Introduction

eechidna (Exploring Election and Census Highly Informative Data Nationally for Australia) is an R package that makes it easy to look at the data from the Australian Federal elections and Censuses that occurred between 2001 and 2019. An Australian Federal election typically takes place every three years (2001, 2004, 2007, 2010, 2013, 2016 and 2019), and a Census of Population and Housing is conducted every five years (2001, 2006, 2011, 2016). The data in this package includes voting results for each polling booth and electoral division (electorate), Census information for each electorate and maps of the electorates that were in place at the time of each event. All data in this package is obtained from the Australian Electoral Commission, the Australian Bureau of Statistics and the Australian Government.

This vignette documents how to access these datasets, and shows a few typical methods to explore the data.

Joining election and Census data

Each electoral division has a unique ID (UniqueID) that can be used to match together Censuses and elections.

library(knitr)
library(dplyr)
library(eechidna)
data(tpp16)
data(abs2016)

# Join 2016 election and Census
data16 <- left_join(tpp16 %>% select(LNP_Percent, UniqueID), abs2016, by = "UniqueID")

# See what it looks like
data16 %>% 
  select(LNP_Percent, DivisionNm, Population, Area, AusCitizen, MedianPersonalIncome, Renting) %>% 
  head() %>% 
  kable

Election results

For each of the six elections, three types of votes are recorded: - First preference votes: a tally of primary votes (as Australia has a preferential voting system). These datasets are labelled fp (e.g. fp16 for 2016). - Two party preferred vote: a measure of preference between the two major parties - the Australian Labor Party (Labor) and the Liberal/National Coalition (Liberal). These are labelled tpp (e.g. tpp16 for 2016). - Two candidate votes: a measure of preference between the two leading candidates in that electorate. These are labelled tcp (e.g. tcp16 for 2016).

The same voting results are available for each polling booth (of which there are around 7500). This data is obtained by calling one of the three functions; firstpref_pollingbooth_download, twoparty_pollingbooth_download and twocand_pollingbooth_download, all of which pull (large) datasets from github. Geocoordinates for each polling booth are also detailed in these datasets (when available).

Let’s have a look at some of the results from the 2016 Federal election.

# Load data
data(fp16)
data(tcp16)

who_won <- tcp16 %>% 
  filter(Elected == "Y") %>% 
  group_by(PartyNm) %>% 
  tally() %>% 
  arrange(desc(n)) 

# Inspect
who_won %>% 
  kable()

total_votes_for_parties <- fp16 %>% 
  select(PartyNm, OrdinaryVotes) %>% 
  group_by(PartyNm) %>% 
  dplyr::summarise(total_votes = sum(OrdinaryVotes, rm.na = TRUE)) %>% 
  ungroup() %>%
  arrange(desc(total_votes))

# Plot the total votes for each party
library(ggplot2)
ggplot(total_votes_for_parties, 
       aes(reorder(PartyNm, total_votes), 
           total_votes)) +
  geom_point(size = 2) + 
  coord_flip() + 
  scale_y_continuous(labels = scales::comma) +
  theme_bw() +
  ylab("Total number of first preference votes") +
  xlab("Party") +
  theme(text = element_text(size=8))

# Download TPP for all elections
tpp_pollingbooth <- twoparty_pollingbooth_download()

# Plot the densities of the TPP vote in each election
tpp_pollingbooth %>% 
  filter(StateAb == "NSW") %>% 
  ggplot(aes(x = year, y = LNP_Percent, fill = factor(year))) + 
  geom_boxplot(alpha = 0.3) +
  theme_minimal() + 
  guides(fill=F) +
  labs(x = "Year", y = "Two party preferred % in favour \nof the Liberal/National Coalition")

Census data

There are four Censuses included in this package, which consist of 85 variables relating to population characteristics of each electorate. The objects abs2001, abs2006, abs2011 and abs2016 correspond with each of the four Censuses. A description of each variable can be found in the corresponding help files.

Let’s have a look at data from the 2016 Census held in abs2016.

# Dimensions
dim(abs2016)

## [1] 150  85

# Preview some of the data
abs2016 %>% 
  select(DivisionNm, State, Population, Area, AusCitizen, BachelorAbv, Indigenous, MedianAge, Unemployed) %>%
  head %>% 
  kable

ggplot(data = abs2016,
       aes(x = reorder(State, -Unemployed),
           y = Unemployed,
           colour = State)) + 
  geom_boxplot() + 
  labs(x = "State",
       y = "% Unemployment") + 
  theme_minimal() + 
  theme(legend.position = "none") 

ggplot(data = abs2016,
       aes(x = reorder(State, -MedianPersonalIncome),
           y = MedianPersonalIncome,
           colour = State)) + 
  geom_boxplot() + 
  geom_jitter(alpha = 0.35, 
              size = 2,
              width = 0.3) +
  theme_minimal() + 
  theme(legend.position = "none") + 
  labs(x = "State", y = "Median Personal Income ($)")

# Load
data(abs2011)
data(abs2006)
data(abs2001)

# Bind and plot 
bind_rows(abs2016 %>% mutate(year = "2016"), abs2011 %>% mutate(year = "2011"), abs2006 %>% mutate(year = "2006"), abs2001 %>% mutate(year = "2001")) %>% 
  ggplot(aes(x = year, y = MedianAge, col = year)) + 
  geom_boxplot() +
  geom_jitter(alpha = 0.3) +
  guides(col = F) + 
  labs(x = "Year", y = "Median Age") +
  theme_minimal()

Maps of the Australian electoral divisions

Electoral divisions change regularly, so many electorates represent different regions from one election to the next. A consequence of this is that the electorate Census data (for example, in 2011) may not align with those in place for any election. As a resource for addressing this problem, and also for plotting visualizations of Australia, a map of electorates for the years 2001, 2004, 2006, 2007, 2010, 2011, 2013 and 2016 are included. The nat_map objects (e.g. nat_map16) are data frames that contain the coordinates of each electoral polygon, and can be plotted using ggplot2 graphics. The accompanying nat_data object (e.g. nat_data16) contains some information about each electoral polygon. These objects can be loaded using the nat_map_download and nat_data_download functions, respectively.

As an example, let’s plot a map of electorates in the 2016 election.

library(ggthemes)
nat_map16 <- nat_map_download(2016)
nat_data16 <- nat_data_download(2016)

ggplot(aes(map_id=id), data=nat_data16) +
  geom_map(aes(fill=state), map=nat_map16, col = "grey50") +
  expand_limits(x=nat_map16$long, y=nat_map16$lat) + 
  theme_map() + coord_equal()

# Get the electorate winners
map.winners <- fp16 %>% filter(Elected == "Y") %>% 
  select(DivisionNm, PartyNm) %>% 
  merge(nat_map16, by.x="DivisionNm", by.y="elect_div")

# Grouping
map.winners$PartyNm <- as.character(map.winners$PartyNm)
map.winners <- map.winners %>% arrange(group, order)

# Combine Liberal and National parties
map.winners <- map.winners %>% 
  mutate(PartyNm = ifelse(PartyNm %in% c("NATIONAL PARTY", "LIBERAL PARTY"), "LIBERAL NATIONAL COALITION", PartyNm))

# Colour cells to match that parties colours
# Order = Australian Labor Party, Independent, Katters, Lib/Nats Coalition, Palmer, The Greens
partycolours = c("#FF0033", "#000000", "#CC3300", "#0066CC", "#FFFF00", "#009900")


ggplot(data=map.winners) + 
  geom_polygon(aes(x=long, y=lat, group=group, fill=PartyNm)) +
  scale_fill_manual(name="Political Party", values=partycolours) +
  theme_map() + coord_equal() + theme(legend.position="bottom")

# Get winners
cart.winners <- fp16 %>% filter(Elected == "Y") %>% 
  select(DivisionNm, PartyNm) %>% 
  merge(nat_data16, by.x="DivisionNm", by.y="elect_div")

# Combine Liberal and National parties
cart.winners <- cart.winners %>% 
  mutate(PartyNm = ifelse(PartyNm %in% c("NATIONAL PARTY", "LIBERAL PARTY"), "LIBERAL NATIONAL COALITION", PartyNm))


# Plot dorling cartogram
ggplot(data=nat_map16) +
  geom_polygon(aes(x=long, y=lat, group=group),
               fill="grey90", colour="white") +
  geom_point(data=cart.winners, aes(x=x, y=y, colour=PartyNm), size = 0.75, alpha=0.8) +
  scale_colour_manual(name="Political Party", values=partycolours) +
  theme_map() + coord_equal() + theme(legend.position="bottom")

Modelling election results using Census data

An interesting exercise is to see how we can model electorate voting outcomes as a function of Census information. In the years 2001 and 2016 both a Census and election occur. The Australian Bureau of Statistics aggregate Census information to electoral boundaries that exactly match those in the election for these years, so we can join this data together and fit some models.

Let’s look at modelling the two party preferred vote (in favour of the Liberal party).

# Join
data16 <- left_join(tpp16 %>% select(LNP_Percent, UniqueID), abs2016, by = "UniqueID")

# Fit a model using all of the available population characteristics
lmod <- data16 %>% 
  select(-c(ends_with("NS"), Area, Population, DivisionNm, UniqueID, State, EmuneratedElsewhere, InternetUse, Other_NonChrist, OtherChrist, EnglishOnly)) %>% 
  lm(LNP_Percent ~ ., data = .)

# See if the variables are jointly significant
library(broom)
lmod %>% 
  glance %>% 
  kable

We see that electoral socio-demographics from the Census are jointly significant in predicting the two party preferred vote.

# See which variables are individually significant
lmod %>% 
  tidy %>% 
  filter(p.value < 0.05) %>% 
  arrange(p.value) %>% 
  kable

Many variables are individually significant too.

For more details on how the data in this package was obtained…

Please see the vignettes on our webpage. These detail the procedures used to obtain the election data, Census data and electorate maps, as well as the Census imputation method.