Our geom
To illustrate how to use ggfx inside a geom, we are going to make an
alternative version of geom_smooth(). Instead of showing
the confidence interval as a ribbon we will show it as a blur with
various sigma depending on the width of the confidence interval. We’ll
ignore for now whether this is a sound approach to showing confidence
intervals.
How to draw a variable blur
ggfx already includes a with_variable_blur() filter
which seems perfect for our task. However, we need to make this work for
each single fit, and we need the blur amount to be based on some data in
the layer. The way we do this is to add the filter to the grobs created
by the geom directly in the draw_group() method:
GeomBlurrySmooth <- ggproto('GeomBlurrySmooth', GeomSmooth,
setup_params = function(data, params) {
params$max_range <- max(data$ymax - data$ymin)
params
},
draw_group = function (data, panel_params, coord, max_range, ...) {
# Create a raster object representing the width oof the ribbon
sigma <- ((data$ymax - data$ymin) / max_range)^1.5
sigma_raster <- as.raster(matrix(sigma, nrow = 1))
# Use the annotate_raster geom to convert it to a raster that spans the x-range
# of the line
sigma_grob <- GeomRasterAnn$draw_panel(
data, panel_params, coord,
raster = sigma_raster,
xmin = min(data$x),
xmax = max(data$x),
ymin = -Inf,
ymax = Inf
)
# Convert it to a reference layer
ref_name <- paste0('GeomBlurrySmooth_<', data$group[1], '>')
sigma_grob <- as_reference(sigma_grob, ref_name)
# Figurer out the maximum sigma relative to the y scale
max_sigma <- 0.5 * max_range / diff(panel_params$y$dimension())
# Create a line grob using geom_line
line_grob <- GeomLine$draw_panel(data, panel_params, coord)
# Add variable blur. Turn off blur in the x-direction and use the calulated max sigma
# in the y direction
line_grob <- with_variable_blur(line_grob, ch_red(ref_name), x_scale = 0, y_scale = unit(max_sigma, 'npc'))
# Return the two grobs combined, making sure that the reference grob comes first
grid::gList(
sigma_grob,
line_grob
)
}
)I’ve tried to annotate the code as much as possible. Most of the code pertains to this particular case, so the main takeaways are that you simply figure out the correct parameters for your filter in the geom and then return a filtered grob using these parameters. Another point of note is that you can create reference layers within your geom and use these - the only thing you need to take care of is that the name is unique to the group, and that the reference grob comes before the filtered grob that uses it.
Without further ado, let’s see if it works - let’s start with the regular smooth geom:
ggplot(mpg, aes(displ, hwy)) +
geom_smooth()
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'
And now for our new version (we are using stat_smooth()
because we haven’t bothered creating a constructor). We’ll increase the
size of the line because otherwise the blur might make it disappear
completely:
ggplot(mpg, aes(displ, hwy)) +
stat_smooth(geom = GeomBlurrySmooth, size = 5, alpha = 1)
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'
It works! Now, there are certainly room for improvements. The obvious place to start is the part of the line that peaks out from outside the blur area. This could easily be solved by padding the raster with some repeated values from the start and end, or we could mask the layer so it only showed the part that is getting blurred. I will leave that as an exercise to the reader.
