| hai_kmeans_user_item_tbl | R Documentation |
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.
hai_kmeans_user_item_tbl(.data, .row_input, .col_input, .record_input) kmeans_user_item_tbl(.data, .row_input, .col_input, .record_input)
.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. |
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.
A aggregated/normalized user item tibble
Steven P. Sanderson II, MPH
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()
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 )