The document discusses analyzing correlation networks between Scottish whisky distilleries. A correlation matrix is created from sensory characteristics of whiskies. This is converted to a graph object where nodes are distilleries and edges represent correlations above 0.8. The graph is analyzed to find clustering of distilleries based on sensory profiles and key central nodes. Visualizations of the network are also created.

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

3. 突然ですが質問です

4. 幸せですか？

6. 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”

7. p.145
“運が良い⼈は到達度が⾼い”

10. 約1年運⽤した結果

20. (node, vertex)
(edge, link)

23. 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

30. #
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()

31. #
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

32. # 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

34. # 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

35. # 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

36. 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

37. 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

38. #
g %>% igraph::graph.density()
[1] 0.06105834
#
g %>% igraph::transitivity()
[1] 0.2797927
# ( 1)
g %>% igraph::reciprocity()
[1] 1

39. #
g <- g %>%
mutate(centrality = centrality_betweenness())
g
# A tbl_graph: 67 nodes and 135 edges
#
# An undirected simple graph with 1 component
#
# Node Data: 67 x 2 (active)
name centrality
<chr> <dbl>
1 Auchroisk 174.
2 Benrinnes 122.
3 Benromach 411.

40. #
g <- g %E>%
mutate(centrality = centrality_edge_betweenness())
g
# A tbl_graph: 67 nodes and 135 edges
#
# An undirected simple graph with 1 component
#
# 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

41. #
g <- g %E>%
mutate(centrality = centrality_edge_betweenness())
g
# A tbl_graph: 67 nodes and 135 edges
#
# An undirected simple graph with 1 component
#
# 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

42. #
g <- g %>%
mutate(community = as.factor(group_fast_greedy(weights = cor)))
g
# A tbl_graph: 67 nodes and 135 edges
#
# An undirected simple graph with 1 component
#
# Node Data: 67 x 2 (active)
name community
<chr> <fct>
1 Auchroisk 2
2 Benrinnes 3
3 Benromach 2

44. g %>%
ggraph(layout = "kk")

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

48. g %>%
ggraph(layout = "kk") +
geom_edge_link(aes(width = cor),
alpha = 0.8,
colour = "lightgray") +
scale_edge_width(range = c(0.1, 1))

50. g %>%
ggraph(layout = "kk") +
geom_edge_link(aes(width = cor),
alpha = 0.8,
colour = "lightgray") +
scale_edge_width(range = c(0.1, 1)) +
geom_node_point(aes(colour = community, size = degree))

52. g %>%
ggraph(layout = "kk") +
geom_edge_link(aes(width = cor),
alpha = 0.8,
colour = "lightgray") +
scale_edge_width(range = c(0.1, 1)) +
geom_node_point(aes(colour = community, size = degree)) +
geom_node_text(aes(label = name), repel = TRUE)

54. g %>%
ggraph(layout = "kk") +
geom_edge_link(aes(width = cor),
alpha = 0.8,
colour = "lightgray") +
scale_edge_width(range = c(0.1, 1)) +
geom_node_point(aes(colour = community, size = degree)) +
geom_node_text(aes(label = name), repel = TRUE) +
theme_graph()

56. g %>%
ggraph(layout = "kk") +
geom_edge_arc(aes(width = cor),
alpha = 0.8,
colour = "lightgray") +
scale_edge_width(range = c(0.1, 1)) +
geom_node_point(aes(colour = community, size = degree)) +
theme_graph(background = "grey20", text_colour = "white")

59. g %>%
mutate(degree = centrality_degree(),
community = as.factor(group_fast_greedy(weights = cor))) %>%
filter(degree >= 6) %E>%
filter(cor > 0.85) %>%
ggraph(layout = "lgl") +
geom_edge_link(aes(width = cor),
alpha = 0.8,
colour = "lightgray") +
scale_edge_width(range = c(0.1, 1)) +
geom_node_point(aes(colour = community, size = degree)) +
geom_node_text(aes(label = name), repel = TRUE) +
theme_graph()

60. g %>% ggraph(layout = "kk") +
geom_edge_fan(aes(width = cor),
alpha = 0.8,
colour = "lightgray") +
scale_edge_width(range = c(0.1, 1)) +
geom_node_point(aes(colour = community, size = degree)) +
geom_node_text(aes(label = name), repel = TRUE) +
theme_graph()

61. g %>% ggraph(layout = "linear") +
geom_edge_arc(aes(width = cor),
alpha = 0.8,
colour = "lightgray") +
scale_edge_width(range = c(0.1, 1)) +
geom_node_point(aes(colour = community, size = degree)) +
geom_node_text(aes(label = name), repel = TRUE) +
theme_graph()

63. g %>% ggraph(layout = "linear", circular = TRUE) +
geom_edge_arc(aes(width = cor),
alpha = 0.8,
colour = "lightgray") +
scale_edge_width(range = c(0.1, 1)) +
geom_node_point(aes(colour = community, size = degree)) +
geom_node_text(aes(label = name), repel = TRUE) +
theme_graph()

65. #
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)

66. g %>%
ggraph(layout = "kk") +
geom_edge_link(aes(width = cor),
alpha = 0.8,
colour = "lightgray") +
scale_edge_width(range = c(0.1, 1)) +
geom_node_point(aes(colour = community, size = degree)) +
geom_node_text(aes(label = name), repel = TRUE) +
theme_graph()

72. 新⼈襲来

73. ⼤量異動

78. Di,j i,j
r Di,j