How to generate n random numbers from negative binomial distribution?

I am trying to make a function in order to generate n random numbers from negative binomial distribution. To generate it, I first made a function to generate n random variables from geometric distribution. My function for generating n random numbers from geometric distribution as follows:

rGE<-function(n,p){
  I<-rep(NA,n)
  for (j in 1:n){
  x<-rBer(1,p)
  i<-1 # number of trials
  while(x==0){
    x<-rBer(1,p)
    i<-i+1
  }
  I[j]<- i
  }
  return(I)
}

I tested this function (rGE), for example for rGE(10,0.5), which is generating 10 random numbers from a geometric distribution with probability of success 0.5, a random result was:

[1] 2 4 2 1 1 3 4 2 3 3

In rGE function I used a function named rBer which is:

rBer<-function(n,p){
  sample(0:1,n,replace = TRUE,prob=c(1-p,p))
}

Now, I want to improve my above function (rGE) in order to make a function for generating n random numbers from a negative binomial function. I made the following function:

rNB<-function(n,r,p){
  I<-seq(n)
  for (j in 1:n){
    x<-0
    x<-rBer(1,p)
    i<-1 # number of trials
    while(x==0 & I[j]!=r){
      x<-rBer(1,p)
      i<-i+1
    }
    I[j]<- i
  }
  return(I)
}

I tested it for rNB(3,2,0.1), which generates 3 random numbers from a negative binomial distribution with parametrs r=2 and p=0.1 for several times:

> rNB(3,2,0.1)
[1] 2 1 7
> rNB(3,2,0.1)
[1] 3 1 4
> rNB(3,2,0.1)
[1] 3 1 2
> rNB(3,2,0.1)
[1] 3 1 3
> rNB(3,2,0.1)
[1] 46  1 13 

As you can see, I think my function (rNB) does not work correctly, because the results always generat 1 for the second random number. Could anyone help me to correct my function (rNB) in order to generate n random numbers from a negative binomial distribution with parametrs n, r, and p. Where r is the number of successes and p is the probability of success?

[[Hint: Explanations regarding geometric distribution and negative binomial distribution: Geometric distribution: In probability theory and statistics, the geometric distribution is either of two discrete probability distributions:

1. The probability distribution of the number X of Bernoulli trials needed to get one success, supported on the set { 1, 2, 3, ... }.
2. The probability distribution of the number Y = X − 1 of failures before the first success, supported on the set { 0, 1, 2, 3, ... }

Negative binomial distribution:A negative binomial experiment is a statistical experiment that has the following properties: The experiment consists of x repeated trials. Each trial can result in just two possible outcomes. We call one of these outcomes a success and the other, a failure. The probability of success, denoted by P, is the same on every trial. The trials are independent; that is, the outcome on one trial does not affect the outcome on other trials. The experiment continues until r successes are observed, where r is specified in advance. ]]