zipcodeR is an all-in-one toolkit of functions and data for working with ZIP codes in R.
This document will introduce the tools provided by zipcodeR for improving your workflow when working with ZIP code-level data. The goal of these examples is to help you quickly get up and running with zipcodeR using real-world examples.
First thing's first: zipcodeR's data & basic search functions are a core component of the package. We'll cover these before showing you how you can implement this package with a real-world example.
The package ships with an offline database containing 24 columns of data for each ZIP code. You can either keep all 24 variables or filter to just one of these depending on what data you need.
The columns of data provided are: zipcode, zipcode_type, major_city, post_office_city, common_city_list, county, state, lat, lng, timezone, radius_in_miles, area_code_list, population, population_density, land_area_in_sqmi, water_area_in_sqmi, housing_units, occupied_housing_units, median_home_value, median_household_income, bounds_west, bounds_east, bounds_north, bounds_south
Let's begin by using zipcodeR to find all ZIP codes within a given state.
Getting all ZIP codes for a single state is simple, you only need to pass a two-digit abbreviation of a state's name to get a tibble of all ZIP codes in that state. Let's start by finding all of the ZIP codes in New York:
search_state('NY')
## # A tibble: 2,208 × 24
## zipcode zipcode…¹ major…² post_…³ common_c…⁴ county state lat lng timez…⁵
## <chr> <chr> <chr> <chr> <blob> <chr> <chr> <dbl> <dbl> <chr>
## 1 00501 Unique Holtsv… <NA> <raw 22 B> Suffo… NY NA NA <NA>
## 2 00544 Unique Holtsv… <NA> <raw 22 B> Suffo… NY NA NA <NA>
## 3 06390 PO Box Fisher… Fisher… <raw 32 B> Suffo… NY 41.3 -72.0 Eastern
## 4 10001 Standard New Yo… New Yo… <raw 20 B> New Y… NY 40.8 -74.0 Eastern
## 5 10002 Standard New Yo… New Yo… <raw 34 B> New Y… NY 40.7 -74.0 Eastern
## 6 10003 Standard New Yo… New Yo… <raw 20 B> New Y… NY 40.7 -74.0 Eastern
## 7 10004 Standard New Yo… New Yo… <raw 37 B> New Y… NY 40.7 -74.0 Eastern
## 8 10005 Standard New Yo… New Yo… <raw 35 B> New Y… NY 40.7 -74.0 Eastern
## 9 10006 Standard New Yo… New Yo… <raw 31 B> New Y… NY 40.7 -74.0 Eastern
## 10 10007 Standard New Yo… New Yo… <raw 20 B> New Y… NY 40.7 -74.0 Eastern
## # … with 2,198 more rows, 14 more variables: radius_in_miles <dbl>,
## # area_code_list <blob>, population <int>, population_density <dbl>,
## # land_area_in_sqmi <dbl>, water_area_in_sqmi <dbl>, housing_units <int>,
## # occupied_housing_units <int>, median_home_value <int>,
## # median_household_income <int>, bounds_west <dbl>, bounds_east <dbl>,
## # bounds_north <dbl>, bounds_south <dbl>, and abbreviated variable names
## # ¹zipcode_type, ²major_city, ³post_office_city, ⁴common_city_list, …
What if you only wanted the actual ZIP codes and no other variables? You can use R's dollar sign operator to select one column at a time from the output of zipcodeR's search functions:
nyzip <- search_state('NY')$zipcode
You can also search for ZIP codes in multiple states at once by passing a vector of state abbreviations to the search_states function like so:
states <- c('NY','NJ','CT')
search_state(states)
## # A tibble: 3,378 × 24
## zipcode zipcode…¹ major…² post_…³ common_c…⁴ county state lat lng timez…⁵
## <chr> <chr> <chr> <chr> <blob> <chr> <chr> <dbl> <dbl> <chr>
## 1 06001 Standard Avon Avon, … <raw 16 B> Hartf… CT 41.8 -72.9 Eastern
## 2 06002 Standard Bloomf… Bloomf… <raw 22 B> Hartf… CT 41.8 -72.7 Eastern
## 3 06006 Unique Windsor <NA> <raw 19 B> Hartf… CT NA NA <NA>
## 4 06010 Standard Bristol Bristo… <raw 19 B> Hartf… CT 41.7 -72.9 Eastern
## 5 06011 PO Box Bristol <NA> <raw 19 B> Hartf… CT NA NA <NA>
## 6 06013 Standard Burlin… Burlin… <raw 36 B> Hartf… CT 41.8 -73.0 Eastern
## 7 06016 Standard Broad … Broad … <raw 46 B> Hartf… CT 41.9 -72.6 Eastern
## 8 06018 Standard Canaan Canaan… <raw 18 B> Litch… CT 42.0 -73.3 Eastern
## 9 06019 Standard Canton Canton… <raw 34 B> Hartf… CT 41.9 -72.9 Eastern
## 10 06020 Standard Canton… Canton… <raw 25 B> Hartf… CT 41.8 -72.9 Eastern
## # … with 3,368 more rows, 14 more variables: radius_in_miles <dbl>,
## # area_code_list <blob>, population <int>, population_density <dbl>,
## # land_area_in_sqmi <dbl>, water_area_in_sqmi <dbl>, housing_units <int>,
## # occupied_housing_units <int>, median_home_value <int>,
## # median_household_income <int>, bounds_west <dbl>, bounds_east <dbl>,
## # bounds_north <dbl>, bounds_south <dbl>, and abbreviated variable names
## # ¹zipcode_type, ²major_city, ³post_office_city, ⁴common_city_list, …
This results in a tibble containing all ZIP codes for the states passed to the search_states() function.
It is also possible to search for ZIP codes located in a particular county within a state.
Let's find all of the ZIP codes located within Ocean County, New Jersey:
search_county('Ocean','NJ')
## # A tibble: 32 × 24
## zipcode zipcode…¹ major…² post_…³ common_c…⁴ county state lat lng timez…⁵
## <chr> <chr> <chr> <chr> <blob> <chr> <chr> <dbl> <dbl> <chr>
## 1 08005 Standard Barneg… Barneg… <raw 20 B> Ocean… NJ 39.8 -74.3 Eastern
## 2 08006 PO Box Barneg… Barneg… <raw 33 B> Ocean… NJ 39.8 -74.1 Eastern
## 3 08008 Standard Beach … Beach … <raw 61 B> Ocean… NJ 39.6 -74.2 Eastern
## 4 08050 Standard Manaha… Manaha… <raw 47 B> Ocean… NJ 39.7 -74.3 Eastern
## 5 08087 Standard Tucker… Tucker… <raw 51 B> Ocean… NJ 39.6 -74.4 Eastern
## 6 08092 Standard West C… West C… <raw 22 B> Ocean… NJ 39.7 -74.3 Eastern
## 7 08527 Standard Jackson Jackso… <raw 19 B> Ocean… NJ 40.1 -74.4 Eastern
## 8 08533 Standard New Eg… New Eg… <raw 21 B> Ocean… NJ 40.0 -74.5 Eastern
## 9 08701 Standard Lakewo… Lakewo… <raw 20 B> Ocean… NJ 40.1 -74.2 Eastern
## 10 08721 Standard Bayvil… Bayvil… <raw 20 B> Ocean… NJ 39.9 -74.2 Eastern
## # … with 22 more rows, 14 more variables: radius_in_miles <dbl>,
## # area_code_list <blob>, population <int>, population_density <dbl>,
## # land_area_in_sqmi <dbl>, water_area_in_sqmi <dbl>, housing_units <int>,
## # occupied_housing_units <int>, median_home_value <int>,
## # median_household_income <int>, bounds_west <dbl>, bounds_east <dbl>,
## # bounds_north <dbl>, bounds_south <dbl>, and abbreviated variable names
## # ¹zipcode_type, ²major_city, ³post_office_city, ⁴common_city_list, …
Sometimes working with county names can be messy and there might not be a 100% match between our database and the name. The search_county() function can be configured to use base R's agrep function for these cases via an optional parameter.
One example where this feature is useful comes from the state of Louisiana. Since Louisiana has parishes, their county names don't line up exactly with how other states name their counties.
This example uses approxmiate matching to retrieve all ZIP codes for St. Bernard Parish in Louisiana:
search_county("ST BERNARD","LA", similar = TRUE)$zipcode
## [1] "70032" "70043" "70044" "70075" "70085" "70092"
Try running the above code with the similar parameter set to FALSE or not present and you'll receive an error.
What if you already have a dataset containing ZIP codes and want to find out more about that particular area?
Using the reverse_zipcode() function, we can get up to 24 more columns of data when given a ZIP code.
To explore how zipcodeR can enhance your data & workflow, we will use a public dataset from the National Association of Realtors containing data about housing market trends in the United States.
This dataset, which is updated monthly, contains 10 observations with current housing market data from the National Association of Realtors hosted on Amazon S3
This is what the data we will be working with looks like:
head(real_estate_data)
## # A tibble: 6 × 2
## postal_code zip_name
## <chr> <chr>
## 1 34102 naples, fl
## 2 64105 kansas city, mo
## 3 33544 wesley chapel, fl
## 4 76550 lampasas, tx
## 5 85335 el mirage, az
## 6 61264 milan, il
Note: The data used in this vignette was filtered to only include valid 5-digit ZIP codes as zipcodeR does not yet have a function for normalizing ZIP codes. The full Realtor dataset will have a different number of rows.
We'll focus on the first row for now, which represents the town of Naples, Fl.
real_estate_data[1,]
## # A tibble: 1 × 2
## postal_code zip_name
## <chr> <chr>
## 1 34102 naples, fl
The Realtor dataset contains a column named postal_code containing the ZIP code that identifies the town. We'll use this to find out more about Naples than what is provided in the housing market data.
So far we've covered the functions provided by zipcodeR for searching ZIP codes across multiple geographies. The package also provides a function for going in reverse, when given a 5-digit ZIP code. Introducing reverse_zipcode():
# Get the ZIP code of the first row of data
zip_code <- real_estate_data[1,]$postal_code
# Pass the ZIP code to the reverse_zipcode() function
reverse_zipcode(zip_code)
## # A tibble: 1 × 24
## zipcode zipcode_…¹ major…² post_…³ common_c…⁴ county state lat lng timez…⁵
## <chr> <chr> <chr> <chr> <blob> <chr> <chr> <dbl> <dbl> <chr>
## 1 34102 Standard Naples Naples… <raw 18 B> Colli… FL 26.1 -81.8 Eastern
## # … with 14 more variables: radius_in_miles <dbl>, area_code_list <blob>,
## # population <int>, population_density <dbl>, land_area_in_sqmi <dbl>,
## # water_area_in_sqmi <dbl>, housing_units <int>,
## # occupied_housing_units <int>, median_home_value <int>,
## # median_household_income <int>, bounds_west <dbl>, bounds_east <dbl>,
## # bounds_north <dbl>, bounds_south <dbl>, and abbreviated variable names
## # ¹zipcode_type, ²major_city, ³post_office_city, ⁴common_city_list, …
You may also be interested in relating data at the ZIP code level to Census data. zipcodeR currently provides a function for getting all Census tracts when provided with a 5-digit ZIP code.
Let's find out how many Census tracts are in the ZIP code from the previous example.
get_tracts(zip_code)
## # A tibble: 11 × 3
## ZCTA5 TRACT GEOID
## <chr> <chr> <dbl>
## 1 34102 000101 12021000101
## 2 34102 000102 12021000102
## 3 34102 000200 12021000200
## 4 34102 000302 12021000302
## 5 34102 000402 12021000402
## 6 34102 000500 12021000500
## 7 34102 000600 12021000600
## 8 34102 000700 12021000700
## 9 34102 010210 12021010210
## 10 34102 010601 12021010601
## 11 34102 010701 12021010701
Now that you have all of the tracts for this ZIP code, it would be very easy to join this with other Census data, such as that which is available from the American Community Survey and other sources.
But ZIP codes alone are not terribly useful for social science research since they are only meant to represent USPS service areas. The Census Bureau has established ZIP code tabulation areas (ZCTAs) that provide a representation of ZIP codes and can be used for joining with Census data. But not every ZIP code is also a ZCTA.
zipcodeR provides a function for testing if a given ZIP code is also a ZIP code tabulation area. When provided with a vector of 5-digit ZIP codes the function will return TRUE or FALSE based upon whether the ZIP code is also a ZCTA.
is_zcta(zip_code)
## [1] TRUE