njelections

This is a data package for R containing the results of statewide elections in NJ, from 2004 to 2025.

Installation

You can install the development version of njelections like so:

# install.packages("devtools")
devtools::install_github("tor-gu/njelections")

Dataset Overview

This package contains the results of statewide general elections for three offices:

• New Jersey Governor
• US Senate
• US President

All elections from 2004 to 2054 are included, at three levels of organization:

• Statewide (election_statewide)
• By county (election_by_county)
• By municipality (election_by_municipality)

Table election_statewide

Table election_statewide contains the following columns, which are common to all tables in this dataset:

Field	Type	Description	Example
year	int	Election year	2004
type	chr	Currently always ‘General’	General
office	chr	‘President’, ‘Senate’, or ‘Governor’	President
candidate	chr	Candidate name	John F. Kerry
party	chr	Candidate party	Democratic
vote	int	Number of votes received	1911430

There is one row in this table for every year, office, and candidate combination.

Example

year	type	office	candidate	party	vote
2004	General	President	John F. Kerry	Democratic	1911430
2004	General	President	George W. Bush	Republican	1670003
2004	General	President	Ralph Nader	Independent	19418

Table election_by_county

Table election_by_county contains all of the columns in election_statewide, plus two more:

Field	Type	Description	Example
GEOID	chr	US Census GEOID for the county	34001
county	chr	County name	Atlantic County

There is one row in this table for every year, office, county and candidate combination. In particular, for a given year and office, every candidate is represented in every county.

Example

year	type	office	GEOID	county	candidate	party	vote
2004	General	President	34001	Atlantic County	John F. Kerry	Democratic	55746
2004	General	President	34003	Bergen County	John F. Kerry	Democratic	207666
2004	General	President	34005	Burlington County	John F. Kerry	Democratic	110411

Table election_by_municipality

Table election_by_municipality contains all of the columns in election_statewide, plus three more:

Field	Type	Description	Example
GEOID	chr	US Census GEOID for the municipality	3400100100
county	chr	County name	Atlantic County
municipality	chr	Municipality name	Absecon city

There is one row in this table for every year, office, county, municipality and candidate combination. In particular, for a given year and office, every candidate is represented in every municipality.

Example

year	type	office	GEOID	county	municipality	candidate	party	vote
2004	General	President	3400100100	Atlantic County	Absecon city	John F. Kerry	Democratic	1800
2004	General	President	3400100100	Atlantic County	Absecon city	George W. Bush	Republican	2177
2004	General	President	3400100100	Atlantic County	Absecon city	Ralph Nader	Independent	25

Notes

Data source

The source for this data is the New Jersey Division of Elections. The data was derived by scraping the PDFs in the election results archive.

NJ municipalities

New Jersey municipalities have not been stable over the course of 2004-2025:

• Several municipalities have changed names or been assigned new GEOIDs by the US Census.
• In 2013, Princeton borough and Princeton township merged
• In 2022, Pine Valley was absorbed by Pine Hill

The njmunicipalities package contains municipality names and GEOIDs across the period 2001-2025. The election_by_municipality table uses the names and GEOIDs from the njmunicipalities package for the year of the election, with the exception of the Princetons for the 2012 election. See Accounting for changing municipal names for a worked example dealing with these issues.

Princeton and the 2012 election

At the time of the 2012 election, Princeton borough and Princeton township were still separate municipalities. However, the official results for Mercer County provide only the combined results for the merged Princeton municipalities.

As a result, the election_by_municipality table uses the 2013 municipality list from njmunicipalities for the 2012 election. The Princeton merger is the only difference in the 2012 and 2013 municipality list.

Candidate and party names

In general, an attempt was made to record candidate and party names exactly as they appear in the official results. However, when the same candidate or party appears in multiple elections with slightly varying names, the most common form of the name was used.

For example, Jeff Boss has appeared in official results variously as ‘Jeff Boss’, ‘Jeffrey Boss’ and ‘Jeffery “Jeff” Boss’. In this package, his name has been standardized to “Jeff Boss”.

Similarly, the Green and Libertarian party names have been standardized to “Green Party” and “Libertarian Party”.

When a candidate does not have a listed party, the party is recorded as “Independent”.

Consistency across levels

State vs county

For every year, office and candidate combination, the vote total across counties exactly matches the vote total in the statewide results:

library(dplyr)
# Statewide election matches sum of county votes for every year and every office
election_by_county |>
  group_by(year, type, office, candidate) |>
  summarize(county_vote = sum(vote), .groups = "drop") |>
  left_join(election_statewide,
             by = c("year", "type", "office", "candidate")) |>
  filter(vote != county_vote) |>
  nrow()
#> [1] 0

County vs municipality

The sum across municipalities does not always match the county total. In many – but not all – cases, the official county results account for the discrepancy. For example, the official 2020 Presidential results from Morris County include federal overseas votes in a separate row, not assigned to any municipality. These discrepancies, even when explicitly included in the official results, are not recorded in this package.

Examples

Displaying in ‘wide’ format

library(dplyr)
library(tidyr)
library(njelections)
hudson_senate_2012 <- election_by_municipality |> 
  filter(year == 2012, 
                office == "Senate", 
                county == "Hudson County") |>
  select(GEOID, municipality, party, vote) |>
  pivot_wider(names_from = party, values_from = vote) |>
  select(GEOID, municipality, Democratic, Republican, 
         Libertarian = `Libertarian Party`, Green = `Green Party`)

GEOID	municipality	Democratic	Republican	Libertarian	Green
3401703580	Bayonne city	12735	5067	98	166
3401719360	East Newark borough	356	59	2	1
3401728650	Guttenberg town	2366	500	20	18
3401730210	Harrison town	2458	520	22	31
3401732250	Hoboken city	12819	5695	210	184
3401736000	Jersey City city	56469	8077	414	565
3401736510	Kearny town	6706	2559	52	93
3401752470	North Bergen township	15187	3196	54	100
3401766570	Secaucus town	3940	1942	21	37
3401774630	Union City city	14094	2169	56	79
3401777930	Weehawken township	3429	915	41	55
3401779610	West New York town	9166	2139	26	60

Accounting for changing municipal names

Over the period 2004-2025, several municipalities changed names and GEOIDs, Princeton township was merged into Princeton borough, and Pine Valley was merged into Pine Hill. The package njmunicipalities is helpful here.

As an example, let consider Mercer county, which includes the merged Princetons, as well as Robbinsville township, previously known as Washington township. Let’s plot the two-party share of votes for each municipality in Mercer, using the current name for each municipality, and combining the totals for the Princetons in the years prior to the merger.

First, generate a cross reference table for the GEOIDs, using the 2025 GEOIDs and municipality names as the reference. We use njmunicipalities::get_geoid_cross_reference and njmunicipalities::get_municipalities for this.

library(njmunicipalities)
geoid_xref <- get_geoid_cross_references(2025, 2004:2025) |>
  dplyr::filter(!is.na(GEOID_ref)) |>
  dplyr::left_join(get_municipalities(2025), by = c("GEOID_ref" = "GEOID"))

year	GEOID_ref	GEOID	county	municipality
2004	3400100100	3400100100	Atlantic County	Absecon city
2004	3400102080	3400102080	Atlantic County	Atlantic City city
2004	3400107810	3400107810	Atlantic County	Brigantine city
2004	3400108680	3400108680	Atlantic County	Buena borough
2004	3400108710	3400108710	Atlantic County	Buena Vista township

Now, generate the two-party share of the vote, combining Princeton borough and township. The constants PRINCETON_TWP_GEOID and PRINCETON_BORO_GEOID come from njmunicipalities.

tpsov <- njelections::election_by_municipality |>
  dplyr::mutate(GEOID = dplyr::if_else(GEOID == PRINCETON_TWP_GEOID,
                                       PRINCETON_BORO_GEOID,
                                       GEOID)) |>
  dplyr::group_by(year, office, GEOID, party) |>
  dplyr::summarize(vote = sum(vote), .groups = "drop") |>
  dplyr::filter(party %in% c("Democratic", "Republican")) |>
  dplyr::group_by(year, office, GEOID) |>
  dplyr::reframe(party = party, two_party_share_of_vote = vote/sum(vote))

year	office	GEOID	party	two_party_share_of_vote
2004	President	3400100100	Democratic	0.4526025
2004	President	3400100100	Republican	0.5473975
2004	President	3400102080	Democratic	0.7595311
2004	President	3400102080	Republican	0.2404689
2004	President	3400107810	Democratic	0.4536190

Finally, combine the two tables and plot.

library(ggplot2)
tpsov |>
  dplyr::left_join(geoid_xref, by = c("year", "GEOID")) |>
  dplyr::filter(county == "Mercer County") |>
  ggplot(aes(x = year, y = two_party_share_of_vote, color = party)) +
  scale_color_manual(values = c("Democratic" = "blue", "Republican" = "red")) +
  geom_point() + 
  geom_smooth(se = FALSE, formula = y ~ x, method = "loess") + 
  facet_wrap("municipality", nrow=4) +
  ylab("Two party share of vote") +
  xlab("Election year") +
  labs(title = "Mercer County, NJ, two party share of vote",
       subtitle = "US Senate, President and Governor races, 2004-2025")