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datagistips / election_choropleth.R
Created June 2, 2012 07:51
Elections Simple Choropleth
library(rgdal)
library(maptools)
library(classInt)
setwd("F:/METHODES/1205_CAMPAGNE_PRES")
#########################
## READING SOURCE DATA ##
#########################
# on intègre les contours des communes GEOFLA
@datagistips
datagistips / elections_distance_coast.R
Created June 2, 2012 07:59
Elections : votes and distance to the coast
## ATTENTION !!: l'utilisation de ce code nécessite d'avoir
# lancé le code précédent (https://gist.github.com/2857180) ,
# en gros d'avoir créé l'objet spatial comm2 avec les résultats du vote.
library(rgdal)
library(spatstat)
library(maptools)
## COERCING DATA for SPATSTAT ##
# on lit le trait de côte téléchargé depuis
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datagistips / Isarithmic_Choropleth-election_results.R
Created June 4, 2012 12:40
Isarithmic Choropleth - election results
library(rgdal)
# DATA
# http://professionnels.ign.fr/ficheProduitCMS.do?idDoc=6185461
cities <- readOGR("C:/DATAS/GEOFLA/COMMUNES/COMMUNE.SHP", "COMMUNE")
# http://www.data-publica.com/opendata/13495--resultats-du-deuxieme-tour-de-l-election-presidentielle-2012
votes <- read.csv2("IN/votesResults2012.csv",sep=",", quote="\"")
# VOTES TO SPATIAL DATA "
votes$pc.sar <- votes$results.sarkozy / votes$results.exprimes
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datagistips / heatmap_candidats_profession.R
Created June 13, 2012 05:43
HeatMap Profession des candidats aux législatives et Parti
library(gdata)
library(reshape)
library(classInt)
###############
# INTEGRATING #
###############
f <- read.xls("IN/Leg 2012 Candidatures T1 31 05 2012.xls", sheet=1)
nuances <- read.xls("IN/Leg 2012 Candidatures T1 31 05 2012.xls", sheet=2, skip=2, header=FALSE)
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datagistips / res_leg_2nd_T.R
Created June 20, 2012 16:27
Fichier des gagnants aux élections législatives 2nd Tour d'après le fichier de Data Publica
library(gdata)
library(stringr)
#Fichier source : http://www.data-publica.com/data/13622--resultat-par-circonscription-du-2nd-tour-des-elections-legislatives-17-juin-2012
res <- read.xls("IN/Leg 2012 Résultats CirLG FE T2.xls") # résultats législatifs
###########################
# RECUPERATION DU GAGNANT #
###########################
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datagistips / gist:3315054
Created August 10, 2012 15:29
Sextante QGIS - R script for analysing neighbord relationships below a certain distance threshold
##[datagistips]=group
##polygons=vector
##distance=number 10
##output=output vector
library("spdep")
nb = poly2nb(polygons, snap=distance)
polygons[["nNeigh"]] = card(nb)
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datagistips / neighbor.R
Created August 10, 2012 15:38
Neighbord relationships below a certain distance threshold
library(rgdal)
library(spdep)
pol <- readOGR(".", "buildings") # read the data using rgdal
nb <- poly2nb(pol, snap=50) # create a graph. Two buildings are connected if they are 50 meters apart from one another.
pol$nNeigh <- card(nb) # card(nb) gives the neighbor counts
pol$neighGroup <- n.comp.nb(nb)$comp.id # n.comp.nb contains the informations about the disconnected compontents
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datagistips / city_names_wordcloud.R
Created September 16, 2012 21:04
city names wordcloud
library(rgdal)
library(wordcloud)
library(reshape)
library(maptools)
library(classInt)
library(FactoMineR)
library(FNN)
### LOAD DATA ###
f <- readOGR(".", "COMMUNE")
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datagistips / per.area_atlas.R
Last active December 11, 2015 13:08
Computing Linear Shape Metric
library(rgdal)
library(spatstat)
library(maptools)
library(rgeos)
setwd("D:/METHODES/1211_ELONGATEDCOUNTRIES/")
countries <- readOGR("D:/DATAS/NATURAL EARTH/ne_110m_admin_0_countries_lakes/ne_110m_admin_0_countries_lakes.shp", "ne_110m_admin_0_countries_lakes")
@datagistips
datagistips / alphabetical_atlas.R
Last active December 11, 2015 17:38
Pays rangés selon l'alphabet
library(rgdal)
countries <- readOGR("D:/DATAS/NATURAL EARTH/ne_110m_admin_0_countries_lakes/ne_110m_admin_0_countries_lakes.shp", "ne_110m_admin_0_countries_lakes")
first <- substr(as.character(countries$name),0,1) # on détermine la première lettre du nom du pays
first[grep("Sahara", countries$name)] <- "S"
first[grep("Guinea", countries$name)] <- "G"
first[grep("Korea", countries$name)] <- "K"
first[grep("Congo", countries$name)] <- "C"
first[grep("Antarctic", countries$name)] <- "A"