geom_abline(mapping = NULL, data = NULL, show.legend = NA, ..., slope, intercept)geom_hline(mapping = NULL, data = NULL, show.legend = NA, yintercept, ...)geom_vline(mapping = NULL, data = NULL, show.legend = NA, xintercept, ...)
aes
or
aes_
. If specified and inherit.aes = TRUE
(the
default), is combined with the default mapping at the top level of the
plot. You only need to supply mapping
if there isn't a mapping
defined for the plot.NA
, the default, includes if any aesthetics are mapped.
FALSE
never includes, and TRUE
always includes.layer
. There are
three types of arguments you can use here:
color = "red"
or size = 3
.
stat
associated with the layer.
data
, mapping
and
show.legend
are overriddenThese 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:
geom_vline
: xintercept
geom_hline
: yintercept
geom_abline
: slope
and intercept
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_segment
for a more general approach to
adding straight line segments to a plot.