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In this tutorial I will show you I build my election maps.

### Preliminaries

Before we start, we need some data and a graphic tool for the plotting:

3.  dgd The graphics are generated with the great R-package ggplot2 developed by Hadley Wickham.

As the data has just the names and a special number of the communes, we need to get the geographic coordinates from them. For this task I used the package ggmap by which you can get the coords for your communes from google (Attention: just 2500 queries per day).

My geocode function:

getCoordsfromGoogle <- function(listofCities, printQuery=TRUE) {
library(ggmap)
n <- length(listofCities)
coords <- data.frame(lon=rep(NA, n),
lat=rep(NA, n))
for (i in 1:n) {
c <- geocodeQueryCheck()
if (printQuery) {
print(c)
}
if (c > 0) {
coords[i, ] <- geocode(listofCities[i])
} else {
return(coords)
}
}
return(coords)
}


Now we have to prepare or election data. I omit some manually preparation of the data. First load the shape data from gadm:

load("DEU_adm3.RData")
bwk <- gadm[which(gadm$NAME_1 == "Baden-Württemberg"), ]  The election data is loaded into a data frame called lw I did a rbind with the coordinates I got by google and calculate the relative election results of the two groups: lw$CDUrel <- (lw$CDU.CSU + lw$FDP) / lw$Gültige.Stimmen lw$SPDrel <- (lw$SPD + lw$GRüNE + lw$DIE.LINKE) / lw$Gültige.Stimmen

lw$abs <- 100*(lw$CDUrel - lw\$SPDrel)


Yet we are able to plot the first map with the raw data.

p <- ggplot() +
geom_point(data    = lw,
mapping = aes(x=lon, y=lat, colour=abs),
size    = 3,
alpha   = 0.8) +
low   = "darkred",
mid   = "white",
high  = "blue",
guide = "colorbar") +
p + geom_path(data    = bwk,
mapping = aes(x=long, y=lat, group=group),
size    = 0.125)


The following graphics shows the result. Part two will follow in the next days. 