19  Overlapping histograms

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


This graph requires:

✅ a categorical variable

✅ a numeric (continuous) variable

19.1 Description

Overlapping histograms allow us to compare distributions across the groups of a categorical (or ordinal) variable.

19.2 Set up

PACKAGES:

Install packages.

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

DATA:

Artwork by allison horst

The penguins data.

show/hide 
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…

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19.3 Grammar

CODE:

  • Create labels with labs()

  • Initialize the graph with ggplot() and provide data

  • Map flipper_length_mm to the x axis and species to fill

  • Set alpha to 2/3 inside geom_histogram()

show/hide 
labs_ovrlp_hist <- labs(
  title = "Adult foraging penguins",
  x = "Flipper length (mm)", 
  fill = "Species")
ggp2_ovrlp_hist <- ggplot(data = penguins,
     aes(x = flipper_length_mm, 
         fill = species)) + 
     geom_histogram(alpha = 2/3) 
ggp2_ovrlp_hist + 
  labs_ovrlp_hist

Experiment with different binwidths when comparing distributions across groups.

GRAPH:

Histograms work by dividing the variable provided to x into bins and counting the number of observations in each bin.