```
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
```

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