Quick normalisation of a coin — qNormalise.coin • COINr

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

Usage

# S3 method for class 'coin'
qNormalise(
  x,
  dset,
  f_n = "n_minmax",
  f_n_para = list(l_u = c(0, 100)),
  directions = NULL,
  ...
)

Arguments

x

A coin

dset

Name of data set to normalise

f_n

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

f_n_para

Any further arguments to pass to f_n, as a named list.

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 be taken from the iMeta table in the coin, if available.

...

arguments passed to or from other methods.

Value

An updated coin with normalised data set.

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

# build example coin
coin <- build_example_coin(up_to = "new_coin", quietly = TRUE)

# normalise raw data set using min max, but change to scale 1-10
coin <- qNormalise(coin, dset = "Raw", f_n = "n_minmax",
                   f_n_para = list(l_u = c(1,10)))
#> Written data set to .$Data$Normalised