nzelect primarily provides convenient access to New Zealand election results and some polling data.
Source data of the voting place aggregated results comes from the New Zealand Electoral Commission.
Polling data back to 2002 is included and is ultimately sourced from a range of polling firms such as Colmar Brunton, Roy Morgan, etc. The data have been scraped from Wikipedia with some manual cleaning and should be treated with caution; see the helpfiles of
polls to clarify.
Some convenience functions for analysis are also provided eg a function to calculate number of seats awarded in Parliament for a given allocation of votes and electorates won.
Some metadata on political parties (eg colours and correct full names) is also included in the
parties_df data objects.
Early versions of the
nzelect package include data from the 2013 New Zealand census to make it easy to combine election results with demographic data. As of July 2016, the census results were separated into their own
nzcensus package, which is only available from GitHub (not CRAN), via:
The separate was made to allow access to the Census results for agencies that did not want them combined with the election results; and to allow the
nzelect package to be small enough to publish on CRAN.
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:
voting_placeshas one row for each of election year - voting place combination
nzgehas one row for each combination of election year, voting place, party, electorate and voting type (Party or Candidate)
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`))
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)) ggpairs(proportions, aes(colour = voting_type), columns = 3:5)
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.
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) # 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)) nzge %>% 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")
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")
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).
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) # Result if there were no 5% minimum threshold: allocate_seats(votes, electorate = electorate, threshold = 0)$seats_v
Two techniques are provided in the
weight_polls function for aggregating opinion polls while giving more weight to more recent polls. These methods aim to replicate the approaches of the Pundit Poll of Polls, which states it is based on FiveThirtyEight's method; and the curia Market Research Public Poll Average. To date, exact replication of Pundit or curia's results has not been possible, probably due in part to the non-inclusion of sample size data so far in the
polls data in
The example below shows the
weight_polls function in action in combination with
allocate_seats, comparing the outcomes of both methods of polling aggregation, on assumption that electorate seats stay as they are in early 2017 (in particular, that ACT, United Future, and Maori party all win at least one electorate seat as needed to keep them in running for the proportional representation part of the seat allocation process).
# electorate seats for Act, Cons, Green, Labour, Mana, Maori, National, NZFirst, United, # assuming that electorates stay as currently allocated. This is critical particularly # for ACT, Maori and United Future, who if they lose their single electorate seat each # will not be represented in parliament electorates <- c(1,0,0,27,0,1,41,1,1) polls %>% filter(MidDate > "2014-12-30" & MidDate < "2017-10-1" & Party != "TOP") %>% mutate(wt_p = weight_polls(MidDate, method = "pundit", refdate = as.Date("2017-09-22")), wt_c = weight_polls(MidDate, method = "curia", refdate = as.Date("2017-09-22"))) %>% group_by(Party) %>% summarise(pundit_perc = round(sum(VotingIntention * wt_p, na.rm = TRUE) / sum(wt_p) * 100, 1), curia_perc = round(sum(VotingIntention * wt_c, na.rm = TRUE) / sum(wt_c) * 100, 1)) %>% ungroup() %>% mutate(pundit_seats = allocate_seats(pundit_perc, electorate = electorates)$seats_v, curia_seats = allocate_seats(curia_perc, electorate = electorates)$seats_v)
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