ECQ Primary Vote data

If you've run the ECQ scraper script you should have a file called primary_votes.csv in your Data folder. The scraper script pulls down the results pages from the ECQ website and extracts the results summary table for each electorate. I realise that there's a downloadable zip file with an XML file but trying to deal with unzipping an XML and parsing the tree seemed to be too difficult. In any case, the HTML data is is all coerced into a tidy data frame with each candidate's party affiliation (IND if none given), raw number of formal, primary votes, and the percentage of the vote for that electorate received. A full distribution of preferences is not available yet.

# source("ecq_scraper.R")
library(tidyverse)
primary_votes <- read_csv("Data/primary_votes.csv")
head(primary_votes)

## # A tibble: 6 x 5
##           Candidate Party Votes Percent Electorate
##               <chr> <chr> <dbl>   <dbl>      <chr>
## 1    LANYON, Darryl   ONP  4963   18.17   Algester
## 2 PATTISON, Clinton   LNP  5916   21.65   Algester
## 3    ENOCH, Leeanne   ALP 14219   52.04   Algester
## 4    O'BRIEN, Patsy   GRN  2223    8.14   Algester
## 5     MELLISH, Bart   ALP 11600   37.50     Aspley
## 6     HANSEN, James   GRN  2871    9.28     Aspley

We need to choose some colours for each party running. These are taken from the Wikipedia template except for CR, the Consumer Rights & No-Tolls party.

cols <- unlist(list(ALP = "#DE3533",
                    GRN = "#39b54a",
                    LNP = "#1456F1",
                    IND = "#808080",
                    KAP = "#b50204",
                    ONP = "#F8F16F",
                    CR = "purple"))

We also need to process the data to show who is independent, and to order the candidates from largest to smallest vote (with 1 being the largest vote)

to_plot <- mutate(primary_votes, 
                  Party = if_else(is.na(Party), 
                                  "IND", 
                                  Party)) %>%
  group_by(Electorate) %>%
  arrange(desc(Percent)) %>%
  mutate(order = 1:n()) %>%
  arrange(Electorate, order)

Now consider all electorates shown as pie charts, with candidates ordered by share of vote

ggplot(data=to_plot,
       aes(x=0, y=Percent)) + 
  geom_col(aes(fill=Party, group=order), 
           position=position_stack(),
           color="black") +
  coord_polar(theta = "y") +
  facet_wrap( ~ Electorate, nrow=16) +
  theme_minimal() + 
  theme(legend.position="top", axis.text = element_blank(), 
        strip.text = element_text(size=7),
        panel.grid = element_blank()) +
  scale_fill_manual(values = cols) +
  xlab("") + ylab("") +
  ggtitle(label = "Primary vote for each candidate in each electorate",
          subtitle = "2017 Queensland State Election, preliminary ECQ results")