# How to Generate a Normal Distribution in R (With Examples)

You can quickly generate a normal distribution in R by using the rnorm() function, which uses the following syntax:

`rnorm(n, mean=0, sd=1)`

where:

• n: Number of observations.
• mean: Mean of normal distribution. Default is 0.
• sd: Standard deviation of normal distribution. Default is 1.

This tutorial shows an example of how to use this function to generate a normal distribution in R.

### Example: Generate a Normal Distribution in R

The following code shows how to generate a normal distribution in R:

```#make this example reproducible
set.seed(1)

#generate sample of 200 obs. that follows normal dist. with mean=10 and sd=3
data <- rnorm(200, mean=10, sd=3)

#view first 6 observations in sample

[1]  8.120639 10.550930  7.493114 14.785842 10.988523  7.538595
```

We can quickly find the mean and standard deviation of this distribution:

```#find mean of sample
mean(data)

[1] 10.10662

#find standard deviation of sample
sd(data)

[1] 2.787292
```

We can also create a quick histogram to visualize the distribution of data values:

```hist(data, col='steelblue')
```

We can even perform a Shapiro-Wilk test to see if the dataset comes from a normal population:

```shapiro.test(data)

Shapiro-Wilk normality test

data:  data
W = 0.99274, p-value = 0.4272
```

The p-value of the test turns out to be 0.4272. Since this value is not less than .05, we can assume the sample data comes from a population that is normally distributed.

This result shouldn’t be surprising since we generated the data using the rnorm() function, which naturally generates a random sample of data that comes from a normal distribution.