Scatterplots in ggplot2
You will learn the layered syntax of ggplot2 for scatterplots in this section. It also demonstrates how regression lines can be added (compared with the base-R syntax shown in the introductory video).
Label points in the scatter plot
Add regression lines
The functions below can be used to add regression lines to a scatter plot:
- geom_smooth() and stat_smooth()
- geom_abline()
Only the function geom_smooth() is covered in this section.
A simplified format is:
geom_smooth(method="auto", se=TRUE, fullrange=FALSE, level=0.95)
- method : smoothing method to be used. Possible values are lm, glm, gam, loess, rlm.
- method = “loess”: This is the default value for small number of observations. It computes a smooth local regression. You can read more about loess using the R code ?loess.
- method =“lm”: It fits a linear model. Note that, it’s also possible to indicate the formula as formula = y ~ poly(x, 3) to specify a degree 3 polynomial.
- se : logical value. If TRUE, confidence interval is displayed around smooth.
- fullrange : logical value. If TRUE, the fit spans the full range of the plot
- level : level of confidence interval to use. Default value is 0.95
# Add the regression line ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point()+ geom_smooth(method=lm) # Remove the confidence interval ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point()+ geom_smooth(method=lm, se=FALSE) # Loess method ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point()+ geom_smooth()