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, ...)
show_guide |
should a legend be drawn? (defaults to
|
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 overlappling points on this layer |
... |
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()