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.

**Related:** A Guide to dnorm, pnorm, qnorm, and rnorm 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 head(data) [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.

**Additional Resources**

How to Plot a Normal Distribution in R

A Guide to dnorm, pnorm, qnorm, and rnorm in R

How to Perform a Shapiro-Wilk Test for Normality in R