I am analysing a drug trafficking network I constructed in R with the igraph package. It is a directed network dataset. I want to illustrate the network on a map of Europe, so I found out how to fetch geodata for the country capitals and fixed it to the network object. I have been trying to have the edges have a colour gradient along their trajectory from origin to destination to visualalise the direction of the edge, since arrows are quite clunky. I sought after a way to make this value independant since I thought this would be possible. For some reason I am really struggling with getting this done. I succeeded in making the edge colour dependent on latitude but that doesnt really help because it has nothing to do with the direction.
This is what I tried so far:
libs<- c("openrouteservice", "sf","tidyverse",
"leaflet","maptiles","tidyterra","rnaturalearth","rnaturalearthdata",
"CoordinateCleaner","igraph","ggraph","tidygraph")
# checking installed libs
installed_libs <- libs %in% rownames(
installed.packages()
)
# installing missing libraries
if(any(installed_libs == F)){
install.packages(
libs[!installed_libs]
)
}
# Activating libraries
invisible(
lapply(libs,library,character.only = T)
)
world <- ne_countries(scale = "medium", returnclass = "sf")
Europe <- world[which(world$continent == "Europe"),]
node_list <-
structure(
list(
country = c(
"Austria",
"Belgium",
"Denmark",
"Finland",
"France",
"Greece",
"Germany",
"Ireland",
"Italy",
"Luxembourg",
"Netherlands",
"Norway",
"Portugal",
"Sweden",
"Spain",
"Switzerland",
"United Kingdom"
),
drug = c(
"Cocaine",
"Cocaine",
"Cocaine",
"Cocaine",
"Cocaine",
"Cocaine",
"Cocaine",
"Cocaine",
"Cocaine",
"Cocaine",
"Cocaine",
"Cocaine",
"Cocaine",
"Cocaine",
"Cocaine",
"Cocaine",
"Cocaine"
),
level.of.sale = c(
"Wholesale",
"Wholesale",
"Wholesale",
"Wholesale",
"Wholesale",
"Wholesale",
"Wholesale",
"Wholesale",
"Wholesale",
"Wholesale",
"Wholesale",
"Wholesale",
"Wholesale",
"Wholesale",
"Wholesale",
"Wholesale",
"Wholesale"
),
wsprice = c(
55753.26,
38039.15,
47449.58,
97254,
38552.79,
45670.23,
44094.82,
35587.19,
42356.55,
55808,
41877,
55525.94,
18895.22,
46800.05,
42754.45,
52472,
38514.44
),
indegree = c(7L,
0L, 1L, 7L, 0L, 4L, 5L, 0L, 5L, 0L, 3L, 1L, 0L, 1L, 2L, 5L, 0L),
outdegree = c(1L, 3L, 4L, 0L, 2L, 2L, 2L, 2L, 5L, 0L, 3L,
0L, 5L, 1L, 8L, 2L, 1L),
iso3 = c(
"AUT",
"BEL",
"DNK",
"FIN",
"FRA",
"GRC",
"DEU",
"IRL",
"ITA",
"LUX",
"NLD",
"NOR",
"PRT",
"SWE",
"ESP",
"CHE",
"GBR"
),
capital = structure(
c(
213L,
41L,
53L,
77L,
148L,
18L,
32L,
62L,
169L,
106L,
9L,
140L,
99L,
191L,
107L,
33L,
102L
),
levels = c(
"Abu Dhabi",
"Abuja",
"Accra",
"Adamstown",
"Addis Ababa",
"Algiers",
"Alofi",
"Amman",
"Amsterdam",
"Andorra la Vella",
"Ankara",
"Antananarivo",
"Apia",
"Ashgabat",
"Asmara ",
"Astana",
"Asuncion",
"Athens",
"Avarua",
"Baghdad",
"Baku",
"Bamako",
"Bandar Seri Begawan",
"Bangkok",
"Bangui",
"Banjul",
"Basseterre",
"Beijing",
"Beirut",
"Belgrade",
"Belmopan",
"Berlin",
"Bern",
"Bishkek",
"Bissau",
"Bogota",
"Brasilia",
"Bratislava",
"Brazzaville",
"Bridgetown",
"Brussels",
"Bucharest",
"Budapest",
"Buenos Aires",
"Bujumbura",
"Cairo",
"Canberra",
"Caracas",
"Castries",
"Chisinau",
"Colombo",
"Conakry",
"Copenhagen",
"Dakar",
"Damascus",
"Dhaka",
"Dili",
"Djibouti",
"Dodoma",
"Doha",
"Douglas",
"Dublin",
"Dushanbe",
"Freetown",
"Funafuti",
"Gaborone",
"George Town",
"Georgetown",
"Gibraltar",
"Grand Turk",
"Guatemala City",
"Hagatna",
"Hamilton",
"Hanoi",
"Harare",
"Havana",
"Helsinki",
"Honiara",
"Islamabad",
"Jakarta",
"Jamestown",
"Jerusalem",
"Juba",
"Kabul",
"Kampala",
"Kathmandu",
"Khartoum",
"Kigali",
"Kingston",
"Kingstown",
"Kinshasa",
"Kuala Lumpur",
"Kuwait City",
"Kyiv (Kiev)",
"La Paz",
"Libreville",
"Lilongwe",
"Lima",
"Lisbon",
"Ljubljana",
"Lome",
"London",
"Longyearbyen",
"Luanda",
"Lusaka",
"Luxembourg",
"Madrid",
"Majuro",
"Malabo",
"Male",
"Managua",
"Manama",
"Manila",
"Maputo",
"Marigot",
"Maseru",
"Mata-Utu",
"Mbabane",
"Melekeok",
"Mexico City ",
"Minsk",
"Mogadishu",
"Monaco",
"Monrovia",
"Montevideo",
"Moroni",
"Moscow",
"Muscat",
"Nairobi",
"Nassau",
"NDjamena",
"New Delhi",
"Niamey",
"Nicosia",
"Nouakchott",
"Noumea",
"Nukualofa",
"Nuuk",
"Oranjestad",
"Oslo",
"Ottawa",
"Ouagadougou",
"Pago Pago",
"Palikir",
"Panama City",
"Papeete ",
"Paramaribo",
"Paris",
"Phnom Penh",
"Plymouth",
"Podgorica",
"Port-au-Prince",
"Port-Vila",
"Port Louis",
"Port Moresby",
"Port of Spain",
"Porto-Novo",
"Prague",
"Praia",
"Pretoria",
"Pyongyang",
"Quito",
"Rabat",
"Rangoon",
"Reykjavik",
"Riga",
"Riyadh",
"Road Town",
"Rome",
"Roseau",
"Saint-Pierre",
"Saint Georges",
"Saint Helier",
"Saint Johns",
"Saint Peter Port",
"Saipan",
"San Jose",
"San Juan",
"San Marino",
"San Salvador",
"Sanaa",
"Santiago",
"Santo Domingo",
"Sao Tome",
"Sarajevo",
"Seoul",
"Singapore",
"Skopje",
"Sofia",
"Stanley",
"Stockholm",
"Suva",
"Taipei",
"Tallinn",
"Tarawa",
"Tashkent",
"Tbilisi",
"Tegucigalpa",
"Tehran",
"The Settlement",
"The Valley",
"Thimphu",
"Tirana",
"Tokyo",
"Torshavn",
"Tripoli ",
"Tunis",
"Ulaanbaatar",
"Vaduz",
"Valletta",
"Vatican City",
"Victoria",
"Vienna",
"Vientiane ",
"Vilnius",
"Warsaw",
"Washington, DC",
"Wellington",
"West Island",
"Windhoek",
"Yamoussoukro",
"Yaounde",
"Yerevan",
"Zagreb"
),
class = "factor"
),
long = c(
16.37,
4.33,
12.58,
24.93,
2.33,
23.73,
13.4,
-6.23,
12.48,
6.12,
4.92,
10.75,
-9.13,
18.05,
-3.68,
7.47,
-0.08
),
lat = c(
48.2,
50.83,
55.67,
60.17,
48.87,
37.98,
52.52,
53.32,
41.9,
49.6,
52.35,
59.92,
38.72,
59.33,
40.4,
46.92,
51.5
)
),
class = "data.frame",
row.names = c(NA, -17L)
)
edge_list <- structure(
list(
country1 = c(
"Austria",
"Belgium",
"Belgium",
"Belgium",
"Denmark",
"Denmark",
"Denmark",
"Denmark",
"France",
"France",
"Germany",
"Germany",
"Greece",
"Greece",
"Ireland",
"Ireland",
"Italy",
"Italy",
"Italy",
"Italy",
"Italy",
"Netherlands",
"Netherlands",
"Netherlands",
"Portugal",
"Portugal",
"Portugal",
"Portugal",
"Portugal",
"Spain",
"Spain",
"Spain",
"Spain",
"Spain",
"Spain",
"Spain",
"Spain",
"Sweden",
"Switzerland",
"Switzerland",
"United Kingdom"
),
country2 = c(
"Finland",
"Italy",
"Netherlands",
"Spain",
"Austria",
"Finland",
"Greece",
"Italy",
"Norway",
"Spain",
"Finland",
"Switzerland",
"Austria",
"Switzerland",
"Austria",
"Finland",
"Austria",
"Finland",
"Germany",
"Greece",
"Switzerland",
"Germany",
"Italy",
"Sweden",
"Germany",
"Greece",
"Italy",
"Netherlands",
"Switzerland",
"Austria",
"Denmark",
"Finland",
"Germany",
"Greece",
"Italy",
"Netherlands",
"Switzerland",
"Austria",
"Austria",
"Finland",
"Germany"
),
difference = c(
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L
),
correlation = c(
0.668950108875404,
0.530220376463286,
0.602603105815811,
0.633894889901975,
0.518996320049688,
0.410621575904769,
0.437541816071113,
0.472938278950377,
0.615664539239391,
0.449799814115612,
0.483598285245482,
0.542463675199482,
0.503746498211967,
0.419154922709794,
0.506287535418966,
0.421561563989063,
0.658838736064947,
0.554557263433874,
0.609431783862321,
0.802671376462743,
0.529923094805411,
0.537097231557396,
0.641754855493876,
0.447827308533041,
0.420920418387666,
0.476851900398108,
0.448455600417252,
0.55536271016908,
0.421037045545927,
0.654889436091643,
0.628137147476826,
0.553861093006277,
0.60965723943248,
0.668105346874185,
0.86414775331133,
0.554894952278287,
0.650120680883999,
0.446939183067011,
0.608148297554411,
0.409046621960521,
0.401572675034778
)
),
class = "data.frame",
row.names = c(NA,
-41L)
)
n <- igraph::graph_from_data_frame(edge_list, directed = T, vertices = node_list )
gr <-tidygraph::as_tbl_graph(n)
# Constructing the map
map <- ggraph::ggraph(
gr,
x = long,
y = lat
) +
geom_sf(
data = Europe,
fill = "grey10",
color = "white",
linewidth = .3,
) +
geom_node_point(size= 2, colour = "#FFFF00")+
ggraph::geom_edge_bundle_path(aes(colour = ..x..),
alpha = .8,
width = .2
) +
scale_colour_gradient(low = "#FFFF00", high = "#FA57B1")+
coord_sf(xlim = c(-20,30), ylim = c(30,65), expand = FALSE)+
theme_void()
map
I did make the color latitude dependant because this is the closest i've gotten but this is not necessary! Edge_list contains 4 Variables one is negligible because it was used to create the directed dataset and is "1" for every edge. The other three are country1 and country2 and the third is correlation which indicates how much countries trade drugs with each other. The node list contains longitude and latitude as well as certain other country characteristics. My apologies for the peacemeal code. I would really appreciate the help since I have been struggling with this for quite some time now. I am new here I hope this makes sense to you. Thanks in advance!
