I am writing a simple R program to execute a t-test repeatedly on data
contained in a data frame. My data looks like this:
Category Value1 Value2
1 .5 .8
1 .3 .9
. . . . . . . . .
2 1.4 1.3
2 1.3 1.3
. . . . . . . . .
15 .2 .3
15 .5 .1
So in all there are 15 categories, and each category contains two sets of
observations which I want to compare. I only want to compare Value1 and
Value2 within each category, but I need to do it 15 times (once for each
category), so I wanted to write an R function to make it easier.
Right now I am using a for() loop to do the comparison. My loop looks like
this:
for(i in 1:21)
{
x <- t.test(Value1[Category == i], Value2[Category == i])
y <- c(y, x$p.value)
}
The loop runs and everything is working well. However, I am not sure how to
translate this code into a function. In particular, I'm not sure how to
write a function that passes a data frame ds (containing Category, Value1,
and Value2 as members) as an argument, and then accessing these members
within the body of the function. I've tried the following:
repeated_test <- function(data)
{
for(i in 1:21)
{
x <- t.test(ds$Value1[ds$Category == i], ds$Value2[ds$Category
== i])
y <- c(y, x$p.value)
}
This will run, but only if the members of the data frame I am passing as an
argument are in fact named Value1, Value2, and Category. This is fine for
now, but in the future I will have to run this function on data where I
cannot be sure this is the case. Rather than change the member names by
hand, I would like to make the function generic to work with any data frame.
How do I do this? Or is there a better way to do this without the for()
loop (for example, using apply())?
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