geom_sina#
- geom_sina(mapping=None, *, data=None, stat=None, position=None, show_legend=None, inherit_aes=None, manual_key=None, sampling=None, tooltips=None, orientation=None, seed=None, show_half=None, quantiles=None, scale=None, trim=None, tails_cutoff=None, kernel=None, bw=None, adjust=None, n=None, fs_max=None, color_by=None, fill_by=None, **other_args)#
A sina plot visualizes a single variable across classes, with jitter width reflecting the data density in each class.
- 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=’sina’
The statistical transformation to use on the data for this layer, as a string.
- positionstr or FeatureSpec, default=position_dodge(width=.95)
Position adjustment. Either a position adjustment name: ‘dodge’, ‘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.
- orientationstr
Specify the axis that the layer’s stat and geom should run along. The default value (None) automatically determines the orientation based on the aesthetic mapping. If the automatic detection doesn’t work, it can be set explicitly by specifying the ‘x’ or ‘y’ orientation.
- seedint
A random seed to make the jitter reproducible. If None (the default value), the seed is initialised with a random value.
- show_halffloat, default=0
If -1, only half of each group is drawn. If 1, another half is drawn. If 0, sina look as usual.
- quantileslist of float, default=[0.25, 0.5, 0.75]
A list of quantiles to be calculated and written to the ..quantile.. variable.
- scale{‘area’, ‘count’, ‘width’}, default=’area’
If ‘area’, all groups have the same area. If ‘count’, areas are scaled proportionally to the number of observations. If ‘width’, all groups have the same maximum width.
- trimbool, default=True
Trim the tails of the violins, which limit the area for sina points, to the range of the data.
- tails_cutofffloat, default=3.0
Extend domain of each violin, which limit the area for sina points, on tails_cutoff * bw if trim=False.
- kernelstr, default=’gaussian’
The kernel we use to calculate the density function. Choose among ‘gaussian’, ‘cosine’, ‘optcosine’, ‘rectangular’ (or ‘uniform’), ‘triangular’, ‘biweight’ (or ‘quartic’), ‘epanechikov’ (or ‘parabolic’).
- bwstr or float
The method (or exact value) of bandwidth. Either a string (choose among ‘nrd0’ and ‘nrd’), or a float.
- adjustfloat
Adjust the value of bandwidth by multiplying it. Change how smooth the frequency curve is.
- nint, default=512
The number of sampled points for plotting the function, that limit the area for sina points.
- fs_maxint, default=500
Maximum size of data to use density computation with ‘full scan’. For bigger data, less accurate but more efficient density computation is applied.
- 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.
- 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
Computed variables:
..violinwidth.. : density scaled for the sina plot, according to area, counts or to a constant maximum width (mapped by default).
..density.. : density estimate.
..count.. : density * number of points.
..scaled.. : density estimate, scaled to maximum of 1.
..quantile.. : quantile estimate.
geom_sina() 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.
weight : used by ‘sina’ stat to compute weighted density.
quantile : quantile values.
width : maximal width of sina plot. Typically ranges between 0 and 1. Values that are greater than 1 lead to overlapping of the geometries.
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 = 100 5np.random.seed(42) 6x = np.random.choice(['a', 'b', 'c'], size=n) 7y = np.random.randint(10, size=n) 8ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \ 9 geom_violin() + \ 10 geom_sina(seed=42)
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 100 5np.random.seed(42) 6x = np.random.choice(['a', 'b', 'b', 'c'], size=n) 7y = np.random.normal(size=n) 8quantiles = [.02, .25, .5, .75, .98] 9ggplot({'x': x, 'y': y}, aes('x', 'y')) + \ 10 geom_violin(aes(fill='..quantile..'), scale='count', alpha=.5, \ 11 quantiles=quantiles, quantile_lines=True) + \ 12 geom_sina(aes(color='..quantile..'), scale='count', quantiles=quantiles, seed=42) + \ 13 scale_continuous(['color', 'fill'], low="black", high="#6baed6")
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 100 5np.random.seed(42) 6x = np.random.choice(["a", "b", "c", "d"], size=n) 7y1 = np.random.normal(size=n) 8y2 = np.random.normal(size=n) 9ggplot({'x': x, 'y1': y1, 'y2': y2}) + \ 10 geom_violin(aes('x', 'y1'), show_half=-1, \ 11 trim=False, fill='#ffffb2') + \ 12 geom_violin(aes('x', 'y2'), show_half=1, \ 13 trim=False, fill='#74c476') + \ 14 geom_sina(aes('x', 'y1'), show_half=-1, \ 15 fill='#ffffb2', shape=24, size=2, seed=42) + \ 16 geom_sina(aes('x', 'y2'), show_half=1, \ 17 fill='#74c476', shape=25, size=2, seed=42)