Q-Q plot with ggplot2::stat_qq, colours, single group

Question

I'm looking for a more convenient way to get a Q-Q plot in ggplot2 where the quantiles are computed for the data set as a whole. but I can use mappings (colour/shapes) for groups in the data.

library(dplyr)
library(ggplot2)
library(broom) ## for augment()

Make up some data:

set.seed(1001)
N <- 1000
G <- 10
dd <- data_frame(x=runif(N),
             f=factor(sample(1:G,size=N,replace=TRUE)),
             y=rnorm(N)+2*x+as.numeric(f))
m1 <- lm(y~x,data=dd)
dda <- cbind(augment(m1),f=dd$f)

Basic plot:

ggplot(dda)+stat_qq(aes(sample=.resid))

if I try to add colour, the groups get separated for the quantile computation (which I don't want):

ggplot(dda)+stat_qq(aes(sample=y,colour=f))

If I use stat_qq(aes(sample=y,colour=f,group=1)) ggplot ignores the colour specification and I get the first plot back.

I want a plot where the points are positioned as in the first case, but coloured as in the second case. I have a qqnorm-based manual solution that I can post but am looking for something nicer ...

Answer 1

You could calculate the quantiles yourself and then plot using geom_point:

dda = cbind(dda, setNames(qqnorm(dda$.resid, plot.it=FALSE), c("Theoretical", "Sample")))

ggplot(dda) + 
  geom_point(aes(x=Theoretical, y=Sample, colour=f))

Ah, I guess I should have read to the end of your question. This is the manual solution you were referring to, right? Although you could just package it as a function:

my_stat_qq = function(data, colour.var) {

  data=cbind(data, setNames(qqnorm(data$.resid, plot.it=FALSE), c("Theoretical", "Sample")))

  ggplot(data) + 
    geom_point(aes_string(x="Theoretical", y="Sample", colour=colour.var))

}

my_stat_qq(dda, "f")