The **exponential** **distribution** is a **continuous** **probability** **distribution** that defines the time between events in a process that occurs continuously and independently at a constant average rate.

**Generate Exponentially Distributed Random Numbers in R**

To generate **exponentially** **distributed** **random** **numbers** in **R**, use the **rexp()** function. The **rexp()** is a built-in R **function** that **returns the values from the exponential distribution.**

The **rexp()** function accepts the following two parameters.

**observations**: It takes observations you want to see.**rate**: It is the estimated rate of events for the distribution; this is usually 1/expected service life or wait time.

**Example**

Generate ten random numbers using the **rexp()** function.

```
set.seed(99)
expo_dist_random_nums <- rexp(10, rate = 0.6)
print(expo_dist_random_nums)
```

**Output**

```
[1] 0.28237284 4.25622953 0.11664528 0.34030952 1.87766520 2.98135398
[7] 0.16272462 0.01817234 3.22831023 0.45634704
```

In this example, we passed **ten** as **observations** and **rate = 0.6; we** got the output of 10 exponentially distributed random numbers.

**Generating a histogram based on an exponential distribution**

To **create** a **histogram** in **R**, use the **hist() **function and pass the exponential random numbers, **probability=TRUE**, **col= gray(.9)**, **main=”exponential mean=1400″** arguments.

```
set.seed(99)
expo_dist_random_nums <- rexp(400, rate = 1/1400)
hist(expo_dist_random_nums, probability=TRUE, col= gray(.9), main="exponential mean=1400")
curve(dexp(x, 1/1400), add= T)
```

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

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