Composite Visualizations
Composite visualizations are those that combine distinct plots to form a single visualization. A simple example of this are subplots, where one stacks plots together either vertically or horizontally. There are several ways to produce composite plots. Let us explore how to do them using Vizagrams.
1. Facet Plots
Facet plots are those where the data is broken into distinct columns. In Vizagrams, this kind of plot can be done in different ways. One way consists in using the plot specification with another plot specification inside. The second way involves grouping the data and then combining the plots using the diagramming operations.
1.1 Facet Plot via Diagramming
Let us start with the diagramming way.
using Vizagrams
using StatsBase
using DataFrames
using Random
using VegaDatasets
df = DataFrame(dataset("cars"));
df[!,:Year] = map(x->parse(Int,x[3:4]),df.Year)
df = dropmissing(df);
facet = ∑(i=:Origin,op=→) do gdf
Plot(
title=gdf.Origin[1],
config=(;legends=NilD()),
data=gdf,
x=(field=:Horsepower,scale_domain=(40,250)),
y=(field=:Miles_per_Gallon,scale_domain=(0,50)),
)
end(df)
draw(facet,width=800)
Note that, in the code above, we are using the ∑ operator to group the dataset df using the column Origin. Once the dataset is grouped, we create a plot. Then, each plot is combined using the beside operator →.
One might wish to apply different colors to each plot. This can be done by creating a scale function, as shown next:
colorscale = infer_scale(data=df.Origin, variable=:color)
facet = ∑(i=:Origin,op=→) do gdf
Plot(
title=gdf.Origin[1],
config=(;legends=NilD()),
data=gdf,
x=(field=:Horsepower,scale_domain=(40,250)),
y=(field=:Miles_per_Gallon,scale_domain=(0,50)),
color=(value=row->colorscale(row.Origin),scale=IdScale()),
)
end(df)
draw(facet,width=800)
1.2 Facet Plot via Plot Specification
It is also possible to create a facet plot using a plot specification.
facet = Plot(
config=(
figsize=(300,200),
coordinate=nothing,
frame=NilD(),
legends=(;transform=T(1030,200)),
),
data=df,
x=(field=:Horsepower,scale=IdScale()),
y=(field=:Miles_per_Gallon,scale=IdScale()),
color=(field=:Origin,),
origin=(field=:Origin,scale=IdScale()),
graphic= data->begin
∑(i=:color,op=(x,y)->x→(T(10,0),y)) do gdf
Plot(
title=gdf.origin[1],
config=(legends=NilD(),xaxis=(;title="")),
data=gdf,
x=(field=:x,scale_domain=(0,250)),
y=(field=:y,scale_domain=(0,50)),
color=(field=:color,scale=IdScale()))
end(data)↓(T(0,-10),TextMark(text="Horsepower",pos=[150,0]))
end
)
draw(facet,width=800)
2. Nesting
Nested visualizations are those where we place a plot inside another plot. In a sense, we can think of the previous facet plots as a nested visualization, as can be more clearly seen in the example using a single plot specification.
Let us start with a common example. We do a pizza plot nested inside a scatter plot.
plt = Plot(
data=df,
figsize=(500,300),
x=(field=:Year,scale_domain=(68,84)),
y=:Cylinders,
color = (field=:Origin,datatype=:n,),
angle = (field=:Origin, scale=IdScale()),
size = (field=:Acceleration, scale_range=(2.0,20)),
graphic= ∑(i=:x,∑(i=:y) do rows
acc = mean(rows.size)
countvalues = sort(countmap(rows.color))
colors = collect(keys(countvalues))
angles = 2π.*values(countvalues)./length(rows.angle)
T(rows.x[1],rows.y[1])U(acc)*
Pizza(angles=angles,colors=colors,style=S(:strokeWidth=>0.5))
end)
)
draw(plt)
Next, we do a more complex example, showcasing the customization capabilities.
plt = Plot(
config=(;
figsize=(400,170),
frame=NilD(),
xaxis=(;axisarrow=NilD(),tickmark=NilD()),
yaxis=(;axisarrow=NilD(),tickmark=S(:opacity=>0)Circle(r=2),ticktextangle=π/2),
xgrid=NilD(),
ygrid=(;style=S(:strokeWidth=>48,:vectorEffect=>"none",:strokeOpacity=>0.03)),
legends=(;transform=T(420,120)U(0.5))
),
data=df,
x=(field=:Cylinders),
y=(field=:Origin),
color=(field=:Acceleration,),
horsepower=(field=:Horsepower,scale=IdScale()),
miles=(field=:Miles_per_Gallon,scale=IdScale()),
cylinder=(field=:Cylinders,scale=IdScale(),datatype=:o),
origin=(field=:Origin,scale=IdScale()),
graphic=
∑(i=:cylinder,orderby=:cylinder,descend=true,
∑(i=:origin,orderby=:origin,) do gdf
T(gdf.x[1],gdf.y[1])*
centralize_graphic(U(0.19)*
Plot(
config=(
frame_style=S(:fill=>:white,:fillOpacity=>1.0,),
legends=NilD(),
yaxis=(;title=""),
xaxis=(;title="")),
data=gdf,
x=(field=:horsepower,scale_domain=(0,250)),
y=(field=:miles,scale_domain=(0,50)),
color=(field=:color,scale=IdScale())
))
end
)
)
draw(plt,width=800)
3. Integrated
Integrated visualizations are those where distinct plots are somehow integrated. A common example of this would be parallel coordinate plots. The difficulty in creating such visualizations involves having to coordinate distict scales in order.
Our example is inspired in one of the visualizations presented in the paper "Dece: Decision explorer with counterfactual explanations for machine learning models" (2020, Cheng, Furui and Ming, Yao and Qu, Huamin).
Let us start by creating a dataset.
Random.seed!(4)
toefl = 10randn(100) .+ 300
rating = 10randn(100) .+ 100
score = 5rand(100) .+ 5;
accept = rand([0,0,1],100);
df = DataFrame(:toefl=>toefl,:rating=>rating,:score=>score,:accept=>accept);Next, we create a horizontal bar plot:
plt_accept = Plot(
title = "Acceptance",
config=(;grid=NilD(),
xaxis=(;title="count"),
yaxis=(;title=""),
),
data=df,
x=(value=row->sum(df.accept .== row.accept),scale_domain=(0,70)),
y=:accept,
graphic=∑(i=:y) do rows
S(:fill=>:steelblue)T(0,rows.y[1])Bar(w=rows.x[1],h=50,orientation=:h)
end
)
draw(plt_accept)Our goal next is to create histograms for the other variables and stack them horizontally. For this, we define a function tohist to envelope our histogram specification.
function tohist(data, title)
Plot(
title = title,
config=(;
grid=NilD(),
xaxis=(;title="count"),
yaxis=(;tickvalues=bin_edges(data),title=""),
),
y = bindata(data),
x = (value=countbin(data),scale_domain=(0,maximum(countbin(data))+2)),
graphic = data-> begin
# compute the bin width
w = let
u = sort(unique(data.y))
u[2]-u[1]-1
end
# draw each bar
∑(i=:y) do row
S(:fillOpacity=>0.9,:fill=>:steelblue)*
T(0,row.y[1])Bar(h=w,w=row.x[1],orientation=:h)
end(data)
end
)
end
data = [toefl,rating,score]
plts = map(x->tohist(x[1],x[2]),zip(data,["TOEFL","RATING","SCORE"]));
plts = vcat(plts,plt_accept)
plt = reduce((x,y)->x→(T(10,0),y),plts);
draw(plt,width=800)
Up until now, this visualization is simply a sort of facet plot. The next step is to draw lines accross this plot in order to illustrate how a single observed row varies accross these different dimensions.
gs = getmarkpath(Plot,plt,G)
lines = mapreduce(+,1:10) do i
pts = map(zip(plts,[:toefl,:rating,:score,:accept],gs)) do row
y = getscale(row[1],:y)(df[i,row[2]])
pos = row[3]([0.0,y])
pos = row[3]([0.0,y])
end
S(:stroke=>:orange)Line(pts)
end
d = plt + lines
draw(d,width=800)
To draw the lines we must obtain the position in each axis and apply the same translation as used in each plot. To do this, we must do two things. First, we must get the scale in each plot, and apply it to the y value in our lines. Secondly, we must get how much each plot is translated left, and use this in the x value. The first task is done using the getscale function, while the second is done using the getmarkpath function.