geom_crossbar#

geom_crossbar(mapping=None, *, data=None, stat=None, position=None, show_legend=None, inherit_aes=None, manual_key=None, sampling=None, tooltips=None, fatten=None, color_by=None, fill_by=None, **other_args)#

Display bars with horizontal median line.

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=’dodge’

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.

fattenfloat, default=2.5

A multiplicative factor applied to size of the middle bar.

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

geom_crossbar() represents a vertical interval, defined by x, ymin, ymax, or a horizontal interval, defined by y, xmin, xmax. The mean is represented by horizontal (vertical) line.

geom_crossbar() understands the following aesthetics mappings:

  • x or y: x-axis or y-axis coordinates for vertical or horizontal bar, respectively.

  • y or x : position of median bar for vertical or horizontal bar, respectively.

  • ymin or xmin: lower bound for vertical or horizontal bar, respectively.

  • ymax or xmax: upper bound for vertical or horizontal bar, respectively.

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

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

  • fill : fill color. For more info see Color and Fill.

  • size : lines width.

  • width : width of a bar. Typically range between 0 and 1. Values that are greater than 1 lead to overlapping of the bars.

  • linetype : type of the line. Accept codes or names (0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’), a hex string (up to 8 digits for dash-gap lengths), or a list pattern [offset, [dash, gap, …]] / [dash, gap, …]. For more info see Line Types.

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

Examples

 1from lets_plot import *
 2LetsPlot.setup_html()
 3data = {
 4    'x': ['a', 'b', 'c', 'd'],
 5    'ymin': [5, 7, 3, 5],
 6    'y': [6.5, 9, 4.5, 7],
 7    'ymax': [8, 11, 6, 9],
 8}
 9ggplot(data, aes(x='x')) + \
10    geom_crossbar(aes(ymin='ymin', y='y', ymax='ymax'))


 1import numpy as np
 2import pandas as pd
 3from lets_plot import *
 4LetsPlot.setup_html()
 5n = 800
 6cat_list = {c: np.random.uniform(3) for c in 'abcdefgh'}
 7np.random.seed(42)
 8x = np.random.choice(list(cat_list.keys()), n)
 9y = np.array([cat_list[c] for c in x]) + np.random.normal(size=n)
10df = pd.DataFrame({'x': x, 'y': y})
11err_df = df.groupby('x').agg({'y': ['min', 'median', 'max']}).reset_index()
12err_df.columns = ['x', 'ymin', 'ymedian', 'ymax']
13ggplot() + \
14    geom_crossbar(aes(x='x', ymin='ymin', y='ymedian', ymax='ymax', fill='x'), \
15                  data=err_df, width=.6, fatten=5) + \
16    geom_jitter(aes(x='x', y='y'), data=df, width=.3, shape=1, color='black', alpha=.5)
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