R/noncensus.r
In ramhiser/noncensus: U.S. Census Regional and Demographic Data
#' U.S. Census Region and Demographic Data
#'
#' The R package \code{noncensus} provides a collection of various regional
#' information determined by the U.S. Census Bureau along with demographic
#' data. We have also included scripts to download, process, and load the
#' original data from their sources.
#'
#' @docType package
#' @name noncensus
#' @aliases noncensus package-noncensus
NULL
#' Demographic Data and Census Regions of U.S. States and Territories
#'
#' A dataset containing demographic information and the census regions of each
#' U.S. state as defined by the U.S. Census Bureau. Also included are the
#' U.S. territories, such as Puerto Rico and Guam.
#'
#' The variables included are:
#'
#' \itemize{
#' \item state. State abbreviation
#' \item name. State name
#' \item region. Region as defined by the U.S. Census Bureau
#' \item division. Subregion as defined by the U.S. Census Bureau
#' \item capital. Capital city.
#' \item area. Land area in square miles
#' \item population. Population from 2010 Census
#' }
#'
#' The U.S. is divided into four regions:
#'
#' \enumerate{
#' \item Midwest
#' \item Northeast
#' \item South
#' \item West
#' }
#'
#' Within each region, states are further partitioned into divisions. For more
#' details about census regions, see:
#' \url{http://en.wikipedia.org/wiki/List_of_regions_of_the_United_States#Census_Bureau-designated_regions_and_divisions}
#'
#' Much of the state data was extracted from
#' \url{http://www.census.gov/popest/data/state/totals/2013/index.html}
#'
#' @docType data
#' @keywords datasets
#' @name states
#' @usage data(states)
#' @format A data frame with 56 rows and 7 variables
NULL
#' Data for U.S. Counties and County-Equivalent Entities
#'
#' Data containing state and county FIPS codes for U.S. counties and
#' county-equivalent entities (CEE) along with county-level demographic
#' data. The CEE includes non-state locations, such as Puerto Rico (PR) and Guam
#' (GU).
#'
#' \itemize{
#' \item county_name. County Name and Legal/Statistical Area Description
#' \item state. State Postal Code
#' \item state_fips. State FIPS Code
#' \item county_fips. County FIPS Code
#' \item fips_class. FIPS Class Code
#' \item CSA. Combined Statistical Area
#' \item CBSA. Core-based Statistical Area
#' \item population. County population from 2010 Census
#' }
#'
#' The U.S. Census Bureau groups counties into CSAs and CBSAs primarily based on
#' county population. We provide listings of both in
#' \code{\link[noncensus]{combined_areas}} and
#' \code{\link[noncensus]{corebased_areas}}.
#'
#' For a detailed description, Wikipedia has excellent discussions of both
#' areas: \url{http://en.wikipedia.org/wiki/Combined_Statistical_Area} and
#' \url{http://en.wikipedia.org/wiki/Core_Based_Statistical_Area}. Also, the
#' following map from Wikipedia is excellent to visualize the areas:
#' \url{http://upload.wikimedia.org/wikipedia/commons/7/7b/Combined_statistical_areas_of_the_United_States_and_Puerto_Rico.gif}
#'
#' NOTE: Not all counties are members of a CSA or CBSA.
#'
#' The following details about FIPS Class Codes have been blatantly taken from
#' the Census Bureau's website:
#'
#' \itemize{
#' \item H1. Identifies an active county or statistically equivalent entity that does not qualify under subclass C7 or H6.
#' \item H4. Identifies a legally defined inactive or nonfunctioning county or statistically equivalent entity that does not qualify under subclass H6.
#' \item H5. Identifies census areas in Alaska, a statistical county equivalent entity.
#' \item H6. Identifies a county or statistically equivalent entity that is areally coextensive or governmentally consolidated with an incorporated place, part of an incorporated place, or a consolidated city.
#' \item C7: Identifies an incorporated place that is an independent city; that is, it also serves as a county equivalent because it is not part of any county, and a minor civil division (MCD) equivalent because it is not part of any MCD.
#' }
#'
#' For more details, see:
#' \url{http://www.census.gov/geo/reference/codes/cou.html}
#'
#' @docType data
#' @keywords datasets
#' @name counties
#' @usage data(counties)
#' @format A data frame with 3235 rows and 6 variables
NULL
#' Data for U.S. Cities by Zip Code
#'
#' This data set considers each zip code throughout the U.S. and provides
#' additional information, including the city and state, latitude and longitude,
#' and the FIPS code for the corresponding county.
#'
#' The ZIP code data was obtained from version 1.0 of the \code{\link[zipcode]{zipcode}}
#' package on CRAN. The county FIPS codes were obtained by querying the FIPS
#' code from each zip's latitude and longitude via the FCC's Census Block
#' Conversions API. For details regarding the API, see
#' \url{http://www.fcc.gov/developers/census-block-conversions-api}.
#'
#' \itemize{
#' \item zip. U.S. ZIP (postal) code
#' \item city. ZIP code's city
#' \item state. ZIP code's state
#' \item latitude. ZIP code's latitude
#' \item longitude. ZIP code's longitude
#' \item fips. County FIPS Code
#' }
#'
#' The FIPS codes are useful for mapping ZIP codes and cities to counties in the
#' \code{\link[noncensus]{counties}} data set.
#'
#' Fun fact: ZIP is an acronym for "Zone Improvement Plan."
#'
#' @docType data
#' @keywords datasets
#' @name zip_codes
#' @usage data(zip_codes)
#' @format A data frame with 43524 rows and 6 variables
NULL
#' Combined Statistical Areas (CSAs)
#'
#' The U.S. Census Bureau groups counties into CSAs primarily based on county
#' population. NOTE: Not all counties are members of a CSA. For a detailed
#' description, Wikipedia has an excellent discussion:
#' \url{http://en.wikipedia.org/wiki/Combined_Statistical_Area}. Also, the
#' following map from Wikipedia is excellent to visualize the areas:
#' \url{http://upload.wikimedia.org/wikipedia/commons/7/7b/Combined_statistical_areas_of_the_United_States_and_Puerto_Rico.gif}
#'
#' @docType data
#' @keywords datasets
#' @name combined_areas
#' @usage data(combined_areas)
#' @format A data frame with 166 rows and 2 variables.
NULL
#' Core-based Statistical Area (CBSAs)
#'
#' The U.S. Census Bureau groups counties into CBSAs primarily based on county
#' population. NOTE: Not all counties are members of a CBSA. For a detailed
#' description, Wikipedia has an excellent discussion:
#' \url{http://en.wikipedia.org/wiki/Core_Based_Statistical_Area}. Also, the
#' following map from Wikipedia is excellent to visualize the areas:
#' \url{http://upload.wikimedia.org/wikipedia/commons/7/7b/Combined_statistical_areas_of_the_United_States_and_Puerto_Rico.gif}
#'
#' @docType data
#' @keywords datasets
#' @name corebased_areas
#' @usage data(corebased_areas)
#' @format A data frame with 917 rows and 4 variables.
NULL
#' County Population Data by Age
#'
#' A dataset containing the population totals and percentages for each US county
#' by age of inhabitant. The variables are as follows:
#'
#' \itemize{
#' \item fips. FIPS code for the county
#' \item age_group. Age groups are in 5 year intervals
#' \item population. Count of people in that county in that age group
#' \item percent. Percent of that county's total population in that age group
#' }
#'
#' @docType data
#' @keywords datasets
#' @name population_age
#' @usage data(population_age)
#' @format A data frame with 56574 rows and 4 variables
NULL
#' Income, Poverty, and Health Insurance in the United States
#'
#' A dataset containing the U.S. Census QuickFacts table of frequently requested
#' data items from various Census Bureau programs.
#' It should be noted that for "firms", "percent_female_firms", "manu_shipments",
#' "merchant_wholesales", "retail_sales", "retail_sales_per_capita",
#' "accom_food_sales", and "building_permits", Skagway Municipality is included
#' with Hoonah-Angoon Census Area and Wrangell City and Borough is included with
#' Petersburg Census Area.
#'
#' \itemize{
#' \item fips. FIPS State and County code. "0" represents national total.
#' \item population_2013. Population, 2013 estimate
#' \item population_2010_base. Population, 2010 (April 1) estimates base
#' \item population_change_percent. Population, percent change from April 1, 2010 to July 1, 2013
#' \item population_2010. Population, 2010
#' \item percent_under_5. Percentage of persons under 5 years
#' \item percent_under_18. Percentge of persons under 18 years
#' \item percent_over_65. Percentage of persons 65 years and over
#' \item percent_female. Percentage of Female persons
#' \item percent_white. Percentage of persons identifying as White alone
#' \item percent_black. Percentage of persons identifying as Black or African American alone
#' \item percent_native. Percentage of persons identifying as American Indian and Alaska Native alone
#' \item percent_asian. Percentage of persons identifying as Asian alone
#' \item percent_hawaiian. Percentage of persons identifying as Native Hawaiian and Other Pacific Islander alone
#' \item percent_two_plus. Percentage of persons identifying as Two or More Races
#' \item percent_hispanic. Percentage of persons identifying as Hispanic or Latino
#' \item percent_white_NH. Percentage of persons identifying as White alone, not Hispanic or Latino
#' \item percent_same_house_1yr. Percentage of persons living in the same house 1 year & over
#' \item percent_foreign. Percentage of foreign born persons
#' \item percent_nonEnglish. Percentage of persons age 5+ who speak a language other than English at home
#' \item percent_high_school_grad. Percentage of persons age 25+ with a high school degree or higher
#' \item percent_BA. Percentage of persons age 25+ with a Bachelor's degree or higher
#' \item veterans. Number of veterans
#' \item mean_travel_time. Mean travel time to work in minutes, workers age 16+
#' \item housing_units. Number of housing units
#' \item homeownership. Homeownership rate
#' \item percent_multi_unit. Percentage of housing units in multi-unit structures
#' \item median_value_housing. Median value of owner-occupied housing units
#' \item households. Number of households
#' \item pph. Persons per household
#' \item per_capita_income. Per capita money income in past 12 months
#' \item med_hh_income. Median household income
#' \item percent_poverty. Percentage of persons below poverty level
#' \item private_non_farm. Number of private nonfarm establishments
#' \item private_non_farm_employ. Private nonfarm employment
#' \item pnfe_percent_change. Percent change in private nonfarm employment
#' \item nonemployer_estab. Number of nonemployer establishments
#' \item firms. Total number of firms
#' \item percent_black_firms. Percentage of Black-owned firms
#' \item percent_native_firms. Percentage of American Indian- and Alaska Native-owned firms
#' \item percent_asian_firms. Percentage of Asian-owned firms
#' \item percent_hawaiian_firms. Percentage of Native Hawaiian- and Other Pacific Islander-owned firms
#' \item percent_hispanic_firms. Percentage of Hispanic-owned firms
#' \item percent_female_firms. Percentage of Woman-owned firms
#' \item manu_shipments. Manufacturers shipments, in thousands of dollars
#' \item merchant_wholesales. Merchant wholesaler sales, in thousands of dollars
#' \item retail_sales. Retail sales, in thousands of dollars
#' \item retail_sales_per_capita. Retail sales per capita
#' \item accom_food_sales. Accommodation and food services sales, in thousands of dollars
#' \item building_permits. Number of building permits
#' \item land_area. Land area in square miles
#' \item population_sq_mi. Population per square mile
#' }
#'
#' @docType data
#' @keywords datasets
#' @name quick_facts
#' @usage data(quick_facts)
#' @format A data frame with 3195 rows and 52 variables
NULL
#' Polygons to Describe Each U.S. County's Geographical Shape
#'
#' Data containing the vertices to describe each U.S. county's geographical
#' shape as a polygon. Vertices are given in terms of latitude and
#' longitude. The order in which the vertices should be drawn are also given by
#' \code{order}.
#'
#' NOTE: A small number of counties have multiple groups. Typically, these
#' counties are separated into multiple polygons by, say, a body of water.
#' Example: Galveston, Texas (FIPS == 48167).
#'
#' U.S. counties are uniquely identified by a FIPS code.
#'
#' The \code{county_polygons} data frame consists of the following variables:
#' \itemize{
#' \item order. The order in which the vertices should be drawn.
#' \item fips. County FIPS Code
#' \item names. Unique name to describe county.
#' \item group. County polygon's group number.
#' \item lat. County's latitude
#' \item long. County's longitude
#' \item county. The county's name.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name county_polygons
#' @usage data(county_polygons)
#' @format A data frame with 91,030 rows and 7 variables
NULL
ramhiser/noncensus documentation built on May 26, 2019, 10:11 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' U.S. Census Region and Demographic Data
#'
#' The R package \code{noncensus} provides a collection of various regional
#' information determined by the U.S. Census Bureau along with demographic
#' data. We have also included scripts to download, process, and load the
#' original data from their sources.
#'
#' @docType package
#' @name noncensus
#' @aliases noncensus package-noncensus
NULL
#' Demographic Data and Census Regions of U.S. States and Territories
#'
#' A dataset containing demographic information and the census regions of each
#' U.S. state as defined by the U.S. Census Bureau. Also included are the
#' U.S. territories, such as Puerto Rico and Guam.
#'
#' The variables included are:
#'
#' \itemize{
#' \item state. State abbreviation
#' \item name. State name
#' \item region. Region as defined by the U.S. Census Bureau
#' \item division. Subregion as defined by the U.S. Census Bureau
#' \item capital. Capital city.
#' \item area. Land area in square miles
#' \item population. Population from 2010 Census
#' }
#'
#' The U.S. is divided into four regions:
#'
#' \enumerate{
#' \item Midwest
#' \item Northeast
#' \item South
#' \item West
#' }
#'
#' Within each region, states are further partitioned into divisions. For more
#' details about census regions, see:
#' \url{http://en.wikipedia.org/wiki/List_of_regions_of_the_United_States#Census_Bureau-designated_regions_and_divisions}
#'
#' Much of the state data was extracted from
#' \url{http://www.census.gov/popest/data/state/totals/2013/index.html}
#'
#' @docType data
#' @keywords datasets
#' @name states
#' @usage data(states)
#' @format A data frame with 56 rows and 7 variables
NULL
#' Data for U.S. Counties and County-Equivalent Entities
#'
#' Data containing state and county FIPS codes for U.S. counties and
#' county-equivalent entities (CEE) along with county-level demographic
#' data. The CEE includes non-state locations, such as Puerto Rico (PR) and Guam
#' (GU).
#'
#' \itemize{
#' \item county_name. County Name and Legal/Statistical Area Description
#' \item state. State Postal Code
#' \item state_fips. State FIPS Code
#' \item county_fips. County FIPS Code
#' \item fips_class. FIPS Class Code
#' \item CSA. Combined Statistical Area
#' \item CBSA. Core-based Statistical Area
#' \item population. County population from 2010 Census
#' }
#'
#' The U.S. Census Bureau groups counties into CSAs and CBSAs primarily based on
#' county population. We provide listings of both in
#' \code{\link[noncensus]{combined_areas}} and
#' \code{\link[noncensus]{corebased_areas}}.
#'
#' For a detailed description, Wikipedia has excellent discussions of both
#' areas: \url{http://en.wikipedia.org/wiki/Combined_Statistical_Area} and
#' \url{http://en.wikipedia.org/wiki/Core_Based_Statistical_Area}. Also, the
#' following map from Wikipedia is excellent to visualize the areas:
#' \url{http://upload.wikimedia.org/wikipedia/commons/7/7b/Combined_statistical_areas_of_the_United_States_and_Puerto_Rico.gif}
#'
#' NOTE: Not all counties are members of a CSA or CBSA.
#'
#' The following details about FIPS Class Codes have been blatantly taken from
#' the Census Bureau's website:
#'
#' \itemize{
#' \item H1. Identifies an active county or statistically equivalent entity that does not qualify under subclass C7 or H6.
#' \item H4. Identifies a legally defined inactive or nonfunctioning county or statistically equivalent entity that does not qualify under subclass H6.
#' \item H5. Identifies census areas in Alaska, a statistical county equivalent entity.
#' \item H6. Identifies a county or statistically equivalent entity that is areally coextensive or governmentally consolidated with an incorporated place, part of an incorporated place, or a consolidated city.
#' \item C7: Identifies an incorporated place that is an independent city; that is, it also serves as a county equivalent because it is not part of any county, and a minor civil division (MCD) equivalent because it is not part of any MCD.
#' }
#'
#' For more details, see:
#' \url{http://www.census.gov/geo/reference/codes/cou.html}
#'
#' @docType data
#' @keywords datasets
#' @name counties
#' @usage data(counties)
#' @format A data frame with 3235 rows and 6 variables
NULL
#' Data for U.S. Cities by Zip Code
#'
#' This data set considers each zip code throughout the U.S. and provides
#' additional information, including the city and state, latitude and longitude,
#' and the FIPS code for the corresponding county.
#'
#' The ZIP code data was obtained from version 1.0 of the \code{\link[zipcode]{zipcode}}
#' package on CRAN. The county FIPS codes were obtained by querying the FIPS
#' code from each zip's latitude and longitude via the FCC's Census Block
#' Conversions API. For details regarding the API, see
#' \url{http://www.fcc.gov/developers/census-block-conversions-api}.
#'
#' \itemize{
#' \item zip. U.S. ZIP (postal) code
#' \item city. ZIP code's city
#' \item state. ZIP code's state
#' \item latitude. ZIP code's latitude
#' \item longitude. ZIP code's longitude
#' \item fips. County FIPS Code
#' }
#'
#' The FIPS codes are useful for mapping ZIP codes and cities to counties in the
#' \code{\link[noncensus]{counties}} data set.
#'
#' Fun fact: ZIP is an acronym for "Zone Improvement Plan."
#'
#' @docType data
#' @keywords datasets
#' @name zip_codes
#' @usage data(zip_codes)
#' @format A data frame with 43524 rows and 6 variables
NULL
#' Combined Statistical Areas (CSAs)
#'
#' The U.S. Census Bureau groups counties into CSAs primarily based on county
#' population. NOTE: Not all counties are members of a CSA. For a detailed
#' description, Wikipedia has an excellent discussion:
#' \url{http://en.wikipedia.org/wiki/Combined_Statistical_Area}. Also, the
#' following map from Wikipedia is excellent to visualize the areas:
#' \url{http://upload.wikimedia.org/wikipedia/commons/7/7b/Combined_statistical_areas_of_the_United_States_and_Puerto_Rico.gif}
#'
#' @docType data
#' @keywords datasets
#' @name combined_areas
#' @usage data(combined_areas)
#' @format A data frame with 166 rows and 2 variables.
NULL
#' Core-based Statistical Area (CBSAs)
#'
#' The U.S. Census Bureau groups counties into CBSAs primarily based on county
#' population. NOTE: Not all counties are members of a CBSA. For a detailed
#' description, Wikipedia has an excellent discussion:
#' \url{http://en.wikipedia.org/wiki/Core_Based_Statistical_Area}. Also, the
#' following map from Wikipedia is excellent to visualize the areas:
#' \url{http://upload.wikimedia.org/wikipedia/commons/7/7b/Combined_statistical_areas_of_the_United_States_and_Puerto_Rico.gif}
#'
#' @docType data
#' @keywords datasets
#' @name corebased_areas
#' @usage data(corebased_areas)
#' @format A data frame with 917 rows and 4 variables.
NULL
#' County Population Data by Age
#'
#' A dataset containing the population totals and percentages for each US county
#' by age of inhabitant. The variables are as follows:
#'
#' \itemize{
#' \item fips. FIPS code for the county
#' \item age_group. Age groups are in 5 year intervals
#' \item population. Count of people in that county in that age group
#' \item percent. Percent of that county's total population in that age group
#' }
#'
#' @docType data
#' @keywords datasets
#' @name population_age
#' @usage data(population_age)
#' @format A data frame with 56574 rows and 4 variables
NULL
#' Income, Poverty, and Health Insurance in the United States
#'
#' A dataset containing the U.S. Census QuickFacts table of frequently requested
#' data items from various Census Bureau programs.
#' It should be noted that for "firms", "percent_female_firms", "manu_shipments",
#' "merchant_wholesales", "retail_sales", "retail_sales_per_capita",
#' "accom_food_sales", and "building_permits", Skagway Municipality is included
#' with Hoonah-Angoon Census Area and Wrangell City and Borough is included with
#' Petersburg Census Area.
#'
#' \itemize{
#' \item fips. FIPS State and County code. "0" represents national total.
#' \item population_2013. Population, 2013 estimate
#' \item population_2010_base. Population, 2010 (April 1) estimates base
#' \item population_change_percent. Population, percent change from April 1, 2010 to July 1, 2013
#' \item population_2010. Population, 2010
#' \item percent_under_5. Percentage of persons under 5 years
#' \item percent_under_18. Percentge of persons under 18 years
#' \item percent_over_65. Percentage of persons 65 years and over
#' \item percent_female. Percentage of Female persons
#' \item percent_white. Percentage of persons identifying as White alone
#' \item percent_black. Percentage of persons identifying as Black or African American alone
#' \item percent_native. Percentage of persons identifying as American Indian and Alaska Native alone
#' \item percent_asian. Percentage of persons identifying as Asian alone
#' \item percent_hawaiian. Percentage of persons identifying as Native Hawaiian and Other Pacific Islander alone
#' \item percent_two_plus. Percentage of persons identifying as Two or More Races
#' \item percent_hispanic. Percentage of persons identifying as Hispanic or Latino
#' \item percent_white_NH. Percentage of persons identifying as White alone, not Hispanic or Latino
#' \item percent_same_house_1yr. Percentage of persons living in the same house 1 year & over
#' \item percent_foreign. Percentage of foreign born persons
#' \item percent_nonEnglish. Percentage of persons age 5+ who speak a language other than English at home
#' \item percent_high_school_grad. Percentage of persons age 25+ with a high school degree or higher
#' \item percent_BA. Percentage of persons age 25+ with a Bachelor's degree or higher
#' \item veterans. Number of veterans
#' \item mean_travel_time. Mean travel time to work in minutes, workers age 16+
#' \item housing_units. Number of housing units
#' \item homeownership. Homeownership rate
#' \item percent_multi_unit. Percentage of housing units in multi-unit structures
#' \item median_value_housing. Median value of owner-occupied housing units
#' \item households. Number of households
#' \item pph. Persons per household
#' \item per_capita_income. Per capita money income in past 12 months
#' \item med_hh_income. Median household income
#' \item percent_poverty. Percentage of persons below poverty level
#' \item private_non_farm. Number of private nonfarm establishments
#' \item private_non_farm_employ. Private nonfarm employment
#' \item pnfe_percent_change. Percent change in private nonfarm employment
#' \item nonemployer_estab. Number of nonemployer establishments
#' \item firms. Total number of firms
#' \item percent_black_firms. Percentage of Black-owned firms
#' \item percent_native_firms. Percentage of American Indian- and Alaska Native-owned firms
#' \item percent_asian_firms. Percentage of Asian-owned firms
#' \item percent_hawaiian_firms. Percentage of Native Hawaiian- and Other Pacific Islander-owned firms
#' \item percent_hispanic_firms. Percentage of Hispanic-owned firms
#' \item percent_female_firms. Percentage of Woman-owned firms
#' \item manu_shipments. Manufacturers shipments, in thousands of dollars
#' \item merchant_wholesales. Merchant wholesaler sales, in thousands of dollars
#' \item retail_sales. Retail sales, in thousands of dollars
#' \item retail_sales_per_capita. Retail sales per capita
#' \item accom_food_sales. Accommodation and food services sales, in thousands of dollars
#' \item building_permits. Number of building permits
#' \item land_area. Land area in square miles
#' \item population_sq_mi. Population per square mile
#' }
#'
#' @docType data
#' @keywords datasets
#' @name quick_facts
#' @usage data(quick_facts)
#' @format A data frame with 3195 rows and 52 variables
NULL
#' Polygons to Describe Each U.S. County's Geographical Shape
#'
#' Data containing the vertices to describe each U.S. county's geographical
#' shape as a polygon. Vertices are given in terms of latitude and
#' longitude. The order in which the vertices should be drawn are also given by
#' \code{order}.
#'
#' NOTE: A small number of counties have multiple groups. Typically, these
#' counties are separated into multiple polygons by, say, a body of water.
#' Example: Galveston, Texas (FIPS == 48167).
#'
#' U.S. counties are uniquely identified by a FIPS code.
#'
#' The \code{county_polygons} data frame consists of the following variables:
#' \itemize{
#' \item order. The order in which the vertices should be drawn.
#' \item fips. County FIPS Code
#' \item names. Unique name to describe county.
#' \item group. County polygon's group number.
#' \item lat. County's latitude
#' \item long. County's longitude
#' \item county. The county's name.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name county_polygons
#' @usage data(county_polygons)
#' @format A data frame with 91,030 rows and 7 variables
NULL
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.