njmunicipalities

This is a data package for R that contains every county and municipality in New Jersey, from 2000 to 2022.

Installation

You can install the development version of njmunicipalities from GitHub with:

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

Changes in NJ municipalites, 2000-2022

Over the period 2000-2022, there have been several changes to the list of municipalities in New Jersey:

• In 2005, South Belmar became Lake Como. The US Census assigned a new GEOID to Lake Como.
• In 2007, Dover township in Ocean County became Toms River and was assigned a new GEOID.
• In 2008, Washington township in Mercer County became Robbinsville township and was assigned a new GEOID.
• In 2009, West Paterson became Woodland Park, and was assigned a new GEOID.
• In 2010, Caldwell borough was assigned a new GEOID from the the US Census, though there was no name change at this time.
• In 2013, Princeton borough and Princeton township merged. The merged municipality retained the Princeton borough GEOID, though the US Census started using the name ‘Princeton’ in place of ‘Princeton borough’ for the merged municipality.
• In 2022, Pine Valley was absorbed by Pine Hill.

This package will return municipality tables for any year from 2000 to 2022, and provides tools for dealing the changes from year to year.

Examples

Get a table of municipalities

Function get_municipalities returns a table of municipalities for a given year. The default is year 2022.

library(njmunicipalities)

# Municipality table for 2022
get_municipalities() |> head(n=5)
#> # A tibble: 5 × 3
#>   GEOID      county          municipality        
#>   <chr>      <chr>           <chr>               
#> 1 3400100100 Atlantic County Absecon city        
#> 2 3400102080 Atlantic County Atlantic City city  
#> 3 3400107810 Atlantic County Brigantine city     
#> 4 3400108680 Atlantic County Buena borough       
#> 5 3400108710 Atlantic County Buena Vista township

If the year is specified (from 2000 to 2022), the table will reflect the names and US Census GEOIDs in effect for that year. Here is the list for 2007.

# Municipality list for 2007
get_municipalities(2007) |> head(n=5)
#> # A tibble: 5 × 3
#>   GEOID      county          municipality        
#>   <chr>      <chr>           <chr>               
#> 1 3400100100 Atlantic County Absecon city        
#> 2 3400102080 Atlantic County Atlantic City city  
#> 3 3400107810 Atlantic County Brigantine city     
#> 4 3400108680 Atlantic County Buena borough       
#> 5 3400108710 Atlantic County Buena Vista township

Get a table of municipalities for one year with GEOIDs from another

If the optional parameter geoid_year is included, the GEOIDs in the returned table will be the ones in effect for that year. For example, to get a municipality list with municipal names effective 2002 and GEOIDs effective 2018:

# Municipality list for 2002 with GEOIDs from 2018
get_municipalities(2002, geoid_year = 2018) |> head(n=5)
#> # A tibble: 5 × 3
#>   GEOID      county          municipality        
#>   <chr>      <chr>           <chr>               
#> 1 3400100100 Atlantic County Absecon city        
#> 2 3400102080 Atlantic County Atlantic City city  
#> 3 3400107810 Atlantic County Brigantine city     
#> 4 3400108680 Atlantic County Buena borough       
#> 5 3400108710 Atlantic County Buena Vista township

If you need both GEOIDs, specify geoid_ref_as_ref_column = TRUE. This will cause the GEOIDs from geoid_year to be returned as a separate column (instead of replacing the GEOID).

# Municipality list for 2002 with GEOIDs from 2018 added as
# separate column
get_municipalities(2002, 
                   geoid_year = 2018, 
                   geoid_ref_as_ref_column = TRUE) |> 
  head(n=5)
#> # A tibble: 5 × 4
#>   GEOID_ref  GEOID      county          municipality        
#>   <chr>      <chr>      <chr>           <chr>               
#> 1 3400100100 3400100100 Atlantic County Absecon city        
#> 2 3400102080 3400102080 Atlantic County Atlantic City city  
#> 3 3400107810 3400107810 Atlantic County Brigantine city     
#> 4 3400108680 3400108680 Atlantic County Buena borough       
#> 5 3400108710 3400108710 Atlantic County Buena Vista township

As an illustration, consider Lake Como, which was known as “South Belmar” before 2005, when it also had a different GEOID. We can see that it appears as a “new” municipality in the 2005 list:

# Lake Como is the only 'new' municipality in 2005
dplyr::anti_join(
  get_municipalities(2005),
  get_municipalities(2004)
)
#> Joining, by = c("GEOID", "county", "municipality")
#> # A tibble: 1 × 3
#>   GEOID      county          municipality     
#>   <chr>      <chr>           <chr>            
#> 1 3402537560 Monmouth County Lake Como borough

If we want to see the old name and GEOID, we can include the 2004 GEOID in the 2005 table and then join with the 2004 table:

# Differences between 2005 and 2004
get_municipalities(2005, 
                   geoid_year = 2004, 
                   geoid_ref_as_ref_column = TRUE) |>
  dplyr::anti_join(get_municipalities(2004)) |>
  dplyr::left_join(get_municipalities(2004), 
                   by = c("GEOID_ref" = "GEOID", "county"),
                   suffix = c("", "_ref"))
#> Joining, by = c("GEOID", "county", "municipality")
#> # A tibble: 1 × 5
#>   GEOID_ref  GEOID      county          municipality      municipality_ref    
#>   <chr>      <chr>      <chr>           <chr>             <chr>               
#> 1 3402568670 3402537560 Monmouth County Lake Como borough South Belmar borough

Generating GEOID cross reference tables

Function get_geoid_cross_references will return a table of GEOID cross-references for a range of years to a specified reference year. Here we map all the GEOIDs from the years 2010-2020 to their 2005 GEOID.

# Cross reference table, comparing GEOID for years 2010-2020 to 
# reference year 2005
get_geoid_cross_references(2005, 2010:2020) |>
  dplyr::arrange(GEOID_ref, year) |> 
  head(n=5)
#> # A tibble: 5 × 3
#>    year GEOID_ref  GEOID     
#>   <int> <chr>      <chr>     
#> 1  2010 3400100100 3400100100
#> 2  2011 3400100100 3400100100
#> 3  2012 3400100100 3400100100
#> 4  2013 3400100100 3400100100
#> 5  2014 3400100100 3400100100

Dealing with the Princetons

Princeton township and Princeton borough merged in 2013. Because the merged municipality retained Princeton borough’s GEOID, Princeton township disappears in 2013. This is one of two examples of a disappearing municipality in the package (see Dealing with Pine Valley and Pine Hill).

The functions get_municipality and get_geoid_cross_references will return NA for a reference year GEOID after 2012 for Princeton township:

# Princeton township existed in 2000 but not 2021
get_municipalities(2000, geoid_year = 2021) |>
  dplyr::filter(is.na(GEOID))
#> # A tibble: 1 × 3
#>   GEOID county        municipality      
#>   <chr> <chr>         <chr>             
#> 1 <NA>  Mercer County Princeton township

For convenience in dealing with this issue, this package includes the constants PRINCETON_TWP_GEOID and PRINCETON_BORO_GEOID.

c(PRINCETON_TWP_GEOID, PRINCETON_BORO_GEOID)
#> [1] "3402160915" "3402160900"

As an example, consider the municipal election data in njelections. For comparisons across years, we may wish to combine the vote totals for the Princetons prior to 2013:

library(njelections)
election_by_municipality_combined <-
  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")

election_by_municipality_combined |> 
  dplyr::filter(GEOID %in% c(PRINCETON_TWP_GEOID, PRINCETON_BORO_GEOID)) |>
  head(5)
#> # A tibble: 5 × 5
#>    year office    GEOID      party               vote
#>   <int> <chr>     <chr>      <chr>              <int>
#> 1  2004 President 3402160900 Constitution Party     5
#> 2  2004 President 3402160900 Democratic          9751
#> 3  2004 President 3402160900 Green Party           12
#> 4  2004 President 3402160900 Independent          111
#> 5  2004 President 3402160900 Libertarian Party     40

Dealing with Pine Valley and Pine Hill

In 2022, Pine Valley borough was merged into Pine Hill borough.

The functions get_municipality and get_geoid_cross_references will return NA for a reference year GEOID after 2021 for Pine Valley:

# Pine Valley existed in 2021 but not 2022
get_municipalities(2021, geoid_year = 2022) |>
  dplyr::filter(is.na(GEOID))
#> # A tibble: 1 × 3
#>   GEOID county        municipality       
#>   <chr> <chr>         <chr>              
#> 1 <NA>  Camden County Pine Valley borough

For convenience in dealing with this issue, this package includes constants PINE_VALLEY_BORO_GEOID and PINE_HILL_BORO_GEOID.

c(PINE_VALLEY_BORO_GEOID, PINE_HILL_BORO_GEOID)
#> [1] "3400758920" "3400758770"

County list

For convenience, this package also includes a table of counties and their GEOIDS. There have been no changes to New Jersey counties from 2000 to 2021.

# County list
counties |> head(n=5)
#> # A tibble: 5 × 2
#>   GEOID county           
#>   <chr> <chr>            
#> 1 34001 Atlantic County  
#> 2 34003 Bergen County    
#> 3 34005 Burlington County
#> 4 34007 Camden County    
#> 5 34009 Cape May County