# geom_abline

##### Line specified by slope and intercept.

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

##### Usage

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

##### Arguments

- show_guide
- should a legend be drawn? (defaults to
`FALSE`

) - mapping
- 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. - 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
`layer`

. This can include aesthetics whose values you want to set, not map. See`layer`

for more details.

##### Details

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.

##### Aesthetics

# 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`

*Documentation reproduced from package ggplot2, version 0.9.3.1, License: GPL-2*