Quick normalisation of a data frame

This is a wrapper function for Normalise(), which offers a simpler syntax but less flexibility. It normalises a data frame using a specified function f_n which is used to normalise each column, with additional function arguments passed by f_n_para. By default, f_n = "n_minmax" and f_n_para is set so that the columns of x are normalised using the min-max method, between 0 and 100.

Usage

# S3 method for data.frame
qNormalise(x, f_n = "n_minmax", f_n_para = NULL, directions = NULL, ...)

Arguments

x

A numeric data frame

f_n

Name of a normalisation function (as a string) to apply to each column of x. Default "n_minmax".

f_n_para

Any further arguments to pass to f_n, as a named list. If f_n = "n_minmax", this defaults to list(l_u = c(0,100)) (scale between 0 and 100).

directions

An optional data frame containing the following columns:

• iCode The indicator code, corresponding to the column names of the data frame

• Direction numeric vector with entries either -1 or 1 If directions is not specified, the directions will all be assigned as 1. Non-numeric columns do not need to have directions assigned.

...

arguments passed to or from other methods.

Value

A normalised data frame

Details

Essentially, this function is similar to Normalise() but brings parameters into the function arguments rather than being wrapped in a list. It also does not allow individual normalisation.

See Normalise() documentation for more details, and vignette("normalise").

Examples

# some made up data
X <- data.frame(uCode = letters[1:10],
                a = runif(10),
                b = runif(10)*100)
# normalise (defaults to min-max)
qNormalise(X)
#>    uCode          a          b
#> 1      a  72.674546   0.000000
#> 2      b  54.294419 100.000000
#> 3      c  24.795524  63.509916
#> 4      d  26.664329  96.224015
#> 5      e   5.394857  36.498074
#> 6      f  60.895229  78.206968
#> 7      g 100.000000   3.645011
#> 8      h   0.000000   9.413429
#> 9      i  89.699042  57.508121
#> 10     j  91.257956  59.682932