Non-standard evaluation

[m-Py]

Maybe, at some point, anticlustering() should also be callable similarly to the following way:

anticlustering(
  iris,
  numeric_vars = c(Sepal.Length, Sepal.Width),
  categorical_vars = Species,
  K = 3
)

That is, the first argument is a generic data argument that includes the entire data frame that users work with and then specify only the column names to select numeric and categorical variables. It would probably just require to add the arguments numeric_vars and categorical_vars to anticlustering(), test if they exist, and then use non-standard-evaluation to extract the relevant data from the first argument. This would also be better integrated into a tidyverse workflow. All of this does not make sense if the data input is a distance matrix, which still has to be supported.

Currently, we would have to use the following, which may be less appealing to users:

anticlustering(
  iris[, c("Sepal.Length", "Sepal.Width")],
  categories = iris$Species,
  K = 3
)

[m-Py]

Given this has no priority whatsoever, I will close for now.