geom_quantile


Geometry fits a quantile regression to the data and draws the fitted quantiles with lines. This is as a continuous analogue to geom_boxplot.

Aesthetics

x, y required position aesthetics
alpha, colour, group, line type, size, weight classic aesthetics properties

Other Properties

lineend line end style (round, butt, square)
linejoin line join style – (round, mitre, bevel)
linemitre line mitre limit (number greater than 1)

Computed Variables

quantile quantile of distribution

Similar Geometries

geom_boxplot, geom_smooth

Description and Details

Using the described geometry, you can insert geometric objects into your data visualization – layer of quantile lines that are defined by two positional aesthetic properties – x and y. You can find this geometry in the ribbon toolbar tab Layers, under the 2D button.

We use the geom_quantile geometry for continuous variables that are mapped to both axes. In the following example, we use the built-in mpg dataset and on the axis were mapped variables displ and 1/hwy. When using point geometry, the result will look like on the following figure.

If you add the geom_quantile layer to the chart and you map the positional aesthetics to the same variables, the result will look like in the following plot.

The individual lines represent fitted quantiles at levels of 0.25, 0.5 and 0.75. These levels are set by default and are not changeable. If you want to use different levels, you can do it using the stat_quantilestatistical layer, which contains more adjustable properties. Statistical layers are described in the following chapter.