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.

Usage

data(HEC)

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

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(HiveR)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/HiveR/HEC.Rd_%03d_medium.png", width=480, height=480)
> ### Name: HEC
> ### Title: A HivePlotData Object of the Hair Eye Color Data Set
> ### Aliases: HEC
> ### 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
[1] FALSE
> sumHPD(HEC)
	HairEyeColor data set
	This hive plot data set contains 64 nodes on 2 axes and 32 edges.
	It is a  2D data set.

		Axis 1 has 32 nodes spanning radii from 1 to 4 
		Axis 2 has 32 nodes spanning radii from 1 to 4 

		Axes 1 and 2 share 16 edges
		Axes 2 and 1 share 16 edges

> 
> ### Step 5.  Plot it.
> 
> # A minimal plot
> plotHive(HEC, ch = 0.1, bkgnd = "white")
> # See ?plotHive for fancier options
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>