Grammar of graphics II

Jeff Stevens

2025-03-31

Introduction

Set-up

Seven components/layers of ggplots

  • Data
  • Mappings – maps data to properties of geom
  • Geom – represents data
  • Stat – transforms data
  • Position – control placement of data on coordinate
  • Facet – split graph into subplots
  • Coordinate – places data in coordinate system

Aesthetics

Map data to visual properties

Map data columns to plot

mpg |>
  ggplot(aes(x = displ, y = hwy)) +
  geom_point()

Size

Map data to aesthetic

mpg |>
  ggplot(aes(x = displ, y = hwy, 
             size = cty)) +
  geom_point()

Apply to all points

mpg |>
  ggplot(aes(x = displ, 
             y = hwy)) +
  geom_point(size = 0.5)

Transparency

mpg |>
  ggplot(aes(x = displ, y = hwy, 
             alpha = cty)) +
  geom_point()

mpg |>
  ggplot(aes(x = displ, 
             y = hwy)) +
  geom_point(alpha = 0.25)

Shape

mpg |>
  ggplot(aes(x = displ, y = hwy, 
             shape = class)) +
  geom_point()

mpg |>
  ggplot(aes(x = displ, 
             y = hwy)) +
  geom_point(shape = 5)

Shapes

Color

mpg |>
  ggplot(aes(x = displ, y = hwy, 
             color = class)) +
  geom_point()

mpg |>
  ggplot(aes(x = displ, 
             y = hwy)) +
  geom_point(color = "dodgerblue")

Color

What happens if we put a single color in the aesthetic?

mpg |>
  ggplot(aes(x = displ, y = hwy, color = "dodgerblue")) +
  geom_point()

Color: points, lines, text, and borders

mpg |>
  ggplot(aes(x = class, 
             color = class)) +
  geom_bar(show.legend = FALSE, linewidth = 2)

mpg |>
  ggplot(aes(x = class)) +
  geom_bar(color = "blue",
           linewidth = 2)

Fill: filled areas

mpg |>
  ggplot(aes(x = class, fill = class)) +
  geom_bar(show.legend = FALSE)

mpg |>
  ggplot(aes(x = class)) +
  geom_bar(fill = "blue")

Lines separated by groups

mpg |>
  ggplot(aes(x = displ, y = hwy, group = drv)) +
  geom_smooth()

Linetype

mpg |>
  ggplot(aes(x = displ, y = hwy, linetype = drv)) +
  geom_smooth()

Apply line aesthetics to groups

mpg |>
  ggplot(aes(x = displ, y = hwy, color = drv)) +
  geom_smooth(show.legend = FALSE)

Apply aesthetics to one geom

mpg |>
  ggplot(aes(x = displ, y = hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth()

Apply aesthetics differently to geoms

mpg |>
  ggplot(aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  geom_smooth(aes(linetype = drv))

Statistical transformations

Statistical transformations

  • ggplot() can calculate statistics on the fly
  • many geoms have underlying statistical transformation

Bar plots

Count observations of variable types with stat_count()

mpg |>
  ggplot(aes(x = class)) +
  geom_bar()

Summarize data with stat_summary() 

mpg |>
  ggplot(aes(x = class, y = hwy)) +
  geom_point()

mpg |>
  ggplot(aes(x = class, y = hwy)) +
  stat_summary()

Summarize data

Plot mean and 95% CI

mpg |>
  ggplot(aes(x = class, y = hwy)) +
  stat_summary(fun.data = mean_cl_normal)

Plot median and range

mpg |>
  ggplot(aes(x = class, y = hwy)) +
  stat_summary(fun.min = min, fun.max = max, fun = median)

Facets

Visualizing groups

Coloring by group difficult to visualize with many groups

mpg |>
  ggplot(aes(x = displ, y = hwy, color = class)) +
  geom_point()

Faceting

Pulls out groups into separate panels

mpg |>
  ggplot(aes(x = displ, y = hwy)) +
  geom_point() +
  facet_wrap(vars(class))

Faceting

Group by two or more variables with facet_grid()

mpg |>
  ggplot(aes(x = displ, y = hwy)) +
  geom_point() +
  facet_grid(rows = vars(year), cols = vars(drv))

Faceting

Allow axes to vary with scales

mpg |>
  ggplot(aes(x = displ, y = hwy)) +
  geom_point() +
  facet_grid(rows = vars(year), cols = vars(drv),
             scales = "free_x")

Facet

Allow axes to vary with scales

mpg |>
  ggplot(aes(x = displ, y = hwy)) +
  geom_point() +
  facet_grid(rows = vars(year), cols = vars(drv),
             scales = "free")

Let’s code!

Grammar of graphics II