Plotting

StatsPlots

JuliaDB has all access to all the power and flexibility of Plots via StatsPlots and the @df macro.

using JuliaDB, StatsPlots

t = table((x = randn(100), y = randn(100)))

@df t scatter(:x, :y)

Plotting Big Data

For large datasets, it isn't feasible to render every data point. The OnlineStats package provides a number of data structures for big data visualization that can be created via the reduce and groupreduce functions.

• Example data:

using JuliaDB, Plots, OnlineStats

x = randn(10^6)
y = x + randn(10^6)
z = x .> 1
z2 = (x .+ y) .> 0
t = table((x=x, y=y, z=z, z2=z2))

Table with 1000000 rows, 4 columns:
x           y          z      z2
───────────────────────────────────
0.883249    1.32075    false  true
0.200336    -0.989121  false  false
-0.421694   -0.397641  false  false
2.23396     2.1823     true   true
-0.881753   -1.28863   false  false
-1.35999    -2.53224   false  false
-1.54985    -1.12409   false  false
1.65567     2.28825    true   true
-0.0242967  -1.69111   false  false
⋮
0.246494    -0.169448  false  true
0.744356    1.07494    false  true
0.0935734   -0.237658  false  false
-1.48985    -1.91953   false  false
0.464175    0.346606   false  true
0.761747    1.49327    false  true
-0.859193   -0.990849  false  false
0.672742    1.31158    false  true

Mosaic Plots

A mosaic plot visualizes the bivariate distribution of two categorical variables.

o = reduce(Mosaic(Bool, Bool), t; select = (3, 4))
plot(o)

Histograms

grp = groupreduce(Hist(-5:.5:5), t, :z, select = :x)
plot(plot.(select(grp, 2))...; link=:all)

grp = groupreduce(KHist(20), t, :z, select = :x)
plot(plot.(select(grp, 2))...; link = :all)

Partition and IndexedPartition

• Partition(stat, n) summarizes a univariate data stream.
  • The stat is fitted over n approximately equal-sized pieces.
• IndexedPartition(T, stat, n) summarizes a bivariate data stream.
  • The stat is fitted over n pieces covering the domain of another variable of type T.

o = reduce(Partition(KHist(10), 50), t; select=:y)
plot(o)

o = reduce(IndexedPartition(Float64, KHist(10), 50), t; select=(:x, :y))
plot(o)

GroupBy

o = reduce(GroupBy{Bool}(KHist(20)), t; select = (:z, :x))
plot(o)
png("groupby.png"); nothing # hide

Convenience function for Partition and IndexedPartition

You can also use the partitionplot function, a slightly less verbose way of plotting Partition and IndexedPartition objects.

# x by itself
partitionplot(t, :x, stat = Extrema())

┌ Warning: `FTSeries(args...; kw...)` is deprecated.  Use `FilterTransform(Series(args...; kw...))` instead.
│   caller = ip:0x0
└ @ Core :-1

# y by x, grouped by z
partitionplot(t, :x, :y, stat = Extrema(), by = :z)
savefig("partitionplot2.png"); nothing # hide