🤺 Exercise 5A
library(tidyverse)
library(sf)
url = "https://results.aec.gov.au/27966/Website/Downloads/HouseDopByDivisionDownload-27966.csv"
election_data <- read_csv(url, skip = 1)
election_data = election_data |>
filter(CalculationType == "Preference Count" &
Elected == "Y" &
CountNumber == 0) |>
mutate(DivisionNm = toupper(DivisionNm))
data_path = "data/vic-july-2021-esri/E_VIC21_region.shp"
vic_election_map <- read_sf(here::here(data_path)) %>%
mutate(DivisionNm = toupper(Elect_div))
vic_election_map = vic_election_map |>
left_join(election_data, by = "DivisionNm")
party_colors <- c(
"ALP" = "#DE3533",
"GVIC" = "#10C25B",
"IND" = "#000000",
"LP" = "#0047AB",
"NP" = "#FFFF00"
)
ggplot(vic_election_map) +
geom_sf(aes(geometry = geometry, fill = PartyAb),
color = "white") +
coord_sf(xlim = c(144.8, 145.2), ylim = c(-38.1, -37.6)) +
scale_fill_manual(values = party_colors) +
ggtitle("Winners of Australian Federal Election in 2019",
subtitle = "Victoria")
crop_map <- vic_election_map %>%
st_crop(xmin = 144.8, xmax = 145.2,
ymin = -38.1, ymax = -37.6)
plot_vic_electorates_cropped <- ggplot(crop_map) +
geom_sf(aes(geometry = geometry, fill = PartyAb),
color = "white") +
ggtitle("Winners of Australian Federal Election in 2019",
subtitle = "Victoria") +
scale_fill_manual(values = party_colors[unique(crop_map$PartyAb)])
plot_vic_electorates_cropped
data_for_labels = crop_map %>%
filter(Elect_div %in% c("Melbourne", "Menzies", "Macnamara"))
plot_vic_electorates_cropped +
geom_sf_label(data = data_for_labels,
aes(label = Elect_div, geometry = geometry),
size = 2)
🗺 Exercise 5B
data(world, package = "spData")
ggplot(world) + geom_sf()
world %>%
st_transform(crs = "+proj=moll") %>%
ggplot() + geom_sf()
world %>%
st_transform(crs = "+proj=laea +x_0=0 +y_0=0 +lon_0=133.78 +lat_0=-25.27") %>%
ggplot() + geom_sf()
Material mainted and updated by Dr. Kate Saunders. Material
orginally developed by Dr. Emi Tanaka.