Usage - 2002 to 2014 results by voting place
The election results are available in two main data frames:
voting_placeshas one row for each of election year - voting place combinationnzgehas one row for each combination of election year, voting place, party, electorate and voting type (Party or Candidate)
Overall results
The code below replicates the published results for the 2011 election at http://www.electionresults.govt.nz/electionresults_2011/e9/html/e9_part1.html
library(nzelect)
library(tidyr)
library(dplyr)## Warning: package 'dplyr' was built under R version 3.4.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionnzge %>%
filter(election_year == 2011) %>%
mutate(voting_type = paste0(voting_type, " Vote")) %>%
group_by(party, voting_type) %>%
summarise(votes = sum(votes)) %>%
spread(voting_type, votes) %>%
ungroup() %>%
arrange(desc(`Party Vote`))## # A tibble: 25 x 3
## party `Candidate Vote` `Party Vote`
## <chr> <dbl> <dbl>
## 1 National Party 1027696 1058636
## 2 Labour Party 762897 614937
## 3 Green Party 155492 247372
## 4 New Zealand First Party 39892 147544
## 5 Conservative Party 51678 59237
## 6 Maori Party 39320 31982
## 7 Mana 29872 24168
## 8 ACT New Zealand 31001 23889
## 9 Informal Party Votes NA 19872
## 10 United Future 18792 13443
## # ... with 15 more rowsComparing party and candidate votes of several parties
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':
##
## nasalibrary(dplyr)
proportions <- nzge %>%
filter(election_year == 2014) %>%
group_by(voting_place, voting_type) %>%
summarise(`proportion Labour` = sum(votes[party == "Labour Party"]) / sum(votes),
`proportion National` = sum(votes[party == "National Party"]) / sum(votes),
`proportion Greens` = sum(votes[party == "Green Party"]) / sum(votes),
`proportion NZF` = sum(votes[party == "New Zealand First Party"]) / sum(votes),
`proportion Maori` = sum(votes[party == "Maori Party"]) / sum(votes))
ggpairs(proportions, aes(colour = voting_type), columns = 3:5)
Geographical location of voting places
These are most reliable and checked for 2014. Please raise an issue on GitHub if you spot anything.
library(ggthemes) # for theme_map()
nzge %>%
filter(voting_type == "Party" & election_year == 2014) %>%
group_by(voting_place, election_year) %>%
summarise(proportion_national = sum(votes[party == "National Party"] / sum(votes))) %>%
left_join(voting_places, by = c("voting_place", "election_year")) %>%
filter(voting_place_suburb != "Chatham Islands") %>%
mutate(mostly_national = ifelse(proportion_national > 0.5,
"Mostly voted National", "Mostly didn't vote National")) %>%
ggplot(aes(x = longitude, y = latitude, colour = proportion_national)) +
geom_point() +
facet_wrap(~mostly_national) +
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.
nzge %>%
filter(election_year == 2014) %>%
filter(voting_type == "Party") %>%
left_join(voting_places, by = c("voting_place", "election_year")) %>%
group_by(REGC2014_N) %>%
summarise(
total_votes = sum(votes),
proportion_national = round(sum(votes[party == "National Party"]) / total_votes, 3)) %>%
arrange(proportion_national)## # A tibble: 17 x 3
## REGC2014_N total_votes proportion_national
## <fctr> <dbl> <dbl>
## 1 Gisborne Region 14342 0.351
## 2 Nelson Region 18754 0.398
## 3 Northland Region 53688 0.427
## 4 Wellington Region 164913 0.430
## 5 Manawatu-Wanganui Region 78841 0.447
## 6 Otago Region 75933 0.447
## 7 <NA> 935376 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 478724 0.486
## 13 Waikato Region 134511 0.512
## 14 Canterbury Region 192120 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?:
nzge %>%
filter(voting_type == "Party" & election_year == 2014) %>%
left_join(voting_places, by = c("voting_place", "election_year")) %>%
filter(is.na(REGC2014_N)) %>%
group_by(voting_place) %>%
summarise(total_votes = sum(votes))## # A tibble: 10 x 2
## voting_place total_votes
## <chr> <dbl>
## 1 Chatham Islands Council Building, 9 Tuku Road, Waitangi 90
## 2 Mount Pleasant Community Centre, 3 Mccormacks Bay Road 457
## 3 Ordinary Votes BEFORE polling day 630775
## 4 Otaki Surf Lifesaving Club, Marine Parade 294
## 5 Overseas Special Votes including Defence Force 38316
## 6 Port Fitzroy Aotea Centre (Nurses Cottage) 36
## 7 Special Votes BEFORE polling day 71362
## 8 Special Votes On polling day 151530
## 9 Votes Allowed for Party Only 40986
## 10 Voting places where less than 6 votes were taken 1530nzge %>%
filter(voting_type == "Party" & election_year == 2014) %>%
left_join(voting_places, by = c("voting_place", "election_year")) %>%
group_by(TA2014_NAM) %>%
summarise(
total_votes = sum(votes),
proportion_national = round(sum(votes[party == "National Party"]) / total_votes, 3)) %>%
arrange(desc(proportion_national)) %>%
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 = proportion_national, y = TA, size = total_votes)) +
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")
