Scaling a numeric matrix in R with values 0 to 1 - r

Question

Here is an excerpt of numeric matrix that I have
[1,] 30 -33.129487 3894754.1 -39.701738 -38.356477 -34.220534
[2,] 29 -44.289487 -8217525.9 -44.801738 -47.946477 -41.020534
[3,] 28 -48.439487 -4572815.9 -49.181738 -48.086477 -46.110534
[4,] 27 -48.359487 -2454575.9 -42.031738 -43.706477 -43.900534
[5,] 26 -38.919487 -2157535.9 -47.881738 -43.576477 -46.330534
[6,] 25 -45.069487 -5122485.9 -47.831738 -47.156477 -42.860534
[7,] 24 -46.207487 -2336325.9 -53.131738 -50.576477 -50.410534
[8,] 23 -51.127487 -2637685.9 -43.121738 -47.336477 -47.040534
[9,] 22 -45.645487 3700424.1 -56.151738 -47.396477 -50.720534
[10,] 21 -56.739487 1572594.1 -49.831738 -54.386577 -52.470534
[11,] 20 -46.319487 642214.1 -39.631738 -44.406577 -41.490534
What I want to do now, is to scale the values for each column to have values from 0 to 1.
I tried to accomplish this using the scale() function on my matrix (default parameters), and I got this
[1,] -0.88123100 0.53812440 -1.05963281 -1.031191482 -0.92872324
[2,] -1.17808251 -1.13538649 -1.19575096 -1.289013031 -1.11327085
[3,] -1.28847084 -0.63180980 -1.31265244 -1.292776849 -1.25141017
[4,] -1.28634287 -0.33914007 -1.12182012 -1.175023107 -1.19143220
[5,] -1.03524267 -0.29809911 -1.27795565 -1.171528133 -1.25738083
[6,] -1.19883019 -0.70775576 -1.27662116 -1.267774342 -1.16320727
[7,] -1.22910054 -0.32280189 -1.41807728 -1.359719044 -1.36810940
[8,] -1.35997055 -0.36443973 -1.15091204 -1.272613537 -1.27664977
[9,] -1.21415156 0.51127451 -1.49868058 -1.274226602 -1.37652260
[10,] -1.50924749 0.21727976 -1.33000083 -1.462151358 -1.42401647
[11,] -1.23207969 0.08873245 -1.05776452 -1.193844887 -1.12602635
Which is already close to what I want, but values from 0:1 were even better. I read the help manual of scale(), but I really don't understand how I would do that.

Answer 1

Try the following, which seems simple enough:
## Data to make a minimal reproducible example
m <- matrix(rnorm(9), ncol=3)
## Rescale each column to range between 0 and 1
apply(m, MARGIN = 2, FUN = function(X) (X - min(X))/diff(range(X)))
# [,1] [,2] [,3]
# [1,] 0.0000000 0.0000000 0.5220198
# [2,] 0.6239273 1.0000000 0.0000000
# [3,] 1.0000000 0.9253893 1.0000000

Answer 2

And if you were still to use scale:
maxs <- apply(a, 2, max)
mins <- apply(a, 2, min)
scale(a, center = mins, scale = maxs - mins)

Answer 3

Install the clusterSim package and run the following command:
normX = data.Normalization(x,type="n4");

Answer 4

scales package has a function called rescale:
set.seed(2020)
x <- runif(5, 100, 150)
scales::rescale(x)
#1.0000000 0.5053362 0.9443995 0.6671695 0.0000000

Answer 5

Not the prettiest but this just got the job done, since I needed to do this in a dataframe.
column_zero_one_range_scale <- function(
input_df,
columns_to_scale #columns in input_df to scale, must be numeric
){
input_df_replace <- input_df
columncount <- length(columns_to_scale)
for(i in 1:columncount){
columnnum <- columns_to_scale[i]
if(class(input_df[,columnnum]) !='numeric' & class(input_df[,columnnum])!='integer')
{print(paste('Column name ',colnames(input_df)[columnnum],' not an integer or numeric, will skip',sep='')) }
if(class(input_df[,columnnum]) %in% c('numeric','integer'))
{
vec <- input_df[,columnnum]
rangevec <- max(vec,na.rm=T)-min(vec,na.rm=T)
vec1 <- vec - min(vec,na.rm=T)
vec2 <- vec1/rangevec
}
input_df_replace[,columnnum] <- vec2
colnames(input_df_replace)[columnnum] <- paste(colnames(input_df)[columnnum],'_scaled')
}
return(input_df_replace)
}