What is the ggplot2/plyr way to calculate statistical tests between two subgroups? - r

Question

I am a rather novice user of R and have come to appreciate the elegance of ggplot2 and plyr. Right now, I am trying to analyze a large dataset that I can not share here, but I have reconstructed my problem with the diamonds dataset (shortened for convenience).
Without further ado:
diam <- diamonds[diamonds$cut=="Fair"|diamonds$cut=="Ideal",]
boxplots <- ggplot(diam, aes(x=cut, price)) + geom_boxplot(aes(fill=cut)) + facet_wrap(~ color)
print(boxplots)
What the plot produces is a set of boxplots, comparing the price of the two cuts "Fair" and "Ideal".
I would now very much like to proceed by statistically comparing the two cuts for each color subgroup (D,E,F,..,J) using either t.test or wilcox.test.
How would I implement this in an way that is as elegant as the ggplot2-syntax? I assume I would use ddply from the plyr-package, but I couldn't figure out how to feed two subgroups into a function that calculates the appropriate statistics..

Answer 1

I think you're looking for:
library(plyr)
ddply(diam,"color",
function(x) {
w <- wilcox.test(price~cut,data=x)
with(w,data.frame(statistic,p.value))
})
(Substituting t.test for wilcox.test seems to work fine too.)
results:
color statistic p.value
1 D 339753.5 4.232833e-24
2 E 591104.5 6.789386e-19
3 F 731767.5 2.955504e-11
4 G 950008.0 1.176953e-12
5 H 611157.5 2.055857e-17
6 I 213019.0 3.299365e-04
7 J 56870.0 2.364026e-01

Answer 2

ddply returns a data frame as output and, assuming that I am reading your question properly, that isn't what you are looking for. I believe you would like to conduct a series of t-tests using a series of subsets of data so the only real task is compiling a list of those subsets. Once you have them you can use a function like lapply() to run a t-test for each subset in your list. I am sure this isn't the most elegant solution, but one approach would be to create a list of unique pairs of your colors using a function like this:
get.pairs <- function(v){
l <- length(v)
n <- sum(1:l-1)
a <- vector("list",n)
j = 1
k = 2
for(i in 1:n){
a[[i]] <- c(v[j],v[k])
if(k < l){
k <- k + 1
} else {
j = j + 1
k = j + 1
}
}
return(a)
}
Now you can use that function to get your list of unique pairs of colors:
> (color.pairs <- get.pairs(levels(diam$color))))
[[1]]
[1] "D" "E"
[[2]]
[1] "D" "F"
...
[[21]]
[1] "I" "J"
Now you can use each of these lists to run a t.test (or whatever you would like) on your subset of your data frame, like so:
> t.test(price~cut,data=diam[diam$color %in% color.pairs[[1]],])
Welch Two Sample t-test
data: price by cut
t = 8.1594, df = 427.272, p-value = 3.801e-15
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
1008.014 1647.768
sample estimates:
mean in group Fair mean in group Ideal
3938.711 2610.820
Now use lapply() to run your test for each subset in your list of color pairs:
> lapply(color.pairs,function(x) t.test(price~cut,data=diam[diam$color %in% x,]))
[[1]]
Welch Two Sample t-test
data: price by cut
t = 8.1594, df = 427.272, p-value = 3.801e-15
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
1008.014 1647.768
sample estimates:
mean in group Fair mean in group Ideal
3938.711 2610.820
...
[[21]]
Welch Two Sample t-test
data: price by cut
t = 0.8813, df = 375.996, p-value = 0.3787
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-260.0170 682.3882
sample estimates:
mean in group Fair mean in group Ideal
4802.912 4591.726