How to loop a test against one designated control group in R? - r

Question

I'm completely new to programming and R, but have a dataset that can only be analyzed with a more powerful statistics program such as R.
I have a large but simple dataset consisting of thousands of different groups with multiple samples that I want to compare against the control group with a mann whitney U test, data structure is pictured below.
Group, Measurements
a 0.14534
cont 0.42574
d 0.36347
c 0.14284
a 0.23593
d 0.36347
cont 0.33514
cont 0.29210
b 0.36345
...
The problem comes from that the nature of the test requires that only two groups are designated. However, as I have more than 1 group it does not work.
This is what I have so far and I as you see it does not work in a repeated fashion and only works if I have two groups in my input file.
data1 = read.csv(file.choose(), header=TRUE, stringsAsFactors=FALSE)
attach(data1)
testoutput <- wilcox.test(group ~ measurement, mu=0, alt="two.sided", conf.int=TRUE, conf.level=0.95, paired=FALSE, exact=FALSE, correct=TRUE)
write.table(testoutput$p.value, file="mwUtest.tsv", sep="\t")
How do I do write and loop the test properly for it to test all my groups against my designated control group? I assume the sapply or lapply functions are used before the wilcox.test, but I dont know how.
I'm sorry if this simple question has been brought up before, but I could not find any previous question regarding this specific problem.

Answer 1

In R, there's often many solutions for the same problem. Here's how I would solve this.
First, I would split my data and have one dataframe with experiments and one with controls:
experiments <- dat[dat$group!="cont",]
controls <- dat[dat$group=="cont",]
Then I would split my experimental data by group, and feed that to my test together with my control measurements. Note that this construction makes it easy to extract more values from the test: just return a (named) vector.
result <- lapply(split(experiments, experiments$group),function(x){
mytest = wilcox.test(x$measurement,controls$measurement,mu=0, alt="two.sided", conf.int=TRUE, conf.level=0.95, paired=FALSE, exact=FALSE, correct=TRUE)
return(mytest$p.value)
})
Combining to a table is then easy:
output <- do.call(rbind,result)
Data used:
set.seed(123)
nobs=100
dat <- data.frame(group=sample(c(LETTERS[1:6],"cont"),nobs,T),
measurement=runif(nobs),stringsAsFactors=F)