Each of the following examples plots one million data points, but can scale to infinitely many observations, since only a summary (
OnlineStat) of the data is plotted.
Partition type summarizes sections of a data stream using any
OnlineStat, and is therefore extremely useful in visualizing huge datasets, as summaries are plotted rather than every single observation.
y = cumsum(randn(10^6)) + 100randn(10^6) o = Partition(KHist(10)) fit!(o, y) plot(o)
o = Partition(Series(Mean(), Extrema())) fit!(o, y) plot(o)
y = rand(["a", "a", "b", "c"], 10^6) o = Partition(CountMap(String), 75) fit!(o, y) plot(o)
Partition type can only track the number of observations in the x-axis. If you wish to plot one variable against another, you can use an
x = randn(10^6) y = x + randn(10^6) o = fit!(IndexedPartition(Float64, KHist(40), 40), zip(x, y)) plot(o)