Add a smoothed conditional mean.


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)


Set of aesthetic mappings created by aes or aes_. If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.
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 to ggplot. A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify for which variables will be created. A function will be called with a single argument, the plot data. The return value must be a data.frame., and will be used as the layer data.
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, like color = "red" or size = 3. They may also be parameters to the paired geom/stat.
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, see gam for more details.
formula to use in smoothing function, eg. y ~ x, y ~ poly(x, 2), y ~ log(x)
display confidence interval around smooth? (TRUE by default, see level to control
If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.
logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.
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 and stat_smooth.
number of points to evaluate smoother at
Controls the amount of smoothing for the default loess smoother. Smaller numbers produce wigglier lines, larger numbers produce smoother lines.
should the fit span the full range of the plot, or just the data
level of confidence interval to use (0.95 by default)
List of additional arguments passed on to the modelling function defined by method.


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 non-standard geom.


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 t-based approximation, and glm where the normal confidence interval is constructed on the link scale, and then back-transformed to the response scale.


geom_smooth understands the following aesthetics (required aesthetics are in bold):

  • x
  • y
  • alpha
  • colour
  • fill
  • linetype
  • size
  • weight

Computed variables

predicted value

lower pointwise confidence interval around the mean

upper pointwise confidence interval around the mean

standard error


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()
Warning message: Computation failed in `stat_smooth()`: y values must be 0 <= y <= 1

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

See also

See individual modelling functions for more details: lm for linear smooths, glm for generalised linear smooths, loess for local smooths