26 Grouped violin plots

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

This graph requires:

✅ a categorical variable

✅ a numeric (continuous) variable

26.1 Description

A ‘violin plot’ is a variation of a density or ridgeline plot, where the distribution is plotted symmetrically, creating a two-sided, smoothed distribution.

26.2 Set up

PACKAGES:

Install packages.

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install.packages("palmerpenguins")
library(palmerpenguins)
library(ggplot2)

DATA:

Remove missing island from penguins

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peng_violin <- filter(penguins, !is.na(island))
glimpse(peng_violin)
#> 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…

CODE:

Create labels with labs()
Initialize the graph with ggplot() and provide data
Map island to the x, bill_length_mm to the y, and island to fill
Set alpha to 2/3
Remove the legend with show.legend = FALSE

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labs_grp_violin <- labs(
  title = "Adult foraging penguins", 
  subtitle = "Palmer Archipelago, Antarctica",
  x = "Island", fill = "Island",
  y = "Bill length (millimeters)")
ggp2_grp_violin <- ggplot(data = peng_violin,
       aes(x = island, 
           y = bill_length_mm, 
        fill = island)) +
  geom_violin(alpha = 2/3, 
      show.legend = FALSE) 
ggp2_grp_violin + 
  labs_grp_violin

GRAPH:

Violin plots can allow us to compare the ‘center’ and ‘spread’ of continuous variables across categorical groups.

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labs_grp_violin <- labs(
  title = "Adult foraging penguins", 
  subtitle = "Palmer Archipelago, Antarctica",
  x = "Island", fill = "Island",
  y = "Bill length (millimeters)")
ggp2_grp_violin <- ggplot(data = peng_violin,
       aes(x = island, 
           y = bill_length_mm, 
        fill = island)) +
  geom_violin(alpha = 2/3, 
      show.legend = FALSE) 
ggp2_grp_violin + 
  labs_grp_violin

26.3 More info

26.3.1 Quartiles

Change the shape of the line with linetype and linewidth.

We can include lines for the 25th, 50th, and 75th quartiles using the draw_quantiles argument.

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ggplot(data = peng_violin,
       aes(x = island, 
           y = bill_length_mm, 
        fill = island)) +
  geom_violin(
      draw_quantiles = c(0.25, 0.5, 0.75),
      alpha = 1/2, 
      linewidth = 0.5, 
      show.legend = FALSE)

26.3.2 Kernel

The kernel argument let’s us change the “kernel density estimate” used to create the violin shape. The possible kernel density estimates are "gaussian", "epanechnikov", "rectangular", "triangular", "biweight", "cosine", and "optcosine"

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ggplot(data = peng_violin,
       aes(x = island, 
           y = bill_length_mm, 
        fill = island)) +
  geom_violin(alpha = 1/2, 
      linewidth = 0.5, 
      kernel = "rectangular",
      show.legend = FALSE)

26.3.3 Bandwidth

We can directly adjust the shape of the violin with the bw argument, which is the standard deviation of the smoothing kernel. The trim argument trim(s) the tails of the violins to the range of the data.

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# bw of 0.5
grp_violin_bw0p5 <- ggplot(data = peng_violin,
       aes(x = island, 
           y = bill_length_mm, 
        fill = island)) +
  geom_violin(bw = 0.5, 
      alpha = 2/3, 
      trim = TRUE,
      show.legend = FALSE) 
grp_violin_bw0p5 + 
    labs_grp_violin + 
    labs(caption = "bw = 0.5")
# bw of 4.5
grp_violin_bw4p5 <- ggplot(data = peng_violin,
       aes(x = island, 
           y = bill_length_mm, 
        fill = island)) +
  geom_violin(bw = 4.5, 
      alpha = 2/3, 
      trim = TRUE,
      show.legend = FALSE) 
grp_violin_bw4p5 + 
    labs_grp_violin + 
    labs(caption = "bw = 4.5")