Find what values were changed to after normalization - r

Question

How can I see the original values post normalization? Or change them in the final output?
I want to change my final output back to there original values. Or at least close to it considering I aggregate and take the mean.
I have a dataset that has 10 columns and 5,000 rows. After cleaning up the data and selecting which columns and rows I want, I run a normalization code.
Then I run a kmeans and get my output. How can I see what the values were changed to after normalization? Like, if I have Region 1, 2, 3, 4, and 5. And post normalization it changes to 0.00, 0.25, 0.5, 0.75, and 1. is there a way to change them back to the original in the kmeans output?
I want to change my final output back to there original values. Or at least close to it considering I aggregate and take the mean.
normalize = function(X) {
return(abs((X-min(X)))/(max(X)-min(X)))
}
df_age_norm = as.data.frame(lapply(df_age,normalize))
clusters = kmeans(df_age_norm, 9)[['cluster']]
df_age_norm$clusters = clusters
df_age_norm =
aggregate(df_age_norm[,1:4],list(df_age_norm$clusters),FUN
= mean)
I want to change my final output back to there original values. Or at least close to it considering I aggregate and take the mean.
Head of dataset before normalization
Age HHIncome Region MaritalStatus group
18 11000 5 0 1
18 11000 5 1 1
18 12000 2 0 1
18 12000 4 0 1
18 13000 1 0 1
Head of dataset after normalization
Age HHIncome Region MaritalStatus group
0 0.001879699 1.00 0 0
0 0.001879699 1.00 1 0
0 0.002819549 0.25 0 0
0 0.002819549 0.75 0 0
0 0.003759398 0.00 0 0

Answer 1

This solution is inspired in base R function scale, that centers and scales the vector by subtracting the mean value and dividing by the standard deviation of the vector x. These two values, mean(x) and sd(x) are returned as attributes.
x <- -4:5
y <- scale(x)
attributes(y)
#$dim
#[1] 10 1
#
#$`scaled:center`
#[1] 0.5
#
#$`scaled:scale`
#[1] 3.02765
I have, therefore, rewritten function normalize to also set and return min(x) and max(x) as attributes. They will be used to later denormalize.
normalize <- function(X, na.rm = FALSE) {
if(na.rm) X <- X[!is.na(X)]
Min <- min(X)
Max <- max(X)
Y <- X - Min
if(Min != Max) Y <- Y/(Max - Min)
attr(Y, "scaled:min") <- Min
attr(Y, "scaled:max") <- Max
Y
}
denormalize <- function(X){
Min <- attr(X, "scaled:min")
Max <- attr(X, "scaled:max")
attr(X, "scaled:min") <- NULL
attr(X, "scaled:max") <- NULL
Y <- if(Min != Max) X*(Max - Min) else X
Y <- Y + Min
Y
}
df_age_norm <- as.data.frame(lapply(df_age, normalize))
df_age_2 <- as.data.frame(lapply(df_age_norm, denormalize))
df_age_2
# Age HHIncome Region MaritalStatus group
#1 18 11000 5 0 1
#2 18 11000 5 1 1
#3 18 12000 2 0 1
#4 18 12000 4 0 1
#5 18 13000 1 0 1
Data.
df_age <- read.table(text = "
Age HHIncome Region MaritalStatus group
18 11000 5 0 1
18 11000 5 1 1
18 12000 2 0 1
18 12000 4 0 1
18 13000 1 0 1
", header = TRUE)