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

**What is Variance**

The Variance is determined as an average of the squared differences from the mean. To calculate the variance, follow these steps:

- 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.

**How to Calculate Variance in R**

To calculate the variance in R, use the var() function. The var() is a built-in function that computes the sample variance of a vector. It is the measure of how much value is away from the mean value.

**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

**Compute the variance of a numeric vector**

To create a numeric vector, use the c() function and pass the multiple numeric arguments. Then pass 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 have to multiply this number by (N–1)/N.

**Calculate the Variance in R of the dataset**

We will use the inbuilt dataset iris in this example. To use the inbuilt R dataset, you need to 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 difference between sample variance and population variance is the correction of – 1. This correction does not really matter for large sample sizes. However, it does really matter when the dataset is small sample sizes.

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 R function computes the sample variance of a numeric input vector. The var() method is used to compute the sample variance, not the population variance. We have already seen how to calculate population variance.

**See Also**

Krunal Lathiya is an Information Technology Engineer by education and web developer by profession. He has worked with many back-end platforms, including Node.js, PHP, and Python. In addition, Krunal has excellent knowledge of Data Science and Machine Learning, and he is an expert in R Language. Krunal has written many programming blogs, which showcases his vast expertise in this field.