nzelect - convenient access to New Zealand election-related data

Introduction

nzelect primarily provides convenient access to New Zealand election results. So far only the results aggregated by voting place of the 2014 General Election are provided, but the intention is to extend this further both backwards and forwards in time for additional elections, opinion polls and possibly referenda.

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 ofr 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_v and parties_df data objects.

Connection to `nzcensus`

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:

devvtools::install_github("ellisp/nzelect/pkg2")

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.

Caveat and disclaimer

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.

Usage - 2014 results by voting place

The 2014 results are available in two main data frames:

Locations2014 has one row for each of the 2,568 voting places
GE2014 has 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))

## # A tibble: 28 × 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
## # ... with 18 more rows

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()

## 
## Attaching package: 'ggthemes'

## The following object is masked _by_ '.GlobalEnv':
## 
##     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")

## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead

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)

## # A tibble: 17 × 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))

## # A tibble: 7 × 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")

Usage - Opinion polls

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'

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).

Usage - convenience functions

Allocating parliamentary seats

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 
##       60       32       14       11        0        0        2        1 
##   United    Other 
##        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 
##       56       30       13       10        5        2        2        1 
##   United    Other 
##        1        1

Weighting opinion polls

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 < "2017-10-1" & Party != "TOP") %>%
    mutate(wt_p = weight_polls(MidDate, method = "pundit"),
           wt_c = weight_polls(MidDate, method = "curia")) %>%
    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() %>%
    filter(pundit_perc > 0) %>%
    mutate(pundit_seats = allocate_seats(pundit_perc, electorate = electorates)$seats_v,
           curia_seats = allocate_seats(curia_perc, electorate = electorates)$seats_v)

## # A tibble: 9 × 5
##           Party pundit_perc curia_perc pundit_seats curia_seats
##           <chr>       <dbl>      <dbl>        <dbl>       <dbl>
## 1           ACT         0.5        0.4            1           1
## 2  Conservative         0.3        0.4            0           0
## 3         Green        12.0       11.8           14          14
## 4        Labour        29.6       30.2           36          37
## 5          Mana         0.2        0.1            0           0
## 6         Maori         2.1        2.4            3           3
## 7      National        46.0       45.8           56          56
## 8      NZ First         8.2        7.8           10          10
## 9 United Future         0.3        0.2            1           1