R/HEC.R

In HiveR: 2D and 3D Hive Plots for R

#' A HivePlotData Object of the Hair Eye Color Data Set
#'
#' This is an \code{\link{HPD}} (\code{HivePlotData} object) derived from the
#' built-in hair eye color data set (see \code{?HairEyeColor}).  It serves as a
#' test 2D data set, and the example below shows how it was built.  While every
#' data set is different and will require a different approach, the example
#' illustrates the general approach to building a hive plot from scratch,
#' step-by-step.
#'
#'
#' @name HEC
#' @docType data
#' @format The format is described in detail at \code{\link{HPD}}.
#' @keywords datasets
#' @examples
#'
#' # An example of building an HPD from scratch
#'
#' ### Step 0.  Get to know your data.
#'
#' data(HairEyeColor) # see ?HairEyeColor for background
#' df <- data.frame(HairEyeColor) # str(df) is useful
#'
#' # Frequencies of the colors can be found with:
#' eyeF <- aggregate(Freq ~ Eye, data = df, FUN = "sum")
#' hairF <- aggregate(Freq ~ Hair, data = df, FUN = "sum")
#' es <- eyeF$Freq / eyeF$Freq[4] # node sizes for eye
#' hs <- hairF$Freq / hairF$Freq[3] # node sizes for hair
#'
#' ### Step 1. Assemble a data frame of the nodes.
#'
#' # There are 32 rows in the data frame, but we are going to
#' # separate the hair color from the eye color and thus
#' # double the number of rows in the node data frame
#'
#' nodes <- data.frame(
#'   id = 1:64,
#'   lab = paste(rep(c("hair", "eye"), each = 32), 1:64, sep = "_"),
#'   axis = rep(1:2, each = 32),
#'   radius = rep(NA, 64)
#' )
#'
#' for (n in 1:32) {
#'   # assign node radius based most common colors
#'   if (df$Hair[n] == "Black") nodes$radius[n] <- 2
#'   if (df$Hair[n] == "Brown") nodes$radius[n] <- 4
#'   if (df$Hair[n] == "Red") nodes$radius[n] <- 1
#'   if (df$Hair[n] == "Blond") nodes$radius[n] <- 3
#'
#'   if (df$Eye[n] == "Brown") nodes$radius[n + 32] <- 1
#'   if (df$Eye[n] == "Blue") nodes$radius[n + 32] <- 2
#'   if (df$Eye[n] == "Hazel") nodes$radius[n + 32] <- 3
#'   if (df$Eye[n] == "Green") nodes$radius[n + 32] <- 4
#'
#'   # now do node sizes
#'   if (df$Hair[n] == "Black") nodes$size[n] <- hs[1]
#'   if (df$Hair[n] == "Brown") nodes$size[n] <- hs[2]
#'   if (df$Hair[n] == "Red") nodes$size[n] <- hs[3]
#'   if (df$Hair[n] == "Blond") nodes$size[n] <- hs[4]
#'
#'   if (df$Eye[n] == "Brown") nodes$size[n + 32] <- es[4]
#'   if (df$Eye[n] == "Blue") nodes$size[n + 32] <- es[3]
#'   if (df$Eye[n] == "Hazel") nodes$size[n + 32] <- es[2]
#'   if (df$Eye[n] == "Green") nodes$size[n + 32] <- es[1]
#' }
#'
#' nodes$color <- rep("black", 64)
#' nodes$lab <- as.character(nodes$lab) # clean up some data types
#' nodes$radius <- as.numeric(nodes$radius)
#'
#' ### Step 2. Assemble a data frame of the edges.
#'
#' edges <- data.frame( # There will be 32 edges, corresponding to the original 32 rows
#'   id1 = c(1:16, 49:64), # This will set up edges between each eye/hair pair
#'   id2 = c(33:48, 17:32), # & put the males above and the females below
#'   weight = df$Freq,
#'   color = rep(c("lightblue", "pink"), each = 16)
#' )
#'
#' edges$color <- as.character(edges$color)
#'
#' # Scale the edge weight (det'd by trial & error to emphasize differences)
#' edges$weight <- 0.25 * log(edges$weight)^2.25
#'
#' ### Step 3. Now assemble the HivePlotData (HPD) object.
#'
#' HEC <- list()
#' HEC$nodes <- nodes
#' HEC$edges <- edges
#' HEC$type <- "2D"
#' HEC$desc <- "HairEyeColor data set"
#' HEC$axis.cols <- c("grey", "grey")
#' class(HEC) <- "HivePlotData"
#'
#' ### Step 4. Check it & summarize
#'
#' chkHPD(HEC) # answer of FALSE means there are no problems
#' sumHPD(HEC)
#'
#' ### Step 5.  Plot it.
#'
#' # A minimal plot
#' plotHive(HEC, ch = 0.1, bkgnd = "white")
#' # See ?plotHive for fancier options
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