1_plot_site_locations.R

#!/usr/bin/env Rscript
library(tidyverse)
library(maps)
library(mapproj)

dir.create("plots", showWarnings=FALSE)


coords <- read_csv("data/site_coordinates.csv") %>%
	group_by(Community_ID) %>%
	summarise(Latitude = mean(Latitude),
	          Longitude = mean(Longitude))

data_map <- read_csv("data/combined_long_data.csv") %>%
	rename(region = Country_full,
	       community = Community_ID,
	       `Site type` = Urb_rural) %>%
	mutate(region = if_else(region == "Congo", "Republic of Congo", region),
	       `Site type` = str_to_title(`Site type`)) %>%
  	group_by(region, community, `Site type`) %>%
	summarise(Participants = n_distinct(Participant_ID))


data_map <- left_join(data_map, coords, by=join_by(community==Community_ID)) 

world <- map_data("world")
selWorld <- left_join(world, select(data_map, region, Participants), by = "region")%>%
  filter(region != "Antarctica")%>%
  mutate(fill = ifelse(is.na(Participants), "out", "in"))  %>%
  filter(! long > 180)

world_map <- selWorld %>%
	rename(Latitude = lat,
	       Longitude = long) %>%
	ggplot(aes(x = Longitude, y = Latitude, group = group)) +
	coord_map(projection="gall", lat0=0) +
	geom_polygon(aes(fill = fill), col = "white", linewidth = 0.10, show.legend=FALSE)  +
	scale_fill_manual(values = c("#3F7979", "#B7D6D6")) +
	geom_point(data = data_map, aes(x = Longitude, y = Latitude, shape=`Site type`), inherit.aes = F, stroke = 0.33) +
	scale_shape_manual(values = c(1, 2)) +
	scale_size(range=c(0.50, 2.0)) +
	theme_light() +
	theme(axis.title = element_text(size=6),
	      axis.text = element_text(size=4),
	      legend.title = element_text(size=6),
	      legend.text = element_text(size=4),
	      strip.text.x = element_text(margin = margin(b = 1, t=1))
	) 

ggsave("plots/world_map.png", plot=world_map, dpi=600, width=180, height=90, units="mm")