Other geoms

Arguments to geom_smooth()

method uses a different model to fit the data:

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

se = FALSE turns off the confidence band:

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

## `geom_smooth()` using method = 'loess'

The rug

Plots marginal distributions of the data close to the axes.

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

To reduce overplotting, the “alpha” aesthetic can be set independently for each geom to a constant value:

ggplot(data = mpg) +
  geom_point(
    mapping = aes(x = displ, y = hwy),
    alpha = 0.3
  ) +
  geom_rug(
    mapping = aes(x = displ, y = hwy)
  )

ggplot(data = mpg) +
  geom_point(
    mapping = aes(x = displ, y = hwy)
  ) +
  geom_rug(
    mapping = aes(x = displ, y = hwy),
    alpha = 0.3
  )

Order of geom_...() calls

The geoms are painted in order of appearance:

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

## `geom_smooth()` using method = 'loess'

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

## `geom_smooth()` using method = 'loess'

Compare highway and city

We could add two layers, each with a different color. But this still doesn’t give us a legend.

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy), color = "red") +
  geom_point(mapping = aes(x = displ, y = cty), color = "blue")

We need better data transformation tools to reformat the data for plotting it in a more natural way.