Nothing
R/data.R
In usa: Updated US State Facts and Figures
#' @importFrom tibble tibble
NULL
# objects from states.R ---------------------------------------------------
#' US State and Territories
#'
#' The 50 states, District of Columbia, and Puerto Rico.
#'
#' @format A tibble with 52 rows and 8 variables:
#' \describe{
#' \item{abb}{2-letter abbreviation}
#' \item{name}{Full legal name}
#' \item{fips}{Federal Information Processing Standard Publication 5-2 code}
#' \item{region}{Census Bureau region}
#' \item{division}{Census Bureau division}
#' \item{area}{Area in square miles}
#' \item{lat}{Center latitudinal coordinate}
#' \item{long}{Center longitudinal coordinate}
#' }
"states"
#' US Territories
#'
#' The 6 non-state territories and federal district.
#'
#' @format A tibble with 7 rows and 6 variables:
#' \describe{
#' \item{abb}{2-letter abbreviation}
#' \item{name}{Full legal name}
#' \item{fips}{Federal Information Processing Standard Publication 5-2 code}
#' \item{area}{Area in square miles}
#' \item{lat}{Center latitudinal coordinate}
#' \item{long}{Center longitudinal coordinate}
#' }
"territory"
#' US State Abbreviations
#'
#' The 2-letter abbreviations for the US state names.
#'
#' @format A character vector of length 52.
#' @source \url{https://www2.census.gov/geo/docs/reference/state.txt}
"state.abb"
#' US Territory Abbreviations
#'
#' The 2-letter abbreviations for the US territory names.
#'
#' @format A character vector of length 52.
#' @source \url{https://www2.census.gov/geo/docs/reference/state.txt}
"territory.abb"
#' US State Areas
#'
#' The area in square miles of the US states.
#'
#' @format A numeric vector of length 52.
#' @source \url{https://tigerweb.geo.census.gov/tigerwebmain/Files/acs19/tigerweb_acs19_state_us.html}
"state.area"
#' US State Areas
#'
#' The area in square miles of the US territories.
#'
#' @format A numeric vector of length 52.
#' @source \url{https://tigerweb.geo.census.gov/tigerwebmain/Files/acs19/tigerweb_acs19_state_us.html}
"territory.area"
#' US State Centers
#'
#' A list with components named `x` and `y` giving the approximate geographic
#' center of each state in negative longitude and latitude.
#'
#' @format A list of length two, each element a numeric vector of length 52.
#' \describe{
#' \item{x}{Center longitudinal coordinate}
#' \item{y}{Center latitudinal coordinate}
#' }
#' @source \url{https://tigerweb.geo.census.gov/tigerwebmain/Files/acs19/tigerweb_acs19_state_us.html}
"state.center"
#' US Territory Centers
#'
#' A list with components named `x` and `y` giving the approximate geographic
#' center of each territory in negative longitude and latitude.
#'
#' @format A list of length two, each element a numeric vector of length 5.
#' \describe{
#' \item{x}{Center longitudinal coordinate}
#' \item{y}{Center latitudinal coordinate}
#' }
#' @source \url{https://tigerweb.geo.census.gov/tigerwebmain/Files/acs19/tigerweb_acs19_state_us.html}
"territory.center"
#' US State Divisions
#'
#' The Census division to which each state belongs, one of nine:
#' 1. New England
#' 2. Middle Atlantic
#' 3. East North Central
#' 4. West North Central
#' 5. South Atlantic
#' 6. East South Central
#' 7. West South Central
#' 8. Mountain
#' 9. Pacific
#'
#' @format A factor vector of length 52.
#' @source \url{https://www2.census.gov/programs-surveys/popest/geographies/2018/state-geocodes-v2018.xlsx}
"state.division"
#' US State Names
#'
#' The full names for the US states.
#'
#' @format A numeric vector of length 52.
#' @source \url{https://tigerweb.geo.census.gov/tigerwebmain/Files/acs19/tigerweb_acs19_state_us.html}
"state.name"
#' US Territory Names
#'
#' The full names for the US territories.
#'
#' @format A numeric vector of length 52.
#' @source \url{https://tigerweb.geo.census.gov/tigerwebmain/Files/acs19/tigerweb_acs19_state_us.html}
"territory.name"
#' US State Regions
#'
#' The Census region to which each state belongs, one of four:
#' 1. Northeast
#' 2. Midwest
#' 3. South
#' 4. West
#'
#' @format A factor vector of length 52.
#' @source \url{https://www2.census.gov/programs-surveys/popest/geographies/2018/state-geocodes-v2018.xlsx}
"state.region"
# objects from info.R -----------------------------------------------------
#' US State Facts
#'
#' Updated version of the [datasets::state.x77] matrix, which provides eights
#' statistics from the 1970's. This version is a modern data frame format
#' with updated (and alternative) statistics.
#'
#' @format A tibble with 52 rows and 9 variables:
#' \describe{
#' \item{name}{Full state name}
#' \item{population}{Population estimate (September 26, 2019)}
#' \item{admission}{The data which the state was admitted to the union}
#' \item{income}{Per capita income (2018)}
#' \item{life_exp}{Life expectancy in years (2017-18)}
#' \item{murder}{Murder rate per 100,000 population (2018)}
#' \item{high}{Percent adult population with at least a high school degree (2019)}
#' \item{bach}{Percent adult population with at least a bachelor's degree or greater (2019)}
#' \item{heat}{Mean number of degree days (temperature requires heating) per year from 1981-2010}
#' }
#' @source
#' * Population: \url{https://www2.census.gov/programs-surveys/popest/datasets/2010-2018/state/detail/SCPRC-EST2018-18+POP-RES.csv}
#' * Income: \url{https://data.census.gov/cedsci/table?tid=ACSST1Y2018.S1903}
#' * GDP: \url{https://www.bea.gov/system/files/2019-11/qgdpstate1119.xlsx}
#' * Literacy: \url{https://nces.ed.gov/naal/estimates/StateEstimates.aspx}
#' * Life Expectancy: \url{https://www.cia.gov/library/publications/the-world-factbook/geos/aq.html}
#' * Murder: \url{https://ucr.fbi.gov/crime-in-the-u.s/2018/crime-in-the-u.s.-2018/tables/table-4/table-4.xls/output.xls}
#' * Education: \url{https://data.census.gov/cedsci/table?q=S1501}
#' * Temperature: \url{ftp://ftp.ncdc.noaa.gov/pub/data/normals/1981-2010/products/temperature/ann-cldd-normal.txt}
"facts"
#' US State and Territory Statistics
#'
#' A matrix version of the [facts] tibble, used to more closely align with the
#' [datasets::state.x77] matrix included with R.
#'
#' @format A tibble with 52 rows and 9 variables:
#' \describe{
#' \item{abb}{2-letter abbreviation}
#' \item{population}{Population estimate as of September 26, 2019}
#' \item{income}{Per capita income (2017)}
#' \item{life_exp}{Life expectancy in years (2017-18)}
#' \item{murder}{Murder rate per 100,000 population (2018)}
#' \item{high}{Percent of population with at least a high school degree (2019)}
#' \item{bach}{Percent of population with at least a bachelor's degree (2019)}
#' \item{heat}{Mean number of "degree days" per year from 1981-2010}
#' }
"state.x19"
# objects from people.R ---------------------------------------------------
#' Synthetic Sample of US population
#'
#' A statistically representative synthetic sample of 20,000 Americans. Each
#' record is a simulated survey respondent.
#'
#' @details
#' This dataset was originally produced by the Pew Research center for their
#' paper entitled [_For Weighting Online Opt-In Samples, What Matters Most?_][1]
#' The synthetic population dataset was created to serve as a reference for
#' making online opt-in surveys more representative of the overall population.
#'
#' See [Appendix B: Synthetic population dataset][2] for a more detailed
#' description of the method for and rationale behind creating this dataset.
#'
#' In short, the dataset was created to overcome the limitations of using large,
#' federal benchmark survey datasets such as the American Community Survey (ACS)
#' or Current Population Survey (CPS). These surveys often do not contain the
#' exact questions asked in online-opt in surveys, keeping them from being used
#' for proper adjustment.
#'
#' This _synthetic_ dataset was created by combining nine separate benchmark
#' datasets. Each had a set of common demographic variables but many added
#' unique variables such as gun ownership or voter registration. The surveys
#' were combined, stratified, sampled, combined, and imputed to fill missing
#' values from each. From this large dataset, the original 20,000 surveys from
#' the ACS were kept to ensure accurate demographic distribution.
#'
#' The names were _RANDOMLY_ assigned to respondents to better simulate a
#' synthetic sample of the population. First names were taken from the
#' `babynames` dataset which contains the Social Security Administration's
#' record of baby names from 1880 to 2017 along with gender and proportion.
#' First names were proportionally randomly assigned by birth year and sex. Last
#' names were taken from the Census Bureau, who provides the 162,254 most common
#' last names in the 2010 Census, covering over 90% of the population. For a
#' given surname, the proportion of that name belonging to members of each race
#' and ethnicity is provided. The last names were proportionally randomly
#' assigned by race.
#'
#' [1]: http://pewrsr.ch/2rNawC7
#' [2]: https://www.pewresearch.org/methods/?p=85
#'
#' @format A tibble with 20,000 rows and 40 variables:
#' \describe{
#' \item{id}{Sequential unique ID}
#' \item{fname}{Random first name, see details}
#' \item{lname}{Random last name, see details}
#' \item{gender}{Biological sex}
#' \item{age}{Age capped at 85}
#' \item{race}{Race and Ethnicity}
#' \item{edu}{Educational attainment}
#' \item{div}{Census regional division}
#' \item{married}{Marital status}
#' \item{house_size}{Household size}
#' \item{children}{Has children}
#' \item{us_citizen}{Is a US citizen}
#' \item{us_born}{Was born in the Us}
#' \item{house_income}{Family income}
#' \item{emp_status}{Employment status}
#' \item{emp_sector}{Employment sector}
#' \item{hours_work}{Hours worked per week}
#' \item{hours_vary}{Hours vary week to week}
#' \item{mil}{Has served in the military}
#' \item{house_own}{Home ownership}
#' \item{metro}{Lives in metropolitan area}
#' \item{internet}{Household has internet access}
#' \item{foodstamp}{Receives food stamps}
#' \item{house_moved}{Moved in the last year}
#' \item{pub_contact}{Contacted or visited a public official}
#' \item{boycott}{}
#' \item{hood_group}{Participated in a community association}
#' \item{hood_talks}{Talked with neighbors}
#' \item{hood_trust}{Trusts neighbors}
#' \item{tablet}{Uses a tablet or e-reader}
#' \item{texting}{Uses text messaging}
#' \item{social}{Uses social media}
#' \item{volunteer}{Volunteered}
#' \item{register}{Is registered to vote}
#' \item{vote}{Voted in the 2014 midterm elections}
#' \item{party}{Political party}
#' \item{religion}{Religious (evangelical) affiliation}
#' \item{ideology}{Political ideology}
#' \item{govt}{Follows government and public affairs}
#' \item{guns}{Owns a gun}
#' }
#' @source “For Weighting Online Opt-In Samples, What Matters Most?” Pew
#' Research Center, Washington, D.C. (January 26, 2018)
#' \url{http://pewrsr.ch/2rNawC7}
"people"
# objects from zipcodes.R -------------------------------------------------
#' US ZIP Code Locations
#'
#' This tibble contains city, state, latitude, and longitude for U.S. ZIP codes
#' from the CivicSpace Database (August 2004) augmented by Daniel Coven's [web
#' site](http://federalgovernmentzipcodes.us/) (updated on January 22, 2012).
#' The data was originally contained in the
#' [`zipcode`](https://CRAN.R-project.org/package=zipcode) CRAN package, which
#' was archived on January 1, 2020.
#'
#' @format A tibble with 52 rows and 9 variables:
#' \describe{
#' \item{zip}{5 digit ZIP code or military postal code (FPO/APO)}
#' \item{city}{USPS official city name}
#' \item{state}{USPS official state, territory abbreviation code}
#' \item{latitude}{Decimal Latitude}
#' \item{longitude}{Decimal Longitude}
#' }
#' @source Daniel Coven's [web site](http://federalgovernmentzipcodes.us/) and
#' the CivicSpace US ZIP Code Database written by Schuyler Erle
#' <schuyler@geocoder.us>, 5 August 2004.
"zipcodes"
#' US ZIP Codes
#'
#' The United States Postal Service's 5-digit codes used to identify a
#' particular postal delivery area.
#'
#' @format A character vector of length 44336.
#' @source Daniel Coven's [web site](http://federalgovernmentzipcodes.us/) and
#' the CivicSpace US ZIP Code Database written by Schuyler Erle
#' <schuyler@geocoder.us>, 5 August 2004.
"zip.code"
#' US ZIP Centers
#'
#' A list with components named `x` and `y` giving the approximate geographic
#' center of each ZIP code in negative longitude and latitude.
#'
#' @format A list of length two, each element a numeric vector of length 44336.
#' \describe{
#' \item{x}{Center longitudinal coordinate}
#' \item{y}{Center latitudinal coordinate}
#' }
#' @source Daniel Coven's [web site](http://federalgovernmentzipcodes.us/) and
#' the CivicSpace US ZIP Code Database written by Schuyler Erle
#' <schuyler@geocoder.us>, 5 August 2004.
"zip.center"
#' US ZIP Cities
#'
#' The United States Postal Service's official names for the cities in which
#' ZIP codes are contained. This vector contains unique values, sorted
#' alphabetically; because of this, they do not line up the other vectors in the
#' way [zip.code] and [zip.center] do.
#'
#' @format A character vector of length 19108.
#' @source Daniel Coven's [web site](http://federalgovernmentzipcodes.us/) and
#' the CivicSpace US ZIP Code Database written by Schuyler Erle
#' <schuyler@geocoder.us>, 5 August 2004.
"city.name"
#' US Counties
#'
#' The county subdivisions of the US states and territories.
#'
#' @format A tibble with 3,232 rows and 3 variables:
#' \describe{
#' \item{fips}{Federal Information Processing Standard Publication 5-2 code}
#' \item{name}{Census county names}
#' \item{state}{USPS official state, territory abbreviation code}
#' }
#' @source \url{https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/home/?cid=nrcs143_013697}
"counties"
#' US County Names
#'
#' The name of distinct US counties.
#'
#' @format A character vector of length 19108.
#' @source \url{https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/home/?cid=nrcs143_013697}
"county.name"
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#' @importFrom tibble tibble
NULL
# objects from states.R ---------------------------------------------------
#' US State and Territories
#'
#' The 50 states, District of Columbia, and Puerto Rico.
#'
#' @format A tibble with 52 rows and 8 variables:
#' \describe{
#' \item{abb}{2-letter abbreviation}
#' \item{name}{Full legal name}
#' \item{fips}{Federal Information Processing Standard Publication 5-2 code}
#' \item{region}{Census Bureau region}
#' \item{division}{Census Bureau division}
#' \item{area}{Area in square miles}
#' \item{lat}{Center latitudinal coordinate}
#' \item{long}{Center longitudinal coordinate}
#' }
"states"
#' US Territories
#'
#' The 6 non-state territories and federal district.
#'
#' @format A tibble with 7 rows and 6 variables:
#' \describe{
#' \item{abb}{2-letter abbreviation}
#' \item{name}{Full legal name}
#' \item{fips}{Federal Information Processing Standard Publication 5-2 code}
#' \item{area}{Area in square miles}
#' \item{lat}{Center latitudinal coordinate}
#' \item{long}{Center longitudinal coordinate}
#' }
"territory"
#' US State Abbreviations
#'
#' The 2-letter abbreviations for the US state names.
#'
#' @format A character vector of length 52.
#' @source \url{https://www2.census.gov/geo/docs/reference/state.txt}
"state.abb"
#' US Territory Abbreviations
#'
#' The 2-letter abbreviations for the US territory names.
#'
#' @format A character vector of length 52.
#' @source \url{https://www2.census.gov/geo/docs/reference/state.txt}
"territory.abb"
#' US State Areas
#'
#' The area in square miles of the US states.
#'
#' @format A numeric vector of length 52.
#' @source \url{https://tigerweb.geo.census.gov/tigerwebmain/Files/acs19/tigerweb_acs19_state_us.html}
"state.area"
#' US State Areas
#'
#' The area in square miles of the US territories.
#'
#' @format A numeric vector of length 52.
#' @source \url{https://tigerweb.geo.census.gov/tigerwebmain/Files/acs19/tigerweb_acs19_state_us.html}
"territory.area"
#' US State Centers
#'
#' A list with components named `x` and `y` giving the approximate geographic
#' center of each state in negative longitude and latitude.
#'
#' @format A list of length two, each element a numeric vector of length 52.
#' \describe{
#' \item{x}{Center longitudinal coordinate}
#' \item{y}{Center latitudinal coordinate}
#' }
#' @source \url{https://tigerweb.geo.census.gov/tigerwebmain/Files/acs19/tigerweb_acs19_state_us.html}
"state.center"
#' US Territory Centers
#'
#' A list with components named `x` and `y` giving the approximate geographic
#' center of each territory in negative longitude and latitude.
#'
#' @format A list of length two, each element a numeric vector of length 5.
#' \describe{
#' \item{x}{Center longitudinal coordinate}
#' \item{y}{Center latitudinal coordinate}
#' }
#' @source \url{https://tigerweb.geo.census.gov/tigerwebmain/Files/acs19/tigerweb_acs19_state_us.html}
"territory.center"
#' US State Divisions
#'
#' The Census division to which each state belongs, one of nine:
#' 1. New England
#' 2. Middle Atlantic
#' 3. East North Central
#' 4. West North Central
#' 5. South Atlantic
#' 6. East South Central
#' 7. West South Central
#' 8. Mountain
#' 9. Pacific
#'
#' @format A factor vector of length 52.
#' @source \url{https://www2.census.gov/programs-surveys/popest/geographies/2018/state-geocodes-v2018.xlsx}
"state.division"
#' US State Names
#'
#' The full names for the US states.
#'
#' @format A numeric vector of length 52.
#' @source \url{https://tigerweb.geo.census.gov/tigerwebmain/Files/acs19/tigerweb_acs19_state_us.html}
"state.name"
#' US Territory Names
#'
#' The full names for the US territories.
#'
#' @format A numeric vector of length 52.
#' @source \url{https://tigerweb.geo.census.gov/tigerwebmain/Files/acs19/tigerweb_acs19_state_us.html}
"territory.name"
#' US State Regions
#'
#' The Census region to which each state belongs, one of four:
#' 1. Northeast
#' 2. Midwest
#' 3. South
#' 4. West
#'
#' @format A factor vector of length 52.
#' @source \url{https://www2.census.gov/programs-surveys/popest/geographies/2018/state-geocodes-v2018.xlsx}
"state.region"
# objects from info.R -----------------------------------------------------
#' US State Facts
#'
#' Updated version of the [datasets::state.x77] matrix, which provides eights
#' statistics from the 1970's. This version is a modern data frame format
#' with updated (and alternative) statistics.
#'
#' @format A tibble with 52 rows and 9 variables:
#' \describe{
#' \item{name}{Full state name}
#' \item{population}{Population estimate (September 26, 2019)}
#' \item{admission}{The data which the state was admitted to the union}
#' \item{income}{Per capita income (2018)}
#' \item{life_exp}{Life expectancy in years (2017-18)}
#' \item{murder}{Murder rate per 100,000 population (2018)}
#' \item{high}{Percent adult population with at least a high school degree (2019)}
#' \item{bach}{Percent adult population with at least a bachelor's degree or greater (2019)}
#' \item{heat}{Mean number of degree days (temperature requires heating) per year from 1981-2010}
#' }
#' @source
#' * Population: \url{https://www2.census.gov/programs-surveys/popest/datasets/2010-2018/state/detail/SCPRC-EST2018-18+POP-RES.csv}
#' * Income: \url{https://data.census.gov/cedsci/table?tid=ACSST1Y2018.S1903}
#' * GDP: \url{https://www.bea.gov/system/files/2019-11/qgdpstate1119.xlsx}
#' * Literacy: \url{https://nces.ed.gov/naal/estimates/StateEstimates.aspx}
#' * Life Expectancy: \url{https://www.cia.gov/library/publications/the-world-factbook/geos/aq.html}
#' * Murder: \url{https://ucr.fbi.gov/crime-in-the-u.s/2018/crime-in-the-u.s.-2018/tables/table-4/table-4.xls/output.xls}
#' * Education: \url{https://data.census.gov/cedsci/table?q=S1501}
#' * Temperature: \url{ftp://ftp.ncdc.noaa.gov/pub/data/normals/1981-2010/products/temperature/ann-cldd-normal.txt}
"facts"
#' US State and Territory Statistics
#'
#' A matrix version of the [facts] tibble, used to more closely align with the
#' [datasets::state.x77] matrix included with R.
#'
#' @format A tibble with 52 rows and 9 variables:
#' \describe{
#' \item{abb}{2-letter abbreviation}
#' \item{population}{Population estimate as of September 26, 2019}
#' \item{income}{Per capita income (2017)}
#' \item{life_exp}{Life expectancy in years (2017-18)}
#' \item{murder}{Murder rate per 100,000 population (2018)}
#' \item{high}{Percent of population with at least a high school degree (2019)}
#' \item{bach}{Percent of population with at least a bachelor's degree (2019)}
#' \item{heat}{Mean number of "degree days" per year from 1981-2010}
#' }
"state.x19"
# objects from people.R ---------------------------------------------------
#' Synthetic Sample of US population
#'
#' A statistically representative synthetic sample of 20,000 Americans. Each
#' record is a simulated survey respondent.
#'
#' @details
#' This dataset was originally produced by the Pew Research center for their
#' paper entitled [_For Weighting Online Opt-In Samples, What Matters Most?_][1]
#' The synthetic population dataset was created to serve as a reference for
#' making online opt-in surveys more representative of the overall population.
#'
#' See [Appendix B: Synthetic population dataset][2] for a more detailed
#' description of the method for and rationale behind creating this dataset.
#'
#' In short, the dataset was created to overcome the limitations of using large,
#' federal benchmark survey datasets such as the American Community Survey (ACS)
#' or Current Population Survey (CPS). These surveys often do not contain the
#' exact questions asked in online-opt in surveys, keeping them from being used
#' for proper adjustment.
#'
#' This _synthetic_ dataset was created by combining nine separate benchmark
#' datasets. Each had a set of common demographic variables but many added
#' unique variables such as gun ownership or voter registration. The surveys
#' were combined, stratified, sampled, combined, and imputed to fill missing
#' values from each. From this large dataset, the original 20,000 surveys from
#' the ACS were kept to ensure accurate demographic distribution.
#'
#' The names were _RANDOMLY_ assigned to respondents to better simulate a
#' synthetic sample of the population. First names were taken from the
#' `babynames` dataset which contains the Social Security Administration's
#' record of baby names from 1880 to 2017 along with gender and proportion.
#' First names were proportionally randomly assigned by birth year and sex. Last
#' names were taken from the Census Bureau, who provides the 162,254 most common
#' last names in the 2010 Census, covering over 90% of the population. For a
#' given surname, the proportion of that name belonging to members of each race
#' and ethnicity is provided. The last names were proportionally randomly
#' assigned by race.
#'
#' [1]: http://pewrsr.ch/2rNawC7
#' [2]: https://www.pewresearch.org/methods/?p=85
#'
#' @format A tibble with 20,000 rows and 40 variables:
#' \describe{
#' \item{id}{Sequential unique ID}
#' \item{fname}{Random first name, see details}
#' \item{lname}{Random last name, see details}
#' \item{gender}{Biological sex}
#' \item{age}{Age capped at 85}
#' \item{race}{Race and Ethnicity}
#' \item{edu}{Educational attainment}
#' \item{div}{Census regional division}
#' \item{married}{Marital status}
#' \item{house_size}{Household size}
#' \item{children}{Has children}
#' \item{us_citizen}{Is a US citizen}
#' \item{us_born}{Was born in the Us}
#' \item{house_income}{Family income}
#' \item{emp_status}{Employment status}
#' \item{emp_sector}{Employment sector}
#' \item{hours_work}{Hours worked per week}
#' \item{hours_vary}{Hours vary week to week}
#' \item{mil}{Has served in the military}
#' \item{house_own}{Home ownership}
#' \item{metro}{Lives in metropolitan area}
#' \item{internet}{Household has internet access}
#' \item{foodstamp}{Receives food stamps}
#' \item{house_moved}{Moved in the last year}
#' \item{pub_contact}{Contacted or visited a public official}
#' \item{boycott}{}
#' \item{hood_group}{Participated in a community association}
#' \item{hood_talks}{Talked with neighbors}
#' \item{hood_trust}{Trusts neighbors}
#' \item{tablet}{Uses a tablet or e-reader}
#' \item{texting}{Uses text messaging}
#' \item{social}{Uses social media}
#' \item{volunteer}{Volunteered}
#' \item{register}{Is registered to vote}
#' \item{vote}{Voted in the 2014 midterm elections}
#' \item{party}{Political party}
#' \item{religion}{Religious (evangelical) affiliation}
#' \item{ideology}{Political ideology}
#' \item{govt}{Follows government and public affairs}
#' \item{guns}{Owns a gun}
#' }
#' @source “For Weighting Online Opt-In Samples, What Matters Most?” Pew
#' Research Center, Washington, D.C. (January 26, 2018)
#' \url{http://pewrsr.ch/2rNawC7}
"people"
# objects from zipcodes.R -------------------------------------------------
#' US ZIP Code Locations
#'
#' This tibble contains city, state, latitude, and longitude for U.S. ZIP codes
#' from the CivicSpace Database (August 2004) augmented by Daniel Coven's [web
#' site](http://federalgovernmentzipcodes.us/) (updated on January 22, 2012).
#' The data was originally contained in the
#' [`zipcode`](https://CRAN.R-project.org/package=zipcode) CRAN package, which
#' was archived on January 1, 2020.
#'
#' @format A tibble with 52 rows and 9 variables:
#' \describe{
#' \item{zip}{5 digit ZIP code or military postal code (FPO/APO)}
#' \item{city}{USPS official city name}
#' \item{state}{USPS official state, territory abbreviation code}
#' \item{latitude}{Decimal Latitude}
#' \item{longitude}{Decimal Longitude}
#' }
#' @source Daniel Coven's [web site](http://federalgovernmentzipcodes.us/) and
#' the CivicSpace US ZIP Code Database written by Schuyler Erle
#' <schuyler@geocoder.us>, 5 August 2004.
"zipcodes"
#' US ZIP Codes
#'
#' The United States Postal Service's 5-digit codes used to identify a
#' particular postal delivery area.
#'
#' @format A character vector of length 44336.
#' @source Daniel Coven's [web site](http://federalgovernmentzipcodes.us/) and
#' the CivicSpace US ZIP Code Database written by Schuyler Erle
#' <schuyler@geocoder.us>, 5 August 2004.
"zip.code"
#' US ZIP Centers
#'
#' A list with components named `x` and `y` giving the approximate geographic
#' center of each ZIP code in negative longitude and latitude.
#'
#' @format A list of length two, each element a numeric vector of length 44336.
#' \describe{
#' \item{x}{Center longitudinal coordinate}
#' \item{y}{Center latitudinal coordinate}
#' }
#' @source Daniel Coven's [web site](http://federalgovernmentzipcodes.us/) and
#' the CivicSpace US ZIP Code Database written by Schuyler Erle
#' <schuyler@geocoder.us>, 5 August 2004.
"zip.center"
#' US ZIP Cities
#'
#' The United States Postal Service's official names for the cities in which
#' ZIP codes are contained. This vector contains unique values, sorted
#' alphabetically; because of this, they do not line up the other vectors in the
#' way [zip.code] and [zip.center] do.
#'
#' @format A character vector of length 19108.
#' @source Daniel Coven's [web site](http://federalgovernmentzipcodes.us/) and
#' the CivicSpace US ZIP Code Database written by Schuyler Erle
#' <schuyler@geocoder.us>, 5 August 2004.
"city.name"
#' US Counties
#'
#' The county subdivisions of the US states and territories.
#'
#' @format A tibble with 3,232 rows and 3 variables:
#' \describe{
#' \item{fips}{Federal Information Processing Standard Publication 5-2 code}
#' \item{name}{Census county names}
#' \item{state}{USPS official state, territory abbreviation code}
#' }
#' @source \url{https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/home/?cid=nrcs143_013697}
"counties"
#' US County Names
#'
#' The name of distinct US counties.
#'
#' @format A character vector of length 19108.
#' @source \url{https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/home/?cid=nrcs143_013697}
"county.name"
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