data-raw/elections/RUN-import-polling-places.R
In ropenscilabs/eechidna: Exploring Election and Census Highly Informative Data Nationally for Australia
# Polling place locations and results
# For 2001 election data, download the folder in the link: https://www.aec.gov.au/About_AEC/Publications/statistics/files/aec-2001-election-statistics.zip
# Change the directory in this code as required (2001 section only).
## IMPORTANT:
## THIS FILE USES A GOOGLE MAPS API TO GEOCODE THE POLLING PLACES
## YOU MUST ENTER YOUR OWN API KEY TO RUN THIS
# -------------------------------------
library(tidyverse)
# Latest version of ggmap from github
# install_github("https://github.com/dkahle/ggmap/")
library(ggmap)
# -------------------------------------
# 2016
pollplace_16 <- read_csv("https://results.aec.gov.au/20499/Website/Downloads/GeneralPollingPlacesDownload-20499.csv", skip = 1)
# 2013
pollplace_13 <- read_csv("http://results.aec.gov.au/17496/Website/Downloads/GeneralPollingPlacesDownload-17496.csv", skip=1)
# 2010
pollplace_10 <- read_csv("https://results.aec.gov.au/15508/Website/Downloads/GeneralPollingPlacesDownload-15508.csv", skip = 1)
# 2007
pollplace_07 <- read_csv("https://results.aec.gov.au/13745/website/Downloads/GeneralPollingPlacesDownload-13745.csv", skip = 1)
# 2004
# Doesn't have lat and long but if IDs and name match other years, it is the same location
pollplace_04_blank <- read_csv("https://results.aec.gov.au/12246/results/Downloads/GeneralPollingPlacesDownload-12246.csv", skip = 1) %>%
left_join(pollplace_07 %>% select(PollingPlaceID, Latitude, Longitude, PollingPlaceNm), by = c("PollingPlaceNm", "PollingPlaceID"))
add1 <- left_join(pollplace_04_blank %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
pollplace_07 %>% select(PollingPlaceNm, PremisesNm, PremisesPostCode, State, Latitude, Longitude), by = c("PremisesNm","PollingPlaceNm", "PremisesPostCode", "State"))
add2 <- left_join(add1 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
pollplace_10 %>% select(PollingPlaceID, PollingPlaceNm, Latitude, Longitude), by = c("PollingPlaceNm", "PollingPlaceID"))
add3 <- left_join(add2 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
pollplace_10 %>% select(PollingPlaceNm, PremisesNm, PremisesPostCode, Latitude, Longitude), by = c("PremisesNm", "PremisesPostCode", "PollingPlaceNm"))
add4 <- left_join(add3 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
pollplace_13 %>% select(PollingPlaceID, PollingPlaceNm, Latitude, Longitude), by = c("PollingPlaceNm", "PollingPlaceID"))
add5 <- left_join(add4 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
pollplace_13 %>% select(PollingPlaceNm, PremisesNm, PremisesPostCode, Latitude, Longitude), by = c("PremisesNm", "PremisesPostCode", "PollingPlaceNm"))
add6 <- left_join(add5 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
pollplace_16 %>% select(PollingPlaceID, PollingPlaceNm, Latitude, Longitude), by = c("PollingPlaceNm", "PollingPlaceID"))
add7 <- left_join(add6 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
pollplace_16 %>% select(PollingPlaceNm, PremisesNm, PremisesPostCode, Latitude, Longitude), by = c("PremisesNm", "PremisesPostCode", "PollingPlaceNm"))
# Google Maps to get remaining geocodes
get_geos <- add7 %>%
filter(is.na(Latitude)) %>%
mutate(address = gsub(" NA ", " ", paste(PremisesNm, PremisesAddress1, PremisesAddress2,
PremisesAddress3, PremisesSuburb, PremisesStateAb, PremisesPostCode, "AUSTRALIA"))) %>%
mutate_geocode(address) %>%
rename(Latitude = lat, Longitude = lon)
get_geos <- get_geos %>%
rename(Latitude = lat, Longitude = lon)
# Now combine into (almost) complete dataset
pollplace_04 <- pollplace_04_blank %>% filter(!is.na(Longitude)) %>%
bind_rows(add1 %>% filter(!is.na(Longitude))) %>%
bind_rows(add2 %>% filter(!is.na(Longitude))) %>%
bind_rows(add3 %>% filter(!is.na(Longitude))) %>%
bind_rows(add4 %>% filter(!is.na(Longitude))) %>%
bind_rows(add5 %>% filter(!is.na(Longitude))) %>%
bind_rows(add6 %>% filter(!is.na(Longitude))) %>%
bind_rows(add7 %>% filter(!is.na(Longitude))) %>%
bind_rows(get_geos) %>%
select(-address)
# Export
#write_csv(pollplace_04, "PollingBooth2004GeoCodes.csv")
# -------------------------------------
# Function to group parties into larger parties and other
group_parties <- function(df, PartyAb = PartyAb) {
out <- df %>%
mutate(PartyAb = ifelse(
PartyAb %in% c("CLP", "LP", "LNP", "NP"), "LNP",
ifelse(PartyAb == "ALP", "ALP",
ifelse(PartyAb == "IND", "IND",
ifelse(PartyAb == "GRN", "GRN",
ifelse(PartyAb %in% c("HAN","ON"), "ON",
"Other"))))))
return(out)
}
# Function to re-label parties so that names are common
relabel_parties <- function(df, PartyNm = PartyNm) {
out <- df %>%
mutate(PartyNm = ifelse(
PartyNm %in% c("Australian Labor Party (Northern Territory) Branch", "Australian Labor Party (ACT Branch)", "Labor"), "Australian Labor Party",
ifelse(PartyNm %in% c("Country Liberals (NT)", "Liberal National Party of Queensland", "The Nationals", "National Party"), "Liberal",
ifelse(PartyNm %in% c("The Greens (WA)"), "The Greens",
ifelse(is.na(PartyNm), "Independent", PartyNm
)))))
return(out)
}
# Function to reabbreviate parties
reabbrev_parties <- function(df, PartyNm = PartyNm) {
out <- df %>%
mutate(PartyAb = ifelse(PartyAb %in% c("CLR", "ALP"), "ALP",
ifelse(PartyAb %in% c("CLP", "LP", "LNP", "NP"), "LNP",
ifelse(PartyAb %in% c("GRN", "GWA", "TG"), "GRN",
ifelse(PartyAb %in% c("HAN","ON"), "ON",
ifelse(is.na(PartyAb), "INFL",
PartyAb))))))
return(out)
}
# Make all character fields upper case
chr_upper <- function(df) {
cols <- sapply(df, class) == 'character'
df[, cols] <- lapply(df[, cols], toupper)
return(df)
}
# -------------------------------------
# Download polling place division of preferences, two party preferred and two candidate preferred (where available)
# In long format, where each candidate has their own row. Alternative is wide format with code in next section.
# ------------------------------------------------------------------------------------------
# 2016
tcp_pp16 <- read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseTcpByCandidateByPollingPlaceDownload-20499.csv", skip = 1) %>%
relabel_parties() %>% reabbrev_parties() %>% chr_upper()
tpp_pp16 <- read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseTppByPollingPlaceDownload-20499.csv", skip = 1) %>%
rename(LNP_Votes = `Liberal/National Coalition Votes`,
LNP_Percent = `Liberal/National Coalition Percentage`,
ALP_Votes = `Australian Labor Party Votes`,
ALP_Percent = `Australian Labor Party Percentage`) %>%
chr_upper()
fp_pp16 <- read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-NSW.csv", skip = 1) %>%
bind_rows(read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-VIC.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-QLD.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-SA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-TAS.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-WA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-NT.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-ACT.csv", skip = 1)) %>%
relabel_parties() %>% reabbrev_parties() %>%
chr_upper()
# ------------------------------------------------------------------------------------------
# 2013
tcp_pp13 <- read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseTcpByCandidateByPollingPlaceDownload-17496.csv", skip = 1) %>%
relabel_parties() %>% reabbrev_parties() %>%
chr_upper()
tpp_pp13 <- read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseTppByPollingPlaceDownload-17496.csv", skip = 1) %>%
rename(LNP_Votes = `Liberal/National Coalition Votes`,
LNP_Percent = `Liberal/National Coalition Percentage`,
ALP_Votes = `Australian Labor Party Votes`,
ALP_Percent = `Australian Labor Party Percentage`) %>%
chr_upper()
fp_pp13 <- read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-NSW.csv", skip = 1) %>%
bind_rows(read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-VIC.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-QLD.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-SA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-TAS.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-WA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-NT.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-ACT.csv", skip = 1)) %>%
relabel_parties() %>% reabbrev_parties() %>%
chr_upper()
# ------------------------------------------------------------------------------------------
# 2010
tcp_pp10 <- read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseTcpByCandidateByPollingPlaceDownload-15508.csv", skip = 1) %>%
relabel_parties() %>% reabbrev_parties() %>%
chr_upper()
tpp_pp10 <- read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseTppByPollingPlaceDownload-15508.csv", skip = 1) %>%
rename(LNP_Votes = `Liberal/National Coalition Votes`,
LNP_Percent = `Liberal/National Coalition Percentage`,
ALP_Votes = `Australian Labor Party Votes`,
ALP_Percent = `Australian Labor Party Percentage`) %>%
chr_upper()
fp_pp10 <- read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-NSW.csv", skip = 1) %>%
bind_rows(read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-VIC.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-QLD.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-SA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-TAS.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-WA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-NT.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-ACT.csv", skip = 1)) %>%
relabel_parties() %>% reabbrev_parties() %>%
chr_upper()
# ------------------------------------------------------------------------------------------
# 2007
tcp_pp07 <- read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseTcpByCandidateByPollingPlaceDownload-13745.csv", skip = 1) %>%
relabel_parties() %>% reabbrev_parties() %>%
chr_upper()
tpp_pp07 <- read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseTppByPollingPlaceDownload-13745.csv", skip = 1) %>%
rename(LNP_Votes = `Liberal/National Coalition Votes`,
LNP_Percent = `Liberal/National Coalition Percentage`,
ALP_Votes = `Australian Labor Party Votes`,
ALP_Percent = `Australian Labor Party Percentage`) %>%
chr_upper()
fp_pp07 <- read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-NSW.csv", skip = 1) %>%
bind_rows(read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-VIC.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-QLD.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-SA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-TAS.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-WA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-NT.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-ACT.csv", skip = 1)) %>%
relabel_parties() %>% reabbrev_parties() %>%
chr_upper()
# ------------------------------------------------------------------------------------------
# 2004
tcp_pp04 <- read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseTcpByCandidateByPollingPlaceDownload-12246.csv", skip = 1) %>%
relabel_parties() %>% reabbrev_parties() %>%
mutate(Elected = ifelse(is.na(SittingMemberFl), "N", "Y")) %>%
select(-SittingMemberFl) %>%
chr_upper()
tpp_pp04 <- read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseTppByPollingPlaceDownload-12246.csv", skip = 1) %>%
rename(LNP_Votes = `Liberal/National Coalition Votes`,
LNP_Percent = `Liberal/National Coalition Percentage`,
ALP_Votes = `Australian Labor Party Votes`,
ALP_Percent = `Australian Labor Party Percentage`) %>%
chr_upper()
fp_pp04 <- read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-NSW.csv", skip = 1) %>%
bind_rows(read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-VIC.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-QLD.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-SA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-TAS.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-WA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-NT.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-ACT.csv", skip = 1)) %>%
relabel_parties() %>% reabbrev_parties() %>%
mutate(Elected = ifelse(is.na(SittingMemberFl), "N", "Y")) %>%
select(-SittingMemberFl) %>%
chr_upper()
# ------------------------------------------------------------------------------------------
# 2001
# Get locations of polling places (extract address from first preferences)
votes <- read_delim("data-raw/elections/data/aec/polling_places_votes_2001/hppdop.txt", delim = ";")
candidates <- read_delim("data-raw/elections/data/aec/polling_places_votes_2001/hcands.txt", delim = ";")
all <- left_join(votes, candidates, by = c("State", "Division", "Ballot Position"))
firstpref <- all %>%
filter(Count == 1) %>%
dplyr::select(-c(`Elected Swing`, `Exhausted Transfer`, Exhausted, `Vote Percent`, Swing, Vote.y, `Vote Distributed`, Count)) %>%
mutate(PollingPlace = gsub("\\s*\\([^\\)]+\\)","",as.character(`Polling Place`) %>% toupper()),
PollingPlaceBracket = `Polling Place` %>% toupper(),
Division = toupper(Division),
Elected = ifelse(is.na(Member), "N", "Y")) %>%
rename(Vote = Vote.x, StateAb = State, DivisionNm = Division)
# Get coordinates from other years
# PollingPlace format varies across years
locations <- firstpref %>%
select(PollingPlace, PollingPlaceBracket, DivisionNm, StateAb) %>%
group_by(PollingPlace, PollingPlaceBracket, DivisionNm, StateAb) %>%
unique() %>%
ungroup()
pollplace_01_start <- locations %>% left_join(
fp_pp04 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(),
by = c("StateAb", "DivisionNm", "PollingPlaceBracket" = "PollingPlace"))
add1 <- left_join(pollplace_01_start %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
fp_pp07 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(), by = c("StateAb", "DivisionNm", "PollingPlaceBracket" = "PollingPlace"))
add2 <- left_join(add1 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
fp_pp10 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(), by = c("StateAb", "DivisionNm", "PollingPlaceBracket" = "PollingPlace"))
add3 <- left_join(add2 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
fp_pp13 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(), by = c("StateAb", "DivisionNm", "PollingPlace"))
add4 <- left_join(add3 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
fp_pp16 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(), by = c("StateAb", "DivisionNm", "PollingPlace"))
# Checking for matches with PPVC suffix
add_ppvc <- add4 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)) %>%
mutate(address = paste(PollingPlace, DivisionNm, StateAb, "AUSTRALIA"),
match = paste(PollingPlace, DivisionNm, "PPVC")) %>%
unique()
add5 <- left_join(add_ppvc, fp_pp10 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(),
by = c("StateAb", "DivisionNm", "match" = "PollingPlace"))
add6 <- left_join(add5 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
fp_pp13 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(),
by = c("StateAb", "DivisionNm", "match" = "PollingPlace"))
add7 <- left_join(add6 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
fp_pp16 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(),
by = c("StateAb", "DivisionNm", "match" = "PollingPlace"))
# Remaining, excluding special votes
remain <- add7 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)) %>%
filter(!PollingPlace %in% c("ABSENT", "POSTAL", "PRE POLL", "PROVISIONAL", "DIVISION SUMMARY"),
!grepl("SPECIAL", PollingPlace)) %>%
mutate(address = paste(PollingPlace, StateAb, "AUSTRALIA"))
special_votes <- add7 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)) %>%
filter(PollingPlace %in% c("ABSENT", "POSTAL", "PRE POLL", "PROVISIONAL", "DIVISION SUMMARY"))
special_place <- add7 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)) %>%
filter(grepl("SPECIAL", PollingPlace))
# ------------------------------------------------------------------------------------------
# Google Maps API to get the remaining polling place locations
# You need to have an API from a Google cloud account (can get 12 month trial for free)
# https://cloud.google.com/maps-platform/
# Ensure you have the latest ggmap version from github
# Enter your
register_google(key = "YOUR-API-KEY-GOES-HERE")
# Search for geocodes
# This is imperfect - the remaining 930 polling places may not be accurate
remain <- remain %>%
mutate_geocode(address) %>%
rename(Latitude = lat, Longitude = lon)
my_geos <- data.frame(Longitude = remain$Longitude, Latitude = remain$Latitude)
# Combine
pollplace_01 <- pollplace_01_start %>% filter(!is.na(Longitude)) %>%
bind_rows(add1 %>% filter(!is.na(Longitude))) %>%
bind_rows(add2 %>% filter(!is.na(Longitude))) %>%
bind_rows(add3 %>% filter(!is.na(Longitude))) %>%
bind_rows(add4 %>% filter(!is.na(Longitude))) %>%
bind_rows(add5 %>% filter(!is.na(Longitude))) %>%
bind_rows(add6 %>% filter(!is.na(Longitude))) %>%
bind_rows(add7 %>% filter(!is.na(Longitude))) %>%
bind_rows(remain %>% filter(!is.na(Longitude))) %>%
bind_rows(special_votes) %>% bind_rows(special_place) %>%
select(-c(address, match, PollingPlaceBracket))
# First preference
fp_pp01 <- firstpref %>%
left_join(pollplace_01, by = c("StateAb", "PollingPlace", "DivisionNm")) %>%
separate(Candidate, c("Surname", "GivenNm"), ", ") %>%
mutate(GivenNm = toupper(GivenNm)) %>%
rename(BallotPosition = `Ballot Position`, OrdinaryVotes = Vote, PartyAb = Party) %>%
select(StateAb, DivisionNm, PollingPlace, Surname, GivenNm, BallotPosition, PartyAb, Elected, OrdinaryVotes, Latitude, Longitude)
# Two party preferred
tpp_pp01 <- read_delim("data-raw/results_pollingplace_2001/htppbypp.txt", delim = ";") %>%
mutate(DivisionNm = toupper(Division),
PollingPlace = gsub("\\s*\\([^\\)]+\\)", "", as.character(`Polling Place`) %>% toupper())) %>%
select(-c(Division, `Polling Place`)) %>%
left_join(pollplace_01, by = c("PollingPlace", "DivisionNm")) %>%
rename(ALP_Votes = `ALP TPP Vote`, LNP_Votes = `Coalition TPP Vote`,
ALP_Percent = `ALP TPP Pc`, Swing = `ALP Swing`) %>%
mutate(TotalVotes = ALP_Votes + LNP_Votes, LNP_Percent = 100 - ALP_Percent) %>%
select(StateAb, DivisionNm, PollingPlace, LNP_Votes, LNP_Percent, ALP_Votes,
ALP_Percent, TotalVotes, Swing, Latitude, Longitude)
# -------------------------------------
# Adding locations of polling places
fp_pp16 <- fp_pp16 %>%
left_join(pollplace_16 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tcp_pp16 <- tcp_pp16 %>%
left_join(pollplace_16 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tpp_pp16 <- tpp_pp16 %>%
left_join(pollplace_16 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
fp_pp13 <- fp_pp13 %>%
left_join(pollplace_13 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tcp_pp13 <- tcp_pp13 %>%
left_join(pollplace_13 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tpp_pp13 <- tpp_pp13 %>%
left_join(pollplace_13 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
fp_pp10 <- fp_pp10 %>%
left_join(pollplace_10 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tcp_pp10 <- tcp_pp10 %>%
left_join(pollplace_10 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tpp_pp10 <- tpp_pp10 %>%
left_join(pollplace_10 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
fp_pp07 <- fp_pp07 %>%
left_join(pollplace_07 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tcp_pp07 <- tcp_pp07 %>%
left_join(pollplace_07 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tpp_pp07 <- tpp_pp07 %>%
left_join(pollplace_07 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
fp_pp04 <- fp_pp04 %>%
left_join(pollplace_04 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tcp_pp04 <- tcp_pp04 %>%
left_join(pollplace_04 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tpp_pp04 <- tpp_pp04 %>%
left_join(pollplace_04 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
# --------------------------------------------------------------------------------------------------
# Still many NA coordinates, so for those that are NA, try to match PollingPlace (and State) with any from other years
# Get polling places via two party preferred
tpp_pp_all <- dplyr::bind_rows(tpp_pp01 %>% mutate(year = 2001),
tpp_pp04 %>% mutate(year = 2004),
tpp_pp07 %>% mutate(year = 2007),
tpp_pp10 %>% mutate(year = 2010),
tpp_pp13 %>% mutate(year = 2013),
tpp_pp16 %>% mutate(year = 2016)
)
# Add coordinates for any NA that have an address in a different year
tpp_nocord <- tpp_pp_all %>%
filter(is.na(Latitude)) %>%
mutate(PollingPlace = toupper(PollingPlace)) %>%
select(-c(Latitude, Longitude))
all_cord <- tpp_pp_all %>%
filter(!is.na(Latitude)) %>%
select(PollingPlace, StateAb, year, Latitude, Longitude) %>%
mutate(PollingPlace = toupper(PollingPlace)) %>%
group_by(PollingPlace, StateAb) %>%
arrange(-year) %>%
filter(row_number(year) == 1) %>%
select(-year)
tpp_nocord_filled <- left_join(tpp_nocord, all_cord, by = c("PollingPlace", "StateAb"))
tpp_pp <- tpp_pp_all %>%
filter(!is.na(Latitude)) %>%
bind_rows(tpp_nocord_filled)
# First preferences
fp_pp_all <- dplyr::bind_rows(fp_pp01 %>% mutate(year = 2001),
fp_pp04 %>% mutate(year = 2004),
fp_pp07 %>% mutate(year = 2007),
fp_pp10 %>% mutate(year = 2010),
fp_pp13 %>% mutate(year = 2013),
fp_pp16 %>% mutate(year = 2016)
)
fp_nocord <- fp_pp_all %>%
filter(is.na(Latitude)) %>%
mutate(PollingPlace = toupper(PollingPlace)) %>%
select(-c(Latitude, Longitude))
fp_nocord_filled <- left_join(fp_nocord, all_cord, by = c("PollingPlace", "StateAb"))
fp_pp <- fp_pp_all %>%
filter(!is.na(Latitude)) %>%
bind_rows(fp_nocord_filled) %>%
ungroup()
# Two candidate preferences
tcp_pp_all <- dplyr::bind_rows(
tcp_pp04 %>% mutate(year = 2004),
tcp_pp07 %>% mutate(year = 2007),
tcp_pp10 %>% mutate(year = 2010),
tcp_pp13 %>% mutate(year = 2013),
tcp_pp16 %>% mutate(year = 2016)
)
tcp_nocord <- tcp_pp_all %>%
filter(is.na(Latitude)) %>%
mutate(PollingPlace = toupper(PollingPlace)) %>%
select(-c(Latitude, Longitude)) %>%
ungroup()
tcp_nocord_filled <- left_join(tcp_nocord, all_cord, by = c("PollingPlace", "StateAb"))
tcp_pp <- tcp_pp_all %>%
filter(!is.na(Latitude)) %>%
bind_rows(tcp_nocord_filled) %>%
ungroup()
# --------------------------------------------------------------------------------
# Save
save(fp_pp, file = "extra-data/fp_pp.rda", compress = "xz")
save(tcp_pp, file = "extra-data/tcp_pp.rda", compress = "xz")
save(tpp_pp, file = "extra-data/tpp_pp.rda", compress = "xz")
ropenscilabs/eechidna documentation built on May 4, 2023, 6:51 a.m.
R Package Documentation
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# Polling place locations and results
# For 2001 election data, download the folder in the link: https://www.aec.gov.au/About_AEC/Publications/statistics/files/aec-2001-election-statistics.zip
# Change the directory in this code as required (2001 section only).
## IMPORTANT:
## THIS FILE USES A GOOGLE MAPS API TO GEOCODE THE POLLING PLACES
## YOU MUST ENTER YOUR OWN API KEY TO RUN THIS
# -------------------------------------
library(tidyverse)
# Latest version of ggmap from github
# install_github("https://github.com/dkahle/ggmap/")
library(ggmap)
# -------------------------------------
# 2016
pollplace_16 <- read_csv("https://results.aec.gov.au/20499/Website/Downloads/GeneralPollingPlacesDownload-20499.csv", skip = 1)
# 2013
pollplace_13 <- read_csv("http://results.aec.gov.au/17496/Website/Downloads/GeneralPollingPlacesDownload-17496.csv", skip=1)
# 2010
pollplace_10 <- read_csv("https://results.aec.gov.au/15508/Website/Downloads/GeneralPollingPlacesDownload-15508.csv", skip = 1)
# 2007
pollplace_07 <- read_csv("https://results.aec.gov.au/13745/website/Downloads/GeneralPollingPlacesDownload-13745.csv", skip = 1)
# 2004
# Doesn't have lat and long but if IDs and name match other years, it is the same location
pollplace_04_blank <- read_csv("https://results.aec.gov.au/12246/results/Downloads/GeneralPollingPlacesDownload-12246.csv", skip = 1) %>%
left_join(pollplace_07 %>% select(PollingPlaceID, Latitude, Longitude, PollingPlaceNm), by = c("PollingPlaceNm", "PollingPlaceID"))
add1 <- left_join(pollplace_04_blank %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
pollplace_07 %>% select(PollingPlaceNm, PremisesNm, PremisesPostCode, State, Latitude, Longitude), by = c("PremisesNm","PollingPlaceNm", "PremisesPostCode", "State"))
add2 <- left_join(add1 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
pollplace_10 %>% select(PollingPlaceID, PollingPlaceNm, Latitude, Longitude), by = c("PollingPlaceNm", "PollingPlaceID"))
add3 <- left_join(add2 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
pollplace_10 %>% select(PollingPlaceNm, PremisesNm, PremisesPostCode, Latitude, Longitude), by = c("PremisesNm", "PremisesPostCode", "PollingPlaceNm"))
add4 <- left_join(add3 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
pollplace_13 %>% select(PollingPlaceID, PollingPlaceNm, Latitude, Longitude), by = c("PollingPlaceNm", "PollingPlaceID"))
add5 <- left_join(add4 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
pollplace_13 %>% select(PollingPlaceNm, PremisesNm, PremisesPostCode, Latitude, Longitude), by = c("PremisesNm", "PremisesPostCode", "PollingPlaceNm"))
add6 <- left_join(add5 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
pollplace_16 %>% select(PollingPlaceID, PollingPlaceNm, Latitude, Longitude), by = c("PollingPlaceNm", "PollingPlaceID"))
add7 <- left_join(add6 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
pollplace_16 %>% select(PollingPlaceNm, PremisesNm, PremisesPostCode, Latitude, Longitude), by = c("PremisesNm", "PremisesPostCode", "PollingPlaceNm"))
# Google Maps to get remaining geocodes
get_geos <- add7 %>%
filter(is.na(Latitude)) %>%
mutate(address = gsub(" NA ", " ", paste(PremisesNm, PremisesAddress1, PremisesAddress2,
PremisesAddress3, PremisesSuburb, PremisesStateAb, PremisesPostCode, "AUSTRALIA"))) %>%
mutate_geocode(address) %>%
rename(Latitude = lat, Longitude = lon)
get_geos <- get_geos %>%
rename(Latitude = lat, Longitude = lon)
# Now combine into (almost) complete dataset
pollplace_04 <- pollplace_04_blank %>% filter(!is.na(Longitude)) %>%
bind_rows(add1 %>% filter(!is.na(Longitude))) %>%
bind_rows(add2 %>% filter(!is.na(Longitude))) %>%
bind_rows(add3 %>% filter(!is.na(Longitude))) %>%
bind_rows(add4 %>% filter(!is.na(Longitude))) %>%
bind_rows(add5 %>% filter(!is.na(Longitude))) %>%
bind_rows(add6 %>% filter(!is.na(Longitude))) %>%
bind_rows(add7 %>% filter(!is.na(Longitude))) %>%
bind_rows(get_geos) %>%
select(-address)
# Export
#write_csv(pollplace_04, "PollingBooth2004GeoCodes.csv")
# -------------------------------------
# Function to group parties into larger parties and other
group_parties <- function(df, PartyAb = PartyAb) {
out <- df %>%
mutate(PartyAb = ifelse(
PartyAb %in% c("CLP", "LP", "LNP", "NP"), "LNP",
ifelse(PartyAb == "ALP", "ALP",
ifelse(PartyAb == "IND", "IND",
ifelse(PartyAb == "GRN", "GRN",
ifelse(PartyAb %in% c("HAN","ON"), "ON",
"Other"))))))
return(out)
}
# Function to re-label parties so that names are common
relabel_parties <- function(df, PartyNm = PartyNm) {
out <- df %>%
mutate(PartyNm = ifelse(
PartyNm %in% c("Australian Labor Party (Northern Territory) Branch", "Australian Labor Party (ACT Branch)", "Labor"), "Australian Labor Party",
ifelse(PartyNm %in% c("Country Liberals (NT)", "Liberal National Party of Queensland", "The Nationals", "National Party"), "Liberal",
ifelse(PartyNm %in% c("The Greens (WA)"), "The Greens",
ifelse(is.na(PartyNm), "Independent", PartyNm
)))))
return(out)
}
# Function to reabbreviate parties
reabbrev_parties <- function(df, PartyNm = PartyNm) {
out <- df %>%
mutate(PartyAb = ifelse(PartyAb %in% c("CLR", "ALP"), "ALP",
ifelse(PartyAb %in% c("CLP", "LP", "LNP", "NP"), "LNP",
ifelse(PartyAb %in% c("GRN", "GWA", "TG"), "GRN",
ifelse(PartyAb %in% c("HAN","ON"), "ON",
ifelse(is.na(PartyAb), "INFL",
PartyAb))))))
return(out)
}
# Make all character fields upper case
chr_upper <- function(df) {
cols <- sapply(df, class) == 'character'
df[, cols] <- lapply(df[, cols], toupper)
return(df)
}
# -------------------------------------
# Download polling place division of preferences, two party preferred and two candidate preferred (where available)
# In long format, where each candidate has their own row. Alternative is wide format with code in next section.
# ------------------------------------------------------------------------------------------
# 2016
tcp_pp16 <- read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseTcpByCandidateByPollingPlaceDownload-20499.csv", skip = 1) %>%
relabel_parties() %>% reabbrev_parties() %>% chr_upper()
tpp_pp16 <- read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseTppByPollingPlaceDownload-20499.csv", skip = 1) %>%
rename(LNP_Votes = `Liberal/National Coalition Votes`,
LNP_Percent = `Liberal/National Coalition Percentage`,
ALP_Votes = `Australian Labor Party Votes`,
ALP_Percent = `Australian Labor Party Percentage`) %>%
chr_upper()
fp_pp16 <- read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-NSW.csv", skip = 1) %>%
bind_rows(read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-VIC.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-QLD.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-SA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-TAS.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-WA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-NT.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/20499/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-20499-ACT.csv", skip = 1)) %>%
relabel_parties() %>% reabbrev_parties() %>%
chr_upper()
# ------------------------------------------------------------------------------------------
# 2013
tcp_pp13 <- read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseTcpByCandidateByPollingPlaceDownload-17496.csv", skip = 1) %>%
relabel_parties() %>% reabbrev_parties() %>%
chr_upper()
tpp_pp13 <- read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseTppByPollingPlaceDownload-17496.csv", skip = 1) %>%
rename(LNP_Votes = `Liberal/National Coalition Votes`,
LNP_Percent = `Liberal/National Coalition Percentage`,
ALP_Votes = `Australian Labor Party Votes`,
ALP_Percent = `Australian Labor Party Percentage`) %>%
chr_upper()
fp_pp13 <- read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-NSW.csv", skip = 1) %>%
bind_rows(read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-VIC.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-QLD.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-SA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-TAS.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-WA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-NT.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/17496/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-17496-ACT.csv", skip = 1)) %>%
relabel_parties() %>% reabbrev_parties() %>%
chr_upper()
# ------------------------------------------------------------------------------------------
# 2010
tcp_pp10 <- read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseTcpByCandidateByPollingPlaceDownload-15508.csv", skip = 1) %>%
relabel_parties() %>% reabbrev_parties() %>%
chr_upper()
tpp_pp10 <- read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseTppByPollingPlaceDownload-15508.csv", skip = 1) %>%
rename(LNP_Votes = `Liberal/National Coalition Votes`,
LNP_Percent = `Liberal/National Coalition Percentage`,
ALP_Votes = `Australian Labor Party Votes`,
ALP_Percent = `Australian Labor Party Percentage`) %>%
chr_upper()
fp_pp10 <- read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-NSW.csv", skip = 1) %>%
bind_rows(read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-VIC.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-QLD.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-SA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-TAS.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-WA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-NT.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/15508/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-15508-ACT.csv", skip = 1)) %>%
relabel_parties() %>% reabbrev_parties() %>%
chr_upper()
# ------------------------------------------------------------------------------------------
# 2007
tcp_pp07 <- read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseTcpByCandidateByPollingPlaceDownload-13745.csv", skip = 1) %>%
relabel_parties() %>% reabbrev_parties() %>%
chr_upper()
tpp_pp07 <- read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseTppByPollingPlaceDownload-13745.csv", skip = 1) %>%
rename(LNP_Votes = `Liberal/National Coalition Votes`,
LNP_Percent = `Liberal/National Coalition Percentage`,
ALP_Votes = `Australian Labor Party Votes`,
ALP_Percent = `Australian Labor Party Percentage`) %>%
chr_upper()
fp_pp07 <- read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-NSW.csv", skip = 1) %>%
bind_rows(read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-VIC.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-QLD.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-SA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-TAS.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-WA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-NT.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/13745/Website/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-13745-ACT.csv", skip = 1)) %>%
relabel_parties() %>% reabbrev_parties() %>%
chr_upper()
# ------------------------------------------------------------------------------------------
# 2004
tcp_pp04 <- read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseTcpByCandidateByPollingPlaceDownload-12246.csv", skip = 1) %>%
relabel_parties() %>% reabbrev_parties() %>%
mutate(Elected = ifelse(is.na(SittingMemberFl), "N", "Y")) %>%
select(-SittingMemberFl) %>%
chr_upper()
tpp_pp04 <- read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseTppByPollingPlaceDownload-12246.csv", skip = 1) %>%
rename(LNP_Votes = `Liberal/National Coalition Votes`,
LNP_Percent = `Liberal/National Coalition Percentage`,
ALP_Votes = `Australian Labor Party Votes`,
ALP_Percent = `Australian Labor Party Percentage`) %>%
chr_upper()
fp_pp04 <- read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-NSW.csv", skip = 1) %>%
bind_rows(read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-VIC.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-QLD.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-SA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-TAS.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-WA.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-NT.csv", skip = 1)) %>%
bind_rows(read_csv("https://results.aec.gov.au/12246/results/Downloads/HouseStateFirstPrefsByPollingPlaceDownload-12246-ACT.csv", skip = 1)) %>%
relabel_parties() %>% reabbrev_parties() %>%
mutate(Elected = ifelse(is.na(SittingMemberFl), "N", "Y")) %>%
select(-SittingMemberFl) %>%
chr_upper()
# ------------------------------------------------------------------------------------------
# 2001
# Get locations of polling places (extract address from first preferences)
votes <- read_delim("data-raw/elections/data/aec/polling_places_votes_2001/hppdop.txt", delim = ";")
candidates <- read_delim("data-raw/elections/data/aec/polling_places_votes_2001/hcands.txt", delim = ";")
all <- left_join(votes, candidates, by = c("State", "Division", "Ballot Position"))
firstpref <- all %>%
filter(Count == 1) %>%
dplyr::select(-c(`Elected Swing`, `Exhausted Transfer`, Exhausted, `Vote Percent`, Swing, Vote.y, `Vote Distributed`, Count)) %>%
mutate(PollingPlace = gsub("\\s*\\([^\\)]+\\)","",as.character(`Polling Place`) %>% toupper()),
PollingPlaceBracket = `Polling Place` %>% toupper(),
Division = toupper(Division),
Elected = ifelse(is.na(Member), "N", "Y")) %>%
rename(Vote = Vote.x, StateAb = State, DivisionNm = Division)
# Get coordinates from other years
# PollingPlace format varies across years
locations <- firstpref %>%
select(PollingPlace, PollingPlaceBracket, DivisionNm, StateAb) %>%
group_by(PollingPlace, PollingPlaceBracket, DivisionNm, StateAb) %>%
unique() %>%
ungroup()
pollplace_01_start <- locations %>% left_join(
fp_pp04 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(),
by = c("StateAb", "DivisionNm", "PollingPlaceBracket" = "PollingPlace"))
add1 <- left_join(pollplace_01_start %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
fp_pp07 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(), by = c("StateAb", "DivisionNm", "PollingPlaceBracket" = "PollingPlace"))
add2 <- left_join(add1 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
fp_pp10 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(), by = c("StateAb", "DivisionNm", "PollingPlaceBracket" = "PollingPlace"))
add3 <- left_join(add2 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
fp_pp13 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(), by = c("StateAb", "DivisionNm", "PollingPlace"))
add4 <- left_join(add3 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
fp_pp16 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(), by = c("StateAb", "DivisionNm", "PollingPlace"))
# Checking for matches with PPVC suffix
add_ppvc <- add4 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)) %>%
mutate(address = paste(PollingPlace, DivisionNm, StateAb, "AUSTRALIA"),
match = paste(PollingPlace, DivisionNm, "PPVC")) %>%
unique()
add5 <- left_join(add_ppvc, fp_pp10 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(),
by = c("StateAb", "DivisionNm", "match" = "PollingPlace"))
add6 <- left_join(add5 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
fp_pp13 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(),
by = c("StateAb", "DivisionNm", "match" = "PollingPlace"))
add7 <- left_join(add6 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)),
fp_pp16 %>% select(PollingPlace, DivisionNm, StateAb, Latitude, Longitude) %>% unique(),
by = c("StateAb", "DivisionNm", "match" = "PollingPlace"))
# Remaining, excluding special votes
remain <- add7 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)) %>%
filter(!PollingPlace %in% c("ABSENT", "POSTAL", "PRE POLL", "PROVISIONAL", "DIVISION SUMMARY"),
!grepl("SPECIAL", PollingPlace)) %>%
mutate(address = paste(PollingPlace, StateAb, "AUSTRALIA"))
special_votes <- add7 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)) %>%
filter(PollingPlace %in% c("ABSENT", "POSTAL", "PRE POLL", "PROVISIONAL", "DIVISION SUMMARY"))
special_place <- add7 %>% filter(is.na(Longitude)) %>% select(-c(Longitude, Latitude)) %>%
filter(grepl("SPECIAL", PollingPlace))
# ------------------------------------------------------------------------------------------
# Google Maps API to get the remaining polling place locations
# You need to have an API from a Google cloud account (can get 12 month trial for free)
# https://cloud.google.com/maps-platform/
# Ensure you have the latest ggmap version from github
# Enter your
register_google(key = "YOUR-API-KEY-GOES-HERE")
# Search for geocodes
# This is imperfect - the remaining 930 polling places may not be accurate
remain <- remain %>%
mutate_geocode(address) %>%
rename(Latitude = lat, Longitude = lon)
my_geos <- data.frame(Longitude = remain$Longitude, Latitude = remain$Latitude)
# Combine
pollplace_01 <- pollplace_01_start %>% filter(!is.na(Longitude)) %>%
bind_rows(add1 %>% filter(!is.na(Longitude))) %>%
bind_rows(add2 %>% filter(!is.na(Longitude))) %>%
bind_rows(add3 %>% filter(!is.na(Longitude))) %>%
bind_rows(add4 %>% filter(!is.na(Longitude))) %>%
bind_rows(add5 %>% filter(!is.na(Longitude))) %>%
bind_rows(add6 %>% filter(!is.na(Longitude))) %>%
bind_rows(add7 %>% filter(!is.na(Longitude))) %>%
bind_rows(remain %>% filter(!is.na(Longitude))) %>%
bind_rows(special_votes) %>% bind_rows(special_place) %>%
select(-c(address, match, PollingPlaceBracket))
# First preference
fp_pp01 <- firstpref %>%
left_join(pollplace_01, by = c("StateAb", "PollingPlace", "DivisionNm")) %>%
separate(Candidate, c("Surname", "GivenNm"), ", ") %>%
mutate(GivenNm = toupper(GivenNm)) %>%
rename(BallotPosition = `Ballot Position`, OrdinaryVotes = Vote, PartyAb = Party) %>%
select(StateAb, DivisionNm, PollingPlace, Surname, GivenNm, BallotPosition, PartyAb, Elected, OrdinaryVotes, Latitude, Longitude)
# Two party preferred
tpp_pp01 <- read_delim("data-raw/results_pollingplace_2001/htppbypp.txt", delim = ";") %>%
mutate(DivisionNm = toupper(Division),
PollingPlace = gsub("\\s*\\([^\\)]+\\)", "", as.character(`Polling Place`) %>% toupper())) %>%
select(-c(Division, `Polling Place`)) %>%
left_join(pollplace_01, by = c("PollingPlace", "DivisionNm")) %>%
rename(ALP_Votes = `ALP TPP Vote`, LNP_Votes = `Coalition TPP Vote`,
ALP_Percent = `ALP TPP Pc`, Swing = `ALP Swing`) %>%
mutate(TotalVotes = ALP_Votes + LNP_Votes, LNP_Percent = 100 - ALP_Percent) %>%
select(StateAb, DivisionNm, PollingPlace, LNP_Votes, LNP_Percent, ALP_Votes,
ALP_Percent, TotalVotes, Swing, Latitude, Longitude)
# -------------------------------------
# Adding locations of polling places
fp_pp16 <- fp_pp16 %>%
left_join(pollplace_16 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tcp_pp16 <- tcp_pp16 %>%
left_join(pollplace_16 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tpp_pp16 <- tpp_pp16 %>%
left_join(pollplace_16 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
fp_pp13 <- fp_pp13 %>%
left_join(pollplace_13 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tcp_pp13 <- tcp_pp13 %>%
left_join(pollplace_13 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tpp_pp13 <- tpp_pp13 %>%
left_join(pollplace_13 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
fp_pp10 <- fp_pp10 %>%
left_join(pollplace_10 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tcp_pp10 <- tcp_pp10 %>%
left_join(pollplace_10 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tpp_pp10 <- tpp_pp10 %>%
left_join(pollplace_10 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
fp_pp07 <- fp_pp07 %>%
left_join(pollplace_07 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tcp_pp07 <- tcp_pp07 %>%
left_join(pollplace_07 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tpp_pp07 <- tpp_pp07 %>%
left_join(pollplace_07 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
fp_pp04 <- fp_pp04 %>%
left_join(pollplace_04 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tcp_pp04 <- tcp_pp04 %>%
left_join(pollplace_04 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
tpp_pp04 <- tpp_pp04 %>%
left_join(pollplace_04 %>% select(PollingPlaceID, PremisesPostCode, Latitude, Longitude), by = "PollingPlaceID")
# --------------------------------------------------------------------------------------------------
# Still many NA coordinates, so for those that are NA, try to match PollingPlace (and State) with any from other years
# Get polling places via two party preferred
tpp_pp_all <- dplyr::bind_rows(tpp_pp01 %>% mutate(year = 2001),
tpp_pp04 %>% mutate(year = 2004),
tpp_pp07 %>% mutate(year = 2007),
tpp_pp10 %>% mutate(year = 2010),
tpp_pp13 %>% mutate(year = 2013),
tpp_pp16 %>% mutate(year = 2016)
)
# Add coordinates for any NA that have an address in a different year
tpp_nocord <- tpp_pp_all %>%
filter(is.na(Latitude)) %>%
mutate(PollingPlace = toupper(PollingPlace)) %>%
select(-c(Latitude, Longitude))
all_cord <- tpp_pp_all %>%
filter(!is.na(Latitude)) %>%
select(PollingPlace, StateAb, year, Latitude, Longitude) %>%
mutate(PollingPlace = toupper(PollingPlace)) %>%
group_by(PollingPlace, StateAb) %>%
arrange(-year) %>%
filter(row_number(year) == 1) %>%
select(-year)
tpp_nocord_filled <- left_join(tpp_nocord, all_cord, by = c("PollingPlace", "StateAb"))
tpp_pp <- tpp_pp_all %>%
filter(!is.na(Latitude)) %>%
bind_rows(tpp_nocord_filled)
# First preferences
fp_pp_all <- dplyr::bind_rows(fp_pp01 %>% mutate(year = 2001),
fp_pp04 %>% mutate(year = 2004),
fp_pp07 %>% mutate(year = 2007),
fp_pp10 %>% mutate(year = 2010),
fp_pp13 %>% mutate(year = 2013),
fp_pp16 %>% mutate(year = 2016)
)
fp_nocord <- fp_pp_all %>%
filter(is.na(Latitude)) %>%
mutate(PollingPlace = toupper(PollingPlace)) %>%
select(-c(Latitude, Longitude))
fp_nocord_filled <- left_join(fp_nocord, all_cord, by = c("PollingPlace", "StateAb"))
fp_pp <- fp_pp_all %>%
filter(!is.na(Latitude)) %>%
bind_rows(fp_nocord_filled) %>%
ungroup()
# Two candidate preferences
tcp_pp_all <- dplyr::bind_rows(
tcp_pp04 %>% mutate(year = 2004),
tcp_pp07 %>% mutate(year = 2007),
tcp_pp10 %>% mutate(year = 2010),
tcp_pp13 %>% mutate(year = 2013),
tcp_pp16 %>% mutate(year = 2016)
)
tcp_nocord <- tcp_pp_all %>%
filter(is.na(Latitude)) %>%
mutate(PollingPlace = toupper(PollingPlace)) %>%
select(-c(Latitude, Longitude)) %>%
ungroup()
tcp_nocord_filled <- left_join(tcp_nocord, all_cord, by = c("PollingPlace", "StateAb"))
tcp_pp <- tcp_pp_all %>%
filter(!is.na(Latitude)) %>%
bind_rows(tcp_nocord_filled) %>%
ungroup()
# --------------------------------------------------------------------------------
# Save
save(fp_pp, file = "extra-data/fp_pp.rda", compress = "xz")
save(tcp_pp, file = "extra-data/tcp_pp.rda", compress = "xz")
save(tpp_pp, file = "extra-data/tpp_pp.rda", compress = "xz")
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