UMAP and K-Means Cluster Visualization

Create a UMAP Projection plot.

Usage

hai_umap_plot(.data, .point_size = 2, .label = TRUE)

umap_plt(.data, .point_size = 2, .label = TRUE)

Arguments

.data: The data from the umap_list() function.
.point_size: The desired size for the points of the plot.
.label: Should ggrepel::geom_label_repel() be used to display cluster user labels.

Value

A ggplot2 UMAP Projection with clusters represented by colors.

Details

This takes in umap_kmeans_cluster_results_tbl from the umap_list() function output.

Author

Steven P. Sanderson II, MPH

Examples

library(healthyR.data)
library(dplyr)
library(broom)
library(ggplot2)

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
)

kmm_tbl <- hai_kmeans_mapped_tbl(uit_tbl)

ump_lst <- hai_umap_list(.data = uit_tbl, kmm_tbl, 3)
#> New names:
#> • `` -> `...1`
#> • `` -> `...2`
#> Joining with `by = join_by(service_line)`

hai_umap_plot(.data = ump_lst, .point_size = 3)