{tidygraph}と{ggraph}によるモダンなネットワーク分析(未公開ver)

> me
$name
[1] "Takashi Kitano"
$twitter
[1] "@kashitan"
$work_in
[1] " "

p.76
“ハピネスと⾝体活動の総量との関係が 
強い相関を⽰している”

p.145
“運が良い⼈は到達度が⾼い”

0 1 0 0 0 0
0 0 1 0 0 0
0 0 0 0 1 0
0 1 0 0 0 0
0 0 0 1 0 1
0 0 0 0 0 0
A B
B C
C E
D B
E D
E F

#
whiskies <- data.table::fread("http://
outreach.mathstat.strath.ac.uk/outreach/nessie/datasets/
whiskies.txt", header = TRUE)
#
cor.mat <- whiskies %>%
select(Body, Sweetness, Smoky, Medicinal, Tobacco, Honey,
Spicy, Winey, Nutty, Malty, Fruity, Floral) %>%
t() %>%
cor()

#
colnames(cor.mat) <- whiskies$Distillery
rownames(cor.mat) <- whiskies$Distillery
#
cor.mat[upper.tri(cor.mat, diag = TRUE)] <- NA
cor.mat[1:5, 1:5]
Aberfeldy Aberlour AnCnoc Ardbeg Ardmore
Aberfeldy NA NA NA NA NA
Aberlour 0.7086322 NA NA NA NA
AnCnoc 0.6973541 0.5030737 NA NA NA
Ardbeg -0.1473114 -0.2285909 -0.1404355 NA NA
Ardmore 0.7319024 0.5118338 0.5570195 0.2316174 NA

# Long-Format 0.8
d <- cor.mat %>%
as.data.frame() %>%
mutate(distillerry1 = whiskies$Distillery) %>%
gather(key = distillerry2, value = cor, -distillerry1) %>%
select(distillerry1, distillerry2, cor) %>%
filter(!is.na(cor) & cor >= 0.80)
head(d)
distillerry1 distillerry2 cor
1 Auchroisk Aberfeldy 0.8238415
2 Benrinnes Aberfeldy 0.8419479
3 Benromach Aberfeldy 0.8554217

# tbl_graph
g <- as_tbl_graph(d, directed = FALSE)
g
# A tbl_graph: 67 nodes and 135 edges
#
# An undirected simple graph with 1 component
#
# Node Data: 67 x 1 (active)
name
<chr>
1 Auchroisk
2 Benrinnes

3 Benromach
4 BlairAthol
5 RoyalLochnagar
6 Speyside
# ... with 61 more rows
#
# Edge Data: 135 x 3
from to cor
<int> <int> <dbl>
1 1 54 0.824
2 2 54 0.842
3 3 54 0.855
# ... with 132 more rows

#
g %>% igraph::graph.density()
[1] 0.06105834
#
g %>% igraph::transitivity()
[1] 0.2797927
# ( 1)
g %>% igraph::reciprocity()
[1] 1

#
g <- g %>%
mutate(centrality = centrality_betweenness())
g
#
#
name centrality
<chr> <dbl>
1 Auchroisk 174.
2 Benrinnes 122.
3 Benromach 411.

#
g <- g %E>%
mutate(centrality = centrality_edge_betweenness())
g
#
#
# Edge Data: 135 x 4 (active)
from to cor centrality
<int> <int> <dbl> <dbl>
1 1 54 0.824 79.3
2 2 54 0.842 42.9
3 3 54 0.855 54.2

#
g <- g %>%
mutate(community = as.factor(group_fast_greedy(weights = cor)))
g
#
#
name community
<chr> <fct>
1 Auchroisk 2
2 Benrinnes 3
3 Benromach 2

g %>%
ggraph(layout = "kk") +
geom_edge_link(aes(width = cor),
alpha = 0.8,
colour = "lightgray")

g %>%
alpha = 0.8,
colour = "lightgray") +
scale_edge_width(range = c(0.1, 1))

g %>%
alpha = 0.8,
scale_edge_width(range = c(0.1, 1)) +
geom_node_point(aes(colour = community, size = degree))

g %>%
alpha = 0.8,
geom_node_point(aes(colour = community, size = degree)) +
geom_node_text(aes(label = name), repel = TRUE)

g %>%
alpha = 0.8,
geom_node_text(aes(label = name), repel = TRUE) +
theme_graph()

g %>%
geom_edge_arc(aes(width = cor),
alpha = 0.8,
theme_graph(background = "grey20", text_colour = "white")

g %>%
mutate(degree = centrality_degree(),
community = as.factor(group_fast_greedy(weights = cor))) %>%
filter(degree >= 6) %E>%
filter(cor > 0.85) %>%
ggraph(layout = "lgl") +
alpha = 0.8,
theme_graph()

g %>% ggraph(layout = "kk") +
geom_edge_fan(aes(width = cor),
alpha = 0.8,
theme_graph()

g %>% ggraph(layout = "linear") +
alpha = 0.8,
theme_graph()

g %>% ggraph(layout = "linear", circular = TRUE) +
alpha = 0.8,
theme_graph()

#
d <- whiskies %>%
select(Body, Sweetness, Smoky, Medicinal, Tobacco, Honey,
Spicy, Winey, Nutty, Malty, Fruity, Floral) %>%
dist()
#
hc <- hclust(d, method="ward.D2")
# tbl_graph
g <- as_tbl_graph(hc)

{tidygraph}と{ggraph}によるモダンなネットワーク分析(未公開ver)

{tidygraph}と{ggraph}によるモダンなネットワーク分析(未公開ver)

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