplot(data, aes(x = weight, y = height)) +
  geom_point(aes(colour = transport, shape = gender), size = 4)

ggplot(data, aes(x = weight, y = height)) +
  geom_point(aes(colour = transport, shape = gender)) +
  geom_smooth()

This defaults to a loess fit

ggplot(data, aes(x = weight, y = height)) +
  geom_point(aes(colour = transport, shape = gender)) +
  geom_smooth(method = "lm", formula = y~x, se = FALSE)

Axis and legend labels can be added using labs()

ggplot(data, aes(x = weight, y = height)) +
  geom_point(aes(colour = transport, shape = gender)) +
  geom_smooth(method = "lm", formula = y~x, se = FALSE) +
  labs(x = "Weight (kg)", y = "Height (cm)", 
       shape = "Gender", colour = "Transport")

(This is my favourite feature)

ggplot(data, aes(x = weight, y = height)) +
  geom_point(aes(colour = transport, shape = gender)) +
  geom_smooth(method = "lm", formula = y~x, se = FALSE) +
  labs(x = "Weight (kg)", y = "Height (cm)", 
       shape = "Gender", colour = "Transport") +
  facet_wrap(~gender) 

Enter geom_ in the Console followed by the tab key

ggplot(data, aes(x = height, fill = gender)) +
  geom_density(alpha = 0.5)

ggplot(data, aes(x = gender, y =height, fill = gender)) +
  geom_boxplot()

We can summarise our data, then pipe into ggplot()

data %>%
  group_by(transport, gender) %>%
  summarise(mn_height = mean(height), sd_height = sd(height)) %>%
  ggplot(aes(x = transport, y = mn_height, fill = transport)) +
  geom_bar(stat = "identity") +
  facet_wrap(~gender) +
  guides(fill =FALSE)

NB: geom_bar() requires stat = "identity"

data %>%
  group_by(transport, gender) %>%
  summarise(mn_height = mean(height), sd_height = sd(height)) %>%
  ggplot(aes(x = transport, y = mn_height, fill = transport)) +
  geom_bar(stat = "identity") +
  geom_errorbar(aes(ymin = mn_height - sd_height,
                    ymax = mn_height + sd_height),
                width = 0.6)+
  facet_wrap(~gender) +
  guides(fill =FALSE)

How could we get histograms for both weight and height using facets?

• The geom to use is geom_histogram()

How could we get histograms for both weight and height using facets?

data %>%
  melt(id.vars = c("gender", "name", "transport")) %>%
  ggplot(aes(x = value, fill = variable)) +
  geom_histogram(bins = 10, colour = "black") +
  facet_wrap(~variable, scales = "free_x") +
  guides(fill = FALSE)

data %>%
  melt(id.vars = c("gender", "name", "transport")) %>%
  ggplot(aes(x =gender, y = value, fill = gender)) +
  geom_boxplot() +
  facet_wrap(~variable, scales = "free_y")