## Normalise data

When you need to compare different data with very different value ranges.

## With clusterSim library

If x is the normalised data and y is the old data, we would like to do y = (x-mean(x))/sd(x)

One way is to use the clusterSim library

```
library(clusterSim)
```

Then a variable can be normalised for mean=0, variance=1 using:

```
data.Normalization(data$FRE,type="n1")
```

As a bonus, there are other types of normalisation (other than type="n1"). For more information look at the help file. `?data.Normalization`

.

## Without any library

Alternatively, use:

```
x<-sweep(y, 2, apply(y, 2, mean), "-")
x<-sweep(x, 2, apply(x, 2, sd), "/")
names(x)<-paste("s", names(x), sep="")
```

Note that if any values are 0 the normalisation will generate infinite values, which may need to be dealt with.