Binomial distribution has two possible outcomes in statistics.

- Success
- 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.

**n**: It is the number of trials.**size**: It is the number of successes.**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] 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

`size`

and `prob`

argumentsTo 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**

`[1] 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.

Krunal Lathiya is a Software Engineer with over eight years of experience. He has developed a strong foundation in computer science principles and a passion for problem-solving. In addition, Krunal has excellent knowledge of Data Science and Machine Learning, and he is an expert in R Language.