This document analyzes correlations between characteristics of different whiskies. It creates a correlation matrix between 12 whisky characteristics for 67 distilleries. It then transforms this into a graph with distilleries as nodes and correlations above 0.8 as edges. Various graph metrics and visualizations are calculated and displayed, including communities detected within the graph.

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

9. 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

16. #
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()

17. #
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

18. # 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

20. # 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

21. # 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

22. 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

23. 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

24. #
g %>% igraph::graph.density()
[1] 0.06105834
#
g %>% igraph::transitivity()
[1] 0.2797927
# ( 1)
g %>% igraph::reciprocity()
[1] 1

25. #
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.

26. #
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

27. #
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

28. #
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

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

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

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

36. 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))

38. 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)

40. 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()

42. 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")

45. 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()

46. 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()

47. 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()

49. 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()

51. #
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)

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) +
theme_graph()