Usage - 2014 results by voting place
The 2014 results are available in two main data frames:
Locations2014has one row for each of the 2,568 voting placesGE2014has one row for each combination voting place, party, electorate andvoting type (Party or Candidate)
Overall results
The code below replicates the published results at http://www.electionresults.govt.nz/electionresults_2014/e9/html/e9_part1.html
library(nzelect)
library(tidyr)
library(dplyr)
GE2014 %>%
mutate(VotingType = paste0(VotingType, "Vote")) %>%
group_by(Party, VotingType) %>%
summarise(Votes = sum(Votes)) %>%
spread(VotingType, Votes) %>%
select(Party, PartyVote, CandidateVote) %>%
ungroup() %>%
arrange(desc(PartyVote))## Source: local data frame [28 x 3]
##
## Party PartyVote CandidateVote
## <chr> <dbl> <dbl>
## 1 National Party 1131501 1081787
## 2 Labour Party 604535 801287
## 3 Green Party 257359 165718
## 4 New Zealand First Party 208300 73384
## 5 Conservative 95598 81075
## 6 Internet MANA 34094 NA
## 7 Maori Party 31849 42108
## 8 ACT New Zealand 16689 27778
## 9 Aotearoa Legalise Cannabis Party 10961 4936
## 10 Informal Party Votes 10857 NA
## .. ... ... ...Comparing party and candidate votes of several parties
library(ggplot2, quietly = TRUE)
library(scales, quietly = TRUE)
library(GGally, quietly = TRUE) # for ggpairs
library(dplyr)
proportions <- GE2014 %>%
group_by(VotingPlace, VotingType) %>%
summarise(ProportionLabour = sum(Votes[Party == "Labour Party"]) / sum(Votes),
ProportionNational = sum(Votes[Party == "National Party"]) / sum(Votes),
ProportionGreens = sum(Votes[Party == "Green Party"]) / sum(Votes),
ProportionNZF = sum(Votes[Party == "New Zealand First Party"]) / sum(Votes),
ProportionMaori = sum(Votes[Party == "Maori Party"]) / sum(Votes))
ggpairs(proportions, aes(colour = VotingType), columns = 3:5)
Geographical location of voting places
library(ggthemes) # for theme_map()
GE2014 %>%
filter(VotingType == "Party") %>%
group_by(VotingPlace) %>%
summarise(ProportionNational = sum(Votes[Party == "National Party"] / sum(Votes))) %>%
left_join(Locations2014, by = "VotingPlace") %>%
filter(VotingPlaceSuburb != "Chatham Islands") %>%
mutate(MostlyNational = ifelse(ProportionNational > 0.5,
"Mostly voted National", "Mostly didn't vote National")) %>%
ggplot(aes(x = WGS84Longitude, y = WGS84Latitude, colour = ProportionNational)) +
geom_point() +
facet_wrap(~MostlyNational) +
coord_map() +
borders("nz") +
scale_colour_gradient2(label = percent, mid = "grey80", midpoint = 0.5) +
theme_map() +
theme(legend.position = c(0.04, 0.5)) +
ggtitle("Voting patterns in the 2014 General Election\n")
See this detailed interactive map of of the 2014 general election built as a side product of this project.
Rolling up results to Regional Council, Territorial Authority, or Area Unit
Because this package matches the location people actually voted with to boundaries of Regional Council, Territorial Authority and Area Unit it’s possible to roll up voting behaviour to those categories. However, a large number of votes cannot be located this way. And it needs to be remembered that people are not necessarily voting near their normal place of residence.
GE2014 %>%
filter(VotingType == "Party") %>%
left_join(Locations2014, by = "VotingPlace") %>%
group_by(REGC2014_N) %>%
summarise(
TotalVotes = sum(Votes),
ProportionNational = round(sum(Votes[Party == "National Party"]) / TotalVotes, 3)) %>%
arrange(ProportionNational)## Source: local data frame [17 x 3]
##
## REGC2014_N TotalVotes ProportionNational
## <fctr> <dbl> <dbl>
## 1 Gisborne Region 14342 0.351
## 2 Nelson Region 18754 0.398
## 3 Northland Region 53688 0.427
## 4 Wellington Region 165207 0.430
## 5 Manawatu-Wanganui Region 78841 0.447
## 6 Otago Region 75933 0.447
## 7 NA 934589 0.451
## 8 Hawke's Bay Region 53833 0.460
## 9 Tasman Region 17935 0.465
## 10 West Coast Region 12226 0.465
## 11 Bay of Plenty Region 89065 0.473
## 12 Auckland Region 478760 0.486
## 13 Waikato Region 134511 0.512
## 14 Canterbury Region 192577 0.520
## 15 Marlborough Region 17474 0.520
## 16 Southland Region 36158 0.528
## 17 Taranaki Region 42586 0.552# what are all those NA Regions?:
GE2014 %>%
filter(VotingType == "Party") %>%
left_join(Locations2014, by = "VotingPlace") %>%
filter(is.na(REGC2014_N)) %>%
group_by(VotingPlace) %>%
summarise(TotalVotes = sum(Votes))## Source: local data frame [7 x 2]
##
## VotingPlace TotalVotes
## <chr> <dbl>
## 1 Chatham Islands Council Building, 9 Tuku Road, Waitangi 90
## 2 Ordinary Votes BEFORE polling day 630775
## 3 Overseas Special Votes including Defence Force 38316
## 4 Special Votes BEFORE polling day 71362
## 5 Special Votes On polling day 151530
## 6 Votes Allowed for Party Only 40986
## 7 Voting places where less than 6 votes were taken 1530GE2014 %>%
filter(VotingType == "Party") %>%
left_join(Locations2014, by = "VotingPlace") %>%
group_by(TA2014_NAM) %>%
summarise(
TotalVotes = sum(Votes),
ProportionNational = round(sum(Votes[Party == "National Party"]) / TotalVotes, 3)) %>%
arrange(desc(ProportionNational)) %>%
mutate(TA = ifelse(is.na(TA2014_NAM), "Special or other", as.character(TA2014_NAM)),
TA = gsub(" District", "", TA),
TA = gsub(" City", "", TA),
TA = factor(TA, levels = TA)) %>%
ggplot(aes(x = ProportionNational, y = TA, size = TotalVotes)) +
geom_point() +
scale_x_continuous("Proportion voting National Party", label = percent) +
scale_size("Number of\nvotes cast", label = comma) +
labs(y = "", title = "Voting in the New Zealand 2014 General Election by Territorial Authority")