Hey guys. I'm new to R so I need a little help

I'm trying to check if the estimators that I'm using for a binomial distribution parameter actually tends to the actual one through a Monte Carlo simulation with 10k replications.

For that I'm basing my code in one that I found for the exponential one. First, I wanna understand the code. So here it is:

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#This file is a Monte Carlo study of the small sample

#behaviour of the sample mean estimators of the Expected value.

#and the power and size of the associated test statistics.

# 1000 REPLICATIONS, SAMPLE SIZES 1000, 200, 20 10

Rp<-10000 # no.of replications

sz <-1000 # sample size

#resulting matrices

TT<-matrix(0,ncol=4,nrow=Rp) # estimates matrix

TS<-matrix(0,ncol=4,nrow=Rp) # Std Err. matrix

#Simulation

for(i in 1:Rp) {

X<-rexp(sz,rate=2)

#mean estimates

TT[i,1]<-1/mean(X) #1 #equation1

TS[i,1]<-1/mean(X)^2/sz #2 #equation2

#mean estimates

TT[i,2]<-1/mean(X[c(1:200)]) #3

TS[i,2]<-(1/mean(X[c(1:200)])^2)/(200) #4

#mean estimates

TT[i,3]<-1/mean(X[c(1:20)]) #5

TS[i,3]<-(1/mean(X[c(1:20)])^2)/(20) #6

#mean estimates

TT[i,4]<-1/mean(X[c(1:10)]) #7

TS[i,4]<-(1/mean(X[c(1:10)])^2)/(10) #8

}

#bias and efficiency

#sample mean

summary(TT[,1])

summary(TT[,2])

summary(TT[,3])

summary(TT[,4])

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I understand that it's being repeated 10k times, so each time he generates a different X, base on the sample size and the rate, to use #equation1 to estimate my parameter [E(x)] in #1. So in the column 1 of the matrix TT, is being stored those estimations. Similar thing being done for the #equation2, but for the variance estimation.

But on #3 , what does the c(1:200) means? does it means that from the sample size of 1000, hes only using 200 of those? So in that case I dont need to change the sample size, SZ, to know the estimation for different sample sizes (100,20 and 10)?