rCarto

library(cartography)
library(sp)
# Load data
data(nuts2006)

# Get a SpatialLinesDataFrame of countries borders
nuts0.contig.spdf <- getBorders(nuts0.spdf)

# Get the GDP per capita
nuts0.df$gdpcap <- nuts0.df$gdppps2008/nuts0.df$pop2008*1000000

rCarto

# World basemap
download.file(url = "https://raw.githubusercontent.com/riatelab/basemaps/master/World/countries.geojson",
              destfile = "country.geojson")
# Graticules layer
download.file(url = "https://raw.githubusercontent.com/riatelab/basemaps/master/World/graticule30.geojson",
              destfile = "graticule.geojson")
# Population data base
download.file(url = "https://population.un.org/wup/Download/Files/WUP2018-F12-Cities_Over_300K.xls",
              destfile = "citypop.xls")

rCarto

library(SpatialPosition)
library(raster)
data(hospital)
# Compute Stewart potentials from known points (hospital) on a
# grid defined by its resolution
pot <- stewart(knownpts = hospital, varname = "capacity",
               typefct = "exponential", span = 750, beta = 3,
               resolution = 100, mask = paris)
# Create a raster of potentials values
ras <- rasterStewart(x = pot)

rCarto

library(cartography)
library(sf)
mtq <- st_read(system.file("gpkg/mtq.gpkg", 
                           package="cartography"))

bks <- getBreaks(v = mtq$MED, nclass = 5, 
                 method = "quantile")
cols <- carto.pal(pal1 = "turquoise.pal", n1 = 5)
choroLayer(x = mtq, var = "MED", 
           breaks = bks, col = cols, 

rCarto

library(cartography)
library(sf)
#> Linking to GEOS 3.7.1, GDAL 2.4.0, PROJ 5.2.0

# create an sf object centered on Tizi Ouzou city
tizi <- data.frame(name = "Tizi Ouzou", longitude = 4.06, latitude = 36.71)
tizi <- st_as_sf(tizi, coords = c("longitude", "latitude"), crs = 4326)
tizi <- st_transform(tizi, 3857)
tizi &lt;- st_buffer(tizi, 5000)

rCarto

library(sf)
nc = st_read(system.file("shape/nc.shp", package="sf"))
nc <- st_transform(nc, 32119)
# regular grid 512096 polygons
grid <- st_make_grid(x = nc, cellsize = 500)

t0 <- Sys.time()
plot(st_geometry(nc))
plot(grid, add = TRUE)
# end of the second plot

rCarto

library(units)
library(sf)
library(cartography)

# library(osmdata)
# 
# # define a bounding box
# q0 <- opq(bbox = c(2.2247, 48.8188, 2.4611, 48.9019)) 
# 
# # extract Paris boundaries

rCarto

library(rnaturalearth)
library(cartography)
library(sf)

lakes <- ne_download(scale = 10, type = "lakes", category = c("physical"), 
                     destdir = tempdir(), load = TRUE, returnclass = c("sf"))
countries <- ne_download(scale = 10, type = "countries", 
                         category = c("cultural"), destdir = tempdir(), 
                         load = TRUE, returnclass = c("sf"))
countries <- st_transform(countries, "ESRI:54017")

rCarto

library(sf)
library(elevatr)
library(raster)
library(tanaka)
# a polygon of bangalore
bangalore <- st_read("export.geojson")
elevation <- get_elev_raster(bangalore, z = 10)
bangalore_elevation <- trim(mask(elevation, bangalore))

# inspect min and max values

rCarto

library(sf)
library(osrm)
library(maptiles)
# build a bbox for Paris
bb <- st_bbox(c(xmin = 643069, ymin = 6857478, 
                xmax = 661079, ymax = 6867081),
              crs = 2154)
# get map tiles 
osm <- get_tiles(x = bb, provider = "CartoDB.PositronNoLabels", 
                 crop = TRUE, zoom = 13)