Joint plot¶

Table of Contents¶

Default Presentation of Joint Plot
Change Geom Types
Geometries Customization
Marginal Layers Customization
Grouping
Additional Layer

In [1]:

%useLatestDescriptors
%use dataframe
%use lets-plot

In [2]:

LetsPlot.getInfo()

Out[2]:

Lets-Plot Kotlin API v.4.9.0. Frontend: Notebook with dynamically loaded JS. Lets-Plot JS v.4.5.1.

In [3]:

val iris = DataFrame.readCSV("https://raw.githubusercontent.com/JetBrains/lets-plot-kotlin/master/docs/examples/data/iris.csv")
iris.head()

Out[3]:

DataFrame: rowsCount = 5, columnsCount = 5

sepal_length	sepal_width	petal_length	petal_width	species
5.100000	3.500000	1.400000	0.200000	setosa
4.900000	3.000000	1.400000	0.200000	setosa
4.700000	3.200000	1.300000	0.200000	setosa
4.600000	3.100000	1.500000	0.200000	setosa
5.000000	3.600000	1.400000	0.200000	setosa

In [4]:

val data = iris.toMap()

1. Default Presentation of Joint Plot¶

In the simplest case, assign x and y to create a scatterplot (using geomPoint()) with marginal histograms (using geomHistogram()).

In [5]:

jointPlot(data, "petal_length", "petal_width")

Out[5]:

2. Change Geom Types¶

Besides the points there are another two types of geoms: tile and density2D(Filled).

In [6]:

jointPlot(data, "petal_length", "petal_width", geom = "tile")

Out[6]:

In [7]:

jointPlot(data, "petal_length", "petal_width", colorBy = "species", geom = "density2d")

Out[7]:

3. Change Geom Parameters¶

Use additional parameters for better customization: color, size, alpha, etc.

In [8]:

jointPlot(data, "petal_length", "petal_width", color = "#756bb1", size = 8, alpha = 0.5, se = false)

Out[8]:

4. Marginal Layers Customization¶

marginal parameter is a shortcut for the ggmarginal() layer.

In [9]:

jointPlot(data, "petal_length", "petal_width", color = "black", marginal = "box:lb:.03,hist:t:.4,hist:r") +
    ggmarginal("tr", layer = geomArea(stat = Stat.density(), color = "magenta", fill = "magenta", alpha = 0.1)) +
    theme(axisLine = "blank")

Out[9]:

5. Grouping¶

The colorBy parameter sets the mapping to the fill and color aesthetics.

In [10]:

jointPlot(data, "petal_length", "petal_width", colorBy = "species", marginal = "hist:tr")

Out[10]:

6. Additional Layer¶

Add any other layer that supports x and y aesthetics (e.g. points layer with the geomPoint() function).

In [11]:

jointPlot(data, "petal_length", "petal_width", geom = "density2df",
          color = "#993404", alpha = 0.3, regLine = false) +
    geomPoint(size = 5, shape = 21, color = "#993404", fill = "#ffffd4") +
    scaleFillGradient(low = "#d95f0e", high = "#fff7bc")

Out[11]: