# How to Generate Binomial Random Numbers in R

Binomial distribution has two possible outcomes in statistics.

1. Success
2. Failure

The probability of success (p) and failure (1 – p) are fixed and do not change from one trial to the next. The number of successes (x) in a given number of trials (n) follows a binomial distribution.

A binomial random number is a random variable that follows a binomial distribution.

## Generate Binomial Random Numbers in R

To generate binomial random numbers in R, use the rbinom() function. The rbinom() is a built-in R function that generates a vector of binomial distributed random variables.

The rbinom() function accepts the following three arguments.

1. n: It is the number of trials.
2. size: It is the number of successes.
3. prob: It is the probability of success for each trial.

### Example

Generate 10 binomial distributed random numbers using the rbinom() function.

``````set.seed(99)

binom_dist_random_nums <- rbinom(10, size = 1, prob = 0.5)

print(binom_dist_random_nums)``````

#### Output

``  1 0 1 1 1 1 1 0 0 0``

We get an output of 10 random numbers with 0 and 1 values. A value of 1 suggests success, and a value of 0 suggests failure.

In this example, we defined a probability of 0.5, which means the output has a minimum of five success outcomes. To increase the success rate of the binomial distribution, increase the probability rate.

## Creating different trials bypassing `size` and `prob` arguments

To create a different number of trials and probability of success,  change the values of the size and prob arguments.

``````set.seed(99)

binom_dist_random_nums <- rbinom(10, size = 30, prob = 0.9)

print(binom_dist_random_nums)``````

#### Output

`` 27 29 26 22 27 24 26 28 28 29``

In this example, we generated 10 binomial random numbers with a probability of success of 0.9 and 30 trials.

## Generating a histogram based on a binomial distribution

To create a histogram in R, use the hist() function and provide the binomial random numbers.

``````set.seed(99)

binom_dist_random_nums <- rbinom(10, size = 30, prob = 0.9)

hist(binom_dist_random_nums)``````

#### Output ## Conclusion

To generate a uniform distribution random numbers in R, use the runif() function.

To generate normal distributed random numbers in R, use the rnorm() function.

To generate exponentially distributed random numbers in R, use the rexp() function.

To generate binomial random numbers in R, use the rbinom() function.

That’s it.