Alternative visualizations

Case Studies: Network Analysis in R

Edmund Hart

Instructor

Introduction to hive plots

library(HiveR)
library(igraph)

# Create random graph
rand_g <- erdos.renyi.game(18, 0.3, "gnp", directed = TRUE)

# Plot random graph
plot(rand_g, vertex.size = 7)

Introduction to hive plots

# Convert to dataframe for hive plots and add weights
rand_g_df <- as.data.frame(get.edgelist(rand_g))
rand_g_df$weight <- 1

# Convert to hive object
rand_hive <- edge2HPD(edge_df = rand_g_df)

# Set the axis and the radius of each node
rand_hive$nodes$axis <- sort(rep(1:3, 6))
rand_hive$nodes$radius <- as.double(rep(1:6, 3))

Introduction to hive plots

# See how nodes are modified
rand_hive$nodes

id lab axis radius size color
1   2    1      1    1 black
2   8    1      2    1 black
3   9    1      3    1 black
4   3    1      4    1 black
5   4    1      5    1 black
6   7    1      6    1 black
7  11    2      1    1 black
8  14    2      2    1 black
9  18    2      3    1 black

# See hive plot
plotHive(rand_hive, method = "abs", bkgnd = "white")

Modifying hive plots

# Setting location of each node
rand_hive$nodes$axis <- sort(rep(1:3, 6))
rand_hive$nodes$radius <- as.double(rep(1:6, 3))

# Add weights to each edge
rand_hive$edges$weight <- as.double(
                      rpois(length(rand_hive$edges$weight), 5)
                          )

# Add color based on edge origination
rand_hive$edges$color[rand_hive$edges$id1 %in% 1:6] <- 'red'
rand_hive$edges$color[rand_hive$edges$id1 %in% 7:12] <- 'blue'
rand_hive$edges$color[rand_hive$edges$id1 %in% 13:18] <- 'green'

# Plot 
plotHive(rand_hive, method = "abs", bkgnd = "white")

Biofabric plots

# Create random graph
rand_g <- erdos.renyi.game(10, 0.3, "gnp", directed = FALSE)
rand_g <- simplify(rand_g)

# Add names to vertices
V(rand_g)$name <- LETTERS[1:length(V(rand_g))]

# Create biofabric plot
biofbc <- bioFabric(rand_g)
bioFabric_htmlwidget(biofbc)

Let's practice!

Case Studies: Network Analysis in R