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Timothée Giraud rCarto

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rCarto / inset.R
Created Mar 3, 2016
Carton cartographique / Map Inset
View inset.R
# Démarrer le package cartography
library(cartography)
# Charger les données stockées sur le web
load((url("http://wukan.ums-riate.fr/bond/bondfiles.RData")))
fdim <- getFigDim(spdf = WorldCountries[WorldCountries$ISO3 %in%
c( "RUS","NZL", "ARG"),],
width = 750, res = 120,
mar = c(0,0,1.2,0))
@rCarto
rCarto / panamapapers.R
Last active Apr 4, 2016
Map of Panama Papers reporters
View panamapapers.R
# load library
library(cartography)
# load data
load(url("http://wukan.ums-riate.fr/panamapapers/panamapapers.RData"))
# get the figures dimensions
fd <- getFigDim(spdf = grat, width = 800,
res = 120, mar = c(0,0,1.2,0))
@rCarto
rCarto / cartomix.R
Last active May 15, 2019
Script to build the cartomix figure
View cartomix.R
library(cartography)
library(sp)
library(sf)
# Load data
data(nuts2006)
# Save image
sizes <- getFigDim(x = nuts0.spdf, width = 700, mar = c(0,0,0,0), res = 100)
png('./img/map8.png', width = sizes[1], height = sizes[2], res = 100)
View trypar.R
library(SpatialPosition)
load("../LargeSpatialPostition/com.RData")
# création d'une grille fine (5km)
grid <- CreateGrid(w = com, resolution = 5000)
# Les calculs de matrices de distances sont (beaucoup) plus rapides quand les
# objets spatiaux sont en WGS84 :
View msd.R
library(cartography)
data("nuts2006")
# GDP par habitants (en euros)
nuts2.df$gdpinh <- 1000000 * nuts2.df$gdppps2008 / nuts2.df$pop2008
boxplot(nuts2.df$gdpinh)
hist(nuts2.df$gdpinh)
rug(nuts2.df$gdpinh)
# Bon d'accord la distribution n'est pas vraiment normale...
View brexit.R
library(sp)
library(raster)
library(rgeos)
## import des données
download.file(url = "http://biogeo.ucdavis.edu/data/gadm2.8/rds/GBR_adm2.rds",
destfile = file.path(tempdir(), "GBR_adm2.rds"))
download.file(url = "http://biogeo.ucdavis.edu/data/gadm2.8/rds/IRL_adm0.rds",
file.path(tempdir(),"IRL_adm0.rds"))
View addresses.csv
We can make this file beautiful and searchable if this error is corrected: Unclosed quoted field in line 6.
"","location","name","housenumber","street","postcode","city","state","country","osm_key","osm_value","lon","lat","msg"
"1","Germany, Augsburg, Berliner Allee 30","Hauptfeuerwache - Berufsfeuerwehr Augsburg","30","Berliner Allee","86153","Augsburg","Bayern","Deutschland","amenity","fire_station",10.9151257435963,48.3738761,NA
"2","Germany, Bautzen, Gesundbrunnenring 23","Feuerwache Bautzen","23","Gesundbrunnenring","02625","Bautzen","Sachsen","Deutschland","amenity","fire_station",14.4383893321343,51.1871298,NA
"3","Germany, Bielefeld, Stadtholz 18","Feuerwache Ost","18","Am Stadtholz","33609","Bielefeld","Nordrhein-Westfalen","Deutschland","amenity","fire_station",8.5537485,52.0256609,NA
"4","Germany, Bochum, Brandwacht 1","Freiwillige Feuerwehr Bochum Brandwacht (LE31)","1","Brandwacht","44894","Bochum","Nordrhein-Westfalen","Deutschland","amenity","fire_station",7.2987397,51.4891521,NA
"5","Germany, Bonn, Lievelingsweg 112","Leitstelle Berufsfeuerwehr Bonn","112","Lievelingsweg","53119","Bonn","Nordrhein-W
View geocode_adressedatagouvfr.R
library(httr)
adresse <- "8 bd du port"
resultraw <- httr::GET(utils::URLencode(paste0('https://api-adresse.data.gouv.fr/search/?q=', adresse)))
result <-jsonlite::fromJSON(httr::content(resultraw, "text"))
result$features
View reprop.R
legpos <- function(pos, x1, x2, y1, y2, delta1, delta2, legend_xsize, legend_ysize){
# Position
if (pos == "bottomleft") {
xref <- x1 + delta1
yref <- y1 + delta1
}
if (pos == "topleft") {
xref <- x1 + delta1
yref <- y2 - 2 * delta1 - legend_ysize
}
View xx.json
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