The **var()** is a built-in **R** function that accepts a **vector** or **matrix** and computes the **sample** **variance** of a **vector** or **matrix**. The **syntax** of the **var** **function** is **var(x)** where **x** is a **vector** or **matrix **and returns the **sample** **variance**.

To **calculate** the **variance** in **R**, use the **var() **function. The var() function measures how much the value is away from the mean value.

The **variance** is an average of the squared differences from the mean. It is a numerical value that shows how widely the individual figures in a data set distribute themselves about the mean.

**Syntax**

`var(x, y=NULL, na.rm=FALSE, use)`

**Parameters**

**x,y**

It is a complex vector or matrix.

**na.rm**

Boolean with default **FALSE** meaning to leave NA values present and **TRUE** meaning to remove them.

**use**

Ignored

**Example 1: Calculating the variance of a numeric vector in R**

To create a numeric vector, use the **c()** function and pass the multiple numeric arguments. Then gives that numeric vector the var() function, which returns the variance of a vector.

```
weights <- c(60, 55, 50, 65, 59)
var(weights)
```

**Output**

`[1] 31.7`

The var() function calculates the estimated variance (with N–1 in the denominator).

To calculate that first variance with N in the denominator, you must multiply this number by (N–1)/N.

**Example 2: Calculating the Variance of the dataset in R**

We will use a built-in dataset **iris** in this example. To use the built-in dataset, write data(dataset name) at the start of your file. Then, you can use that dataset.

`data(iris)`

We will find the variance of the **petal.length** of the iris dataset.

```
data(iris)
ln <- iris$Petal.Length
var(ln)
```

**Output**

`[1] 3.116278`

The variance of the petal.length is 3.116278.

**Sample Variance vs. Population Variance**

The **main** **difference** between a **sample** and **population** **variance** relates to a variance calculation. **Population variance** refers to the value of **variance** calculated from population data, and **sample** **variance** is the **variance** calculated from **sample** **data**.

The correction does not matter for large sample sizes. However, it does matter when the dataset is small sample sizes. When the variance is calculated from population data, n equals the number of elements.

To calculate the population variance, use the following function.

`mean((x - mean(x)) ^ 2)`

Let’s see how to calculate population variance in R.

```
population_variance <- function(rv) {
mean((rv - mean(rv)) ^ 2)
}
weights <- c(60, 55, 50, 65, 59)
population_variance(weights)
```

**Output**

`[1] 25.36`

**Conclusion**

The var() function is used to compute the variance of a numeric vector or matrix. It takes one or two arguments – the first argument is the numeric vector or matrix for which the variance is to be computed.

The **variance** is a standard of variability. It is calculated by taking the average squared deviations from the mean. It shows you the degree of spread in your dataset. The more separated the data, the larger the variance is about the mean.

To **calculate** the **variance** in **mathematics**,

- First, calculate the mean, which is an average of the numbers.

- Second, for each number: subtract the Mean and square the result (the squared difference).

- In the last step, check out the average of those squared differences.

**Further reading**

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.