24  Ridgeline plots

This graph is largely complete and just needs final proof reading.


This graph requires:

✅ a categorical variable

✅ a numeric (continuous) variable

24.1 Description

If we want to plot density curves but retain the interpretability of the axes, consider comparing multiple distributions using the ggridges package.

24.2 Set up

PACKAGES:

Install packages.

show/hide 
install.packages("palmerpenguins")
library(palmerpenguins)
library(ggplot2)

DATA:

Artwork by allison horst

Remove missing island from penguins

show/hide 
peng_ridges <- filter(penguins, !is.na(island))
glimpse(peng_ridges)
#> Rows: 344
#> Columns: 8
#> $ species           <fct> Adelie, Adelie, Adelie…
#> $ island            <fct> Torgersen, Torgersen, …
#> $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, …
#> $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, …
#> $ flipper_length_mm <int> 181, 186, 195, NA, 193…
#> $ body_mass_g       <int> 3750, 3800, 3250, NA, …
#> $ sex               <fct> male, female, female, …
#> $ year              <int> 2007, 2007, 2007, 2007…

24.3 Grammar

CODE:

  • Create labels with labs()

  • Initialize the graph with ggplot() and provide data

  • Map bill_length_mm to the x axis

  • Map island to the y axis and fill

  • Add the ggridges::geom_density_ridges() layer (with alpha set to 2/3)

  • Remove the legend with show.legend = FALSE

show/hide 
labs_ridges <- labs(
  title = "Adult foraging penguins",
  x = "Bill length (millimeters)",
  y = "Island", fill = "Island")
ggp2_ridges <- ggplot(data = peng_ridges,
       aes(x = bill_length_mm, 
           y = island, 
        fill = island)) +
  ggridges::geom_density_ridges(alpha = 2/3, 
      show.legend = FALSE)
ggp2_ridges + 
  labs_ridges

GRAPH:

Ridgeline plots are excellent for comparing continuous distributions across groups.