lets_plot.geom_bar

lets_plot.geom_bar(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, labels=None, orientation=None, color_by=None, fill_by=None, **other_args)

Display a bar chart which makes the height of the bar proportional to the number of observed variable values, mapped to x axis.

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=’count’

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=’stack’

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.

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.

labelslayer_labels

Result of the call to the layer_labels() function. Specify style and content of the annotations.

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.

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_bar() makes the height of the bar proportional to the number of observed variable values, mapped to x axis. Is intended to use for discrete data. If used for continuous data with stat=’bin’ produces histogram for binned data. geom_bar() handles no group aesthetics.

Computed variables:

• ..count.. : number of points with same x-axis coordinate.

• ..sum.. : total number of points with same x-axis coordinate.

• ..prop.. : groupwise proportion.

• ..proppct.. : groupwise proportion in percent.

• ..sumprop.. : proportion of points with same x-axis coordinate among all points in the dataset.

• ..sumpct.. : proportion of points with same x-axis coordinate among all points in the dataset in percent.

geom_bar() understands the following aesthetics mappings:

• x : x-axis value (this value will produce cases or bins for bars).

• y : y-axis value (this value will be used to multiply the case’s or bin’s counts).

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

• color (colour) : color of the geometry lines. String in the following formats: RGB/RGBA (e.g. “rgb(0, 0, 255)”); HEX (e.g. “#0000FF”); color name (e.g. “red”); role name (“pen”, “paper” or “brush”).

• fill : fill color. String in the following formats: RGB/RGBA (e.g. “rgb(0, 0, 255)”); HEX (e.g. “#0000FF”); color name (e.g. “red”); role name (“pen”, “paper” or “brush”).

• size : line width. Define bar line width.

• weight : used by ‘count’ stat to compute weighted sum instead of simple count.

Examples

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
np.random.seed(42)
data = {'x': np.random.randint(10, size=100)}
ggplot(data, aes(x='x')) + geom_bar()

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
np.random.seed(42)
n = 10
x = np.arange(n)
y = 1 + np.random.randint(5, size=n)
ggplot() + \
    geom_bar(aes(x='x', y='y', fill='x'), data={'x': x, 'y': y}, \
             stat='identity', show_legend=False) + \
    scale_fill_discrete()

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
np.random.seed(42)
n = 5000
x = np.random.normal(size=n)
c = np.random.choice(list('abcde'), size=n)
ggplot({'x': x, 'class': c}, aes(x='x')) + \
    geom_bar(aes(group='class', fill='class', color='class'), \
             stat='bin', sampling=sampling_pick(n=500), alpha=.3, \
             tooltips=layer_tooltips().line('@|@class')
                                      .line('count|@..count..'))