You will learn how to plot smooth line using ggplot2.
Related BookGGPlot2 Essentials for Great Data Visualization in R
- Load the ggplot2 package and set the default theme to
library(ggplot2) theme_set( theme_bw() + theme(legend.position = "top") )
- Demo dataset:
## speed dist ## 1 4 2 ## 2 4 10 ## 3 7 4 ## 4 7 22 ## 5 8 16 ## 6 9 10
Key R function: geom_smooth()
- Key R function:
geom_smooth()for adding smoothed conditional means / regression line.
- Key arguments:
linetype: Change the line color, size and type.
fill: Change the fill color of the confidence region.
A simplified format of the function `geom_smooth():
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
To add a regression line on a scatter plot, the function
geom_smooth() is used in combination with the argument
method = lm.
lm stands for linear model.
p <- ggplot(cars, aes(speed, dist)) + geom_point() # Add regression line p + geom_smooth(method = lm) # loess method: local regression fitting p + geom_smooth(method = "loess")
Loess method for local regression fitting
# loess method: local regression fitting p + geom_smooth(method = "loess")
# Remove the confidence bande: se = FALSE p + geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE)
spline.d <- as.data.frame(spline(cars$speed, cars$dist)) p + geom_line(data = spline.d, aes(x = x, y = y))
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