# Tutorial 🐧

This is a gentle and lighthearted tutorial on how to use tools from AlgebraOfGraphics, using as example dataset a collection of measurements on penguins. See the Palmer penguins website for more information.

To follow along this tutorial, you will need to install a few packages. All the required packages can be installed with the following command.

julia> import Pkg; Pkg.add(["AlgebraOfGraphics", "CairoMakie", "DataFrames", "LIBSVM", "PalmerPenguins"])

After the above command completes, we are ready to go.

using PalmerPenguins, DataFrames

first(penguins, 6)
6×7 DataFrame
Rowspeciesislandbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gsex
String15String15Float64Float64Int64Int64String7

## Frequency plots

Let us start by getting a rough idea of how the data is distributed.

Note

Due to julia's compilation model, the first plot may take a while to appear.

using AlgebraOfGraphics, CairoMakie
set_aog_theme!()

axis = (width = 225, height = 225)
penguin_frequency = data(penguins) * frequency() * mapping(:species)

draw(penguin_frequency; axis = axis) ### Small intermezzo: saving the plot

If you are working in an interactive enviroment with inline plotting support, such VSCode or Pluto.jl, the above should have displayed a bar plot. If you are working directly in the console, you can simply save the plot and inspect it in the file explorer.

fg = draw(penguin_frequency; axis = axis)
save("figure.png", fg, px_per_unit = 3) # save high-resolution png

### Styling by categorical variables

Next, let us see whether the distribution is the same across islands.

plt = penguin_frequency * mapping(color = :island)
draw(plt; axis = axis) Oops! The bars are in the same spot and are hiding each other. We need to specify how we want to fix this. Bars can either dodge each other, or be stacked on top of each other.

plt = penguin_frequency * mapping(color = :island, dodge = :island)
draw(plt; axis = axis) This is our first finding. Adelie is the only species of penguins that can be found on all three islands. To be able to see both which species is more numerous and how different species are distributed across islands in a unique plot, we could have used stack.

plt = penguin_frequency * mapping(color = :island, stack = :island)
draw(plt; axis = axis) ## Correlating two variables

Now that we have understood the distribution of these three penguin species, we can start analyzing their features.

penguin_bill = data(penguins) * mapping(:bill_length_mm, :bill_depth_mm)
draw(penguin_bill; axis = axis) We would actually prefer to visualize these measures in centimeters, and to have cleaner axes labels. As we want this setting to be preserved in all of our bill visualizations, let us save it in the variable penguin_bill, to be reused in subsequent plots.

penguin_bill = data(penguins) * mapping(
:bill_length_mm => (t -> t / 10) => "bill length (cm)",
:bill_depth_mm => (t -> t / 10) => "bill depth (cm)",
)
draw(penguin_bill; axis = axis) Much better! Note the parentheses around the function t -> t / 10. They are necessary to specify that the function maps t to t / 10, and not to t / 10 => "bill length (cm)".

There does not seem to be a strong correlation between the two dimensions, which is odd. Maybe dividing the data by species will help.

plt = penguin_bill * mapping(color = :species)
draw(plt; axis = axis) Ha! Within each species, penguins with a longer bill also have a deeper bill. We can confirm that with a linear regression

plt = penguin_bill * linear() * mapping(color = :species)
draw(plt; axis = axis)