AlgebraOfGraphics.jlIn this tutorial, we’ll explore different statistical transformations that we can apply to our visualizations with AoG.jl.
These can be added to any AoG.jl layer with the * operator.
We will also cover some geometries that are commonly used with the statistical visualization functions: Contour and Heatmap
Statistical transformations are paired with defaults for visual(), so you don’t need to specify a visual() yourself. You just add the data() layer along with a mapping() layer and finalize with a statistical transformation layer.
We’ll cover 6 statistical transformation functions:
histogram()density()frequency()expectation()linear()smooth()stat_*() - AoG.jl TableThe table below references ggplot2’s stat_*() to AoG.jl statistical visualizations functions:
ggplot2 |
AoG.jl |
|---|---|
geom_bar() or stat_count() |
frequency() |
stat_summary(fun = "mean") |
expectation() |
geom_histogram() or stat_bin() |
histogram() |
geom_density() or stat_density() |
density() |
geom_smooth() or stat_smooth() |
smooth() |
geom_smooth(method = "lm") or stat_smooth(method = "lm") |
linear() |
AoG.jlTo start let us load AoG.jl, data wrangling libraries and the DataFrame we’ve used previously:
using PharmaDatasets
using DataFramesMeta
df = dataset("demographics_1")
first(df, 5)| Row | ID | AGE | WEIGHT | SCR | ISMALE | eGFR |
|---|---|---|---|---|---|---|
| Int64 | Float64 | Float64 | Float64 | Int64 | Float64 | |
| 1 | 1 | 34.823 | 38.212 | 1.1129 | 0 | 42.635 |
| 2 | 2 | 32.765 | 74.838 | 0.8846 | 1 | 126.0 |
| 3 | 3 | 35.974 | 37.303 | 1.1004 | 1 | 48.981 |
| 4 | 4 | 38.206 | 32.969 | 1.1972 | 1 | 38.934 |
| 5 | 5 | 33.559 | 47.139 | 1.5924 | 0 | 37.198 |
We will also do some columns transformations to CategoricalArrays:
using CategoricalArrays
@transform! df :SEX = categorical(:ISMALE);
@transform! df :SEX = recode(:SEX, 0 => "female", 1 => "male");
@transform! df :WEIGHT_cat = cut(:WEIGHT, 2; labels = ["light", "heavy"])And now load AoG.jl along with CairoMakie.jl:
using CairoMakie
using AlgebraOfGraphicshistogram()The first statistical transformation is the histogram() function, which performs a binning operation on the data and outputs a histogram.
For example, if we want to visualize a histogram of the column :AGE, we can do easily:
data(df) * mapping(:AGE) * histogram() |> draw
We can also add faceting to our histogram by specifying either a layout, row or col as keyword arguments inside mapping():
data(df) * mapping(:AGE; layout = :SEX) * histogram() |> draw
If you want to customize other aspects of the histogram’s underlying bar plot, you can add a visual() layer without any plotting type inside and just with the keyword desired customizations as keywords arguments:
data(df) * mapping(:AGE; layout = :SEX) * histogram() * visual(; color = :blue) |> draw
The number of bins to use is determined automatically. But if you want, you can customize with the keyword argument bins inside histogram():
data(df) * mapping(:AGE) * histogram(; bins = 20) |> draw
histogram() also has a normalization keyword argument which lets you specify a normalization scheme. There are 4 possible normalizations schemes and you can specify them using the corresponding Symbols:
:none: the default, no normalization applied, i.e. raw count.:pdf: normalize by sum of weights and bin sizes. The resulting histogram will behave as a probability density function (PDF) which the sum of all bins sums to 1.:density: normalize by bin sizes only. The resulting histogram represents count density of input and does not sum to 1.:probability: normalize by sum of weights only. The resulting histogram represents the fraction of probability mass for each bin and does not sum to 1.Our advice is to use either :none (the default) for a raw histogram or :pdf for a relative histogram.
data(df) * mapping(:AGE) * histogram(; normalization = :pdf) |> draw
Note that the default y-axis label changes with normalization. In the default case, :none, the y-label is count and in the :pdf case pdf.
You can also specify a two-axes histogram() transformation, i.e. a 2-D histogram plot.
This is similar to geom_tile() in ggplot2.
For example, let’s add the column :eGFR to take a look at the relationship between age and kidney function:
data(df) * mapping(:AGE, :eGFR) * histogram() |> draw
Notice that by default a 2-D histogram uses the visual(Heatmap) under the hood:
data(df) * mapping(:AGE, :eGFR) * histogram() * visual(Heatmap) |> draw
We can also change the colormap keyword argument inside visual(). Let’s make a black and white printer-friendly visualization with colormap = Reverse(:greys):
data(df) * mapping(:AGE, :eGFR) * histogram() * visual(; colormap = Reverse(:greys)) |> draw
density()Our second statistical function is density() which fits a kernel density estimation (KDE) of the underlying data.
Using the same example column :AGE as before, we just change the function to density():
Both AoG.jl and CairoMakie.jl exports a function named density(). So if we just blindly use density() we get an error:
density()WARNING: both AlgebraOfGraphics and CairoMakie export "density"; uses of it in module Notebook must be qualifiedUndefVarError: UndefVarError(:density)Thus, we need to qualify which density() function we want with Package.function naming. This is why need to call AlgebraOfGraphics.density().
Or we can also use an import statement:
import AlgebraOfGraphics: densityto tell Julia which density() function to use.
data(df) * mapping(:AGE) * AlgebraOfGraphics.density() |> draw
As before we can customize our plot with keyword arguments using a visual() layer without any plotting type inside:
data(df) * mapping(:AGE) * AlgebraOfGraphics.density() * visual(; color = (:blue, 0.75)) |>
draw
Makie.jl lets us specify colors either as Symbols, e.g. :blue; or as a tuple with length 2 where the first element is a Symbol representing the desired color and the second element is a Float representing the desired transparency (i.e. alpha).
Since AoG.jl uses Makie.jl as a backend we can use Makie.jl’s multiple dispatch on the color argument.
For a complete list of named colors that Makie.jl has access to, see here.
Also as before, we can perform faceting by specifying either a layout, row or col as keyword arguments inside mapping():
data(df) * mapping(:AGE, col = :SEX) * AlgebraOfGraphics.density() |> draw
Similar to histogram(), you can also specify a two-axes density() transformation, i.e. a 2-D density plot.
Let’s use the same example as before with :AGE and :eGFR to take a look at the relationship between age and kidney function:
data(df) * mapping(:AGE, :eGFR) * AlgebraOfGraphics.density() |> draw
Note that, just like histogram(), density() in 2-D visualizations by default uses visual(Heatmap) under the hood:
data(df) * mapping(:AGE, :eGFR) * AlgebraOfGraphics.density() * visual(Heatmap) |> draw
You can change Heatmap for Contour to make a contour plot instead of a heatmap plot:
data(df) * mapping(:AGE, :eGFR) * AlgebraOfGraphics.density() * visual(Contour) |> draw
As before, you can also specify a custom colormap inside visual():
data(df) *
mapping(:AGE, :eGFR) *
AlgebraOfGraphics.density() *
visual(Contour; colormap = Reverse(:greys)) |> draw
density() has some nice extra features. You can also use it with 3-D visualizations by passing an Axis3 as type inside the axis NamedTuple customization inside the draw() function. This is done with the Surface plotting type inside visual():
draw(
data(df) * mapping(:AGE, :eGFR) * AlgebraOfGraphics.density() * visual(Surface);
axis = (; type = Axis3),
)
This also works for facetting. Let’s add :SEX as layout inside mapping():
draw(
data(df) *
mapping(:AGE, :eGFR; layout = :SEX) *
AlgebraOfGraphics.density() *
visual(Surface);
axis = (; type = Axis3),
)
We will cover much more customizations in Tutorial 4 - Customization of AlgebraOfGraphics.jl Plots. Don’t forget to check it out.
frequency()The third statistical function we will cover is the frequency() function which computes a raw frequency table of the arguments.
frequency() does not take any arguments.
The simplest example could be just computing the frequency of the column :SEX:
data(df) * mapping(:SEX) * frequency() |> draw
As before we can add faceting with either a layout, row or col as keyword arguments inside mapping(); and customize with keyword arguments inside an empty visual() layer:
data(df) * mapping(:SEX; layout = :WEIGHT_cat) * frequency() * visual(; color = :blue) |>
draw
A nice plot to have up your sleeve is a frequency() plot using stacked bars.
This can be done by specifying the keyword arguments color and stack to a desired column inside mapping():
data(df) * mapping(:SEX; color = :WEIGHT_cat, stack = :WEIGHT_cat) * frequency() |> draw
frequency() can also be paired with a 2-D visualization in order to have a heatmap plot of raw counts.
For example, the previous stacked bar frequency plot can be done as a 2-D heatmap frequency plot:
data(df) * mapping(:SEX, :WEIGHT_cat) * frequency() |> draw
Also, it supports a custom colormap:
data(df) *
mapping(:SEX, :WEIGHT_cat) *
frequency() *
visual(; colormap = Reverse(:greys)) |> draw
expectation()Our fourth statistical visualization function is expectation() which is the mathematical term for the “mean” of a random variable and used extensively in fields like probability.
expectation computes the expected value, i.e. “mean” of a random variable, of the second argument conditioned on the values of the first argument inside mapping(). In other words, the mean of the y column conditioned on the x column.
Here is an example with only the columns :SEX and :AGE:
data(df) * mapping(:SEX, :AGE) * expectation() |> draw
As before we can add faceting with either a layout, row or col as keyword arguments inside mapping(); and customize with keyword arguments inside an empty visual() layer:
data(df) *
mapping(:SEX, :AGE; layout = :WEIGHT_cat) *
expectation() *
visual(; color = :blue) |> draw
Another nice plot to have up your sleeve is a grouped expected value bar plot.
This is accomplished with adding keyword arguments of color along with dodge inside mapping():
data(df) *
mapping(:SEX, :AGE; color = :WEIGHT_cat, dodge = :WEIGHT_cat) *
expectation() *
visual(; color = :blue) |> draw
expectation() does not take any arguments.
linear()Our fifth statistical visualization function is linear() which draws a linear trend line between two variables. This is similar to geom_smooth(method = "lm") in ggplot2. It computes a linear fit using the following formula: y ~ 1 + x.
Let’s see an example using :AGE vs :eGFR:
data(df) * mapping(:AGE, :eGFR) * linear() |> draw
If we also add a visual(Scatter) to see the data points along with our linear trend line, we get something that is not our original intention. This is because AoG.jl has two algebraic operations: addition with + and multiplication with *. To superimpose layers you need to use the + and not the * operator. We’ll discuss more in-depth AoG.jl’s algebraic operations in Tutorial 5 - Grammar of Graphics with AlgebraOfGraphics.jl. Don’t forget to check it out.
data(df) * mapping(:AGE, :eGFR) * (linear() + visual(Scatter)) |> draw
Of course we can also use customizations inside mapping() or even inside visual():
data(df) *
mapping(:AGE, :eGFR; color = :SEX) *
(linear() + visual(Scatter; marker = '+', markersize = 20)) |> draw
smooth()Our final statistical visualization is smooth(). It adds a smooth trend curve to our data. It fills the same need as geom_smooth() in ggplot2.
Here is the previous example, but now using smooth():
data(df) * mapping(:AGE, :eGFR) * smooth() |> draw
By default it uses a LOESS (Locally Estimated Scatterplot Smoothing) of degree 2. You change the degree to make it less or more “smooth”:
data(df) * mapping(:AGE, :eGFR) * smooth(; degree = 5) |> draw
data(df) * mapping(:AGE, :eGFR) * smooth(; degree = 1) |> draw
Like all of the previous statistical functions, it supports all keyword arguments and mapping() customizations:
data(df) *
mapping(:AGE, :eGFR; color = :SEX) *
(smooth() + visual(Scatter; marker = '+', markersize = 20)) |> draw