lets_plot.geom_abline

lets_plot.geom_abline(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, slope=None, intercept=None, color_by=None, **other_args)

Add a straight line with specified slope and intercept to the plot.

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.

positionstr or FeatureSpec, default=’identity’

Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.

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”).

slopefloat

The line slope.

interceptfloat

The value of y at the point where the line crosses the y axis.

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

Define the color 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_abline() understands the following aesthetics mappings:

  • slope : line slope.

  • intercept : line y-intercept.

  • 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”).

  • size : lines width.

  • linetype : type of the line. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’.

Examples

1from lets_plot import *
2LetsPlot.setup_html()
3ggplot() + geom_abline(slope=0)


 1import numpy as np
 2import pandas as pd
 3from lets_plot import *
 4LetsPlot.setup_html()
 5n, m = 10, 3
 6np.random.seed(42)
 7ids = np.arange(m).astype(str)
 8x = np.linspace(0, 1, n)
 9y = x + np.random.uniform(size=(m, n))
10df = pd.DataFrame({'id': np.repeat(ids, n),
11                   'x': np.tile(x, m),
12                   'y': y.reshape(m * n)})
13slope = np.corrcoef(y, x)[0, :-1] * y.std(axis=1) / x.std()
14intercept = y.mean(axis=1) - slope * x.mean()
15reg_df = pd.DataFrame({'id': ids, 'slope': slope, 'intercept': intercept})
16ggplot() + \
17    geom_abline(aes(slope='slope', intercept='intercept', color='id'), \
18                data=reg_df, size=1, linetype='dashed') + \
19    geom_point(aes(x='x', y='y', color='id', fill='id'), \
20               data=df, size=4, shape=21, alpha=.5)