**dnorm()** function in R allows you to calculate the probability density function for a normal distribution for a given mean and standard deviation. This guide shows you how to use the **dnorm()** function in R.

#### dnorm() syntax

`dnorm(x, mean = 0, sd = 1)`

#### Arguments

**x**= numeric value to test for probability**mean**= mean of the distribution**sd**= standard deviation of the distribution

The mean and sd arguments are optional. If you do not pass a value to them, they will take the default values as mean = 0 and sd = 1.

### Example 1: dnorm() function with a single value

Suppose we want to calculate the probability density function of 5 with a mean of 10 and a standard deviation of 3.

`x <- dnorm(5, mean=10, sd=3)`

#### Output

`[1] 0.03315905`

### Example 2: dnorm() Function with a vector of values

Say we want to calculate the probability density function of each element inside a vector with a mean of 10 and a standard deviation of 3.

```
#creating a vector
my_data <-c(1,2,3,4,5)
#calculating probability density function of the vector
x <- dnorm(my_data, mean=10, sd=3)
x
```

#### Output

`[1] 0.001477283 0.003798662 0.008740630 0.017996989 0.033159046`