Question

Generate a scatter plot of this data, and include a smoothed function using smooth.spline function in R. Use predict function in R to calculate the 95% bounds (confidence band) for the mean, and plot them on the same scatterplot (use lty=2, and col=3).

How would I go about writing this code in R?

Answer 1

ggplot(mpg, aes(displ, hwy)) + geom_point() + geom_smooth()

#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

# 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)

#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

# 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)

#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'