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), is combined with the default mapping at the top level of the plot. You only need to supply
mappingif there isn't a mapping defined for the plot.
layer. There are three types of arguments you can use here:
color = "red"or
size = 3.
statassociated with the layer.
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.