geom_smooth
Add a smoothed conditional mean.
Aids the eye in seeing patterns in the presence of overplotting.
geom_smooth
and stat_smooth
are effectively aliases: they
both use the same arguments. Use geom_smooth
unless you want to
display the results with a nonstandard geom.
Usage
geom_smooth(mapping = NULL, data = NULL, stat = "smooth", position = "identity", ..., method = "auto", formula = y ~ x, se = TRUE, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
stat_smooth(mapping = NULL, data = NULL, geom = "smooth", position = "identity", ..., method = "auto", formula = y ~ x, se = TRUE, n = 80, span = 0.75, fullrange = FALSE, level = 0.95, method.args = list(), na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
Arguments
 mapping
 Set of aesthetic mappings created by
aes
oraes_
. If specified andinherit.aes = TRUE
(the default), it is combined with the default mapping at the top level of the plot. You must supplymapping
if there is no plot mapping.  data
 The data to be displayed in this layer. There are three
options:
If
NULL
, the default, the data is inherited from the plot data as specified in the call toggplot
.A
data.frame
, or other object, will override the plot data. All objects will be fortified to produce a data frame. Seefortify
for which variables will be created.A
function
will be called with a single argument, the plot data. The return value must be adata.frame.
, and will be used as the layer data.  position
 Position adjustment, either as a string, or the result of a call to a position adjustment function.
 ...
 other arguments passed on to
layer
. These are often aesthetics, used to set an aesthetic to a fixed value, likecolor = "red"
orsize = 3
. They may also be parameters to the paired geom/stat.  method
 smoothing method (function) to use, eg. lm, glm, gam, loess,
rlm. For datasets with n < 1000 default is
loess
. For datasets with 1000 or more observations defaults to gam, seegam
for more details.  formula
 formula to use in smoothing function, eg.
y ~ x
,y ~ poly(x, 2)
,y ~ log(x)
 se
 display confidence interval around smooth? (TRUE by default, see level to control
 na.rm
 If
FALSE
(the default), removes missing values with a warning. IfTRUE
silently removes missing values.  show.legend
 logical. Should this layer be included in the legends?
NA
, the default, includes if any aesthetics are mapped.FALSE
never includes, andTRUE
always includes.  inherit.aes
 If
FALSE
, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g.borders
.  geom, stat
 Use to override the default connection between
geom_smooth
andstat_smooth
.  n
 number of points to evaluate smoother at
 span
 Controls the amount of smoothing for the default loess smoother. Smaller numbers produce wigglier lines, larger numbers produce smoother lines.
 fullrange
 should the fit span the full range of the plot, or just the data
 level
 level of confidence interval to use (0.95 by default)
 method.args
 List of additional arguments passed on to the modelling
function defined by
method
.
Details
Calculation is performed by the (currently undocumented)
predictdf
generic and its methods. For most methods the standard
error bounds are computed using the predict
method  the
exceptions are loess
which uses a tbased approximation, and
glm
where the normal confidence interval is constructed on the link
scale, and then backtransformed to the response scale.
Aesthetics
geom_smooth
understands the following aesthetics (required aesthetics are in bold):

x

y

alpha

colour

fill

linetype

size

weight
Computed variables
 y
 predicted value
 ymin
 lower pointwise confidence interval around the mean
 ymax
 upper pointwise confidence interval around the mean
 se
 standard error
See Also
See individual modelling functions for more details:
lm
for linear smooths,
glm
for generalised linear smooths,
loess
for local smooths
Examples
ggplot(mpg, aes(displ, hwy)) +
geom_point() +
geom_smooth()
# Use span to control the "wiggliness" of the default loess smoother
# The span is the fraction of points used to fit each local regression:
# small numbers make a wigglier curve, larger numbers make a smoother curve.
ggplot(mpg, aes(displ, hwy)) +
geom_point() +
geom_smooth(span = 0.3)
# Instead of a loess smooth, you can use any other modelling function:
ggplot(mpg, aes(displ, hwy)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
ggplot(mpg, aes(displ, hwy)) +
geom_point() +
geom_smooth(method = "lm", formula = y ~ splines::bs(x, 3), se = FALSE)
# Smoothes are automatically fit to each group (defined by categorical
# aesthetics or the group aesthetic) and for each facet
ggplot(mpg, aes(displ, hwy, colour = class)) +
geom_point() +
geom_smooth(se = FALSE, method = "lm")
ggplot(mpg, aes(displ, hwy)) +
geom_point() +
geom_smooth(span = 0.8) +
facet_wrap(~drv)
binomial_smooth < function(...) {
geom_smooth(method = "glm", method.args = list(family = "binomial"), ...)
}
# To fit a logistic regression, you need to coerce the values to
# a numeric vector lying between 0 and 1.
ggplot(rpart::kyphosis, aes(Age, Kyphosis)) +
geom_jitter(height = 0.05) +
binomial_smooth()
ggplot(rpart::kyphosis, aes(Age, as.numeric(Kyphosis)  1)) +
geom_jitter(height = 0.05) +
binomial_smooth()
ggplot(rpart::kyphosis, aes(Age, as.numeric(Kyphosis)  1)) +
geom_jitter(height = 0.05) +
binomial_smooth(formula = y ~ splines::ns(x, 2))
# But in this case, it's probably better to fit the model yourself
# so you can exercise more control and see whether or not it's a good model