stat_quantile {ggplot2} | R Documentation |
Continuous quantiles.
stat_quantile(mapping = NULL, data = NULL, geom = "quantile", position = "identity", quantiles = c(0.25, 0.5, 0.75), formula = NULL, method = "rq", na.rm = FALSE, ...)
quantiles |
conditional quantiles of y to calculate and display |
formula |
formula relating y variables to x variables |
method |
Quantile regression method to use.
Currently only supports |
na.rm |
If |
mapping |
The aesthetic mapping, usually constructed
with |
data |
A layer specific dataset - only needed if you want to override the plot defaults. |
geom |
The geometric object to use display the data |
position |
The position adjustment to use for overlappling points on this layer |
... |
other arguments passed on to
|
a data.frame with additional columns:
quantile |
quantile of distribution |
stat_quantile
understands the following aesthetics (required aesthetics are in bold):
x
y
msamp <- movies[sample(nrow(movies), 1000), ] m <- ggplot(msamp, aes(year, rating)) + geom_point() m + stat_quantile() m + stat_quantile(quantiles = 0.5) q10 <- seq(0.05, 0.95, by=0.05) m + stat_quantile(quantiles = q10) # You can also use rqss to fit smooth quantiles m + stat_quantile(method = "rqss") # Note that rqss doesn't pick a smoothing constant automatically, so # you'll need to tweak lambda yourself m + stat_quantile(method = "rqss", lambda = 10) m + stat_quantile(method = "rqss", lambda = 100) # Use 'votes' as weights for the quantile calculation m + stat_quantile(aes(weight=votes)) # Change scale m + stat_quantile(aes(colour = ..quantile..), quantiles = q10) m + stat_quantile(aes(colour = ..quantile..), quantiles = q10) + scale_colour_gradient2(midpoint = 0.5) # Set aesthetics to fixed value m + stat_quantile(colour = "red", size = 2, linetype = 2) # Use qplot instead qplot(year, rating, data=movies, geom="quantile")