Nothing
R/data.R
In rolap: Obtaining Star Databases from Flat Tables
#' Mortality Reporting System
#'
#' Selection of 20 rows from the 122 Cities Mortality Reporting System.
#'
#' The original dataset covers from 1962 to 2016. For each week, in 122 US cities,
#' mortality figures by age group and cause, considered separately, are included.
#' In the cause, only a distinction is made between pneumonia or influenza and
#' others.
#'
#' @family mrs example data
#' @seealso \code{\link{mrs_cause_schema}}
#'
#' @format A `tibble`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"ft"
#' Mortality Reporting System with numerical measures
#'
#' Selection of 20 rows from the 122 Cities Mortality Reporting System. Measures
#' have been defined as integer values.
#'
#' The original dataset covers from 1962 to 2016. For each week, in 122 US cities,
#' mortality figures by age group and cause, considered separately, are included.
#' In the cause, only a distinction is made between pneumonia or influenza and
#' others.
#'
#' @family mrs example data
#' @seealso \code{\link{mrs_cause_schema}}
#' @examples
#' # The operations to obtain it from the `ft` data set are:
#'
#' ft_num <- ft |>
#' dplyr::mutate(`Pneumonia and Influenza Deaths` = as.integer(`Pneumonia and Influenza Deaths`)) |>
#' dplyr::mutate(`All Deaths` = as.integer(`All Deaths`))
#'
#' @format A `tibble`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"ft_num"
#' Mortality Reporting System by Age Group
#'
#' Selection data from the 122 Cities Mortality Reporting System by age group.
#'
#' The original dataset covers from 1962 to 2016. For each week, in 122 US cities,
#' mortality figures by age group and cause, considered separately, are included.
#'
#' @family mrs example data
#' @seealso \code{\link{mrs_age_schema}}
#' @examples
#' # The operations to obtain it from the `ft` data set are:
#'
#' if (rlang::is_installed("stringr")) {
#' ft_age <- ft |>
#' dplyr::select(-`Pneumonia and Influenza Deaths`, -`All Deaths`) |>
#' tidyr::gather("Age", "All Deaths", 7:11) |>
#' dplyr::mutate(`All Deaths` = as.integer(`All Deaths`)) |>
#' dplyr::mutate(Age = stringr::str_replace(Age, " \\(all cause deaths\\)", ""))
#' }
#'
#' @format A `tibble`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"ft_age"
#' Star schema for Mortality Reporting System by Cause
#'
#' Definition of schemas for facts and dimensions for the Mortality Reporting System
#' considering the cause classification.
#'
#' Dimension schemes can be defined using variables so that you do not have to repeat
#' the definition in several multidimensional designs.
#'
#' @family mrs example schema
#' @seealso \code{\link{ft_num}}
#' @examples
#' # Defined by:
#'
#' when <- dimension_schema(name = "When",
#' attributes = c("Year"))
#' where <- dimension_schema(name = "Where",
#' attributes = c("REGION",
#' "State",
#' "City"))
#' mrs_cause_schema <- star_schema() |>
#' define_facts(name = "MRS Cause",
#' measures = c("Pneumonia and Influenza Deaths",
#' "All Deaths")) |>
#' define_dimension(when) |>
#' define_dimension(where)
#'
#' @format A `star_schema` object.
"mrs_cause_schema"
#' Star schema for Mortality Reporting System by Age
#'
#' Definition of schemas for facts and dimensions for the Mortality Reporting System
#' considering the age classification.
#'
#' Dimension schemes can be defined using variables so that you do not have to repeat
#' the definition in several multidimensional designs.
#'
#' @family mrs example schema
#' @seealso \code{\link{ft_age}}
#' @examples
#' # Defined by:
#'
#' when <- dimension_schema(name = "When",
#' attributes = c("Year"))
#' where <- dimension_schema(name = "Where",
#' attributes = c("REGION",
#' "State",
#' "City"))
#' mrs_age_schema <- star_schema() |>
#' define_facts(name = "MRS Age",
#' measures = c("All Deaths")) |>
#' define_dimension(when) |>
#' define_dimension(where) |>
#' define_dimension(name = "Who",
#' attributes = c("Age"))
#'
#' @format A `star_schema` object.
"mrs_age_schema"
#' Mortality Reporting System by Age
#'
#' Selection of data from the 122 Cities Mortality Reporting System by age
#' group, for the first 9 weeks of 1962 and 4 cities.
#'
#' The original dataset begins in 1962. For each week, in 122 US cities,
#' mortality figures by age group and cause, considered separately, are included
#' (i.e., the combination of age group and cause is not included). In the cause,
#' only a distinction is made between pneumonia or influenza and others.
#'
#' Two additional dates have been generated, which were not present in the
#' original dataset.
#'
#' @family mrs example data
#' @seealso \code{\link{mrs_age_schema}}
#'
#' @format A `tibble`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"ft_age_rpd"
#' Mortality Reporting System by Cause
#'
#' Selection of data from the 122 Cities Mortality Reporting System by cause,
#' for the first 9 weeks of 1962 and 4 cities.
#'
#' The original dataset begins in 1962. For each week, in 122 US cities,
#' mortality figures by age group and cause, considered separately, are included
#' (i.e., the combination of age group and cause is not included). In the cause,
#' only a distinction is made between pneumonia or influenza and others.
#'
#' Two additional dates have been generated, which were not present in the
#' original dataset.
#'
#' @family mrs example data
#' @seealso \code{\link{mrs_cause_schema}}
#'
#' @format A `tibble`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"ft_cause_rpd"
#' Star schema for Mortality Reporting System by Cause with additional dates
#'
#' Definition of schemas for facts and dimensions for the Mortality Reporting System
#' considering the cause classification with additional dates to be used as role
#' playing dimensions..
#'
#' @family mrs example schema
#' @seealso \code{\link{ft_cause_rpd}}
#' @examples
#' # Defined by:
#'
#' mrs_cause_schema_rpd <- star_schema() |>
#' define_facts(fact_schema(
#' name = "mrs_cause",
#' measures = c(
#' "Pneumonia and Influenza Deaths",
#' "All Deaths"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "When",
#' attributes = c(
#' "Year",
#' "WEEK",
#' "Week Ending Date"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "When Available",
#' attributes = c(
#' "Data Availability Year",
#' "Data Availability Week",
#' "Data Availability Date"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "When Received",
#' attributes = c(
#' "Reception Year",
#' "Reception Week",
#' "Reception Date"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "where",
#' attributes = c(
#' "REGION",
#' "State",
#' "City"
#' )
#' ))
#'
#' @format A `star_schema` object.
"mrs_cause_schema_rpd"
#' Star schema for Mortality Reporting System by Age with additional dates
#'
#' Definition of schemas for facts and dimensions for the Mortality Reporting System
#' considering the cause classification with additional dates to be used as role
#' playing dimensions..
#'
#' @family mrs example schema
#' @seealso \code{\link{ft_age_rpd}}
#' @examples
#' # Defined by:
#'
#' mrs_age_schema_rpd <- star_schema() |>
#' define_facts(fact_schema(
#' name = "mrs_age",
#' measures = c(
#' "Deaths"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "When",
#' attributes = c(
#' "Year",
#' "WEEK",
#' "Week Ending Date"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "When Available",
#' attributes = c(
#' "Data Availability Year",
#' "Data Availability Week",
#' "Data Availability Date"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "When Arrived",
#' attributes = c(
#' "Arrival Year",
#' "Arrival Week",
#' "Arrival Date"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "Who",
#' attributes = c(
#' "Age Range"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "where",
#' attributes = c(
#' "REGION",
#' "State",
#' "City"
#' )
#' ))
#'
#' @format A `star_schema` object.
"mrs_age_schema_rpd"
#' Flat table generated from MRS file
#'
#' The original dataset covers from 1962 to 2016. For each week, in 122 US cities,
#' from the original file, we have stored in the package a file with the same
#' format as the original file but that includes only 1% of its data, selected at
#' random.
#'
#' @family mrs example data
#'
#' @format A `flat_table`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"mrs_ft"
#' Flat table generated from MRS file
#'
#' The original dataset covers from 1962 to 2016. For each week, in 122 US cities,
#' from the original file, we have stored in the package a file with the same
#' format as the original file but that includes only 0,1% of its data, selected at
#' random to test the incremental refresh.
#'
#' @family mrs example data
#'
#' @format A `flat_table`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"mrs_ft_new"
#' Constellation generated from MRS file
#'
#' The original dataset covers from 1962 to 2016. For each week, in 122 US cities,
#' from the original file, we have stored in the package a file with the same
#' format as the original file but that includes only 1% of its data, selected at
#' random.
#'
#' From these data the constellation in the vignette titled 'Obtaining and
#' transforming flat tables' has been generated. This variable contains the defined
#' constellation.
#'
#' @family mrs example data
#'
#' @format A `star_database`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"mrs_db"
#' Constellation generated from MRS file through a query and with geographic information
#'
#' The original dataset covers from 1962 to 2016. For each week, in 122 US cities,
#' from the original file, we have stored in the package a file with the same
#' format as the original file but that includes only 1% of its data, selected at
#' random.
#'
#' From these data the constellation in the vignette titled 'Obtaining and
#' transforming flat tables' has been generated. This variable contains the defined
#' constellation.
#'
#' @family mrs example data
#' @examples
#' # Defined by:
#'
#' sq <- mrs_db |>
#' star_query() |>
#' select_dimension(name = "where",
#' attributes = "state") |>
#' select_dimension(name = "when",
#' attributes = "year") |>
#' select_fact(
#' name = "mrs_age",
#' measures = "all_deaths"
#' ) |>
#' select_fact(
#' name = "mrs_cause",
#' measures = "pneumonia_and_influenza_deaths"
#' )
#'
#' db <- mrs_db |>
#' run_query(sq)
#'
#' mrs_db_geo <- db |>
#' define_geoattribute(
#' dimension = "where",
#' attribute = "state",
#' from_layer = us_layer_state,
#' by = "STUSPS"
#' )
#'
#' @format A `star_database`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"mrs_db_geo"
#' Census of US States, by sex and age
#'
#' Census of US States, by sex and age, obtained from the United States Census
#' Bureau (USCB), American Community Survey (ACS). Obtained from the variables
#' defined in reports, classifying the concepts according to the defined subjects.
#'
#' U.S. Census Bureau. “Government Units: US and State: Census Years 1942 - 2022.”
#' Public Sector, PUB Public Sector Annual Surveys and Census of Governments,
#' Table CG00ORG01, 2022,
#' https://data.census.gov/table/GOVSTIMESERIES.CG00ORG01?q=census+state+year.
#' Accessed on October 25, 2023.
#'
#' @format A `tibble`.
#' @source
#' \url{https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-data.2021.html}
"us_census_state"
#' Geographic layer of US States
#'
#' Geographic layer with data from the States of the USA in polygon format, with
#' simplified geometry so that it takes up less space.
#'
#' It has been obtained from the geographic data included in the US census prepared
#' by the U.S. Census Bureau.
#'
#' @format A `sf`.
#' @source
#' \url{https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-data.2021.html}
"us_layer_state"
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#' Mortality Reporting System
#'
#' Selection of 20 rows from the 122 Cities Mortality Reporting System.
#'
#' The original dataset covers from 1962 to 2016. For each week, in 122 US cities,
#' mortality figures by age group and cause, considered separately, are included.
#' In the cause, only a distinction is made between pneumonia or influenza and
#' others.
#'
#' @family mrs example data
#' @seealso \code{\link{mrs_cause_schema}}
#'
#' @format A `tibble`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"ft"
#' Mortality Reporting System with numerical measures
#'
#' Selection of 20 rows from the 122 Cities Mortality Reporting System. Measures
#' have been defined as integer values.
#'
#' The original dataset covers from 1962 to 2016. For each week, in 122 US cities,
#' mortality figures by age group and cause, considered separately, are included.
#' In the cause, only a distinction is made between pneumonia or influenza and
#' others.
#'
#' @family mrs example data
#' @seealso \code{\link{mrs_cause_schema}}
#' @examples
#' # The operations to obtain it from the `ft` data set are:
#'
#' ft_num <- ft |>
#' dplyr::mutate(`Pneumonia and Influenza Deaths` = as.integer(`Pneumonia and Influenza Deaths`)) |>
#' dplyr::mutate(`All Deaths` = as.integer(`All Deaths`))
#'
#' @format A `tibble`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"ft_num"
#' Mortality Reporting System by Age Group
#'
#' Selection data from the 122 Cities Mortality Reporting System by age group.
#'
#' The original dataset covers from 1962 to 2016. For each week, in 122 US cities,
#' mortality figures by age group and cause, considered separately, are included.
#'
#' @family mrs example data
#' @seealso \code{\link{mrs_age_schema}}
#' @examples
#' # The operations to obtain it from the `ft` data set are:
#'
#' if (rlang::is_installed("stringr")) {
#' ft_age <- ft |>
#' dplyr::select(-`Pneumonia and Influenza Deaths`, -`All Deaths`) |>
#' tidyr::gather("Age", "All Deaths", 7:11) |>
#' dplyr::mutate(`All Deaths` = as.integer(`All Deaths`)) |>
#' dplyr::mutate(Age = stringr::str_replace(Age, " \\(all cause deaths\\)", ""))
#' }
#'
#' @format A `tibble`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"ft_age"
#' Star schema for Mortality Reporting System by Cause
#'
#' Definition of schemas for facts and dimensions for the Mortality Reporting System
#' considering the cause classification.
#'
#' Dimension schemes can be defined using variables so that you do not have to repeat
#' the definition in several multidimensional designs.
#'
#' @family mrs example schema
#' @seealso \code{\link{ft_num}}
#' @examples
#' # Defined by:
#'
#' when <- dimension_schema(name = "When",
#' attributes = c("Year"))
#' where <- dimension_schema(name = "Where",
#' attributes = c("REGION",
#' "State",
#' "City"))
#' mrs_cause_schema <- star_schema() |>
#' define_facts(name = "MRS Cause",
#' measures = c("Pneumonia and Influenza Deaths",
#' "All Deaths")) |>
#' define_dimension(when) |>
#' define_dimension(where)
#'
#' @format A `star_schema` object.
"mrs_cause_schema"
#' Star schema for Mortality Reporting System by Age
#'
#' Definition of schemas for facts and dimensions for the Mortality Reporting System
#' considering the age classification.
#'
#' Dimension schemes can be defined using variables so that you do not have to repeat
#' the definition in several multidimensional designs.
#'
#' @family mrs example schema
#' @seealso \code{\link{ft_age}}
#' @examples
#' # Defined by:
#'
#' when <- dimension_schema(name = "When",
#' attributes = c("Year"))
#' where <- dimension_schema(name = "Where",
#' attributes = c("REGION",
#' "State",
#' "City"))
#' mrs_age_schema <- star_schema() |>
#' define_facts(name = "MRS Age",
#' measures = c("All Deaths")) |>
#' define_dimension(when) |>
#' define_dimension(where) |>
#' define_dimension(name = "Who",
#' attributes = c("Age"))
#'
#' @format A `star_schema` object.
"mrs_age_schema"
#' Mortality Reporting System by Age
#'
#' Selection of data from the 122 Cities Mortality Reporting System by age
#' group, for the first 9 weeks of 1962 and 4 cities.
#'
#' The original dataset begins in 1962. For each week, in 122 US cities,
#' mortality figures by age group and cause, considered separately, are included
#' (i.e., the combination of age group and cause is not included). In the cause,
#' only a distinction is made between pneumonia or influenza and others.
#'
#' Two additional dates have been generated, which were not present in the
#' original dataset.
#'
#' @family mrs example data
#' @seealso \code{\link{mrs_age_schema}}
#'
#' @format A `tibble`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"ft_age_rpd"
#' Mortality Reporting System by Cause
#'
#' Selection of data from the 122 Cities Mortality Reporting System by cause,
#' for the first 9 weeks of 1962 and 4 cities.
#'
#' The original dataset begins in 1962. For each week, in 122 US cities,
#' mortality figures by age group and cause, considered separately, are included
#' (i.e., the combination of age group and cause is not included). In the cause,
#' only a distinction is made between pneumonia or influenza and others.
#'
#' Two additional dates have been generated, which were not present in the
#' original dataset.
#'
#' @family mrs example data
#' @seealso \code{\link{mrs_cause_schema}}
#'
#' @format A `tibble`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"ft_cause_rpd"
#' Star schema for Mortality Reporting System by Cause with additional dates
#'
#' Definition of schemas for facts and dimensions for the Mortality Reporting System
#' considering the cause classification with additional dates to be used as role
#' playing dimensions..
#'
#' @family mrs example schema
#' @seealso \code{\link{ft_cause_rpd}}
#' @examples
#' # Defined by:
#'
#' mrs_cause_schema_rpd <- star_schema() |>
#' define_facts(fact_schema(
#' name = "mrs_cause",
#' measures = c(
#' "Pneumonia and Influenza Deaths",
#' "All Deaths"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "When",
#' attributes = c(
#' "Year",
#' "WEEK",
#' "Week Ending Date"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "When Available",
#' attributes = c(
#' "Data Availability Year",
#' "Data Availability Week",
#' "Data Availability Date"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "When Received",
#' attributes = c(
#' "Reception Year",
#' "Reception Week",
#' "Reception Date"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "where",
#' attributes = c(
#' "REGION",
#' "State",
#' "City"
#' )
#' ))
#'
#' @format A `star_schema` object.
"mrs_cause_schema_rpd"
#' Star schema for Mortality Reporting System by Age with additional dates
#'
#' Definition of schemas for facts and dimensions for the Mortality Reporting System
#' considering the cause classification with additional dates to be used as role
#' playing dimensions..
#'
#' @family mrs example schema
#' @seealso \code{\link{ft_age_rpd}}
#' @examples
#' # Defined by:
#'
#' mrs_age_schema_rpd <- star_schema() |>
#' define_facts(fact_schema(
#' name = "mrs_age",
#' measures = c(
#' "Deaths"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "When",
#' attributes = c(
#' "Year",
#' "WEEK",
#' "Week Ending Date"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "When Available",
#' attributes = c(
#' "Data Availability Year",
#' "Data Availability Week",
#' "Data Availability Date"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "When Arrived",
#' attributes = c(
#' "Arrival Year",
#' "Arrival Week",
#' "Arrival Date"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "Who",
#' attributes = c(
#' "Age Range"
#' )
#' )) |>
#' define_dimension(dimension_schema(
#' name = "where",
#' attributes = c(
#' "REGION",
#' "State",
#' "City"
#' )
#' ))
#'
#' @format A `star_schema` object.
"mrs_age_schema_rpd"
#' Flat table generated from MRS file
#'
#' The original dataset covers from 1962 to 2016. For each week, in 122 US cities,
#' from the original file, we have stored in the package a file with the same
#' format as the original file but that includes only 1% of its data, selected at
#' random.
#'
#' @family mrs example data
#'
#' @format A `flat_table`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"mrs_ft"
#' Flat table generated from MRS file
#'
#' The original dataset covers from 1962 to 2016. For each week, in 122 US cities,
#' from the original file, we have stored in the package a file with the same
#' format as the original file but that includes only 0,1% of its data, selected at
#' random to test the incremental refresh.
#'
#' @family mrs example data
#'
#' @format A `flat_table`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"mrs_ft_new"
#' Constellation generated from MRS file
#'
#' The original dataset covers from 1962 to 2016. For each week, in 122 US cities,
#' from the original file, we have stored in the package a file with the same
#' format as the original file but that includes only 1% of its data, selected at
#' random.
#'
#' From these data the constellation in the vignette titled 'Obtaining and
#' transforming flat tables' has been generated. This variable contains the defined
#' constellation.
#'
#' @family mrs example data
#'
#' @format A `star_database`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"mrs_db"
#' Constellation generated from MRS file through a query and with geographic information
#'
#' The original dataset covers from 1962 to 2016. For each week, in 122 US cities,
#' from the original file, we have stored in the package a file with the same
#' format as the original file but that includes only 1% of its data, selected at
#' random.
#'
#' From these data the constellation in the vignette titled 'Obtaining and
#' transforming flat tables' has been generated. This variable contains the defined
#' constellation.
#'
#' @family mrs example data
#' @examples
#' # Defined by:
#'
#' sq <- mrs_db |>
#' star_query() |>
#' select_dimension(name = "where",
#' attributes = "state") |>
#' select_dimension(name = "when",
#' attributes = "year") |>
#' select_fact(
#' name = "mrs_age",
#' measures = "all_deaths"
#' ) |>
#' select_fact(
#' name = "mrs_cause",
#' measures = "pneumonia_and_influenza_deaths"
#' )
#'
#' db <- mrs_db |>
#' run_query(sq)
#'
#' mrs_db_geo <- db |>
#' define_geoattribute(
#' dimension = "where",
#' attribute = "state",
#' from_layer = us_layer_state,
#' by = "STUSPS"
#' )
#'
#' @format A `star_database`.
#' @source \url{https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system}
"mrs_db_geo"
#' Census of US States, by sex and age
#'
#' Census of US States, by sex and age, obtained from the United States Census
#' Bureau (USCB), American Community Survey (ACS). Obtained from the variables
#' defined in reports, classifying the concepts according to the defined subjects.
#'
#' U.S. Census Bureau. “Government Units: US and State: Census Years 1942 - 2022.”
#' Public Sector, PUB Public Sector Annual Surveys and Census of Governments,
#' Table CG00ORG01, 2022,
#' https://data.census.gov/table/GOVSTIMESERIES.CG00ORG01?q=census+state+year.
#' Accessed on October 25, 2023.
#'
#' @format A `tibble`.
#' @source
#' \url{https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-data.2021.html}
"us_census_state"
#' Geographic layer of US States
#'
#' Geographic layer with data from the States of the USA in polygon format, with
#' simplified geometry so that it takes up less space.
#'
#' It has been obtained from the geographic data included in the US census prepared
#' by the U.S. Census Bureau.
#'
#' @format A `sf`.
#' @source
#' \url{https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-data.2021.html}
"us_layer_state"
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