Day #25 - Data Transformations in R

This post is on performing Data Transformations in R. This would be part of feature modelling. Advanced PCA will be done during later stages

	#Data for column attributes
	Y0 = 1:20 + rnorm(100,sd=3)
	Y1 = 1:20 + rnorm(100,sd=2)
	Y2 = 1:20 + rnorm(100,sd=2)
	Y3 = 1:20 + rnorm(100,sd=2)
	Y4 = 1:20 + rnorm(100,sd=2)
	Y5 = 1:20 + rnorm(100,sd=3)
	Y6 = 1:10 + rnorm(100,sd=3)
	Y7 = 1:10 + rnorm(100,sd=2)
	Y8 = 1:20 + rnorm(100,sd=1)
	#Create Data Frame
	data <- data.frame(Y0,Y1,Y2,Y3,Y4,Y5,Y6,Y7,Y8)
	#sample few records
	head(data)
	#Ways to normalize Data
	data$Y0 = (x-min(data$Y0))/(max(data$Y0)-min(data$Y0))
	#Raises Y to the power of 3
	data$Y1 <- (data$Y1)^3
	#Takes the ninth root of Y
	data$Y2 <- (data$Y2)^(1/9)
	#Takes the natural logarithm (ln) of Y
	data$Y3 <- log(data$Y3)
	#Takes the base-10 logarithm of Y
	data$Y4 <- log10(data$Y4)
	#Raises the constant e to the power of Y
	data$Y5 <- exp(data$Y5)
	#Finds the absolute value of Y
	data$Y6 <- abs(data$Y6)
	#Calculates the sine of Y
	data$Y7 <- sin(data$Y7)
	#Calculates the inverse sine (arcsine) of Y
	data$Y8 <- asin(data$Y8)
	#check modified data
	head(data)

view raw DataTransformation.R hosted with ❤ by GitHub

Data Normalization in Python

Happy Learning!!!