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(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

# 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(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

library(sf)
library(cartography)
library(png)
# import background image
if (!file.exists("img/background.png")) {
  githubURL <- "https://raw.githubusercontent.com/gadenbuie/ggpomological/master/inst/images/pomological_background.png"
  download.file(githubURL, "img/background.png")
}
img <- readPNG("img/background.png")
# import Communes of Martinique (sf dataframe within cartography package)

rCarto

library(sf)
library(cartography)
library(osrm)

# set osrm to my own server
options(osrm.server = "http://address.of.my.server/", osrm.profile = "driving")

# destination point (useR2019 conference in Toulouse)
dst <- data.frame(id="dst", x = 1.4344, y = 43.6113)
dst <- st_as_sf(dst, coords = c('x','y'), crs = 4326)

rCarto

library(rnaturalearth)
library(sf)
library(wbstats)
library(popcircle)
library(cartography)

# Get countries
ctry <- ne_countries(scale = 50, returnclass = "sf")
ctry <- st_transform(ctry, 54030)

rCarto

library(raster)
library(sf)
library(viridis)
library(cartography)
library(tanaka)

temp <- tempfile()
data_url <- "http://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/GHSL/GHS_POP_GPW4_GLOBE_R2015A/GHS_POP_GPW42015_GLOBE_R2015A_54009_1k/V1-0/GHS_POP_GPW42015_GLOBE_R2015A_54009_1k_v1_0.zip"
download.file(data_url, temp)
unzip(temp, exdir = "pop")

rCarto

library(raster)
library(cartography)
library(sf)
library(SpatialPosition)

mtq <- st_read(system.file("shape/martinique.shp", package="cartography"))
# use WGS84 proj
mtq_latlon <- st_transform(mtq, 4326)
# this call throw an error but seems to work...
getData('SRTM', lon=-61, lat=14)

rCarto

library(sf)
library(spatstat)
library(sp)
library(maptools)
library(raster)
library(cartography)
library(SpatialPosition)

## import dataset
feat <- sf::st_read("https://gist.githubusercontent.com/rCarto/747164575e3f216a123c3092d0ce9162/raw/f12390464f255b5f9760c577ab6bf5456cf61a40/iris75.geojson")