5  Density plots

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

This graph requires:

✅ a numeric (continuous) variable

5.1 Description

A density plot displays data distribution using a smooth curve instead of bars. It helps compare multiple sets of data and the area under the curve represents the total probability. Instead of dividing the x axis into discrete ‘bins’ to create groupings for the variable’s values, density plots transform the distribution according to a kernel density estimate. Legends are used to explain each curve, and different colors are used to differentiate them.

5.2 Set up

PACKAGES:

Install packages.

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

DATA:

Artwork by Allison Horst

The penguins data.

penguins <- palmerpenguins::penguins
glimpse(penguins)
#> 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…

5.3 Grammar

CODE:

Create labels with labs()

Initialize the graph with ggplot() and provide data

Map flipper_length_mm to the x axis

Add the geom_density() layer

labs_density <- labs(
  title = "Adult foraging penguins",
  subtitle = "Distribution of flipper length",
  x = "Flipper length (millimeters)")
ggp2_density <- ggplot(data = penguins, 
        aes(x = flipper_length_mm)) + 
      geom_density() 

ggp2_density +
  labs_density

GRAPH:

A downside of using density plots is the lack of interpretability of the y axis.