Österreich NRW 2019 - Wien: Abstand zwischen stärkster und zweitstärkster Partei je Bezirk

library(tidyverse)
library(ggtext)  
library(lemon)  

wdr <- getwd()

Read, clean, manipulate data

read data; all files at once; include file name as file-id; locale ‘latin1’ for Umlaute (ü etc).

tbl <- read_plus <- function(flnm) 
{ read_csv2(flnm,
            locale=locale(encoding="latin1")) %>%
    mutate(filename = flnm) } 

df <- list.files(path=paste0(wdr, "/data"),
  pattern = "*.csv", full.names = T) %>%
  map_df(~read_plus(.))

extract year from filename.

df <- df %>% 
  janitor::clean_names() %>% 
  rename(gkz=x1) %>% 
  mutate(jahr=case_when(str_detect(filename,"nrw17") ~ 2017,
                        str_detect(filename, "2019") ~ 2019,
      TRUE ~ NA_real_))

take only those rows pertaining to Vienna and district levels; sort (leader and runners-up)

Wien <- df %>% 
  #filter(str_detect(gebietsname, "Wien")) #%>% 
  filter(str_detect(gkz, regex("^G9[0-9]{2}0{2}"))) %>% 
  filter(!gkz=="G90000") %>% 
  select(jahr, gkz:neos) %>% 
  pivot_longer(cols=spo:neos, 
               names_to="party",
               values_to = "votes_abs") %>% 
  mutate(votes_rel=votes_abs/gultige*100) %>% 
  group_by(jahr, gkz) %>% 
  arrange(desc(jahr), desc(votes_rel), .by_group=T) %>% 
  slice(1:2) %>% 
  mutate(pos=row_number())

get leaders and runners-up into one row; calculate vote difference;

Wien_x <- Wien %>% 
  mutate_at(vars(party:votes_rel), .funs=list(runnersup=~lead(.,1))) %>% 
  filter(pos==1) %>% 
  arrange(desc(gkz), jahr) %>% 
  mutate(votes_diff=votes_rel-votes_rel_runnersup) 

identify those district in which leader changed between 2017 and 2019

spo_loss <- Wien_x %>% 
  select(jahr, gebietsname, party, gkz) %>% 
  pivot_wider(id_cols=c(gkz, gebietsname, jahr), names_from="jahr", values_from = "party", names_prefix = "year") %>% 
  mutate(spo_loss=case_when(year2017=="spo" & year2019!="spo" ~ "loss",
                            TRUE ~ as.character("no loss")))

Plot

plot_wien <- Wien_x %>% 
  ungroup() %>% 
  mutate(jahr=as_factor(jahr)) %>% 
  ggplot()+
  geom_rect(data=spo_loss %>% filter(spo_loss=="loss"), 
            aes(xmin=-Inf, xmax=Inf,
                ymin=-Inf, ymax=Inf),
            fill="grey30",
            alpha=0.2)+
  geom_segment(aes(x=jahr, xend=jahr,
                   y=votes_rel, yend=votes_rel_runnersup))+
  geom_text(aes(x=jahr,
                y=votes_rel_runnersup+votes_diff/2,
                label=round(votes_diff, 1)),
            nudge_x=0.2,
            size=3)+
  geom_point(aes(y=votes_rel_runnersup,
                 x=jahr,
                 color=party_runnersup),
             size=3) +
    geom_point(aes(x=jahr, 
                 y=votes_rel,
                 color=party),
             size=3)+
  facet_rep_wrap(vars(gebietsname), repeat.tick.labels = T)+
  scale_y_continuous(limits=c(0, 50), 
                     breaks=seq(0, 50, 20),
                     labels=scales::percent_format(scale=1))+
  theme_minimal()+
  scale_color_manual(values=c("grune"="#A3C630", "neos"="#EA5290", "fpo"="#005DA8",
                              "spo"="#FC0204", "ovp"="#5DC2CC"),
                     labels=c("grune"="Grüne", "neos"="Neos", "fpo"="FPÖ",
                              "ovp"="ÖVP", "spo"="SPÖ"))+
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.major.x = element_blank(),
        plot.caption = element_markdown(color="grey30"),
        legend.position = "bottom",
        axis.title.x = element_blank(),
        axis.title.y = element_text(size=rel(0.8), hjust=1, color="grey30"),
        #plot.title = element_markdown(),
        legend.title = element_blank(),
        strip.text = element_text(face="bold", hjust=0))+
  labs(title="Nationalratswahl 2019 - Wien: Abstand zwischen stärkster und zweitstärkster Partei je Bezirk",
       subtitle="Graue Unterlegung: SPÖ verliert Position als stärkste Partei. Vergleich zu NRW 2017.",
       y="% Stimmen",
       caption="Daten: data.gv.at, Stand: 2019-09-30, 21:34:06<br>Roland Schmidt | @zoowalk | <span style='color:black'>**werk.statt.codes**</span>")

move legend to bottom-right empty space.

lemon::reposition_legend(plot_wien, position='bottom right', panel='panel-5-5')

#Wienliebe