Lines: horizontal, vertical, and specified by slope and intercept.


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)


Set of aesthetic mappings created by 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.
A data frame. If specified, overrides the default data frame defined at the top level of the plot.
other arguments passed on to layer. There are three types of arguments you can use here:
  • Aesthetics: to set an aesthetic to a fixed value, like color = "red" or size = 3.
  • Other arguments to the layer, for example you override the default stat associated with the layer.
  • Other arguments passed on to the stat.
If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.
logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.
xintercept, yintercept, slope, intercept
Parameters that control the position of the line. If these are set, data, mapping and show.legend are overridden


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:

  • 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 -5.344472
p + 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)

See also

See geom_segment for a more general approach to adding straight line segments to a plot.