geom_jitter#

geom_jitter(mapping=None, *, data=None, stat=None, position=None, show_legend=None, inherit_aes=None, manual_key=None, sampling=None, tooltips=None, width=None, height=None, color_by=None, fill_by=None, seed=None, **other_args)#

Display jittered points, especially for discrete plots or dense plots.

Parameters:
mappingFeatureSpec

Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.

datadict or Pandas or Polars DataFrame

The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.

statstr, default=’identity’

The statistical transformation to use on the data for this layer, as a string. Supported transformations: ‘identity’ (leaves the data unchanged), ‘count’ (counts number of points with same x-axis coordinate), ‘bin’ (counts number of points with x-axis coordinate in the same bin), ‘smooth’ (performs smoothing - linear default), ‘density’ (computes and draws kernel density estimate).

positionstr or FeatureSpec, default=’jitter’

Position adjustment. Either a position adjustment name: ‘dodge’, ‘dodgev’, ‘jitter’, ‘nudge’, ‘jitterdodge’, ‘fill’, ‘stack’ or ‘identity’, or the result of calling a position adjustment function (e.g., position_dodge() etc.).

show_legendbool, default=True

False - do not show legend for this layer.

inherit_aesbool, default=True

False - do not combine the layer aesthetic mappings with the plot shared mappings.

manual_keystr or layer_key

The key to show in the manual legend. Specify text for the legend label or advanced settings using the layer_key() function.

samplingFeatureSpec

Result of the call to the sampling_xxx() function. To prevent any sampling for this layer pass value “none” (string “none”).

tooltipslayer_tooltips

Result of the call to the layer_tooltips() function. Specify appearance, style and content. Set tooltips=’none’ to hide tooltips from the layer.

widthfloat, default=0.4

Amount of horizontal variation. The jitter is added in both directions, so the total spread is twice the specified parameter. Typically ranges between 0 and 0.5. Values that are greater than 0.5 lead to overlapping of the points.

heightfloat, default=0.4

Amount of vertical variation. The jitter is added in both directions, so the total spread is twice the specified parameter. Typically ranges between 0 and 0.5. Values that are greater than 0.5 lead to overlapping of the points.

color_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’color’

Define the color aesthetic for the geometry.

fill_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’fill’

Define the fill aesthetic for the geometry.

seedint

A random seed to make the jitter reproducible. If None (the default value), the seed is initialised with a random value.

other_args

Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.

Returns:
LayerSpec

Geom object specification.

Notes

The jitter geometry is used to create jittered points. The scatterplot is useful for displaying the relationship between two discrete variables.

geom_jitter() understands the following aesthetics mappings:

  • x : x-axis value.

  • y : y-axis value.

  • alpha : transparency level of a point. Accept values between 0 and 1.

  • color (colour) : color of the geometry. For more info see Color and Fill.

  • fill : fill color. Is applied only to the points of shapes having inner area. For more info see Color and Fill.

  • shape : shape of the point, an integer from 0 to 25. For more info see Point Shapes.

  • size : size of the point.

  • stroke : width of the shape border. Applied only to the shapes having border.

To hide axis tooltips, set ‘blank’ or the result of element_blank() to the axis_tooltip, axis_tooltip_x or axis_tooltip_y parameter of the theme().

Examples

 1import numpy as np
 2from lets_plot import *
 3LetsPlot.setup_html()
 4n = 1000
 5np.random.seed(42)
 6x = np.random.randint(-5, 6, size=n)
 7y = np.random.randint(10, size=n)
 8ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
 9    geom_point(color='red', shape=3, size=10) + \
10    geom_jitter()


 1import numpy as np
 2from lets_plot import *
 3LetsPlot.setup_html()
 4n = 6000
 5np.random.seed(42)
 6x = np.random.choice(list('abcde'), size=n)
 7y = np.random.normal(size=n)
 8ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
 9    geom_jitter(aes(color='x', size='y'), \
10                sampling=sampling_random(n=600, seed=60), \
11                seed=37, show_legend=False, width=.25) + \
12    scale_color_grey(start=.75, end=0) + \
13    scale_size(range=[1, 3])
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