geom_abline(mapping = NULL, data = NULL, stat = "abline",
position = "identity", show_guide = FALSE, ...)
aes
or aes_string
. Only needs to be set
at the layer level if you are overriding the plot defaults.FALSE
)# 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)) 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) 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()
stat_smooth
to add lines derived from the data,
geom_hline
for horizontal lines,
geom_vline
for vertical lines
geom_segment
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.