lets_plot.geom_polygon

lets_plot.geom_polygon(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, map=None, map_join=None, **other_args)

Display a filled closed path defined by the vertex coordinates of individual polygons.

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

  • data (dict or DataFrame or GeoDataFrame) – 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.

  • stat (str, default=’identity’) – The statistical transformation to use on the data for this layer, as a string.

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

  • show_legend (bool, default=True) – False - do not show legend for this layer.

  • sampling (FeatureSpec) – Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.

  • tooltips (layer_tooltips) – Result of the call to the layer_tooltips() function. Specifies appearance, style and content.

  • map (GeoDataFrame or Geocoder) – Data contains coordinates of polygon vertices on map.

  • map_join (str or list) – Keys used to join map coordinates with data. First value in pair - column/columns in data. Second value in pair - column/columns in map.

  • 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

Geom object specification.

Return type

LayerSpec

Note

geom_polygon() draws polygons, which are filled paths. Each vertex of the polygon requires a separate row in the data.

geom_polygon() understands the following aesthetics mappings:

  • x : x-axis coordinates of the vertices of the polygon.

  • y : y-axis coordinates of the vertices of the polygon.

  • alpha : transparency level of a layer. Understands numbers between 0 and 1.

  • color (colour) : color of a geometry lines. Can be continuous or discrete. For continuous value this will be a color gradient between two colors.

  • size : lines width. Defines line width.

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

Note

The data and map parameters of GeoDataFrame type support shapes Polygon and MultiPolygon.

The map parameter of Geocoder type implicitly invoke boundaries() function.

Note

The conventions for the values of map_join parameter are as follows.

  • Joining data and GeoDataFrame object

    Data has a column named ‘State_name’ and GeoDataFrame has a matching column named ‘state’:

    • map_join=[‘State_Name’, ‘state’]

    • map_join=[[‘State_Name’], [‘state’]]

  • Joining data and Geocoder object

    Data has a column named ‘State_name’. The matching key in Geocoder is always ‘state’ (providing it is a state-level geocoder) and can be omitted:

    • map_join=’State_Name’

    • map_join=[‘State_Name’]

  • Joining data by composite key

    Joining by composite key works like in examples above, but instead of using a string for a simple key you need to use an array of strings for a composite key. The names in the composite key must be in the same order as in the US street addresses convention: ‘city’, ‘county’, ‘state’, ‘country’. For example, the data has columns ‘State_name’ and ‘County_name’. Joining with a 2-keys county level Geocoder object (the Geocoder keys ‘county’ and ‘state’ are omitted in this case):

    • map_join=[‘County_name’, ‘State_Name’]

Examples

 1import numpy as np
 2from lets_plot import *
 3LetsPlot.setup_html()
 4n = 7
 5t = np.linspace(0, 2 * np.pi, 2 * n + 1)
 6r = np.concatenate((np.tile([1, .5], n), [1]))
 7data = {'x': r * np.cos(t), 'y': r * np.sin(t)}
 8ggplot(data, aes(x='x', y='y')) + \
 9    geom_polygon() + \
10    coord_fixed()