**scale()** function in R allows us to **standardize** a vector or matrix. Standardization is the process of scaling a vector or matrix so that it has a **mean of 0 **and a **standard deviation of 1**. This is useful for many machine learning algorithms, such as regression and support vector machines, which require that data be normalized.

#### Syntax

`scale(x, center=TRUE, scale=TRUE)`

#### Arguments

**x**= a vector or matrix to be scaled or standardized**center**= a logical value that tells the function whether to center the data before scaling. The default value is**TRUE**.**scale**= a logical value that tells the function whether to scale the data. The default value is**TRUE**.

### Example

Letâ€™s create a vector of data, then standardize it using the scale() function.

```
#creating a vector
x = c(1,2,3,4,5)
#scaling the vector x
scale(x)
```

#### Output

```
[1,] -1.2649111
[2,] -0.6324555
[3,] 0.0000000
[4,] 0.6324555
[5,] 1.2649111
attr(,"scaled:center")
[1] 3
attr(,"scaled:scale")
[1] 1.581139
```

The output shows the standardized values of the data. To confirm if the data is standardized or not, calculate the mean and standard deviation of the output:

```
mean(c(-1.2649111,-0.6324555,0.0000000,0.6324555,1.2649111))
sd(c(-1.2649111,-0.6324555,0.0000000,0.6324555,1.2649111))
```

#### Output

```
[1] 0
[1] 1
```

As we can see, the mean of the data is 0, and the standard deviation is 1.