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UMAP and K-Means Cluster Visualization

Source: R/umap_list.R
umap_plt.Rd

Create a UMAP Projection plot.

Usage

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.

See also

Author

Steven P. Sanderson II, MPH

Examples

library(healthyR.data)
library(healthyR)
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 <- kmeans_user_item_tbl(
   .data           = data_tbl
   , .row_input    = service_line
   , .col_input    =  payer_grouping
   , .record_input = record
 )

kmm_tbl <- kmeans_mapped_tbl(uit_tbl)

ump_lst <- umap_list(.data = uit_tbl, kmm_tbl, 3)
#> Joining, by = "service_line"

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