How to Normalize data in R [3 easy methods]

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

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

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