HEC: A HivePlotData Object of the Hair Eye Color Data Set

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

A HivePlotData Object of the Hair Eye Color Data Set

Description

This is an HPD (HivePlotData object) derived from the built-in hair eye color data set (see ?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.

Format

The format is described in detail at HPD.

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

HiveR documentation built on Sept. 12, 2024, 7:25 a.m.