ea_normalise

A function to normalise, or re-scale, a numerical vector such as a condition variable to become a value between 0 and 1, bound by an upper and a lower reference level.

Usage

ea_normalise(
  data = NULL,
  vector = NULL,
  upper_reference_level = NULL,
  lower_reference_level = 0,
  scaling_function = "linear",
  reverse = FALSE,
  break_point = NULL,
  optimum = NULL,
  plot = FALSE
)

Arguments

data

Data set of class sf

vector

Numerical vector inside data which should be normalised

upper_reference_level

The upper reference level against which to normalise the vector. Can be a single number or a vector of length equal to vector. If the indicator direction is negative, upper_reference_level should still represent the highest variable limit, i.e. the worst condition in that case.

lower_reference_level

The lower reference level against which to normalise the vector. Defaults to 0. Can be a single number or a vector of length equal to vector. If the indicator direction is negative, lower_reference_level should still represent the lowest variable limit, i.e. the best condition in that case.

scaling_function

one of c("linear", "sigmoid", "exponential convex", "exponential concave")

reverse

Logical. Is the indicator direction negative (i.e. a high variable value should give a low indicator value)

break_point

Numerical vector or single value indicating the value of the variable which should be scaled to 0.6 in the indicator.

optimum

Numerical vector or single value indicating the upper reference value for a two-sided indicator.

plot

Logical. Wheter to return a plot comparing normalised and raw values, or to return the normalised values (default).

Value

If plot = TRUE return a ggplot comparing normalised and raw values. If plot = False return the numerical vector, a normalised version of vetcor

Examples

data("ex_polygons")
ea_normalise(data = ex_polygons,
            vector = "condition_variable_2",
            upper_reference_level = 8,
            plot=TRUE)