boris@boris-All-Series:~/IGRAPH$ cat RIgraph02.R
nodes <- read.csv("/home/boris/IGRAPH/Dataset1-Media-Example-NODES.csv", header=T, as.is=T)
links <- read.csv("/home/boris/IGRAPH/Dataset1-Media-Example-EDGES.csv", header=T, as.is=T)
library('igraph')
net <- graph_from_data_frame(d=links, vertices=nodes, directed=T)
E(net) # The edges of the "net" object
V(net) # The vertices of the "net" object
E(net)$type # Edge attribute "type"
V(net)$media # Vertex attribute "media"
# Find nodes and edges by attribute:
# (that returns oblects of type vertex sequence/edge sequence)
V(net)[media=="BBC"]
E(net)[type=="mention"]
as_edgelist(net, names=T)
as_adjacency_matrix(net, attr="weight")
# Plot with curved edges (edge.curved=.1) and reduce arrow size:
# Note that using curved edges will allow you to see multiple links
# between two nodes (e.g. links going in either direction, or multiplex links)
plot(net, edge.arrow.size=.4, edge.curved=.1)
$ sudo apt -y install r-cran-rjson
boris@boris-All-Series:~/IGRAPH$ cat CoutryGraph1.py
library(assertthat)
library(dplyr)
library(purrr)
library(igraph)
library(ggplot2)
library(ggraph)
library(ggmap)
country_coords_txt <- "
1 3.00000 28.00000 Algeria
2 54.00000 24.00000 UAE
3 139.75309 35.68536 Japan
4 45.00000 25.00000 'Saudi Arabia'
5 9.00000 34.00000 Tunisia
6 5.75000 52.50000 Netherlands
7 103.80000 1.36667 Singapore
8 124.10000 -8.36667 Korea
9 -2.69531 54.75844 UK
10 34.91155 39.05901 Turkey
11 -113.64258 60.10867 Canada
12 77.00000 20.00000 India
13 25.00000 46.00000 Romania
14 135.00000 -25.00000 Australia
15 10.00000 62.00000 Norway"
# nodes come from the above table and contain geo-coordinates for some
# randomly picked countries
nodes <- read.delim(text = country_coords_txt, header = FALSE,
quote = "'", sep = "",
col.names = c('id', 'lon', 'lat', 'name'))
set.seed(123) # set random generator state for the same output
N_EDGES_PER_NODE_MIN <- 1
N_EDGES_PER_NODE_MAX <- 4
N_CATEGORIES <- 4
# edges: create random connections between countries (nodes)
edges <- map_dfr(nodes$id, function(id) {
n <- floor(runif(1, N_EDGES_PER_NODE_MIN, N_EDGES_PER_NODE_MAX+1))
to <- sample(1:max(nodes$id), n, replace = FALSE)
to <- to[to != id]
categories <- sample(1:N_CATEGORIES, length(to), replace = TRUE)
weights <- runif(length(to))
data_frame(from = id, to = to, weight = weights, category = categories)
})
edges <- edges %>% mutate(category = as.factor(category))
g <- graph_from_data_frame(edges, directed = FALSE, vertices = nodes)
edges_for_plot <- edges %>%
inner_join(nodes %>% select(id, lon, lat), by = c('from' = 'id')) %>%
rename(x = lon, y = lat) %>%
inner_join(nodes %>% select(id, lon, lat), by = c('to' = 'id')) %>%
rename(xend = lon, yend = lat)
assert_that(nrow(edges_for_plot) == nrow(edges))
nodes$weight = degree(g)
maptheme <- theme(panel.grid = element_blank()) +
theme(axis.text = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(axis.title = element_blank()) +
theme(legend.position = "bottom") +
theme(panel.grid = element_blank()) +
theme(panel.background = element_rect(fill = "#596673")) +
theme(plot.margin = unit(c(0, 0, 0.5, 0), 'cm'))
country_shapes <- geom_polygon(aes(x = long, y = lat, group = group),
data = map_data('world'),
fill = "#CECECE", color = "#515151",
size = 0.15)
mapcoords <- coord_fixed(xlim = c(-150, 180), ylim = c(-55, 80))
ggplot(nodes) + country_shapes +
geom_curve(aes(x = x, y = y, xend = xend, yend = yend, # draw edges as arcs
color = category, size = weight),
data = edges_for_plot, curvature = 0.33,
alpha = 0.5) +
scale_size_continuous(guide = FALSE, range = c(0.25, 2)) + # scale for edGe widths
geom_point(aes(x = lon, y = lat, size = weight), # draw nodes
shape = 21, fill = 'white',
color = 'black', stroke = 0.5) +
scale_size_continuous(guide = FALSE, range = c(1, 6)) + # scale for node size
geom_text(aes(x = lon, y = lat, label = name), # draw text labels
hjust = 0, nudge_x = 1, nudge_y = 4,
size = 3, color = "white", fontface = "bold") +
mapcoords + maptheme
boris@boris-All-Series:~/IGRAPH$ cat CoutryGraph2.py
library(assertthat)
library(dplyr)
library(purrr)
library(igraph)
library(ggplot2)
library(ggraph)
library(ggmap)
country_coords_txt <- "
1 3.00000 28.00000 Algeria
2 54.00000 24.00000 UAE
3 139.75309 35.68536 Japan
4 45.00000 25.00000 'Saudi Arabia'
5 9.00000 34.00000 Tunisia
6 5.75000 52.50000 Netherlands
7 103.80000 1.36667 Singapore
8 124.10000 -8.36667 Korea
9 -2.69531 54.75844 UK
10 34.91155 39.05901 Turkey
11 -113.64258 60.10867 Canada
12 77.00000 20.00000 India
13 25.00000 46.00000 Romania
14 135.00000 -25.00000 Australia
15 10.00000 62.00000 Norway"
# nodes come from the above table and contain geo-coordinates for some
# randomly picked countries
nodes <- read.delim(text = country_coords_txt, header = FALSE,
quote = "'", sep = "",
col.names = c('id', 'lon', 'lat', 'name'))
set.seed(123) # set random generator state for the same output
N_EDGES_PER_NODE_MIN <- 1
N_EDGES_PER_NODE_MAX <- 4
N_CATEGORIES <- 4
# edges: create random connections between countries (nodes)
edges <- map_dfr(nodes$id, function(id) {
n <- floor(runif(1, N_EDGES_PER_NODE_MIN, N_EDGES_PER_NODE_MAX+1))
to <- sample(1:max(nodes$id), n, replace = FALSE)
to <- to[to != id]
categories <- sample(1:N_CATEGORIES, length(to), replace = TRUE)
weights <- runif(length(to))
data_frame(from = id, to = to, weight = weights, category = categories)
})
edges <- edges %>% mutate(category = as.factor(category))
g <- graph_from_data_frame(edges, directed = FALSE, vertices = nodes)
edges_for_plot <- edges %>%
inner_join(nodes %>% select(id, lon, lat), by = c('from' = 'id')) %>%
rename(x = lon, y = lat) %>%
inner_join(nodes %>% select(id, lon, lat), by = c('to' = 'id')) %>%
rename(xend = lon, yend = lat)
assert_that(nrow(edges_for_plot) == nrow(edges))
nodes$weight = degree(g)
maptheme <- theme(panel.grid = element_blank()) +
theme(axis.text = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(axis.title = element_blank()) +
theme(legend.position = "bottom") +
theme(panel.grid = element_blank()) +
theme(panel.background = element_rect(fill = "#596673")) +
theme(plot.margin = unit(c(0, 0, 0.5, 0), 'cm'))
country_shapes <- geom_polygon(aes(x = long, y = lat, group = group),
data = map_data('world'),
fill = "#CECECE", color = "#515151",
size = 0.15)
mapcoords <- coord_fixed(xlim = c(-150, 180), ylim = c(-55, 80))
ggplot(nodes) + country_shapes +
geom_curve(aes(x = x, y = y, xend = xend, yend = yend, # draw edges as arcs
color = category, size = weight),
data = edges_for_plot, curvature = 0.33,
alpha = 0.5) +
scale_size_continuous(guide = FALSE, range = c(0.25, 2)) + # scale for edGe widths
geom_point(aes(x = lon, y = lat, size = weight), # draw nodes
shape = 21, fill = 'white',
color = 'black', stroke = 0.5) +
scale_size_continuous(guide = FALSE, range = c(1, 6)) + # scale for node size
geom_text(aes(x = lon, y = lat, label = name), # draw text labels
hjust = 0, nudge_x = 1, nudge_y = 4,
size = 3, color = "white", fontface = "bold") +
mapcoords + maptheme
theme_transp_overlay <- theme(
panel.background = element_rect(fill = "transparent", color = NA),
plot.background = element_rect(fill = "transparent", color = NA)
)
p_base <- ggplot() + country_shapes + mapcoords + maptheme
p_edges <- ggplot(edges_for_plot) +
geom_curve(aes(x = x, y = y, xend = xend, yend = yend, # draw edges as arcs
color = category, size = weight),
curvature = 0.33, alpha = 0.5) +
scale_size_continuous(guide = FALSE, range = c(0.5, 2)) +
mapcoords + maptheme + theme_transp_overlay +
theme(legend.position = c(0.5, -0.1),
legend.direction = "horizontal")
p_nodes <- ggplot(nodes) +
geom_point(aes(x = lon, y = lat, size = weight),
shape = 21, fill = "white", color = "black", # draw nodes
stroke = 0.5) +
scale_size_continuous(guide = FALSE, range = c(1, 6)) + # scale for node size
geom_text(aes(x = lon, y = lat, label = name), # draw text labels
hjust = 0, nudge_x = 1, nudge_y = 4,
size = 3, color = "white", fontface = "bold") +
mapcoords + maptheme + theme_transp_overlay
p <- p_base +
annotation_custom(ggplotGrob(p_edges), ymin = -74) +
annotation_custom(ggplotGrob(p_nodes), ymin = -74)
print(p)
https://www.r-bloggers.com/2018/05/three-ways-of-visualizing-a-graph-on-a-map/
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