The different filters provided by ggfx are applicable to a wide range of object types. Rather than documenting how to use them with each type in every documentation entry, the information is collected here. While the examples will use with_blur() they are general and applicable to all filters in ggfx.

## Value

All filters will generally return a new version of the same object, the only exception being filtering of rasters, functions, and references which returns a Layer object

## Method specific arguments

• id: A string that identifies the result of this filter, to be referenced by other filters in the same graphic.

• include: A logical flag that indicates whether the filtered image should be displayed. By default, the result will not be displayed if it is given an id (as it is assumed that it is meant for later use), but this can be overewritten by setting include = TRUE.

• ignore_background: Should the background of the plot be removed before applying the filter and re-added afterwards?

• background: A grob to draw below the result of the filter. Mainly for internal use for supporting ignore_background.

## Filtering layers

This is perhaps the most common and obvious use of ggfx, and the one show-cased in the respective docs of each filter. In order to apply a filter to a ggplot2 layer you wrap it around the layer constructor (usually a geom_*() function) and pass in additional parameters after it:

ggplot(mtcars) +
with_blur(
geom_point(aes(x = mpg, y = disp)),
sigma = 4
)

Apart from the arguments specific to the filter, layer filters also take an id, and include argument. Providing an id (as a string) will make this filter be referable by other filters. By default this turns of rendering of the result, but setting include = TRUE will turn rendering back on (while still making it referable). Referable layers should always come before whatever other layer ends up referring to them, since ggfx does not have control over the rendering order. Not following this rule will have undefined consequences (either an error or a weird plot - or maybe the correct result)

## Filtering layer references

While the first argument to a filter is mostly some sort of graphic generating object, it can also be a text string referring to another filter. This allows you to string together filters, should you so choose. The big caveat is that filtering a reference will always result in a layer - i.e. it is not compatible outside of ggplot2.

ggplot(mtcars) +
with_blur(
geom_point(aes(x = mpg, y = disp)),
sigma = 4,
id = 'blurred_points'
) +
'blurred_points'
)

## Filtering guides

ggplot2 does not only consist of layers - there are all sort of other graphic elements around them. Guides are one such type of element and these can be filtered by wrapping the filter around the guide constructor:

ggplot(mtcars) +
geom_point(aes(x = mpg, y = disp, colour = gear)) +
guides(colour = with_blur(guide_colourbar(), sigma = 4))

There is a caveat here in that it is not possible to use this with the string shorthand (i.e. with_blur('colourbar') won't work) — you have to use the functional form.

## Filtering theme elements

Theme elements, like guides, is another non-layer graphic that is amenable to filtering. It can be done by wrapping the element_*() constructor with a filter:

ggplot(mtcars) +
geom_point(aes(x = mpg, y = disp)) +
ggtitle("A blurry title") +
theme(plot.title = with_blur(element_text(), sigma = 4))

There is a caveat here as well. The filtering doesn't get carried through inheritance so you cannot set filtering at a top-level element and expect all child elements to be filtered.

## Filtering ggplots

While you normally only want to add a filter to a part of the plot, it is also possible to add it to everthing, simply by wrapping the filter function around the plot. You can elect to remove the background element while applying the filter and add it back on afterwards by setting ignore_background = TRUE on the filter

p <- ggplot(mtcars) +
geom_point(aes(x = mpg, y = disp))

with_blur(p, sigma = 4)

An alternative is to put the filter around the ggplot() call, which will have the same effect and may fit better with your plot construction code

with_blur(ggplot(mtcars), sigma = 4) +
geom_point(aes(x = mpg, y = disp))

## Filtering grobs

At the lowest level, it is possible to apply a filter to a grob. This is what powers all of the above at some level and that power is also available to you. It is done in the same manner as all of the above, by wrapping the grob in a filter:

blurred_circle <- with_blur(circleGrob(), sigma = 4)

grid.newpage()
grid.draw(blurred_circle)

As with layers, filters applied to grobs also take an id and include argument and they have the same effect. It should be noted that it can be difficult to grasp the rendering order of elements in a manually created grid graphics, so take care when using filters that refer to each other as the rule about the rendering order still applies.

There are not a lot of people who use grid directly, but if you develop ggplot2 extensions the ability to apply filters to grobs means that you can create geoms with filters build right into them!