library(eechidna)
library(plyr)
library(dplyr)
library(purrr)
library(knitr)
library(broom)
library(tidyr)
library(purrrlyr)
# inspect the data frame
glimpse(aec2013_fp)

# show the first few rows
head(aec2013_fp) %>% kable

who_won <- aec2013_2pp_electorate %>% 
  group_by(PartyNm) %>% 
  tally() %>% 
  arrange(desc(n)) 

# inspect
who_won %>% 
  kable()

# plot
library(ggplot2)
library(scales)
ggplot(who_won, 
       aes(reorder(PartyNm, n), 
           n)) +
  geom_point(size = 2) + 
  coord_flip() + 
  scale_y_continuous(labels = comma) +
  theme_bw() +
  ylab("Total number of electorates") +
  xlab("Party") +
  theme(text = element_text(size=10))

total_votes_for_parties <- aec2013_fp %>% 
  select(PartyNm, OrdinaryVotes) %>% 
  group_by(PartyNm) %>% 
  summarise(total_votes = sum(OrdinaryVotes, rm.na = TRUE)) %>% 
  ungroup() %>%
  arrange(desc(total_votes))

#  inspect
total_votes_for_parties %>% 
  head %>% # just the first 10 rows
  kable

ggplot(total_votes_for_parties, 
       aes(reorder(PartyNm, total_votes), 
           total_votes)) +
  geom_point(size = 2) + 
  coord_flip() + 
  scale_y_continuous(labels = comma) +
  theme_bw() +
  ylab("Total ordinary votes") +
  xlab("Party") +
  theme(text = element_text(size=10))

who_most_votes <- 
aec2013_fp %>% 
  filter(CandidateID != 999) %>% #exclude informal votes
  mutate(candidate_full_name = paste0(GivenNm, " ", Surname, " (", CandidateID, ")")) %>% 
  group_by(candidate_full_name) %>% 
  summarise(total_votes_for_candidate = sum(OrdinaryVotes, rm.na = TRUE)) %>% 
  arrange(desc(total_votes_for_candidate))
# inspect
who_most_votes %>% 
  mutate(total_votes_for_candidate = 
           prettyNum(total_votes_for_candidate, 
                     big.mark = ","))  %>% 
  head %>% 
  kable

who_most_votes_prop <- 
aec2013_fp %>% 
  filter(CandidateID != 999) %>% #exclude informal votes
  mutate(candidate_full_name = 
           paste0(GivenNm, " ", Surname, " (", CandidateID, ")")) %>% 
  group_by(Electorate, candidate_full_name) %>% 
  summarise(sum_votes = sum(OrdinaryVotes))  %>% 
  mutate(prop_votes = round(sum_votes / sum(sum_votes), 3),
         sum_votes = prettyNum(sum_votes, ",")) %>% 
  ungroup %>% 
  arrange(desc(prop_votes))

# inspect
who_most_votes_prop %>% 
  data.frame %>% 
  head %>% 
  kable

# summarise and compute proportion of votes for a particular party
p <- aec2013_fp %>%
    filter(CandidateID != 999) %>% # exclude informal votes
  group_by(Electorate, State) %>%
  summarise(
    TotalVotes = sum(OrdinaryVotes),
    ProportionLabor = round(sum(OrdinaryVotes[PartyNm == "Australian Labor Party"]) / TotalVotes, 3)) %>%
  filter(TotalVotes != 0) %>% 
  arrange(desc(ProportionLabor)) %>% 
  group_by(State) %>% 
  # send the data to the plotting function
  do(plots=ggplot(data = .) + 
       aes(x = ProportionLabor, 
           y = reorder(Electorate, ProportionLabor), 
           size = TotalVotes, 
           label = State) +
       geom_point() +
       ylab("Electorate") +
       labs(title = .$State) + 
       scale_x_continuous("Proportion voting Labor Party", 
                          label = percent) +
       scale_size("Number of\nvotes cast", 
                  label = comma)  +
       theme_bw() +
       theme(text = element_text(size=10)))

# draw the plots
library(gridExtra)
n <- length(p$plots)
nCol <- floor(sqrt(n))
do.call("grid.arrange", c(p$plots, ncol=nCol))

# Comparing party and candidate votes of several parties -------
proportions <- aec2013_fp %>%
  filter(CandidateID != 999) %>% # exclude informal votes
  group_by(Electorate) %>%
  summarise(Prop_Labour = sum(OrdinaryVotes[PartyNm == "Australian Labor Party"]) / sum(OrdinaryVotes),
            Prop_Coalition = sum(
              OrdinaryVotes[PartyNm == "Liberal"],
              OrdinaryVotes[PartyNm == " Country Liberal Party"],
              OrdinaryVotes[PartyNm == "The Nationals"],
              OrdinaryVotes[PartyNm == "Country Liberals (NT)"]
              ) / sum(OrdinaryVotes),
            Prop_Greens = sum(OrdinaryVotes[PartyNm == "The Greens"]) / sum(OrdinaryVotes)) 

# make a scatterplot matrix
library(GGally)
ggpairs(proportions, columns = 2:ncol(proportions)) + theme_bw()

census_and_election <- left_join(aec2013_2pp_electorate, 
                                 abs2011,
                                 by = c("Electorate" = "Electorate"))

# subset only the columns we want for the model
census_and_election_subset <-  
  census_and_election %>% 
  ungroup %>% 
  select(Electorate, 
         Average_Australian_Labor_Party_Percentage_in_electorate,
         Population:NotOwned)  %>% 
  rename(ALP_perc = Average_Australian_Labor_Party_Percentage_in_electorate)

library(corrplot)
M <- cor(census_and_election_subset[, c(2:ncol(census_and_election_subset))], 
         use = "pairwise.complete.obs")
corrplot.mixed(M, 
               lower="ellipse", 
               upper="number", 
               tl.pos = "lt",
               tl.cex = 0.5,
               tl.col = "black",
               number.cex= 0.5)

options(scipen = 10) # for more readable numbers
census_variables <- names(abs2011)[-c(1:3)]

# compute the multiple regressions
mutliple_regression_model <- 
  census_and_election %>% 
  ungroup %>% 
  select(
         Average_Australian_Labor_Party_Percentage_in_electorate,
         Population:NotOwned) %>% 
  lm(Average_Australian_Labor_Party_Percentage_in_electorate ~ ., 
     data = . )

mutliple_regression_model %>% 
  glance %>% 
  dmap(round, 3) %>% 
  kable

# find the variables with a significant effect
mutliple_regression_model %>% 
  tidy %>% 
  filter(p.value < 0.05) %>% 
  dmap_if(is.numeric, round, 3) %>% 
  arrange(p.value) %>% 
  kable

# model that includes all census variables
all_vars <- mutliple_regression_model %>% 
  tidy %>% 
  filter(p.value < 0.05) %>% 
  arrange(p.value) 

census_and_election %>% 
  ungroup %>% 
  select_(.dots = all_vars$term,
          "Average_Australian_Labor_Party_Percentage_in_electorate")  %>% 
  gather(variable, 
         value, 
         -Average_Australian_Labor_Party_Percentage_in_electorate) %>% 
  ggplot(aes(value, Average_Australian_Labor_Party_Percentage_in_electorate)) +
  geom_point() +
  geom_smooth(method = "lm") +
  facet_wrap( ~ variable, scales = "free") +
  theme_bw()

library(readr)
senate <- read_csv("http://results.aec.gov.au/17496/Website/Downloads/SenateSenatorsElectedDownload-17496.csv", 
    skip = 1)

coalition <- c("Country Liberals (NT)", "Liberal", "Liberal National Party of Queensland", 
    "The Nationals")

labor <- c("Australian Labor Party", "Australian Labor Party (Northern Territory) Branch", 
    "Labor")

greens <- c("The Greens", "Australian Greens", "The Greens (WA)")

senate <- senate %>% mutate(PartyNm = ifelse(as.character(PartyNm) %in% coalition, 
    "Liberal National Coalition", PartyNm))

senate <- senate %>% mutate(PartyNm = ifelse(as.character(PartyNm) %in% labor, 
    "Australian Labor Party", PartyNm))

senate <- senate %>% mutate(PartyNm = ifelse(as.character(PartyNm) %in% greens, 
    "Australian Greens", PartyNm))

senate$PartyNm <- factor(senate$PartyNm, levels = names(sort(table(senate$PartyNm), 
    decreasing = T)))

# Order = Liberal National Coalition, Australian Labor Party, The Greens,
# Palmer, motoring, sports, family first, Lib Dems, Nick Xenophon
partycolours = c("#0066CC", "#FF0033", "#009900", "#FFFF00", "#00008B", "#0000FF", 
    "#87CEFA", "#C71585", "#FF4500")

ggplot(data = senate, aes(x = PartyNm, fill = PartyNm)) + geom_bar() + xlab("") + 
    ylab("") + scale_fill_manual(name = "Party", values = partycolours) + coord_flip() + 
    theme(legend.position = "None")

senate$StateAb <- factor(senate$StateAb, levels = c("QLD", "NSW", "WA", "VIC", 
    "SA", "TAS", "ACT", "NT"))

ggplot(data = senate, aes(x = StateAb, fill = PartyNm)) + geom_bar() + xlab("") + 
    ylab("") + scale_fill_manual(name = "Party", values = partycolours)