2019 Altmetric Top 100 geocoded affiliations on world map

2019altmTop100aff.R

library(tidyverse)
library(readxl)
library(tmaptools)
library(maps)
library(sf)

url <- "https://altmetric.figshare.com/ndownloader/files/20202618"
temp <- tempfile()
download.file(url, temp)
data <- read_excel(temp)
unlink(temp)

data_sep <- data %>%
  separate(`Affiliations (GRID)`, paste("org", c("1", "2", "3", "4", "5", "6", "7", "8"), sep = "_"), sep = ";")

aff <- data_sep %>% 
  select(starts_with("org_")) %>% 
  gather("key", "value", na.rm = TRUE)  %>% 
  group_by(value) %>%
  summarise(Count = n()) %>% 
  arrange(desc(Count)) %>% 
  filter(value != "") 

aff_geocode <- geocode_OSM(aff$value, as.sf = TRUE)
# Get count for those whose location was found
aff_geocode_count <- left_join(aff_geocode, aff, by = c("query"="value"))

countries_map <- map_data("world")

ggplot() + 
  geom_map(data = countries_map, 
           map = countries_map, aes(map_id = region, group = group),
           fill = "white", color = "black", size = 0.1) +
  scale_size_continuous(range = c(2, 15)) +
  geom_sf(data = aff_geocode_count, colour = "sienna2", aes(size = Count), alpha = 0.7) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        legend.position = "none",
        plot.title = element_text(size = 12*4, color = "#BDBDBD"),
        plot.subtitle = element_text(size = 12*2, color = "light grey"),
        plot.caption = element_text(size = 12*0.8, color = "#BDBDBD")) +
  labs(x = NULL, y = NULL, 
       title = "2019 Altmetric Top 100",
       subtitle = "Affiliations",
       caption = "2019 Altmetric Top 100 - dataset.\nhttps://doi.org/10.6084/m9.figshare.11371860.v2") 

saveRDS(aff_geocode_count, "altmtop100_aff_geocode.RDS")

ggsave(
  "altmtop100world.png",
  width = 35, 
  height = 20, 
  dpi = 72, 
  units = "cm", 
  device = 'png'
)