Distance computations play a big role in many data analytics techniques. We know that variables with higher values tend to dominate distance computations and you may want to rescale the values to be in the range of 0 - 1.

Install the scales package and read the data-conversion.csv file from the book's data for this chapter into your R environment's working directory:

> install.packages("scales")
> library(scales)
> students <- read.csv("data-conversion.csv")

To rescale the Income variable to the range [0,1], use the following code snippet:

> students$Income.rescaled <- rescale(students$Income) 

By default, the rescale() function makes the lowest value(s) zero and the highest value(s) one. It rescales all the other values proportionately. The following two expressions provide identical results:

> rescale(students$Income) 
> (students$Income - min(students$Income)) /     (max(students$Income) - min(students$Income)) 

To rescale a different range than [0,1], use the to argument. The following snippet rescales students$Income to the range (0,100):

> rescale(students$Income, to = c(1, 100)) 

When using distance-based techniques, you may need to rescale several variables. You may find it tedious to scale one variable at a time.

Use the following function to rescale variables:

rescale.many <- function(dat, column.nos) { 
  nms <- names(dat) 
  for(col in column.nos) { 
    name <- paste(nms[col],".rescaled", sep = "") 
    dat[name] <- rescale(dat[,col]) 
  } 
  cat(paste("Rescaled ", length(column.nos),      " variable(s)n")) 
  dat 
} 

With the preceding function defined, we can do the following to rescale the first and fourth variables in the data frame:

> rescale.many(students, c(1,4)) 

• The Normalizing or standardizing data in a data frame recipe in this chapter.