| geom_abline {ggplot2} | R Documentation |
The abline geom adds a line with specified slope and intercept to the plot.
geom_abline(mapping = NULL, data = NULL, stat = "abline", position = "identity", show_guide = FALSE, ...)
mapping |
The aesthetic mapping, usually constructed with
|
data |
A layer specific dataset - only needed if you want to override the plot defaults. |
stat |
The statistical transformation to use on the data for this layer. |
position |
The position adjustment to use for overlapping points on this layer |
show_guide |
should a legend be drawn? (defaults to |
... |
other arguments passed on to |
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
stat_smooth to add lines derived from the data,
geom_hline for horizontal lines,
geom_vline for vertical lines
geom_segment
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))
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()