Hello, readers! In this article, we will be having a look at 3 Easy Ways to **Normalize data in R** programming.

So, let us begin!! ðŸ™‚

## What is Normalization?

**Feature Scaling** is an essential step prior to modeling while solving prediction problems in Data Science. Machine Learning algorithms work well with the data that belongs to a smaller and standard scale.

This is when **Normalization **comes into picture. Normalization techniques enables us to reduce the scale of the variables and thus it affects the statistical distribution of the data in a positive manner.

In the subsequent sections, we will be having a look at some of the techniques to perform Normalization on the data values.

### 1. Normalize data in R – Log Transformation

In the real world scenarios, to work with the data, we often come across situations wherein we find the datasets that are unevenly distributed. That is, they are either skewed or do not follow normalization of values.

In such cases, the easiest way to get values into proper scale is to scale them through the individual log values.

In the below example, we have scaled the huge data values present in the data frame ‘data’ using log() function from the R documentation.

**Example:**

```
rm(list = ls())
data = c(1200,34567,3456,12,3456,0985,1211)
summary(data)
log_scale = log(as.data.frame(data))
```

**Output:**

```
data
1 7.090077
2 10.450655
3 8.147867
4 2.484907
5 8.147867
6 6.892642
7 7.099202
```

### 2. Normalize Data with Min-Max Scaling in R

Another efficient way of Normalizing values is through the Min-Max Scaling method.

With **Min-Max Scaling**, we scale the data values between a range of 0 to 1 only. Due to this, the effect of outliers on the data values suppresses to a certain extent. Moreover, it helps us have a smaller value of the standard deviation of the data scale.

In the below example, we have used ‘**caret**‘ library to pre-process and scale the data. The `preProcess() function`

enables us to scale the value to a range of 0 to 1 using `method = c('range')`

as an argument. The `predict() `

method applies the actions of the preProcess() function on the entire data frame as shown below.

**Example:**

```
rm(list = ls())
data = c(1200,34567,3456,12,3456,0985,1211)
summary(data)
library(caret)
process <- preProcess(as.data.frame(data), method=c("range"))
norm_scale <- predict(process, as.data.frame(data))
```

**Output:**

```
data
1 0.03437997
2 1.00000000
3 0.09966720
4 0.00000000
5 0.09966720
6 0.02815801
7 0.03469831
```

### 3. Normalize Data with Standard Scaling in R

In Standard scaling, also known as Standardization of values, we scale the data values such that the overall statistical summary of every variable has a **mean value of zero** and an **unit variance** value.

The `scale() function`

enables us to apply standardization on the data values as it centers and scales the

```
rm(list = ls())
data = c(1200,34567,3456,12,3456,0985,1211)
summary(data)
scale_data <- as.data.frame(scale(data))
```

**Output:**

As seen below, the mean value of the data frame before scaling is 6412. Whereas, after performing scaling of values, the mean has reduced to Zero.

```
Min. 1st Qu. Median Mean 3rd Qu. Max.
12 1092 1211 6412 3456 34567
V1
1 -0.4175944
2 2.2556070
3 -0.2368546
4 -0.5127711
5 -0.2368546
6 -0.4348191
7 -0.4167131
V1
Min. :-0.5128
1st Qu.:-0.4262
Median :-0.4167
Mean : 0.0000
3rd Qu.:-0.2369
Max. : 2.2556
```

## Conclusion

By this, we have come to the end of this topic. Feel free to comment below, in case you come across any question. For more such posts related to R programming, stay tuned with us!

Till then, Happy Learning!! ðŸ™‚