hai_kmeans_user_item_tbl: K-Means User Item Tibble

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

K-Means User Item Tibble

Description

Takes in a data.frame/tibble and transforms it into an aggregated/normalized user-item tibble of proportions. The user will need to input the parameters for the rows/user and the columns/items.

Usage

hai_kmeans_user_item_tbl(.data, .row_input, .col_input, .record_input)

kmeans_user_item_tbl(.data, .row_input, .col_input, .record_input)

Arguments

.data	The data that you want to transform
.row_input	The column that is going to be the row (user)
.col_input	The column that is going to be the column (item)
.record_input	The column that is going to be summed up for the aggregation and normalization process.

Details

This function should be used before using a k-mean model. This is commonly referred to as a user-item matrix because "users" tend to be on the rows and "items" (e.g. orders) on the columns. You must supply a column that can be summed for the aggregation and normalization process to occur.

Value

A aggregated/normalized user item 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_tidy_tbl()

Examples

library(healthyR.data)
library(dplyr)

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()

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

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