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

Description Format Examples

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
# 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 May 2, 2019, 2:08 a.m.