Baugrundstückspreise nach Bezirken, 2017

estateprices.r

# Download XLS, merge sheets and export as csv: http://www.statistik.at/web_de/statistiken/wirtschaft/preise/immobilien_durchschnittspreise/index.html
immodat <- read_csv2("baugrundstueckspreise_2017.csv",skip = 3) %>%
  select(iso=B.Nr., preis=starts_with("Euro")) %>% add_row(iso=900,preis=1000) %>% 
  filter(!is.na(iso), !is.na(preis)) %>% mutate(preis=round(preis,-1)) %>%
  mutate(preis=cut(preis,breaks=c(0,50,100,200,500,3000),
                   labels=c("<50€","50-100€","100-200€","200-500€",">500€")))

source("plotmap.R")

plotbezirke(dataset=immodat,fillvar="preis",wienbezirke=F,colpal=msc_palette[5:1],
            tit="Baugrundstückspreise in Österreich",subtit="Durchschnittspreis pro Quadratmeter nach politischen Bezirken, 2017",
            captit="Daten: Statistik Austria, Grafik: @matschnetzer",
            savfile="grundstückspreise.png",
            legpos="right")

plotmap.r

plotbezirke <- function(dataset,fillvar,colpal,wienbezirke=T,tit,subtit,captit,savfile,legpos="none"){

require(sf)
require(tidyverse)
require(ggrepel)
require(msthemes)

# Load JSON map from https://github.com/ginseng666/GeoJSON-TopoJSON-Austria
map <- ifelse(wienbezirke==T, "bezirke_wien_gross_geo.json","bezirke_999_geo.json")  

geodat <- st_read(map, quiet=TRUE, stringsAsFactors=FALSE) %>%
  mutate(
    center = map(geometry, st_centroid),
    centercoord = map(center, st_coordinates),
    ccordx = map_dbl(centercoord, 1),
    ccordy = map_dbl(centercoord, 2)
  ) %>%
    mutate(iso = as.numeric(iso)) %>%
    mutate(name = str_replace_all(name,c("\\(Stadt\\)"="","-Stadt"=""," Stadt"=""," am Wörthersee"=""))) %>%
    mutate(labselect=as.factor(iso %in% c(101,201,302,401,501,601,701,802,900)))


labels <- data.frame(iso = c(802,701,501,401,302,101,601,201,900),
                     nudgey=c(0.4,-0.5,0.1,0.5,1.2,-0.5,-1.5,-1.7,0.1),
                     nudgex=c(0,0.2,-0.6,0,0.6,1,0,-0.8,0.9))

df <- geodat %>% left_join(labels,by="iso") %>% left_join(immodat,by="iso")

df %>%
  ggplot() +
  geom_sf(aes(fill=get(paste(fillvar)))) +
  coord_sf(datum=NA) + # datum=NA to supress long and lat labels
  geom_label_repel(data=subset(df,labselect==TRUE),aes(x = ccordx,y = ccordy,label = name),
                   color="black",size=2.5,nudge_y=df$nudgey[df$labselect==TRUE],
                   nudge_x=df$nudgex[df$labselect==TRUE],segment.size = 0.3) +
  scale_fill_manual(values=colpal) +
  theme_ms(alttf = F) + theme(legend.title = element_blank(), legend.position = legpos) +
  labs(x="",y="",title=tit,subtitle=subtit,caption=captit) +
  ggsave(savfile)
}