Line specified by slope and intercept.


geom_abline(mapping = NULL, data = NULL, stat = "abline", position = "identity", 
  show_guide = FALSE, ...)


should a legend be drawn? (defaults to FALSE)
The aesthetic mapping, usually constructed with aes or aes_string. Only needs to be set at the layer level if you are overriding the plot defaults.
A layer specific dataset - only needed if you want to override the plot defaults.
The statistical transformation to use on the data for this layer.
The position adjustment to use for overlappling points on this layer
other arguments passed on to layer. This can include aesthetics whose values you want to set, not map. See layer for more details.


The abline geom adds a line with specified slope and intercept to the plot.


With its siblings geom_hline and geom_vline, it's useful for annotating plots. You can supply the parameters for geom_abline, intercept and slope, in two ways: either explicitly as fixed values, or in a data frame. If you specify the fixed values (geom_abline(intercept=0, slope=1)) then the line will be the same in all panels. If the intercept and slope are stored in the data, then they can vary from panel to panel. See the examples for more ideas.


geom_abline understands the following aesthetics (required aesthetics are in bold):

  • alpha
  • colour
  • linetype
  • size


p <- qplot(wt, mpg, data = mtcars) # Fixed slopes and intercepts 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)

p + geom_abline(intercept = 10, colour = "red", size = 2)

# See ?stat_smooth for fitting smooth models to data p + stat_smooth(method="lm", se=FALSE)

# Slopes and intercepts as data p <- ggplot(mtcars, aes(x = wt, y=mpg), . ~ cyl) + geom_point() df <- data.frame(a=rnorm(10, 25), b=rnorm(10, 0)) p + geom_abline(aes(intercept=a, slope=b), data=df)

# Slopes and intercepts from linear model library(plyr) coefs <- ddply(mtcars, .(cyl), function(df) { m <- lm(mpg ~ wt, data=df) data.frame(a = coef(m)[1], b = coef(m)[2]) }) str(coefs)
'data.frame': 3 obs. of 3 variables: $ cyl: num 4 6 8 $ a : num 39.6 28.4 23.9 $ b : num -5.65 -2.78 -2.19
p + geom_abline(data=coefs, aes(intercept=a, slope=b))

# It's actually a bit easier to do this with stat_smooth p + geom_smooth(aes(group=cyl), method="lm")

p + geom_smooth(aes(group=cyl), method="lm", fullrange=TRUE)

# With coordinate transforms p + geom_abline(intercept = 37, slope = -5) + coord_flip()

p + geom_abline(intercept = 37, slope = -5) + coord_polar()

See also

stat_smooth to add lines derived from the data, geom_hline for horizontal lines, geom_vline for vertical lines geom_segment