New Zealand election results data in convenient form of an R package
Currently only available via GitHub. I'll consider a CRAN version later.
devtools::install_github("ellisp/nzelect/pkg")
library(nzelect)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
## .. ... ... ...
library(ggplot2, quietly = TRUE)
library(scales, quietly = TRUE)
library(GGally, quietly = TRUE) # for ggpairs##
## Attaching package: 'GGally'
## The following object is masked from 'package:dplyr':
##
## nasa
library(gridExtra, quietly = TRUE) # for grid.arrange##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
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)
source("https://gist.githubusercontent.com/briatte/4718656/raw/2c4e71efe6d46f37e7ea264f5c9e1610511bcb09/ggplot2-map-theme.R")
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, 0.55)) +
ggtitle("Voting patterns in the 2014 General Election\n")##
## # maps v3.1: updated 'world': all lakes moved to separate new #
## # 'lakes' database. Type '?world' or 'news(package="maps")'. #
## Loading required package: grid
See this detailed interactive map of of the 2014 general election built as a side product of this project.
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 1530
GE2014 %>%
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")