CODEWITHSUNDEEP
rnormalization
Jael
Jael

Reputation: 369

R- Normalizing certain columns from 0 to 1 with values equal to 0

I recently started with are and I would like to scale my data matrix. I found a way to do that here Scale a series between two points 

x <- data.frame(step = c(1,2,3,4,5,6,7,8,9,10))
normalized <- (x-min(x))/(max(x)-min(x))

As my data consists of several columns whereof I only want to normalize certain columns using a function was suggested.

normalized <- function(x) (x- min(x))/(max(x) - min(x))
x[] <- lapply(x, normalized)

Additionally, I realized that some of the data points in my dataset equal 0 such that the presented formula doesn't work anymore. I added an extension suggested here: scaling r dataframe to 0-1 with NA values

normalized <- function(x, ...) {(x - min(x, ...)) / (max(x, ...) - min(x, ...))}

But I don't understand how I have to code it. For example, I would like to have column 4,5,6 and 10 normalized but I would like to have the remaining columns as they were in the data set? I tried it for column 4:

data <- lapply(data[,4],normalized,na.rm= TRUE)

But it did not work (instead of a data frame a list resulted :-(...), does anybody knows how I could fix it?

Thanks a lot already in advance!

Upvotes: 5

Views: 14186

Answers (1)

Terru_theTerror
Terru_theTerror

Reputation: 5017

Try this, I have modified normalized function considering NA values:

db<-data.frame(a=c(22,33,28,51,25,39,54,NA,50,66),
               b=c(22,33,NA,51,25,39,54,NA,50,66))

normalized<-function(y) {

  x<-y[!is.na(y)]

  x<-(x - min(x)) / (max(x) - min(x))

  y[!is.na(y)]<-x

  return(y)
  }

 apply(db[,c(1,2)],2,normalized)

Your output:

               a          b
 [1,] 0.00000000 0.00000000
 [2,] 0.25000000 0.25000000
 [3,] 0.13636364         NA
 [4,] 0.65909091 0.65909091
 [5,] 0.06818182 0.06818182
 [6,] 0.38636364 0.38636364
 [7,] 0.72727273 0.72727273
 [8,]         NA         NA
 [9,] 0.63636364 0.63636364
[10,] 1.00000000 1.00000000

Upvotes: 6

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