[R] Permutation or Bootstrap to obtain p-value for one sample

[francy]

Hi, 

I am having trouble understanding how to approach a simulation:

I have a sample of n=250 from a population of N=2,000 individuals, and I
would like to use either permutation test or bootstrap to test whether this
particular sample is significantly different from the values of any other
random samples of the same population. I thought I needed to take random
samples (but I am not sure how many simulations I need to do) of n=250 from
the N=2,000 population and maybe do a one-sample t-test to compare the mean
score of all the simulated samples, + the one sample I am trying to prove
that is different from any others, to the mean value of the population. But
I don't know:
(1) whether this one-sample t-test would be the right way to do it, and how
to go about doing this in R
(2) whether a permutation test or bootstrap methods are more appropriate

This is the data frame that I have, which is to be sampled:
df<-
i.e.
x y
1 2
3 4
5 6
7 8
. .
. .
. .
2,000

I have this sample from df, and would like to test whether it is has extreme
values of y. 
sample1<-
i.e.
x y
3 4
7 8
. .
. .
. .
250

For now I only have this: 

R=999 #Number of simulations, but I don't know how many...
t.values =numeric(R)	 #creates a numeric vector with 999 elements, which
will hold the results of each simulation. 
for (i in 1:R) {
sample1 <- df[sample(nrow(df), 250, replace=TRUE),] 

But I don't know how to continue the loop: do I calculate the mean for each
simulation and compare it to the population mean? 
Any help you could give me would be very appreciated,
Thank you. 


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