geom_abline(mapping = NULL, data = NULL, ..., slope, intercept, na.rm = FALSE, show.legend = NA)geom_hline(mapping = NULL, data = NULL, ..., yintercept, na.rm = FALSE, show.legend = NA)geom_vline(mapping = NULL, data = NULL, ..., xintercept, na.rm = FALSE, show.legend = NA)
aes_. If specified and
inherit.aes = TRUE(the default), it is combined with the default mapping at the top level of the plot. You must supply
mappingif there is no plot mapping.
NULL, the default, the data is inherited from the plot data as specified in the call to
data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See
fortifyfor which variables will be created. A
functionwill be called with a single argument, the plot data. The return value must be a
data.frame., and will be used as the layer data.
layer. These are often aesthetics, used to set an aesthetic to a fixed value, like
color = "red"or
size = 3. They may also be parameters to the paired geom/stat.
FALSE(the default), removes missing values with a warning. If
TRUEsilently removes missing values.
NA, the default, includes if any aesthetics are mapped.
FALSEnever includes, and
These paired geoms and stats add straight lines to a plot, either horizontal, vertical or specified by slope and intercept. These are useful for annotating plots.
These geoms act slightly different to other geoms. You can supply the
parameters in two ways: either as arguments to the layer function,
or via aesthetics. If you use arguments, e.g.
geom_abline(intercept = 0, slope = 1), then behind the scenes
the geom makes a new data frame containing just the data you've supplied.
That means that the lines will be the same in all facets; if you want them
to vary across facets, construct the data frame yourself and use aesthetics.
Unlike most other geoms, these geoms do not inherit aesthetics from the plot default, because they do not understand x and y aesthetics which are commonly set in the plot. They also do not affect the x and y scales.
These geoms are drawn using with
geom_line so support the
same aesthetics: alpha, colour, linetype and size. They also each have
aesthetics that control the position of the line:
p <- ggplot(mtcars, aes(wt, mpg)) + geom_point() # Fixed values p + geom_vline(xintercept = 5)
p + geom_vline(xintercept = 1:5)
p + geom_hline(yintercept = 20)
p + geom_abline() # Can't see it - outside the range of the data
p + geom_abline(intercept = 20)
# Calculate slope and intercept of line of best fit coef(lm(mpg ~ wt, data = mtcars))(Intercept) wt 37.285126 -5.344472p + geom_abline(intercept = 37, slope = -5)
# But this is easier to do with geom_smooth: p + geom_smooth(method = "lm", se = FALSE)
# To show different lines in different facets, use aesthetics p <- ggplot(mtcars, aes(mpg, wt)) + geom_point() + facet_wrap(~ cyl) mean_wt <- data.frame(cyl = c(4, 6, 8), wt = c(2.28, 3.11, 4.00)) p + geom_hline(aes(yintercept = wt), mean_wt)
# You can also control other aesthetics ggplot(mtcars, aes(mpg, wt, colour = wt)) + geom_point() + geom_hline(aes(yintercept = wt, colour = wt), mean_wt) + facet_wrap(~ cyl)
geom_segmentfor a more general approach to adding straight line segments to a plot.