stat_density2d(). This makes a 2D kernel density estimate from the data. First we’ll plot the density contour along with the data points (Figure 6.34, left):
# Save a base plot object ggplot(faithful, aes(x = eruptions, y = waiting)) faithful_p <- + faithful_p geom_point() + stat_density2d()
It’s also possible to map the height of the density curve to the color of the contour lines, by using
..level.. (Figure 6.34, right):
# Contour lines, with "height" mapped to color + faithful_p stat_density2d(aes(colour = ..level..))
The two-dimensional kernel density estimate is analogous to the one-dimensional density estimate generated by
stat_density(), but of course, it needs to be viewed in a different way. The default is to use contour lines, but it’s also possible to use tiles and to map the density estimate to the fill color, or to the transparency of the tiles, as shown in Figure 6.35:
# Map density estimate to fill color + faithful_p stat_density2d(aes(fill = ..density..), geom = "raster", contour = FALSE) # With points, and map density estimate to alpha + faithful_p geom_point() + stat_density2d(aes(alpha = ..density..), geom = "tile", contour = FALSE)
geom = "raster"in the first of the preceding examples and
geom = "tile"in the second. The main difference is that the raster geom renders more efficiently than the tile geom. In theory they should appear the same, but in practice they often do not. If you are writing to a PDF file, the appearance depends on the PDF viewer. On some viewers, when tile is used there may be faint lines between the tiles, and when raster is used the edges of the tiles may appear blurry (although it doesn’t matter in this particular case).
As with the one-dimensional density estimate, you can control the bandwidth of the estimate. To do this, pass a vector for the x and y bandwidths to
h. This argument gets passed on to the function that actually generates the density estimate,
kde2d(). In this example (Figure 6.36), we’ll use a smaller bandwidth in the x and y directions, so that the density estimate is more closely fitted (perhaps overfitted) to the data:
+ faithful_p stat_density2d( aes(fill = ..density..), geom = "raster", contour = FALSE, h = c(.5, 5) )
The relationship between
stat_bin2d() is the same as the relationship between their one-dimensional counterparts, the density curve and the histogram. The density curve is an estimate of the distribution under certain assumptions, while the binned visualization represents the observed data directly. See Recipe 5.5 for more about binning data.
If you want to use a different color palette, see Recipe 12.6.
stat_density2d() passes options to
?kde2d for information on the available options.