Preserving Data–Statistic Bijection in Lets-Plot-Kotlin

Some statistical geometries in Lets-Plot-Kotlin (such as geomSina()) generate their own statistical data, while still keeping a one-to-one correspondence with the original input data points. Previously, this correspondence was not preserved in the mapping: if you mapped an aesthetic (e.g., color) to a column from the original dataset, all points could end up with an aggregated value.

Now, Lets-Plot-Kotlin preserves the bijection between data and statistics for such geometries. This means you can safely map aesthetics to variables from the original dataset, and they will be correctly aligned with the statistical output.

%useLatestDescriptors
%use dataframe
%use lets-plot

LetsPlot.getInfo()

Lets-Plot Kotlin API v.4.12.0. Frontend: Notebook with dynamically loaded JS. Lets-Plot JS v.4.8.1.
Outputs: Web (HTML+JS), Kotlin Notebook (Swing), Static SVG (hidden)

val url = "https://raw.githubusercontent.com/JetBrains/lets-plot-docs/refs/heads/master/data/mpg.csv"
val df = DataFrame.readCSV(url)
val data = df.toMap()
println("${df.rowsCount()} x ${df.columnsCount()}")
df.head()

234 x 12

DataFrame: rowsCount = 5, columnsCount = 12

untitled	manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
1	audi	a4	1,800000	1999	4	auto(l5)	f	18	29	p	compact
2	audi	a4	1,800000	1999	4	manual(m5)	f	21	29	p	compact
3	audi	a4	2,000000	2008	4	manual(m6)	f	20	31	p	compact
4	audi	a4	2,000000	2008	4	auto(av)	f	21	30	p	compact
5	audi	a4	2,800000	1999	6	auto(l5)	f	16	26	p	compact

Map Columns to the Aesthetics

Sina Stat

letsPlot(data) { x = "drv"; y = "hwy" } +
    geomViolin() +
    geomSina(seed = 42) {
        color = "displ"
        size = "cyl"
    } +
    scaleSize(range = 2.0 to 4.0)    

Q-Q Stat

letsPlot(data) +
    geomQQ {
        sample = "hwy"
        color = "displ"
        size = "cyl"
    } +
    scaleSize(range = 3.0 to 6.0)

Show Column Values in Tooltips

For the above-mentioned statistics, the tooltips can display not only the mapped values, but also any columns from the original dataframe.

letsPlot(data) { sample = "hwy" } +
    geomQQLine(color = "teal") +
    geomQQ(
        size = 3.0,
        shape = 21,
        color = "black",
        fill = "gold",
        alpha = 0.5,
        tooltips = layerTooltips()
            .title("@manufacturer @model")
            .line("theoretical|@..theoretical..")
            .line("highway mileage (sample)|@..sample..")
            .line("city mileage|@cty")
            .line("engine displacement in liters|@displ")
            .line("year of manufacturing|@year")
            .line("number of cylinders|@cyl")
            .line("type of transmission|@trans")
            .line("drive type|@drv")
            .line("fuel type|@fl")
            .line("vehicle class|@class")
            .format("year", "d")
            .minWidth(300)
            .anchor("bottom_right")
    ) +
    ggsize(1000, 600)