R/plotCells.R

In muHVT: Constructing Hierarchical Voronoi Tessellations and Overlay Heatmap for Data Analysis

#' @name plotCells
#' @title Plot the identified outlier cell(s) in the voronoi tessellations map.
#' 
#' Plotting function to construct hierarchical voronoi tessellations and highlight the cells using the 
#' compressed HVT map.
#'
#' @param plot.cells Vector. A vector indicating the cells to be highlighted in the map
#' @param hvt.map List. A list containing the output of \code{HVT} function
#' which has the details of the tessellations to be plotted.
#' @param line.width Numeric Vector. A vector indicating the line widths of the
#' tessellation boundaries for each level.
#' @param color.vec Vector. A vector indicating the colors of the boundaries of
#' the tessellations at each level.
#' @param pch1 Numeric. Symbol type of the centroids of the tessellations
#' (parent levels). Refer \code{\link{points}}. (default = 21)
#' @param centroid.size Numeric. Size of centroids of first level
#' tessellations. (default = 3)
#' @param title String. Set a title for the plot. (default = NULL)
#' @param maxDepth Numeric. An integer indicating the number of levels. (default = NULL)
#' @author Shantanu Vaidya <shantanu.vaidya@@mu-sigma.com>
#' @seealso \code{\link{HVT}} \cr \code{\link{hvtHmap}}
#' @keywords hplot
#' @importFrom magrittr %>%
#' @import ggplot2
#' 
#' @export plotCells


# library(ggplot2)
plotCells <-
  function(plot.cells,
           hvt.map,
           line.width = c(0.6),
           color.vec = c("#141B41"),
           pch1 = 21,
           centroid.size = 0.5,
           title = NULL,
           maxDepth = 1) {
    # browser()
    
    hvt_list <- hvt.map
    outlier_cell_k = plot.cells
    
    maxDepth = min(maxDepth,max(hvt_list[[3]][["summary"]] %>% stats::na.omit() %>% dplyr::select("Segment.Level")))
    
    min_x = 1e9
    min_y = 1e9
    max_x = -1e9
    max_y = -1e9
    depthVal = c()
    clusterVal = c()
    childVal = c()
    value = c()
    x_pos = c()
    y_pos = c()
    x_cor = c()
    y_cor = c()
    depthPos = c()
    clusterPos = c()
    childPos = c()
    levelCluster = c()
    for (clusterNo in 1:length(hvt_list[[2]][[1]][[1]])) {
      bp_x = hvt_list[[2]][[1]][[1]][[clusterNo]][["x"]]
      bp_y = hvt_list[[2]][[1]][[1]][[clusterNo]][["y"]]
      
      
      if (min(bp_x) < min_x)
        min_x = min(bp_x)
      if (max(bp_x) > max_x)
        max_x = max(bp_x)
      if (min(bp_y) < min_y)
        min_y = min(bp_y)
      if (max(bp_y) > max_y)
        max_y = max(bp_y)
      
    }
    
    for (depth in 1:maxDepth) {
      for (clusterNo in 1:length(hvt_list[[2]][[depth]])) {
        for (childNo in 1:length(hvt_list[[2]][[depth]][[clusterNo]])) {
          current_cluster = hvt_list[[2]][[depth]][[clusterNo]][[childNo]]
          
          x = as.numeric(current_cluster[["x"]])
          y = as.numeric(current_cluster[["y"]])
          x_cor = c(x_cor, as.numeric(current_cluster[["pt"]][["x"]]))
          y_cor = c(y_cor, as.numeric(current_cluster[["pt"]][["y"]]))
          depthVal = c(depthVal, depth)
          clusterVal = c(clusterVal, clusterNo)
          childVal = c(childVal, childNo)
          depthPos = c(depthPos, rep(depth, length(x)))
          clusterPos = c(clusterPos, rep(clusterNo, length(x)))
          childPos = c(childPos, rep(childNo, length(x)))
          x_pos = c(x_pos, x)
          y_pos = c(y_pos, y)
          levelCluster = c(levelCluster, depth)
        }
      }
    }
    
    # outlier_cell_k = c(53)
  
    
    valuesDataframe <- data.frame(depth = depthVal,
                                  cluster = clusterVal,
                                  child = childVal)
    valuesDataframe <- valuesDataframe %>% 
      dplyr::mutate(outlier_cell = ifelse((child %in% outlier_cell_k),
                                          "Outlier_Cell", ""))
    
    positionsDataframe <- data.frame(
      depth = depthPos,
      cluster = clusterPos,
      child = childPos,
      x = x_pos,
      y = y_pos
    )
    
    
    centroidDataframe <-
      data.frame(x = x_cor, y = y_cor, lev = levelCluster)
    
    datapoly <-
      merge(valuesDataframe,
            positionsDataframe,
            by = c("depth", "cluster", "child"))
    
    datapoly_outlier_cell <- datapoly[which(datapoly$outlier_cell == "Outlier_Cell"), ]
    datapoly_non_outlier_cell <- datapoly[which(datapoly$outlier_cell != "Outlier_Cell"), ]
    
    
    p <- ggplot2::ggplot()
    
    for (i in maxDepth:1) {
      p <-
        p + ggplot2::geom_polygon(
          data = datapoly_outlier_cell[which(datapoly_outlier_cell$depth == i), ],
          ggplot2::aes(
            x = x,
            y = y,
            color = factor(depth),
            size = factor(depth),
            group = interaction(depth, cluster, child),
            
          ),
          fill = "red"
        ) +
        ggplot2::geom_polygon(
          data = datapoly_non_outlier_cell[which(datapoly_non_outlier_cell$depth == i), ],
          ggplot2::aes(
            x = x,
            y = y,
            color = factor(depth),
            size = factor(depth),
            group = interaction(depth, cluster, child),
            
          ),
          fill = NA
        ) +
        ggplot2::scale_colour_manual(values = color.vec) +
        ggplot2::scale_size_manual(values = line.width, guide = "none") +
        ggplot2::labs(color = "Level")
    }
    
    for (depth in 1:maxDepth) {
      p <-  p + ggplot2::geom_point(
        data = centroidDataframe[centroidDataframe["lev"] == depth, ],
        ggplot2::aes(x = x, y = y),
        size = (centroid.size / (2 ^ (depth - 1))),
        pch = 21,
        fill = color.vec[depth],
        color = color.vec[depth] ) +
        ggplot2::geom_point(
          data = centroidDataframe[centroidDataframe["lev"] == depth, ],
          ggplot2::aes(x = x, y = y),
          size = (centroid.size / (2 ^ (depth - 1))),
          pch = 21,
          fill = color.vec[depth],
          color = color.vec[depth] ) 
    }
    
    p <- p +
      ggplot2::scale_color_manual(name = "Outlier Cell",
                         values = color.vec) +
      ggplot2::theme_bw() + ggplot2::theme(
        plot.background = ggplot2::element_blank()
        ,
        plot.title = element_text(
          size = 20,
          hjust = 0.5,
          margin = margin(0, 0, 20, 0)
        )
        ,
        panel.grid = ggplot2::element_blank()
        ,
        panel.border = ggplot2::element_blank()
        ,
        axis.ticks = element_blank()
        ,
        axis.text = element_blank()
        ,
        axis.title = element_blank()
        ,
        panel.background = element_blank()
      ) + ggplot2::theme(plot.title = element_text(hjust = 0.5)) +
      ggplot2::scale_x_continuous(expand = c(0, 0)) +
      ggplot2::scale_y_continuous(expand = c(0, 0)) +
      ggplot2::geom_label(label = centroidDataframe$outlier_cell,
                 nudge_x=0.45, nudge_y=0.1,
                 check_overlap=T,
                 label.padding=unit(0.55, "lines"),
                 label.size=0.4,
                 color="white",
                 fill="#038225" ) +
      theme(legend.position = "none")

    
    return(suppressMessages(p))
    
    
  }