Add highlight transformation
#551
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This PR adds the `aggregate` analysis which can aggregate one or more
mapped columns grouped by one or more other columns. The aggregation
functions can be chosen freely and the plotting function should just be
picked as usual with `visual`, so it's a very flexible analysis layer.
The reason for this is that it's usually a bit annoying having to add
data wrangling just for some simple visualizations that should be done
on the fly, where you are not interested in keeping the aggregated data
around. This way you don't have to come up with a variable name for it,
plus it works with all table inputs and not just the typical
`DataFrame`.
For example, let's say we have some categories and associated
measurements. We can plot these as a normal scatter:
```julia
using AlgebraOfGraphics
using CairoMakie
using Statistics
df = (
cats = repeat(["low", "mid", "high"], 30),
vals = repeat([1, 2, 3], 30) .+ randn.(),
)
base = data(df) * mapping(:cats, :vals)
scat = base * visual(Scatter)
draw(scat)
```
<img width="606" height="438" alt="image"
src="https://github.com/user-attachments/assets/94a1523a-924f-4bd9-ad0b-06044a2f9108"
/>
Let's say we want to show the median of each group. We can do this with
`aggregate`. Every mapped column needs to be either a grouping column or
an aggregated column. Grouping columns are denoted by a `:`.
```julia
med = base * aggregate(:, median) * visual(Scatter, markersize = 20, color = :red)
draw(scat + med)
```
<img width="592" height="440" alt="image"
src="https://github.com/user-attachments/assets/75396a20-7f81-4dd5-a340-34b65eae7545"
/>
Each column can only have one function applied, but this function may
return multiple values per group, for example as a tuple. There can then
be multiple functions that are applied on the result, each of which can
be assigned to a different output mapping. This can be used, for
example, to draw error bars or confidence intervals. Let's compute the
25th and 75th percentiles and draw the interval.
```julia
interval = base * aggregate(
:,
(x -> quantile(x, [0.25, 0.75])) => [
first => 2,
last => 3,
]
) * visual(Rangebars, linewidth = 3, color = :red)
draw(scat + med + interval)
```
<img width="591" height="438" alt="image"
src="https://github.com/user-attachments/assets/44a703b3-7000-42af-9f53-231a6a09585a"
/>
With `=> 2` and `=> 3` we assign the first and second quantile to
positional mappings 2 and 3 for `Rangebars`. If you don't specify a
remapping, the initial mapping is kept, but there can only be one output
assigned to a mapping.
In this case, it might look nice to apply a dodge, so both components
can be discriminated better.
```julia
with_dodge = scat * mapping(dodge_x = direct("A")) +
(med + interval) * mapping(dodge_x = direct("B"))
draw(with_dodge, scales(DodgeX = (; width = 0.2)))
```
<img width="592" height="437" alt="image"
src="https://github.com/user-attachments/assets/3c640481-435c-4c00-960d-61f09994b5d1"
/>
Grouping by multiple mappings also works, for example to compute a
heatmap by summing all values of a given group (the empty cells are
combinations of x and y that don't exist by chance):
```julia
df = (;
x = rand(1:5, 100),
y = rand(1:5, 100),
z = randn(100)
)
data(df) * mapping(:x, :y, :z) * aggregate(:, :, sum) *
visual(Heatmap) |> draw
```
<img width="585" height="451" alt="image"
src="https://github.com/user-attachments/assets/3f7ab201-9084-407a-8860-6d76ece8525b"
/>
Additionally, the outputs can be renamed with the pair syntax, plus you
can assign a scale id like with a normal `mapping`, because `aggregate`
freely creates new mapped columns:
```julia
data(df) * mapping(:x, :y, :z) *
aggregate(
:,
:,
sum => "Sum of all z values" => scale(:sumcolor)
) *
visual(Heatmap) |>
draw(scales(sumcolor = (; colormap = :plasma)))
```
<img width="591" height="435" alt="image"
src="https://github.com/user-attachments/assets/899cc4d0-73b6-4615-a26a-d06e3ec0b313"
/>
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The
highlighttransformation has a similar effect as the gghighlight extension from the ggplot ecosystem.You can pass predicates which are then evaluated on the subgroups created by the given grouping variables in
mapping.For example, this plot highlights all series with standard deviation of positional arg 2 (the y data) over some threshold:
You can also repeat the data split over facets in each facet. Whatever transformation has been done previously on that data in the facetted context will remain (so for example the histogram binning when using
histogram()):