dobtools: dobtools

#' K-Means Cluster
#'
#' From beginning to end
#' @param df A dataframe
#' @param vars Variables from that dataframe to keep, some of which will be scaled and used for clustering.
#' @param to_scale A subset of vars, all numeric, to be scaled and used for clustering.
#' @param n_centers The number of clusters you want.
#' @param algorithm kmeans algo to use
#' @param trace Trace progress?
#' @keywords cluster
#' @export
#' @examples
#'
#' v <- names(iris)
#' t_s <- v[-which(v == "Species" | v == "Petal.Length")]
#'
#' clustered_iris <- do_cluster(iris, vars = v, to_scale = t_s, n_centers = 3)
#' \dontrun{
#' ggplot(data = clustered_iris %>%
#'                   trim_outliers(exclude = c("cluster_assignment", "Species"))) +
#'   geom_text(aes(Sepal.Width_scaled, Sepal.Length_scaled,
#'                 colour = cluster_assignment,
#'                 label = Species)) +
#'   theme_bw()
#' }


do_cluster <- function (df, vars, to_scale, n_centers = 5, algorithm = "Hartigan-Wong",
                        trace = FALSE) {

  df_for_clustering <- df %>% dplyr::select(!!vars) %>% tidyr::drop_na()

  # Scale the ones to be scaled and append _scaled to their names
  df_vars_scale <- df_for_clustering %>% dplyr::select(!!to_scale) %>%
    scale() %>% tibble::as_tibble()
  names(df_vars_scale) <- names(df_vars_scale) %>% stringr::str_c("_scaled")

  # Do the clustering on the scaled data
  clusters_out <- stats::kmeans(x = df_vars_scale, centers = n_centers, trace = trace,
                         algorithm = algorithm)

  # Combine cluster assignment, scaled data, and unscaled rest of data
  clustered_df <- dplyr::bind_cols(
    cluster_assignment = factor(clusters_out$cluster),   # Cluster assignment
    df_vars_scale,
    df_for_clustering
  )

  return(clustered_df)
}