`hai_umap_plot()`

[spsanderson]

Function:

#' UMAP and K-Means Cluster Visualization
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @description Create a UMAP Projection plot.
#'
#' @seealso
#' *  \url{https://cran.r-project.org/package=uwot} (CRAN)
#' *  \url{https://github.com/jlmelville/uwot} (GitHub)
#' *  \url{https://github.com/jlmelville/uwot} (arXiv paper)
#'
#' @details This takes in `umap_kmeans_cluster_results_tbl` from the [umap_list()]
#' function output.
#'
#' @param .data The data from the [umap_list()] function.
#' @param .point_size The desired size for the points of the plot.
#' @param .label Should [ggrepel::geom_label_repel()] be used to display cluster
#' user labels.
#'
#' @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)
#'
#' umap_plt(.data = ump_lst, .point_size = 3)
#'
#' @return A ggplot2 UMAP Projection with clusters represented by colors.
#'
#' @export
#'

umap_plt <- function(.data, .point_size = 2, .label = TRUE) {

    # * Checks ----
    if(!is.list(.data)){
        stop(call. = FALSE,"(.data) is not a list")
    }

    # * Data ----
    ump_lst   <- .data
    ump_tbl   <- ump_lst$umap_kmeans_cluster_results_tbl
    optimal_k <- max(ump_lst$kmeans_obj$cluster)

    umap_plt <- ump_tbl %>%
        ggplot2::ggplot(
            mapping = ggplot2::aes(
                x = x
                , y = y
            )
        ) +
        ggplot2::geom_point(size = .point_size, ggplot2::aes(col = .cluster)) +
        ggplot2::theme_minimal()
        ggplot2::labs(
            subtitle = "UMAP 2D Projection with K-Means Cluster Assignment"
            , caption = stringr::str_c(
                "Conclusion:"
                , optimal_k
                , "Clusters Identified"
                , sep = " "
            )
            , color = "Cluster"
        )

    if(.label){
        ump_label <- ump_lst$umap_kmeans_cluster_results_tbl[[3]]

        umap_plt <- umap_plt +
            ggrepel::geom_label_repel(
                mapping = ggplot2::aes(
                    label = ump_label
                )
            )
    }

    # * Return ----
    print(umap_plt)

}

Example:

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