New Zealand election results, polling data and census results data in convenient form of two R packages. Each of the two packages can be installed separately, but they have been developed together and get good results working together.
nzelect is on CRAN, but nzcensus is too large so will remain on GitHub only.
# install stable version of nzelect from CRAN:
install.packages("nzelect")
# or install dev version of nzelect (with the very latest data) from GitHub:
devtools::install_github("ellisp/nzelect/pkg1")
# install nzcensus from GitHub:
devtools::install_github("ellisp/nzelect/pkg2")
library(nzelect)
library(nzcensus)
The New Zealand Electoral Commission had no involvement in preparing this package and bear no responsibility for any errors. In the event of any uncertainty, refer to the definitive source materials on their website.
nzelect is a very small voluntary project. Please report any issues or bugs on GitHub.
The election results are available in two main data frames:
distinct_voting_placeshas one row for each distinct voting place that could be located in a geographical pointnzgehas one row for each combination of election year, voting place, party, electorate and voting type (Party or Candidate).
The voting_place_id column is shared between distinct_voting_places and nzge and is the only column that should be used to join the two.
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)
nzge %>%
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`))## `summarise()` regrouping output by 'party' (override with `.groups` argument)
## # 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 rows
library(ggplot2, quietly = TRUE)
library(scales, quietly = TRUE)
library(GGally, quietly = TRUE) # for ggpairs
library(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))## `summarise()` regrouping output by 'voting_place' (override with `.groups` argument)
ggpairs(proportions, aes(colour = voting_type), columns = 3:5)
These are available from 2008 onwards and can be obtained by joining the nzge and distinct_voting_places data frames by the voting_place_id column.
library(ggthemes) # for theme_map()
nzge %>%
filter(voting_type == "Party" & election_year == 2014) %>%
group_by(voting_place_id, election_year) %>%
summarise(proportion_national = sum(votes[party == "National Party"] / sum(votes))) %>%
left_join(distinct_voting_places, by = c("voting_place_id")) %>%
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")## `summarise()` regrouping output by 'voting_place_id' (override with `.groups` argument)
## Warning: Removed 1 rows containing missing values (geom_point).
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.
nzge %>%
filter(election_year == 2017) %>%
filter(voting_type == "Party") %>%
left_join(distinct_voting_places, by = "voting_place_id") %>%
group_by(REGC2014_N) %>%
summarise(
total_votes = sum(votes),
proportion_national = round(sum(votes[party == "National Party"]) / total_votes, 3)) %>%
arrange(proportion_national)## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 17 x 3
## REGC2014_N total_votes proportion_national
## <chr> <dbl> <dbl>
## 1 Gisborne Region 19402 0.347
## 2 Nelson Region 25317 0.354
## 3 <NA> 462345 0.376
## 4 Wellington Region 254815 0.386
## 5 West Coast Region 15959 0.393
## 6 Northland Region 79713 0.404
## 7 Otago Region 112242 0.408
## 8 Manawatu-Wanganui Region 113102 0.431
## 9 Tasman Region 29183 0.432
## 10 Hawke's Bay Region 79729 0.442
## 11 Bay of Plenty Region 140818 0.466
## 12 Canterbury Region 282927 0.48
## 13 Auckland Region 656654 0.483
## 14 Marlborough Region 24602 0.491
## 15 Taranaki Region 56442 0.494
## 16 Waikato Region 201022 0.496
## 17 Southland Region 48417 0.523
# what are some of those NA Regions?:
nzge %>%
filter(voting_type == "Party" & election_year == 2017) %>%
left_join(distinct_voting_places, by = c("voting_place_id")) %>%
filter(is.na(REGC2014_N)) %>%
group_by(voting_place) %>%
summarise(total_votes = sum(votes))## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 395 x 2
## voting_place total_votes
## <chr> <dbl>
## 1 Advance Voting Place - Mobile Team 210
## 2 Ashburton Hospital & Rest Homes Team - Taken in Rangitata 310
## 3 Auckland Hospital Mobile & Advance Voting 816
## 4 Central Mobile Team 212
## 5 Chatham Islands Council Building, 9 Tuku Road, Waitangi 230
## 6 Christchurch Mobile Voting Facility One, Central Christchurch and South City Mall 982
## 7 Defence Force Team, Powles Road 94
## 8 Duvauchelle Community Centre, Main Road 185
## 9 Herald Island Community Hall, 57 Ferry Parade 274
## 10 Hospital & Rest Homes - Team 1 - Taken in New Plymouth 643
## # ... with 385 more rows
nzge %>%
filter(voting_type == "Party" & election_year == 2017) %>%
left_join(distinct_voting_places, by = "voting_place_id") %>%
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 2017 General Election by Territorial Authority")## `summarise()` ungrouping output (override with `.groups` argument)
Opinion poll data from 2002 onwards has been tidied and collated into a single data object, polls. Note that at the time of writing, sample sizes are not yet available. The example below illustrates use of the few years of polling data since the 2014 election, in conjunction with the parties_v object which provides colours to use in representing political parties in graphics.
library(forcats)
polls %>%
filter(MidDate > as.Date("2014-11-20") & !is.na(VotingIntention)) %>%
filter(Party %in% c("National", "Labour", "Green", "NZ First")) %>%
mutate(Party = fct_reorder(Party, VotingIntention, .desc = TRUE),
Party = fct_drop(Party)) %>%
ggplot(aes(x = MidDate, y = VotingIntention, colour = Party, linetype = Pollster)) +
geom_line(alpha = 0.5) +
geom_point(aes(shape = Pollster)) +
geom_smooth(aes(group = Party), se = FALSE, colour = "grey15", span = .4) +
scale_colour_manual(values = parties_v) +
scale_y_continuous("Voting intention", label = percent) +
scale_x_date("") +
facet_wrap(~Party, scales = "free_y") ## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: The shape palette can deal with a maximum of 6 discrete values because more than 6 becomes difficult to
## discriminate; you have 8. Consider specifying shapes manually if you must have them.
## Warning: Removed 8 rows containing missing values (geom_point).
Note that it is not appropriate to frequently update the version of nzelect on CRAN, so polling data will generally be out of date. The development version of nzelect from GitHub will be kept more up to date (but no promises exactly how much).
The allocate_seats function uses the Sainte-Lague allocation method to allocate seats to a Parliament given proportions or counts of vote per party. When used with the default settings, it should give the same result as the New Zealand Electoral Commission; this means a five percent threshold to be included in the main algorithm, but parties below five percent of total votes but with at least one electorate seat get total seats proportionate to their votes. Here is the allocate_seats function in action with the actual vote counts from the 2014 General Election:
votes <- c(National = 1131501, Labour = 604535, Green = 257359,
NZFirst = 208300, Cons = 95598, IntMana = 34094,
Maori = 31849, Act = 16689, United = 5286,
Other = 20411)
electorate = c(41, 27, 0,
0, 0, 0,
1, 1, 1,
0)
# Actual result:
allocate_seats(votes, electorate = electorate)## $seats_df
## proportionally_allocated electorate_seats final party
## 1 60 41 60 National
## 2 32 27 32 Labour
## 3 14 0 14 Green
## 4 11 0 11 NZFirst
## 5 0 0 0 Cons
## 6 0 0 0 IntMana
## 7 2 1 2 Maori
## 8 1 1 1 Act
## 9 0 1 1 United
## 10 0 0 0 Other
##
## $seats_v
## National Labour Green NZFirst Cons IntMana Maori Act United Other
## 60 32 14 11 0 0 2 1 1 0
# Result if there were no 5% minimum threshold:
allocate_seats(votes, electorate = electorate, threshold = 0)$seats_v## National Labour Green NZFirst Cons IntMana Maori Act United Other
## 56 30 13 10 5 2 2 1 1 1
Two techniques are provided in the weight_polls function for aggregating opinion polls while giving more weight to more recent polls. These methods aim to replicate the approaches of the Pundit Poll of Polls, which states it is based on FiveThirtyEight's method; and the curia Market Research Public Poll Average. To date, exact replication of Pundit or curia's results has not been possible, probably due in part to the non-inclusion of sample size data so far in the polls data in nzelect package.
The example below shows the weight_polls function in action in combination with allocate_seats, comparing the outcomes of both methods of polling aggregation, on assumption that electorate seats stay as they are in early 2017 (in particular, that ACT, United Future, and Maori party all win at least one electorate seat as needed to keep them in running for the proportional representation part of the seat allocation process).
# electorate seats for Act, Cons, Green, Labour, Mana, Maori, National, NZFirst, United,
# assuming that electorates stay as currently allocated. This is critical particularly
# for ACT, Maori and United Future, who if they lose their single electorate seat each
# will not be represented in parliament
electorates <- c(1,0,0,27,0,1,41,1,1)
polls %>%
filter(MidDate > "2014-12-30" & MidDate < "2017-09-21" & Party != "TOP") %>%
mutate(wt_p = weight_polls(MidDate, method = "pundit", refdate = as.Date("2017-09-22")),
wt_c = weight_polls(MidDate, method = "curia", refdate = as.Date("2017-09-22"))) %>%
group_by(Party) %>%
summarise(pundit_perc = round(sum(VotingIntention * wt_p, na.rm = TRUE) / sum(wt_p) * 100, 1),
curia_perc = round(sum(VotingIntention * wt_c, na.rm = TRUE) / sum(wt_c) * 100, 1)) %>%
ungroup() %>%
mutate(pundit_seats = allocate_seats(pundit_perc, electorate = electorates)$seats_v,
curia_seats = allocate_seats(curia_perc, electorate = electorates)$seats_v)## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 9 x 5
## Party pundit_perc curia_perc pundit_seats curia_seats
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ACT 0.5 0.6 1 1
## 2 Conservative 0.5 0.5 0 0
## 3 Green 7.1 6.6 9 8
## 4 Labour 38.8 40.5 47 50
## 5 Mana 0.1 0.1 0 0
## 6 Maori 0.9 1.1 1 1
## 7 National 43.1 41.5 53 51
## 8 NZ First 7 7.2 9 9
## 9 United Future 0.1 0.1 1 1
library(nzcensus)
library(ggrepel)
ggplot(REGC2013, aes(x = PropPubAdmin2013, y = PropPartnered2013, label = REGC2013_N) ) +
geom_point() +
geom_text_repel(colour = "steelblue") +
scale_x_continuous("Proportion of workers in public administration", label = percent) +
scale_y_continuous("Proportion of individuals who stated status that have partners", label = percent) +
ggtitle("New Zealand census 2013")
ggplot(Meshblocks2013, aes(x = WGS84Longitude, y = WGS84Latitude, colour = MedianIncome2013)) +
borders("nz", fill = terrain.colors(5)[3], colour = NA) +
geom_point(alpha = 0.1) +
coord_map(xlim = c(166, 179)) +
theme_map() +
ggtitle("Locations of centers of meshblocks in 2013 census") +
scale_colour_gradientn(colours = c("blue", "white", "red"), label = dollar) +
theme(legend.position = c(0.1, 0.6))## Warning: Removed 642 rows containing missing values (geom_point).
To be provided later.