map assignment

Data Source: Data is from the Global Footprint Network, which has the goal of measuring a region’s ecological productivity along with the demands that region places on ecosystems. This is broken down by grazing land, cropland, forest land, and fisheries.

Spatial Units: Latitude and Longitude

Purpose of the map: I chose to produce a map that displays cropland productivity by country, as this variable had a wider range and was therefore more interesting visually. The image shows how much productive capacity a given country has relative to its population.

library(ggplot2)
library(maps)
library(viridisLite)
library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
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## ✔ purrr   0.3.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
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## ✖ purrr::map()    masks maps::map()

countries <- read.csv('countries_energy_use.csv')
#head(countries)

countries$Country[countries$Country == "United States of America"] <- "USA"
#countries

#filter(countries, Country == "USA")



#countries_land_use <- map_data('world')
#head(countries_land_use)
#countries_land_use[countries_land_use == "USA"] <- "United States of America"
#filter(countries_land_use, region == "USA")

countries_land_use <- map_data("world") %>%
  mutate(region = str_to_title(region),
         subregion = str_to_title(subregion)) %>%
  left_join(countries, by = c('region' = "Country"))

#head(countries_land_use)

land_use_map <- ggplot(data = countries_land_use,
            mapping = aes(x = long, y = lat,
                          group = group, 
                          fill = Cropland))
land_use_map <- land_use_map + geom_polygon(color="white")
land_use_map <- land_use_map + scale_fill_viridis_c(option="plasma", direction=-1)
land_use_map <- land_use_map + guides(fill=guide_legend(title="Cropland Productivity"))
#land_use_map <- land_use_map + coord_map()
land_use_map