hai_kmeans_tidy_tbl: K-Means Object Tidy Functions

In healthyR.ai: The Machine Learning and AI Modeling Companion to 'healthyR'

K-Means Object Tidy Functions

Description

K-Means tidy functions

Usage

hai_kmeans_tidy_tbl(.kmeans_obj, .data, .tidy_type = "tidy")

kmeans_tidy_tbl(.kmeans_obj, .data, .tidy_type = "tidy")

Arguments

.kmeans_obj	A stats::kmeans() object
.data	The user item tibble created from hai_kmeans_user_item_tbl()
.tidy_type	"tidy","glance", or "augment"

Details

Takes in a k-means object and its associated user item tibble and then returns one of the items asked for. Either: broom::tidy(), broom::glance() or broom::augment(). The function defaults to broom::tidy().

Value

A tibble

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Kmeans: hai_kmeans_automl_predict(), hai_kmeans_automl(), hai_kmeans_mapped_tbl(), hai_kmeans_obj(), hai_kmeans_scree_data_tbl(), hai_kmeans_scree_plt(), hai_kmeans_user_item_tbl()

Examples

library(healthyR.data)
library(dplyr)
library(broom)

data_tbl <- healthyR_data %>%
  filter(ip_op_flag == "I") %>%
  filter(payer_grouping != "Medicare B") %>%
  filter(payer_grouping != "?") %>%
  select(service_line, payer_grouping) %>%
  mutate(record = 1) %>%
  as_tibble()

uit_tbl <- hai_kmeans_user_item_tbl(
  .data = data_tbl,
  .row_input = service_line,
  .col_input = payer_grouping,
  .record_input = record
)

km_obj <- hai_kmeans_obj(uit_tbl)

hai_kmeans_tidy_tbl(
  .kmeans_obj = km_obj,
  .data = uit_tbl,
  .tidy_type = "augment"
)

hai_kmeans_tidy_tbl(
  .kmeans_obj = km_obj,
  .data = uit_tbl,
  .tidy_type = "glance"
)

hai_kmeans_tidy_tbl(
  .kmeans_obj = km_obj,
  .data = uit_tbl,
  .tidy_type = "tidy"
) %>%
  glimpse()

healthyR.ai documentation built on April 3, 2023, 5:24 p.m.