Gallery

library(ggEDA)
library(datarium)
library(palmerpenguins)
#> 
#> Attaching package: 'palmerpenguins'
#> The following objects are masked from 'package:datasets':
#> 
#>     penguins, penguins_raw

Examples with real datasets

Chicken Weights by Feed

Horsebean feed consistently produces the lowest chicken weights.

ggstack(
  chickwts,
  col_sort = "weight",
  options = ggstack_options(
    show_legend = TRUE,
    legend_position = "bottom",
    legend_nrow = 1,
    legend_text_size = 18
  )
)

Iris

The setosa species have drastically smaller petals then other iris species.

ggstack(
  iris,
  options = ggstack_options(show_legend = TRUE)
)

Titanic Survival by Class

Most deaths in the titanic were male adults.

ggstack(
  titanic.raw,
  palettes = list(
    Sex = c(Male = "#0072B2", Female = "#CC79A7"),
    Survived = c(Yes = "#32A02D", No = "#E31A1C"),
    Class = c("Crew" = "#F9F7EAFF", "1st" = "#006E37FF", "2nd" = "#8DB580FF", "3rd" = "#D9E5BDFF"),
    Age = c(Child = "#35B779FF", Adult = "#440154FF")
  ),
  options = ggstack_options(show_legend = TRUE)
)

Lets make the same plot except heirarchically sort on multiple arguments

ggstack(
  titanic.raw,
  col_sort = c("Class", "Age", "Sex"), sort_type = "frequency",
  palettes = list(
    Sex = c(Male = "#0072B2", Female = "#CC79A7"),
    Survived = c(Yes = "#32A02D", No = "#E31A1C"),
    Class = c("Crew" = "#F9F7EAFF", "1st" = "#006E37FF", "2nd" = "#8DB580FF", "3rd" = "#D9E5BDFF"),
    Age = c(Child = "#35B779FF", Adult = "#440154FF")
  ),
  options = ggstack_options(show_legend = TRUE)
)

Palmer Penguins

Gentoo penguins from Biscoe Island have the shallowest bills depths.

library(palmerpenguins)

# Drop Year Column
penguins$year <- NULL

ggstack(
  penguins,
  palettes = list(
    species = c(Chinstrap = "#C55BCC", Adelie = "#FF7F02", Gentoo = "#047476"),
    sex = c(male = "#0072B2", female = "#CC79A7"),
    island = c(Biscoe = "#E69F00", Dream = "#56B4E9", Torgersen = "#009E73")
  ),
  options = ggstack_options(
    relative_height_numeric = 1.2,
    show_legend = TRUE,
  )
)
#> 
#> ── Running ggstack ─────────────────────────────────────────────────────────────
#> 
#> ── Sorting
#> ℹ Sorting X axis by: Order of appearance
#> 
#> ── Generating Plot
#> ℹ Found 7 plottable columns in data
#> ✔ Plotting column species
#> ✔ Plotting column island
#> ✔ Plotting column bill_length_mm
#> ✔ Plotting column bill_depth_mm
#> ✔ Plotting column flipper_length_mm
#> ✔ Plotting column body_mass_g
#> ✔ Plotting column sex
#> ℹ Stacking plots vertically
#> ℹ Making plot interactive since `interactive = TRUE`

Artificial Data

Inbuilt ggEDA example

# Plot data, sort by Glasses
ggstack(
  baseballfans,
  col_id = "ID",
  col_sort = "Glasses",
  options = ggstack_options(legend_nrow = 2)
)

Lazy Birdwatcher

The simulated “Lazy Birdwatcher” dataset records magpie observations by Robert and Catherine. Robert doesn’t birdwatch on weekends, resulting in missing data for him on those days. This pattern of missingness, dependent on both person and day, is hard to detect using one-dimensional EDA tools like skimr or two-dimensional tools like ggpairs from the GGally package, as they don’t capture multi-variable interactions contributing to the missingness.

# Plot lazy birdwatcher dataset by Number of Magpies Observed
ggstack(
  lazy_birdwatcher,
  col_sort = "Magpies",
  palettes = list(
    Birdwatcher = c(Robert = "#E69F00", Catherine = "#999999"),
    Day = c(Weekday = "#999999", Weekend = "#009E73")
  ),
  options = ggstack_options(
    show_legend = TRUE,
    fontsize_barplot_y_numbers = 12,
    legend_text_size = 16,
    legend_key_size = 1,
    legend_nrow = 1,
  )
)