Last updated: 2021-05-23

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Map usingozmaps

#ozmaps data not on CRAN
#devtools::install_github("mdsumner/ozmaps.data")

Load libraries

pkgs <- c("ozmaps", "sf", "sp", "dplyr", "rgdal",
          "raster", "ggplot2", "viridis", "readr",
          "paletteer", "rmapshaper", "ggrepel",
          "tidyverse", "ozmaps.data", "readxl")
lapply(pkgs, require, character.only = TRUE)

Import data

site_map <- read_excel("data/site_map.xlsx")
site_map_lab = column_to_rownames(site_map, var = "Site name")

site_map <- read_excel(“data/site_map.xlsx”, col_types = c(“text”, “text”, “numeric”, “numeric”, “text”, “numeric”))

Ozmaps

package data

Simple Aus map

ozmap()

Plot of states

sf_oz <- ozmap_data("states")
if (utils::packageVersion("paletteer") < '1.0.0') {
 pal <- paletteer::paletteer_d(package = "ochRe", palette = "namatjira_qual")
} else {
  pal <- paletteer::paletteer_d(palette = "ochRe::namatjira_qual")
} 

mycols = c("#a19a11", "#D3D3D3", "#D3D3D3", "#D3D3D3", "#D3D3D3", "#D3D3D3", "#D3D3D3", "#365350", "#a19a11", "#3f4482")
opal <- colorRampPalette(pal)
nmjr <- opal(nrow(sf_oz))
plot(st_geometry(sf_oz), col = mycols)

Plot the ABS layers (from 2016).

ozmap("abs_ced", col = opal(nrow(abs_ced)))

Plot of map with city regions abs_gccsa

citycols = c("#D3D3D3", "#3f4482",  "#D3D3D3",  "#D3D3D3",  "#D3D3D3",  "#D3D3D3",  "#D3D3D3",  "#D3D3D3",  "#3f4482",  "#D3D3D3",  "#D3D3D3",  "#D3D3D3",  "#D3D3D3",  "#D3D3D3",  "#D3D3D3",  "#D3D3D3")
plot(st_transform(abs_gccsa, LCC), main = "Greater Capital City Statistical Areas", col = citycols)

Enlarge northern beaches

library(dplyr)
kbor <- abs_lga %>% dplyr::filter(grepl("Northern Beaches", NAME))
bb <- st_bbox(kbor)

layout(matrix(c(1, 1, 1, 2, 2, 2, 2, 2, 2), nrow = 3))
plot(kbor, reset = FALSE, main = "Northern Beaches (NSW)")
rect(bb["xmin"], bb["ymin"], bb["xmax"], bb["ymax"])
library(mapdata)
#> Loading required package: maps
par(mar = rep(0, 4))
plot(c(110, 160), c(-45, -5), type = "n", asp = 1/cos(mean(bb[c(2, 4)]) * pi/180), axes = FALSE, xlab = "", ylab = "")
plot = maps::map(database = "worldHires", regions = "australia", xlim = c(110, 160), ylim = c(-45, -5), add = TRUE)
rect(bb["xmin"], bb["ymin"], bb["xmax"], bb["ymax"])


buffer <- 0.11
min_lon <- 110
max_lon <- 160
min_lat <- -45
max_lat <- -5

geo_bounds <- c(left = min_lon, bottom = min_lat, right = max_lon, top = max_lat)

Tutorial

here

oz_states <- ozmaps::ozmap_states
ggplot(oz_states) + 
  geom_sf() + 
  coord_sf()

oz_states <- ozmaps::ozmap_states %>% filter(NAME != "Other Territories")
oz_votes <- rmapshaper::ms_simplify(ozmaps::abs_ced)
ggplot() + 
  geom_sf(data = oz_states, mapping = aes(fill = NAME), show.legend = FALSE) +
  geom_sf(data = oz_votes, fill = NA) + 
  coord_sf()

# large map
map1 = ggplot() + 
  geom_sf(data = oz_votes) + 
  geom_sf(data = oz_states, colour = "black", fill = "NA") + 
  geom_point(data = site_map_lab, mapping = aes(x = lon, y = lat, color = Site, shape=Site, size= Size)) + 
  coord_sf() + theme_classic() + theme(legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank()) + scale_color_manual(values=c("#E69F00", "#56B4E9")) + scale_shape_manual(values=c(8, 16)) + xlim (114,155)

# base map for inserts
map2 = ggplot() + 
  geom_sf(data = oz_votes)  +
  geom_sf(data = oz_states, colour = "black", fill = "NA") + 
  geom_point(data = site_map_lab, mapping = aes(x = lon, y = lat, color = Site, shape=Site, size= Size)) + 
  coord_sf() + theme_classic() + theme(legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank()) + scale_color_manual(values=c("#999999", "#E69F00"))

# zoom in on Sydney region
map3 = map2 + coord_sf(xlim = c(150, 152), ylim = c(-35, -33.0)) + theme(legend.position = "none") + theme(axis.title.x = element_blank(), axis.title.y = element_blank())
map4 = map2 + coord_sf(xlim = c(115, 117), ylim = c(-32.5, -31)) + theme(legend.position = "none") + theme(axis.title.x = element_blank(), axis.title.y = element_blank())

ggsave("map_base.pdf", plot = map1, path = "output/plots", width = 30, height = 15, units = "cm")
ggsave("map_NSW.pdf", plot = map3, path = "output/plots", width = 10, height = 10, units = "cm")
ggsave("map_WA.pdf", plot = map4, path = "output/plots", width = 10, height = 10, units = "cm")

Capital cities

oz_capitals <- tibble::tribble( 
  ~city,           ~lat,     ~lon,
  "Sydney",    -33.8688, 151.2093,  
  "Melbourne", -37.8136, 144.9631, 
  "Brisbane",  -27.4698, 153.0251, 
  "Adelaide",  -34.9285, 138.6007, 
  "Perth",     -31.9505, 115.8605, 
  "Hobart",    -42.8821, 147.3272, 
  "Canberra",  -35.2809, 149.1300, 
  "Darwin",    -12.4634, 130.8456, 
)

Alternative

Following tutorial at this link

Load libraries

pkgs <- c("sp", "dplyr", "rgdal", 
          "raster", "ggplot2", "readxl",
          "viridis", "readr")
lapply(pkgs, require, character.only = TRUE)

Load your point data file

site_map <- read_excel("data/site_map.xlsx")

Make map of Australia

Create buffer around your data points

# Make boundaries
buffer <- 0.11
min_lon <- 110
max_lon <- 160
min_lat <- -45
max_lat <- -5

geo_bounds <- c(left = min_lon, bottom = min_lat, right = max_lon, top = max_lat)


# Now map with boundaries
Sites.grid <- expand.grid(lon_bound = c(geo_bounds[1], geo_bounds[3]), 
                       lat_bound = c(geo_bounds[2], geo_bounds[4]))
coordinates(Sites.grid) <- ~ lon_bound + lat_bound

Load mapping files Downloaded from GEODATA COAST 100K 2004 shapefiles available here

Aus <- readOGR(dsn = "~/Documents/Programs/R/Maps/Rpackage_Geodata-coast/australia",layer = "cstauscd_r")

Remove coastline clutter from map

Aus_coast <- subset(Aus, FEAT_CODE != "sea")
plot <- plot(Aus_coast)

Now apply boundaries to the map without coastline

Aus_crop <- crop(Aus_coast, extent(Sites.grid))
plot(Aus_crop)

Plot map with axis

aus_plot1 <- ggplot() + 
 geom_polygon(data = Aus_crop, aes(x=long, y=lat, group=group), fill = "gray98", colour="black") +
 coord_equal() +
 labs(x="Longitude", y="Latitude") +
 theme_classic()
aus_plot2 = aus_plot1 + geom_point(data = site_map, aes (x=lon, y=lat, color=Site, stroke=1)) +
  geom_text(label=(site_map$Site)) +  scale_color_brewer(palette = "Set1") + theme_classic()
ggsave("aus_plot2.pdf", plot = aus_plot2, path = "output/plots", width = 15, height = 15, units = "cm")

Test

tutorial

# load library
library(OpenStreetMap)
# extract map
AustraliaMap <- openmap(c(-8,110),
    c(-45,160),
#   type = "osm",
#   type = "esri",
    type = "nps",
    minNumTiles=7)
plot(AustraliaMap)
library(DT)
opt <- c("osm", "osm-bw","maptoolkit-topo", "waze", "bing", "stamen-toner", "stamen-terrain", "stamen-watercolor", "osm-german", "osm-wanderreitkarte", "mapbox", "esri", "esri-topo", "nps", "apple-iphoto", "skobbler", "hillshade", "opencyclemap", "osm-transport", "osm-public-transport", "osm-bbike", "osm-bbike-german")
opt <- data.frame(opt)
# extract map
queensland1 <- openmap(c(-8,135),
    c(-30,160),
    type = "osm",
    minNumTiles=6)
queensland2 <- openmap(c(-8,135),
    c(-30,160),
    type = "esri",
    minNumTiles=6)
# plot maps
par(mfrow = c(1, 2)) # display plots in 1 row/2 columns
plot(queensland1); plot(queensland2); par(mfrow = c(1, 1)) # restore original settings

Leaflet here

# load package
library(leaflet)
# load library
m <- leaflet() %>% setView(lng = 153.05, lat = -27.45, zoom = 12)
# display map
m %>% addTiles()
# add ERSI theme
esri <- grep("^Esri", providers, value = TRUE)
m %>% addTiles() %>% addLayersControl(baseGroups = names(esri))


leaflet(data = site_map) %>% addTiles() %>%
  addMarkers(~lon, ~lat, icon=oceanIcons)