tame (version 0.2.0)

refactor: Refactor Cluster Levels

Description

Refactor the levels of the chosen clusters.

Usage

refactor(object, ..., inheret_parameters = TRUE)

Value

A medic object with relevant clusterings refactored.

Arguments

object

A medic object.

...

<data-masking> Name-value pairs. ... is passed to dplyr::mutate, and therefor inherits its behavior:

The name gives the name of the new clustering in the output. The value can be:

• A vector of length 1, which will be recycled to the correct length.

• A function of another clustering.

When a recording uses the name of an existing clustering, this new clustering will overwrite the existing one.

inheret_parameters

A logical. If TRUE a new clustering overwriting an existing clustering inherits the parameters of the old.

Examples

clust <- medic(complications, id = id, atc = atc, k = 3:4)

# Refactor one clustering
refactor(
  clust,
  `cluster_1_k=4` = dplyr::recode(`cluster_1_k=4`, IV = "III")
)

# Refactor all clusterings
refactor(
  clust,
  dplyr::across(
    dplyr::everything(),
    ~dplyr::recode(., IV = "III")
  )
)