How to Normalize Data in R?

In this article, we will discuss how to normalize data in the R programming language.

Normalizing Data is the approach to scale the data into a fixed range usually 0 to 1 so that it reduces the scale of the variables.

Method 1: Normalize data with log transformation in base R

In this approach to normalize the data with its log transformation, the user needs to call the log() which is an inbuilt function and pass the data frame as its parameter to transform the given data to its log and the resulting data will then be transformed to the scale.

log() function is used to compute logarithms, by default natural logarithm.

Syntax:

log(x)

Parameters:

• x: a numeric or complex vector.

Example: Normalize data 

R

# Create data
gfg < - c(244, 753, 596, 645, 874, 141,
          639, 465, 999, 654)

# normalizing data
gfg < -log(gfg)
gfg

Output:

  [1] 5.497168 6.624065 6.390241 6.469250 6.773080 4.948760 6.459904 6.142037 6.906755 6.483107

Method 2: Normalize Data with Standard Scaling in R

In this method to normalize the data, the user simply needs to call the scale() function which is an inbuilt function, and pass the data which is needed to be scaled, and further this will be resulting in normalized data from range -1 to 1 in the R programming language.

Scale() is a generic function whose default method centers and/or scales the columns of a numeric matrix.

Syntax:

scale(x)

Parameters:

• x: Data

Example: Normalize data

R

# Create data
gfg <- c(244,753,596,645,874,141,639,465,999,654)

# normalizing data
gfg <- as.data.frame(scale(gfg))
gfg

Output:

            V1
1  -1.36039519
2   0.57921588
3  -0.01905315
4   0.16766775
5   1.04030220
6  -1.75289016
7   0.14480397
8  -0.51824578
9   1.51663105
10  0.20196343

Method 3: Normalize Data using Min-Max Scaling

In this method to normalize, the user has to first install and import the caret package in the R working console, and then the user needs to call the preProcess() function with the method passed as the range as its parameters, and then the user calls the predict() function to get the final normalize data which will lead to the normalization of the given data to the scale from 0 to 1 in the R programming language.

perProcess() function is used for transformation can be estimated from the training data and applied to any data set with the same variables.

Syntax:

preProcess(x,method)

Parameters:

• x: Data
• method: a character vector specifying the type of processing.

Example: Normalize data

R

library(caret)

# Create data
gfg <- c(244,753,596,645,874,141,639,465,999,654)

# normalizing data
ss <- preProcess(as.data.frame(gfg), method=c("range"))

gfg <- predict(ss, as.data.frame(gfg))
gfg

Output:

         gfg
1  0.1200466
2  0.7132867
3  0.5303030
4  0.5874126
5  0.8543124
6  0.0000000
7  0.5804196
8  0.3776224
9  1.0000000
10 0.5979021