Choosing a Projection

This lesson is called Choosing a Projection, part of the Mapping with R course. This lesson is called Choosing a Projection, part of the Mapping with R course.

Transcript

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View code shown in video

library(tidyverse)
library(rnaturalearth)
library(sf)
library(tigris)
library(crsuggest)

all_countries <-
  ne_countries() |>
  select(sovereignt)

all_countries

all_countries |>
  ggplot() +
  geom_sf()

# Robinson

all_countries |>
  st_transform(
    "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"
  ) |>
  ggplot() +
  geom_sf()

# Gall-Peters

all_countries |>
  st_transform(
    "+proj=cea +lon_0=0 +lat_ts=45 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"
  ) |>
  ggplot() +
  geom_sf()

# Mollweide

all_countries |>
  st_transform("+proj=moll +datum=WGS84 +units=m") |>
  ggplot() +
  geom_sf()

# Lambert Azimuthal Equal-Area Projection

all_countries |>
  st_transform("+proj=laea +x_0=0 +y_0=0 +lon_0=0 +lat_0=0") |>
  ggplot() +
  geom_sf()

africa <-
  ne_countries(continent = "Africa") |>
  select(sovereignt)

africa |> 
  ggplot() +
  geom_sf()

africa_crs <- 
africa |> 
  suggest_top_crs()

africa |> 
  st_transform(africa_crs) |> 
  ggplot() +
  geom_sf()


continental_us_states <-
  states() |>
  select(NAME) |>
  filter(NAME %in% state.name) |>
  filter(NAME != "Alaska") |>
  filter(NAME != "Hawaii")

us_crs <- 
continental_us_states |> 
  suggest_top_crs()

continental_us_states |>
  st_transform(us_crs) |> 
  ggplot() +
  geom_sf()

Your Turn

Solution

library(sf)
library(tidyverse)
library(scales)
library(ggrepel)
library(crsuggest)

all_countries <-
  read_sf("https://raw.githubusercontent.com/rfortherestofus/mapping-with-r-v2/refs/heads/main/data/refugees.geojson") |> 
  st_transform(
    "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"
  ) 

all_countries |> 
  suggest_top_crs()

top_three_countries <-
  all_countries |>
  slice_max(
    order_by = number_of_refugees,
    n = 3
  ) |> 
  st_transform(
    "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"
  ) 


ggplot() +
  geom_sf(
    data = all_countries,
    aes(fill = number_of_refugees)
  ) +
  geom_text_repel(
    data = top_three_countries,
    aes(
      label = country_abbreviation,
      geometry = geometry
    ),
    stat = "sf_coordinates",
    seed = 1234,
    box.padding = unit(10, "pt"),
    segment.color = "white",
    segment.curvature = -0.1,
    arrow = arrow(
      length =
        unit(2, "pt"),
      type = "closed"
    ),
    color = "white",
    bg.color = "grey30",
    bg.r = 0.15
  ) +
  # geom_sf_text(
  #   data = top_three_countries,
  #   aes(label = country_abbreviation),
  #   color = "white"
  # ) +
  scale_fill_viridis_c(
    labels = comma_format()
  ) +
  labs(
    fill = NULL,
    title = "Top Refugee-Sending Countries"
  ) +
  theme_void() +
  theme(
    legend.position = "top",
    legend.key.width = unit(2, "cm"),
    legend.key.height = unit(0.5, "cm"),
    plot.title = element_text(
      hjust = 0.5,
      face = "bold",
      size = 16,
      margin = margin(
        b = 0.25,
        unit = "cm"
      )
    )
  )

Improve the map you made in the last lesson on the number of refugees from each country in the world by using the crsuggest package to find the best projection for the data.
Once you’ve done this, redo your map with this projection.