The **rnorm()** **function** in** R **is** “used to** **generate a vector of normally distributed random numbers”**. It takes three arguments: **n** is the **sample** **size**, the **mean** is the **mean** of the **normal** **distribution**, and **sd** is the **standard** **deviation**.

**Syntax**

`rnorm(n, mean, sd)`

**Parameters**

**n:** It is the number of observations(sample size).

**mean:** It is the mean value of the sample data. Its default value is zero.

**sd:** It is the standard deviation. Its default value is 1.

**Example 1: Simple R program to show the use of the rnorm() function**

```
data <- rnorm(10)
data
```

**Output**

```
[1] -0.359721535 1.516744916 -0.380787719 0.345410241 0.321703671
[6]-0.436644645 -0.267328311 -1.640269174 -0.190012636 -0.004461941
```

**Example 2: Control the number of observations generated in the rnorm() function**

You can control the observations generated by the **rnorm() function** by providing the **n** argument.

For example, using the code below, you can generate a random sample of size **20** from a normal distribution with a mean of **0** and a standard deviation of **1**.

`random_sample <- rnorm(n = 20, mean = 0, sd = 1)`

The “n” argument defines the number of random samples generated from the normal distribution in the above code. Pass the **“n”** argument per your requirement to generate random samples from a random distribution.

**Example 3: Specify the mean and standard deviation of the generated data**

You can use the **“mean” **and **“sd” **arguments in the **rnorm() ****function **to define the normal distribution from which the random samples will be taken.

`output <- rnorm(n = 10, mean = 0, sd = 1)`

In this example, we passed mean = 0 and sd = 1.

So, the normal distribution will be generated based on these values(mean and sd).

**Example 4: Passing customized values of mean to rnorm() function**

Let’s find normalized values that cumulatively have a mean of 1.9.

```
data <- rnorm(1:21, 1.9)
print(data)
summary(data)
```

**Output**

```
[1] 0.38755150 2.75291745 1.85497073 3.06377678 2.19992803 2.19544549
[7] 3.06391987 2.11362968 0.08858567 1.80981794 0.62872918 1.95926405
[13] 1.00410509 3.33564571 1.56683186 1.07596651 1.19985883 3.28332289
[19] 2.07253467 0.85443979 3.74952206
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.08859 1.07597 1.95926 1.91718 2.75292 3.74952
```

In this example, we checked that the mean of the formed normalized values is **1.9** using the summary() function.

The distributed values are up to 21 because our vector size is 21.

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.

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