while loop within a user-defined R function is not breaking - r

Question

I have created a user defined function as follows:
HAC.sim(K = 1, N, Hstar, m = 0, probs, perms, p = 0.95)
and within the body of this function, I would like to put the following 'while' loop (because I want to eventually create a package for easy use).
while(R < p){
HAC.sim(K = 1, N = Nstar, Hstar, m = 0, probs, perms, p = 0.95)
}
R is a variable that is defined in the main function body.
The problem is that the 'while' loop is not breaking when the condition is met (i.e., the moment when R < p).
My routine works (i.e., it breaks successfully) when the 'while' loop is placed outside of the function, but fails when I place it within the main function body.
I have also tried implementing a repeat loop with break, but the same problem occurs.
Any ideas on how I can fix the issue?
Here is the full function (quite long)
HAC.sim <- function(K = 1, N, Hstar, probs, m = 0, perms = 10000, p = 0.95){
### Set up a container to hold the identity of each individual from each permutation
pop <- array(dim = c(c(perms, ceiling((1 - m) * N / K)), K))
### Create an ID for each haplotype
haps <- as.character(1:Hstar)
### Generate permutations, we assume each permutation has Npop individuals, and we sample those individuals' haplotypes from the probabilities
specs <- 1:ceiling((1 - m) * N / K)
for(j in 1:perms){
for(i in 1:K){
pop[j, specs, i] <- sample(haps, size = ceiling((1 - m) * N / K), replace = TRUE, prob = probs)
}
}
### Make a matrix to hold the 1:N individuals from each permutation
HAC.mat <- array(dim = c(c(perms, ceiling((1 - m) * N / K)), K))
for(k in specs){
for(j in 1:perms){
for(i in 1:K){
ind.index <- sample(specs, size = k, replace = FALSE) ## which individuals will we sample
hap.plot <- pop[sample(1:nrow(pop), size = 1, replace = TRUE), ind.index, sample(i, size = 1, replace = TRUE)] ## pull those individuals from a permutation
HAC.mat[j, k, i] <- length(unique(hap.plot)) ## how many haplotypes did we get for a given sampling intensity (j) from each permutation (i)
}
}
}
### Calculate the mean and CI for number of haplotypes at each sampling intensity (k)
means <- apply(HAC.mat, MARGIN = 2, mean)
lower <- apply(HAC.mat, MARGIN = 2, function(x) quantile(x, 0.025))
upper <- apply(HAC.mat, MARGIN = 2, function(x) quantile(x, 0.975))
assign("d", data.frame(specs, means), envir = .GlobalEnv)
### Compute Measures of Sampling Closeness
P <- max(means)
Q <- Hstar - max(means)
R <- assign("R", max(means) / Hstar, envir = .GlobalEnv)
S <- (Hstar - max(means)) / Hstar
Nstar <- assign("Nstar", (N * Hstar) / max(means), envir = .GlobalEnv)
X <- ((N * Hstar) / max(means)) - N
cat("\n Input parameters \n \n Number of (sub)populations: ", K, "\n Number of individuals: ", N, "\n Number of haplotypes: ", Hstar, "\n Haplotype distribution: ", probs, "\n Migration rate: ", m, "\n Number of permutations: ", perms, "\n Proportion of haplotypes to recover: ", p, "\n \n \n Measures of Sampling Closeness \n \n Mean number of haplotypes sampled: " , P, "\n Mean number of haplotypes not sampled: " , Q, "\n Proportion of haplotypes sampled: " , R, "\n Proportion of haplotypes not sampled: " , S, "\n \n Calculated mean value of N*: ", Nstar, "\n Mean number of individuals not sampled: ", X, "\n \n")
if(R < p){
cat("Desired level of H* has not yet been reached")
} else{
cat("Desired level of H* has been reached")
}
### Plot the curve and frequency barplot
par(mfrow = c(1, 2))
plot(specs, means, type = "n", xlab = "Specimens sampled", ylab = "Unique haplotypes", ylim = c(1, Hstar))
polygon(x = c(specs, rev(specs)), y = c(lower, rev(upper)), col = "gray")
lines(specs, means, lwd = 2)
HAC.bar <- barplot(ceiling((1 - m) * N / K)*probs, xlab = "Unique haplotypes", ylab = "Specimens sampled", names.arg = 1:Hstar)
while(R < p){
HAC.sim(K = K, N = ceiling(Nstar), Hstar = Hstar, probs = probs, m = m, perms = perms, p = p)
}
}
### Run simulation
HAC.sim(K = K, N = N, Hstar = Hstar, probs = probs, m = m, perms = perms, p = p)