data <- read.table("O://Genetik//MA//Silke//RCode//TestData//2pop.txt", header=T)
# read data; change path name according to where you saved the file "2pop.txt"
data[1,]
Loci <- data[,3:8]
Pop <- data[,9]

#### witout resampling #####
source("O:\\Genetik\\MA\\Silke\\RCode\\GeneticDistance//genetic_dist_source_v3.R")
#change path name according to the location on your computer
gen.dist <- genetic.dist(Data=Loci, Pop)
gen.dist$D.Nei.Min #Nei's minimum genetic distance
# Reference: Takesaki,N.&Nei,M.(1996)Genetics144:389-399. 
# Consult the "ReadMe.txt" file for finding out which formulae 
# contain bugs and thus should _NOT_ be used.

#### with resampling #####
attach(data)
source("O:\\Genetik\\MA\\Silke\\RCode\\GeneticDistance//genetic_dist_source_v3.R")
source("O:\\Genetik\\MA\\Silke\\RCode\\Resampling//Code_Resampling.R")
#change path names according to the location on your computer

nr.res <- 3 # enter the number of resamplings 
data.res <- list()
for(i in 1:nr.res)
	{
	data.res[[i]] <- resample(data)
	}

#Assign "Loci.1" and "Pop.1" to each resampled data set
Loci.1 <- list()
Pop.1 <- list()
for(n in 1:nr.res)
	{
	Loci.1[[n]] <- data.res[[n]][,3:8] #change the columns if your data file differs
	Pop.1[[n]] <- data.res[[n]][,9] #change the columns if your data file differs
	}

test1 <- list()
n <- 1
for(i in 1:nr.res)
	{
	Loci <- Loci.1[[i]]
	Pop <- Pop.1[[i]]
	test1[[n]] <- genetic.dist(Data=Loci, Pop)
	n <- n+1
	}
test1[[1]]$D.Nei.Min 
# returns Nei's minimum genetic distance of the first
# resampled data set.

## Calculate the mean genetic distance over resamplings
Dm <- matrix(NA,length(unique(Pop)),length(unique(Pop)),dimnames=list(unique(Pop),unique(Pop)))
a <- test1[[1]]$D.Nei.Min ##change if necessary
for(k in 2:nr.res)
	{
	for(i in 1:(length(unique(Pop))-1))
		{
		for(j in (i+1):length(unique(Pop)))
			{
			Dm[j,i] <- a[j,i] + test1[[k]]$D.Nei.Min[j,i]	
			}
		}
	}
Dm <- Dm/nr.res
Dm