how to plot networks over a map with the least overlap

Question

I have some authors with their city or country of affiliation. I would like to know if it is possible to plot the coauthors\' networks (figure 1), on the map, having the coordin

Answer 1

Are you looking for a solution using exactly the packages you used, or would you be happy to use suite of other packages? Below is my approach, in which I extract the graph properties from the network object and plot them on a map using the ggplot2 and map package.

---

First I recreate the example data you gave.

library(tidyverse)
library(sna)
library(maps)
library(ggrepel)
set.seed(1)

coauthors <- matrix(
  c(0,3,1,1,3,0,1,0,1,1,0,0,1,0,0,0),
  nrow = 4, ncol = 4, 
  dimnames = list(c('BENNETT MD', 'LEITCH IJ', 'HANSON L', 'SOMEONE ELSE'),
                  c('BENNETT MD', 'LEITCH IJ', 'HANSON L', 'SOMEONE ELSE')))

coords <- data_frame(
  country = c('Argentina', 'Brazil', 'USA'),
  coord_lon = c(-63.61667, -51.92528, -95.71289),
  coord_lat = c(-38.41610, -14.23500, 37.09024))

authorcountry <- data_frame(
  author = c('LEITCH IJ', 'HANSON L', 'BENNETT MD', 'SOMEONE ELSE'),
  country = c('Argentina', 'USA', 'Brazil', 'Brazil'))

Now I generate the graph object using the snp function network

# Generate network
bip <- network(coauthors,
               matrix.type = "adjacency",
               ignore.eval = FALSE,
               names.eval = "weights")

# Graph with ggnet2 for centrality
gdata <- ggnet2(bip, color = "Country", legend.position = "right",label = TRUE,  
               alpha = 0.9, label.size = 3, edge.size="weights", 
               size="degree", size.legend="Degree Centrality") + theme(legend.box = "horizontal")

From the network object we can extract the values of each edge, and from the ggnet2 object we can get degree of centrality for nodes as below:

# Combine data
authors <- 
  # Get author numbers
  data_frame(
    id = seq(1, nrow(coauthors)),
    author = sapply(bip$val, function(x) x$vertex.names)) %>%
  left_join(
    authorcountry,
    by = 'author') %>%
  left_join(
    coords,
    by = 'country') %>%
  # Jittering points to avoid overlap between two authors
  mutate(
    coord_lon = jitter(coord_lon, factor = 1),
    coord_lat = jitter(coord_lat, factor = 1))

# Get edges from network
networkdata <- sapply(bip$mel, function(x) 
  c('id_inl' = x$inl, 'id_outl' = x$outl, 'weight' = x$atl$weights)) %>%
  t %>% as_data_frame

dt <- networkdata %>%
  left_join(authors, by = c('id_inl' = 'id')) %>%
  left_join(authors, by = c('id_outl' = 'id'), suffix = c('.from', '.to')) %>%
  left_join(gdata$data %>% select(label, size), by = c('author.from' = 'label')) %>%
  mutate(edge_id = seq(1, nrow(.)),
         from_author = author.from,
         from_coord_lon = coord_lon.from,
         from_coord_lat = coord_lat.from,
         from_country = country.from,
         from_size = size,
         to_author = author.to,
         to_coord_lon = coord_lon.to,
         to_coord_lat = coord_lat.to,
         to_country = country.to) %>%
  select(edge_id, starts_with('from'), starts_with('to'), weight)

Should look like this now:

dt
# A tibble: 8 × 11
  edge_id  from_author from_coord_lon from_coord_lat from_country from_size    to_author to_coord_lon
                                                             
1       1   BENNETT MD      -51.12756     -16.992729       Brazil         6    LEITCH IJ    -65.02949
2       2   BENNETT MD      -51.12756     -16.992729       Brazil         6     HANSON L    -96.37907
3       3   BENNETT MD      -51.12756     -16.992729       Brazil         6 SOMEONE ELSE    -52.54160
4       4    LEITCH IJ      -65.02949     -35.214117    Argentina         4   BENNETT MD    -51.12756
5       5    LEITCH IJ      -65.02949     -35.214117    Argentina         4     HANSON L    -96.37907
6       6     HANSON L      -96.37907      36.252312          USA         4   BENNETT MD    -51.12756
7       7     HANSON L      -96.37907      36.252312          USA         4    LEITCH IJ    -65.02949
8       8 SOMEONE ELSE      -52.54160      -9.551913       Brazil         2   BENNETT MD    -51.12756
# ... with 3 more variables: to_coord_lat , to_country , weight 

Now moving on to plotting this data on a map:

world_map <- map_data('world') 
myMap <- ggplot() +
  # Plot map
  geom_map(data = world_map, map = world_map, aes(map_id = region),
           color = 'gray85',
           fill = 'gray93') +
  xlim(c(-120, -20)) + ylim(c(-50, 50)) + 
  # Plot edges
  geom_segment(data = dt, 
               alpha = 0.5,
               color = "dodgerblue1",
               aes(x = from_coord_lon, y = from_coord_lat,
                   xend = to_coord_lon, yend = to_coord_lat,
                   size = weight)) +
  scale_size(range = c(1,3)) +
  # Plot nodes
  geom_point(data = dt,
             aes(x = from_coord_lon,
                 y = from_coord_lat,
                 size = from_size,
                 colour = from_country)) +
  # Plot names
  geom_text_repel(data = dt %>% 
                    select(from_author, 
                           from_coord_lon, 
                           from_coord_lat) %>% 
                    unique,
                  colour = 'dodgerblue1',
                  aes(x = from_coord_lon, y = from_coord_lat, label = from_author)) + 
  coord_equal() +
  theme_bw()

Obviously you can change the colour and design in the usual way with ggplot2 grammar. Notice that you could also use geom_curve and the arrow aesthetic to get a plot similar to the one in the uber post linked in the comments above.