nswgeo

A collection of geospatial datasets and map plotting helpers for working with New South Wales maps. The maps are registered with {cartographer}. This package is not an official publication, merely a collection of handy data from public sources.

Installation

You can install nswgeo like so:

# CRAN release
install.packages('nswgeo')

# development version
install.packages('nswgeo', repos = c('https://cidm-ph.r-universe.dev', 'https://cloud.r-project.org'))

Examples

The data can be used directly with ggplot:

library(nswgeo)
library(ggplot2)

ggplot(nswgeo::nsw) +
  geom_sf(fill = "white", data = nswgeo::australia) +
  geom_sf(fill = "#9BCBEB", colour = NA) +
  geom_sf(fill = NA, colour = "white", data = nswgeo::lhd) +
  geom_sf(fill = NA, colour = "#002664", linewidth = .8) +
  annotate(
    "text", x = 144, y = -27, label = "New South Wales",
    angle = -5, size = 5, fontface = "italic", colour = "#002664"
  ) +
  coord_sf(crs = crs_gda2020_albers(), default_crs = crs_gda2020()) +
  theme_minimal() +
  theme(
    panel.background = element_rect(fill = "grey97", colour = NA),
    axis.title = element_blank(),
  )

Alternatively, the data can be accessed through {cartographer}:

library(cartographer)
head(covid_cases_nsw)
#> # A tibble: 6 × 5
#>   postcode lga       lhd                 year type 
#>   <chr>    <chr>     <chr>              <int> <chr>
#> 1 2427     Mid-Coast Hunter New England  2022 B    
#> 2 2761     Blacktown Western Sydney      2021 A    
#> 3 2426     Mid-Coast Hunter New England  2022 B    
#> 4 2148     Blacktown Western Sydney      2022 B    
#> 5 2768     Blacktown Western Sydney      2021 A    
#> 6 2766     Blacktown Western Sydney      2021 B

covid_cases_nsw |>
  dplyr::count(lga) |>
  add_geometry(lga, feature_type = "nswgeo.lga") |>
  ggplot() +
  geom_sf(aes(fill = n)) +
  geom_sf(fill = NA, data = map_sf("nswgeo.lga")) +
  scale_fill_viridis_b("Cases") +
  theme_void()

A note on resolution

Some of the source datasets are very high resolution and would make this package too large if they were bundled. Accordingly, most data has been processed to reduce the resolution. You can see exactly what was done by looking at the scripts in data-raw/, which also help to download source data.

If you need higher resolution shapes, such as when zooming into specific postal areas, you’re better off using the original datasets directly.

Other Australian data

• strayr helps with working with data from the Australian Bureau of Statistics (ABS).
• absmapsdata contains some processed ABS geospatial data.