diff --git a/.claude/settings.local.json b/.claude/settings.local.json new file mode 100644 index 0000000..348e6d9 --- /dev/null +++ b/.claude/settings.local.json @@ -0,0 +1,10 @@ +{ + "permissions": { + "allow": [ + "Bash(dpkg:*)", + "Bash(pkg-config:*)", + "Bash(apt list:*)", + "Bash(Rscript:*)" + ] + } +} diff --git a/DESCRIPTION b/DESCRIPTION index 15229ab..f202f20 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -15,7 +15,7 @@ Authors@R: c( Description: This packages combines a set of utilities for acquiring and processing climate and climate-adjacent datasets under a consistent API. It takes opinionated stances on how to manipulate raw data in an effort to produce standard workflows - that enable project teams to devote more time to substantive analysis and inference-making. + that enable project teams to devote more time to substantive analysis. License: MIT + file LICENSE Encoding: UTF-8 Roxygen: list(markdown = TRUE) @@ -23,6 +23,7 @@ RoxygenNote: 7.3.3 Imports: arrow, censusapi, + sfarrow, dplyr, esri2sf (>= 0.1.1), ellmer, diff --git a/NAMESPACE b/NAMESPACE index 2103982..3918c56 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -23,6 +23,7 @@ export(get_sheldus) export(get_spatial_extent_census) export(get_structures) export(get_system_username) +export(get_wildfire_burn_zones) export(inflation_adjust) export(interpolate_demographics) export(polygons_to_linestring) diff --git a/R/get_current_fire_perimeters.R b/R/get_current_fire_perimeters.R index 1d81d97..dcc0905 100644 --- a/R/get_current_fire_perimeters.R +++ b/R/get_current_fire_perimeters.R @@ -1,14 +1,30 @@ -# Author: Will Curran-Groome - #' @importFrom magrittr %>% -#' @title Acquire wildfire perimeters +#' @title Acquire current wildfire perimeters +#' +#' @description Retrieves current wildfire perimeter data from the NIFC (National +#' Interagency Fire Center) via the Wildland Fire Interagency Geospatial Services +#' (WFIGS) API. +#' #' @param geography Included only for API consistency; this must be NULL. #' @param file_path Included only for API consistency; this must be NULL. #' @param bbox Optionally, an sf::st_bbox() object, or an object that can be converted to such. #' @param api Included only for API consistency; this must be TRUE. #' -#' @returns A library(sf) enabled dataframe comprising perimeters of current wildfires. +#' @details Data are from the NIFC WFIGS service. See +#' \url{https://data-nifc.opendata.arcgis.com/datasets/nifc::wfigs-interagency-fire-perimeters/about}. +#' +#' @returns An sf dataframe comprising perimeters of current wildfires. Columns include: +#' \describe{ +#' \item{unique_id}{Unique identifier for each observation (generated).} +#' \item{incident_name}{Name of the fire incident (title case).} +#' \item{incident_size_acres}{Size of the fire in acres.} +#' \item{incident_short_description}{Brief description of the incident.} +#' \item{percent_contained}{Percent of fire contained (0-100).} +#' \item{identified_date}{Date/time the fire was discovered.} +#' \item{updated_date}{Date/time the record was last updated.} +#' \item{geometry}{Polygon geometry of the fire perimeter.} +#' } #' @export #' #' @examples diff --git a/R/get_emergency_managerment_performance.R b/R/get_emergency_managerment_performance.R index d04ac52..9022c0b 100644 --- a/R/get_emergency_managerment_performance.R +++ b/R/get_emergency_managerment_performance.R @@ -1,17 +1,30 @@ -#' Get EMPG data +#' Get Emergency Management Performance Grant (EMPG) data +#' +#' @description Retrieves Emergency Management Performance Grant (EMPG) award data +#' from FEMA, which supports state and local emergency management agencies. #' #' @param file_path Path to the downloaded dataset on Box. -#' @param api Logical indicating whether to use the OpenFEMA API to retrieve the data. Default is TRUE. +#' @param api Logical indicating whether to use the OpenFEMA API to retrieve the data. +#' Default is TRUE. +#' +#' @details Data are from FEMA's OpenFEMA API. See +#' \url{https://www.fema.gov/openfema-data-page/emergency-management-performance-grants-v2}. #' -#' @return A tibble containing Emergency Management Performance Grant (EMPG) data with the following columns: -#' \describe{ -#' \item{state_name}{Character. The name of the state receiving the grant (renamed from original "state" column).} -#' \item{year_project_start}{Numeric. The year the project started, with corrections applied for known data entry errors in the source data.} -#' \item{state_code}{Character. Two-digit FIPS state code.} -#' \item{state_abbreviation}{Character. Two-letter USPS state abbreviation.} -#' \item{...}{Additional columns from the OpenFEMA EMPG dataset, cleaned via `janitor::clean_names()`.} -#' } -#' Data are filtered to records with `year_project_start > 2012`. A warning is issued noting data completeness concerns for 2024-2025. +#' @return A data frame containing emergency management performance grant (EMPG) data. +#' Columns include: +#' \describe{ +#' \item{id}{Unique identifier for the grant record.} +#' \item{state_name}{Full state name.} +#' \item{state_code}{Two-digit state FIPS code.} +#' \item{state_abbreviation}{Two-letter state abbreviation.} +#' \item{year_project_start}{Year the project started.} +#' \item{project_start_date}{Date the project started.} +#' \item{project_end_date}{Date the project ended.} +#' \item{grant_amount}{Total grant amount in dollars.} +#' \item{federal_share}{Federal portion of the grant in dollars.} +#' \item{non_federal_share}{Non-federal cost share in dollars.} +#' \item{program}{EMPG program type.} +#' } #' @export get_emergency_management_performance = function( diff --git a/R/get_fema_disaster_declarations.R b/R/get_fema_disaster_declarations.R index b7b3584..6e8137a 100644 --- a/R/get_fema_disaster_declarations.R +++ b/R/get_fema_disaster_declarations.R @@ -1,9 +1,28 @@ -# Author: Kameron Lloyd - #' @title Get major disaster declarations by county +#' +#' @description Retrieves FEMA Major Disaster Declarations at the county level, +#' aggregated by year and month. Tribal declarations are stored separately as +#' an attribute. +#' #' @param file_path The path (on Box) to the file containing the raw data. #' @param api If TRUE (default), access data from the API. Else, read locally from `file_path`. -#' @returns A dataframe comprising Major Disaster Declarations by month by year by county. Tribal declarations are stored as an attribute of the primary dataframe called `tribal_declarations`. +#' +#' @details Data are from FEMA's OpenFEMA API. See +#' \url{https://www.fema.gov/openfema-data-page/disaster-declarations-summaries-v2}. +#' Statewide declarations are expanded to all counties in the state. +#' +#' @returns A dataframe comprising Major Disaster Declarations by month by year by county. +#' Tribal declarations are stored as an attribute (`tribal_declarations`). Columns include: +#' \describe{ +#' \item{unique_id}{Unique identifier for each observation.} +#' \item{GEOID}{Five-digit county FIPS code.} +#' \item{year_declared}{Year the disaster was declared.} +#' \item{month_declared}{Month the disaster was declared (1-12).} +#' \item{declaration_title}{Title(s) of the disaster declaration(s).} +#' \item{incidents_all}{Total count of disaster declarations in the county-month.} +#' \item{incidents_natural_hazard}{Count of natural hazard declarations.} +#' \item{incidents_*}{Additional columns for other incident types, each of which reflects the count of the given incident type.} +#' } #' @export #' @examples #' \dontrun{ diff --git a/R/get_government_finances.R b/R/get_government_finances.R index 97007b2..de9ca11 100644 --- a/R/get_government_finances.R +++ b/R/get_government_finances.R @@ -1,21 +1,31 @@ #' Get government unit-level expenses from the Census of Governments #' +#' @description Retrieves government unit-level finance data from the Census of Governments, +#' including expenses by category for state, county, city, township, special district, +#' and school district government units. +#' #' @param year A four-digit year. The default is 2022. #' -#' @return A tibble containing government unit-level financial data aggregated by unit, with the following columns: -#' \describe{ -#' \item{unit_id}{Character. Unique identifier for the government unit.} -#' \item{year_data}{Numeric. The year of the financial data.} -#' \item{amount_thousands}{Numeric. Total expenditure amount in thousands of dollars.} -#' \item{government_type}{Character. Type of government unit: "State", "County", "City", "Township", "Special District", or "School District/Educational Service Agency".} -#' \item{data_quality}{Numeric. Proportion of records that were reported (vs. imputed or from alternative sources), ranging from 0 to 1.} -#' \item{unit_name}{Character. Name of the government unit.} -#' \item{county_name}{Character. County name where the unit is located.} -#' \item{state_code}{Character. Two-digit state FIPS code.} -#' \item{population}{Numeric. Population served by the government unit.} -#' \item{enrollment}{Numeric. Student enrollment (for school districts; NA for other unit types).} -#' \item{amount_per_capita}{Numeric. Expenditure per capita (or per enrolled student for school districts).} -#' } +#' @details Data are from the U.S. Census Bureau's Annual Survey of State and Local +#' Government Finances and Census of Governments. See +#' \url{https://www.census.gov/programs-surveys/gov-finances.html}. +#' +#' @return A dataframe containing government unit-level expenses for the specified year. +#' Columns include: +#' \describe{ +#' \item{unit_id}{Unique identifier for the government unit.} +#' \item{year_data}{Year of the financial data.} +#' \item{amount_thousands}{Total expenses in thousands of dollars.} +#' \item{government_type}{Type of government (State, County, City, Township, Special District, School District).} +#' \item{data_quality}{Proportion of expense items that were reported (vs. imputed).} +#' \item{state_code}{Two-digit state FIPS code.} +#' \item{county_code}{Three-digit county FIPS code.} +#' \item{unit_name}{Name of the government unit.} +#' \item{county_name}{Name of the county.} +#' \item{population}{Population served by the unit.} +#' \item{enrollment}{Student enrollment (for school districts).} +#' \item{amount_per_capita}{Expenses per capita or per enrolled student.} +#' } #' @export get_government_finances = function(year = 2022) { diff --git a/R/get_hazard_mitigation_assistance.R b/R/get_hazard_mitigation_assistance.R index 935cffb..29ad50b 100644 --- a/R/get_hazard_mitigation_assistance.R +++ b/R/get_hazard_mitigation_assistance.R @@ -26,11 +26,39 @@ clean_county = function(county) { #' Get Hazard Mitigation Assistance (HMA) Project Details #' -#' @param file_path_old_grant_system The file path to raw data for HMA applications from the older grant-reporting system. These data are typically available from: https://www.fema.gov/openfema-data-page/hazard-mitigation-assistance-projects-v4 -#' @param file_path_new_grant_system The file path to raw data for HMA applications from the newer (FEMA GO) grant-reporting system. These data are typically available from: https://www.fema.gov/openfema-data-page/hma-subapplications-v2 -#' @param state_abbreviations NULL by default, in which case data are returned for all 51 states. Provide a vector of two-character USPS state abbreviations to obtain data for a sub-selection of states. +#' @description Retrieves Hazard Mitigation Assistance project data from both the legacy +#' HMA Projects dataset and the newer FEMA GO subapplications dataset, harmonized +#' at the project-county level. #' -#' @return A dataframe of project-county HMA application data, aggregated across both old and new grant reporting systems. +#' @param file_path_old_grant_system The file path to raw data for HMA applications from +#' the older grant-reporting system. These data are typically available from: +#' \url{https://www.fema.gov/openfema-data-page/hazard-mitigation-assistance-projects-v4} +#' @param file_path_new_grant_system The file path to raw data for HMA applications from +#' the newer (FEMA GO) grant-reporting system. These data are typically available from: +#' \url{https://www.fema.gov/openfema-data-page/hma-subapplications-v2} +#' @param state_abbreviations NULL by default, in which case data are returned for all 51 +#' states. Provide a vector of two-character USPS state abbreviations to obtain data for +#' a sub-selection of states. +#' +#' @details Data are from FEMA's OpenFEMA API, combining two data sources: the legacy +#' Hazard Mitigation Assistance Projects (v4) and the newer HMA Subapplications (v2). +#' Multi-county projects are split across counties based on population proportions. +#' +#' @return A dataframe of project-county HMA application data. Only `project_cost_federal_split` +#' should be used for county-level aggregations. Columns include: +#' \describe{ +#' \item{data_source}{"hma-projects" (legacy) or "hma-subapplications" (FEMA GO).} +#' \item{project_id}{Unique project identifier.} +#' \item{disaster_number}{FEMA disaster number (if disaster-related).} +#' \item{project_program_area}{HMA program: HMGP, BRIC, FMA, or PDM.} +#' \item{project_fiscal_year}{Fiscal year of the project.} +#' \item{state_name}{Full state name.} +#' \item{county_geoid}{Five-digit county FIPS code.} +#' \item{county_population}{County population used for allocation.} +#' \item{project_status}{Current project status (e.g., "Closed", "Active").} +#' \item{project_cost_federal}{Total federal cost at project level.} +#' \item{project_cost_federal_split}{Federal cost allocated to this county.} +#' } #' @export #' #' @examples diff --git a/R/get_ihp_registrations.R b/R/get_ihp_registrations.R index f6d499f..37ec2aa 100644 --- a/R/get_ihp_registrations.R +++ b/R/get_ihp_registrations.R @@ -2,24 +2,41 @@ #' @title Get Individuals and Households Program (IHP) registrations #' -#' @param state_fips A character vector of two-letter state abbreviations. If NULL (default), return data for all 51 states. Otherwise return data for the specified states. +#' @description Retrieves FEMA Individual and Households Program (IHP) registration data, +#' which captures applications for disaster assistance from individuals and households. +#' +#' @param state_fips A character vector of two-letter state abbreviations. If NULL (default), +#' return data for all 51 states. Otherwise return data for the specified states. #' @param file_name The name (not the full path) of the Box file containing the raw data. #' @param api If TRUE, query the API. If FALSE (default), read from disk. #' @param outpath The path to save the parquet-formatted datafile. Applicable only when `api = FALSE`. #' -#' @return A tibble containing Individual and Households Program (IHP) registration data at the household level, joined to county-level geography. Due to ZIP-to-county crosswalking, records may be duplicated across counties (see warning). The returned object includes: -#' \describe{ -#' \item{unique_id}{Character. A UUID uniquely identifying each original IHP registration.} -#' \item{allocation_factor_zcta_to_county}{Numeric. The proportion of the ZCTA's population in this county (0-1). Used to apportion registrations when a ZIP spans multiple counties.} -#' \item{geoid_county}{Character. Five-digit FIPS county code.} -#' \item{zcta_code}{Character. Five-digit ZCTA (ZIP Code Tabulation Area) code.} -#' \item{geoid_tract}{Character. 11-digit Census tract GEOID (may have missingness).} -#' \item{geoid_block_group}{Character. 12-digit Census block group GEOID (may have missingness).} -#' \item{disaster_number}{Character. FEMA disaster number associated with the registration.} -#' \item{amount_individual_housing_program, amount_housing_assistance, amount_other_needs_assistance, amount_rental_assistance, amount_repairs, amount_replacement, amount_personal_property}{Numeric. Various IHP assistance amounts in dollars.} -#' \item{amount_flood_insurance_premium_paid_by_fema}{Numeric. Flood insurance premium paid by FEMA in dollars.} -#' \item{state_name, state_abbreviation, state_code}{Character. State identifiers.} -#' } +#' @details Data are from FEMA's OpenFEMA API. See +#' \url{https://www.fema.gov/openfema-data-page/individuals-and-households-program-valid-registrations-v2}. +#' +#' @returns A dataframe comprising IHP registrations. Note that records are duplicated +#' due to a many-to-many join with a ZCTA-to-county crosswalk; use `allocation_factor_zcta_to_county` +#' to properly aggregate. Columns include: +#' \describe{ +#' \item{unique_id}{Unique identifier for the original registration.} +#' \item{allocation_factor_zcta_to_county}{Weight for attributing registration to county.} +#' \item{geoid_county}{Five-digit county FIPS code.} +#' \item{zcta_code}{Five-digit ZIP Code Tabulation Area.} +#' \item{geoid_tract}{11-digit census tract FIPS code.} +#' \item{geoid_block_group}{12-digit census block group FIPS code.} +#' \item{disaster_number}{FEMA disaster number.} +#' \item{amount_individual_housing_program}{Total IHP assistance amount in dollars.} +#' \item{amount_housing_assistance}{Housing assistance amount in dollars.} +#' \item{amount_other_needs_assistance}{Other needs assistance amount in dollars.} +#' \item{amount_rental_assistance}{Rental assistance amount in dollars.} +#' \item{amount_repairs}{Repair assistance amount in dollars.} +#' \item{amount_replacement}{Replacement assistance amount in dollars.} +#' \item{amount_personal_property}{Personal property assistance amount in dollars.} +#' \item{amount_flood_insurance_premium_paid_by_fema}{FEMA-paid flood insurance premium.} +#' \item{state_name}{Full state name.} +#' \item{state_abbreviation}{Two-letter state abbreviation.} +#' \item{state_code}{Two-digit state FIPS code.} +#' } #' @export #' #' @examples diff --git a/R/get_lodes.R b/R/get_lodes.R index 4c23caa..a636486 100644 --- a/R/get_lodes.R +++ b/R/get_lodes.R @@ -91,7 +91,6 @@ rename_lodes_variables = function(.df) { } #' Get LEHD Origin-Destination Employment Statistics (LODES) data -#' Returned data are from LODES Version 8, which is enumerated in 2020-vintage geometries. #' #' @param lodes_type One of c("rac", "wac", "od"). "rac" = Residence Area #' Characteristics, where jobs are associated with employees' residences. diff --git a/R/get_nfip_claims.R b/R/get_nfip_claims.R index ffc1bd1..e280682 100644 --- a/R/get_nfip_claims.R +++ b/R/get_nfip_claims.R @@ -1,10 +1,15 @@ #' @title Access county-level data on NFIP claims -#' @param county_geoids A character vector of five-digit county codes. NULL by default; must be non-NULL if `api = TRUE`. +#' +#' @description Retrieves National Flood Insurance Program (NFIP) claims data at +#' the county level, including damage amounts, payments, and building characteristics. +#' +#' @param county_geoids A character vector of five-digit county codes. NULL by default; +#' must be non-NULL if `api = TRUE`. #' @param file_name The name (not the full path) of the Box file containing the raw data. #' @param api If TRUE, query the API. FALSE by default. #' -#' @details -#' These data are from: https://www.fema.gov/openfema-data-page/fima-nfip-redacted-claims-v2. +#' @details Data are from FEMA's OpenFEMA API. See +#' \url{https://www.fema.gov/openfema-data-page/fima-nfip-redacted-claims-v2}. #' Per FEMA: This data set represents more than 2,000,000 NFIP claims transactions. It is #' derived from the NFIP system of record, staged in the NFIP reporting platform and #' redacted to protect policy holder personally identifiable information. The @@ -114,7 +119,8 @@ get_nfip_claims = function( df1b = df1a |> tidytable::mutate( ### taking county_code if it exists, if not extract from census_tract - county_geoid = tidytable::if_else(!is.na(county_code), county_code, stringr::str_sub(census_tract, 1, 5))) + county_geoid = tidytable::if_else( + !is.na(county_code), county_code, stringr::str_sub(census_tract, 1, 5))) if (!is.null(county_geoids)) { df1b = df1b |> @@ -138,46 +144,49 @@ get_nfip_claims = function( #state_abbreviation = state, ## this is unreliable--other fields appear to be more consistent county_geoid, county_name, - occupancy_type = dplyr::case_when(occupancy_type %in% c(1, 11) ~ "single family", - occupancy_type %in% c(2, 3, 12, 13, 16, 15) ~ "multi-family", - occupancy_type %in% c(14) ~ "mobile/manufactured home", - occupancy_type %in% c(4, 6, 17, 18, 19) ~ "non-residential"), + occupancy_type = dplyr::case_when( + occupancy_type %in% c(1, 11) ~ "single family", + occupancy_type %in% c(2, 3, 12, 13, 16, 15) ~ "multi-family", + occupancy_type %in% c(14) ~ "mobile/manufactured home", + occupancy_type %in% c(4, 6, 17, 18, 19) ~ "non-residential"), year_loss = year_of_loss, year_construction = lubridate::year(original_construction_date), count_units_insured = policy_count, ## number of insured units associated with the claim #cause_of_damage, #flood_event_name = flood_event, #flood_zone_firm_current = flood_zone_current, - deductible_building = dplyr::case_when(building_deductible_code == "0" ~ 500, - building_deductible_code == "1" ~ 1000, - building_deductible_code == "2" ~ 2000, - building_deductible_code == "3" ~ 3000, - building_deductible_code == "4" ~ 4000, - building_deductible_code == "5" ~ 5000, - building_deductible_code == "9" ~ 750, - building_deductible_code == "A" ~ 10000, - building_deductible_code == "B" ~ 15000, - building_deductible_code == "C" ~ 20000, - building_deductible_code == "D" ~ 25000, - building_deductible_code == "E" ~ 50000, - building_deductible_code == "F" ~ 1250, - building_deductible_code == "G" ~ 500, - building_deductible_code == "H" ~ 200), - deductible_contents = dplyr::case_when(contents_deductible_code == "0" ~ 500, - contents_deductible_code == "1" ~ 1000, - contents_deductible_code == "2" ~ 2000, - contents_deductible_code == "3" ~ 3000, - contents_deductible_code == "4" ~ 4000, - contents_deductible_code == "5" ~ 5000, - contents_deductible_code == "9" ~ 750, - contents_deductible_code == "A" ~ 10000, - contents_deductible_code == "B" ~ 15000, - contents_deductible_code == "C" ~ 20000, - contents_deductible_code == "D" ~ 25000, - contents_deductible_code == "E" ~ 50000, - contents_deductible_code == "F" ~ 1250, - contents_deductible_code == "G" ~ 500, - contents_deductible_code == "H" ~ 200), + deductible_building = dplyr::case_when( + building_deductible_code == "0" ~ 500, + building_deductible_code == "1" ~ 1000, + building_deductible_code == "2" ~ 2000, + building_deductible_code == "3" ~ 3000, + building_deductible_code == "4" ~ 4000, + building_deductible_code == "5" ~ 5000, + building_deductible_code == "9" ~ 750, + building_deductible_code == "A" ~ 10000, + building_deductible_code == "B" ~ 15000, + building_deductible_code == "C" ~ 20000, + building_deductible_code == "D" ~ 25000, + building_deductible_code == "E" ~ 50000, + building_deductible_code == "F" ~ 1250, + building_deductible_code == "G" ~ 500, + building_deductible_code == "H" ~ 200), + deductible_contents = dplyr::case_when( + contents_deductible_code == "0" ~ 500, + contents_deductible_code == "1" ~ 1000, + contents_deductible_code == "2" ~ 2000, + contents_deductible_code == "3" ~ 3000, + contents_deductible_code == "4" ~ 4000, + contents_deductible_code == "5" ~ 5000, + contents_deductible_code == "9" ~ 750, + contents_deductible_code == "A" ~ 10000, + contents_deductible_code == "B" ~ 15000, + contents_deductible_code == "C" ~ 20000, + contents_deductible_code == "D" ~ 25000, + contents_deductible_code == "E" ~ 50000, + contents_deductible_code == "F" ~ 1250, + contents_deductible_code == "G" ~ 500, + contents_deductible_code == "H" ~ 200), value_building = building_property_value, value_contents = contents_property_value, replacement_cost_building = building_replacement_cost, diff --git a/R/get_nfip_policies.R b/R/get_nfip_policies.R index 0770af4..aaf34ff 100644 --- a/R/get_nfip_policies.R +++ b/R/get_nfip_policies.R @@ -1,10 +1,15 @@ #' @title Access county-level data on NFIP policies +#' +#' @description Retrieves National Flood Insurance Program (NFIP) policy data at +#' the county level, including both current and historical policies. +#' #' @param state_abbreviation A 2 letter state abbreviation (e.g. TX). #' @param county_geoids A character vector of five-digit county codes. #' @param file_name The name (not the full path) of the Box file containing the raw data. #' @param api If TRUE, query the API. If FALSE (default), read from `file_name`. #' -#' @details +#' @details Data are from FEMA's OpenFEMA API. See +#' \url{https://www.fema.gov/openfema-data-page/fima-nfip-redacted-policies-v2}. #' #' The following dataset houses information on NFIP policies (both historic and current). #' In order to filter to current policies, the analyst will need to filter on the @@ -88,7 +93,8 @@ get_nfip_policies = function( tidytable::mutate(county_geoid = tidytable::if_else( !is.na(county_code), county_code, - stringr::str_sub(census_tract, 1, 5))) ### taking county_code if it exists, if not extract from census_tract + ### taking county_code if it exists, if not extract from census_tract + stringr::str_sub(census_tract, 1, 5))) if (!is.null(county_geoids)) { df1b = df1b |> @@ -119,10 +125,11 @@ get_nfip_policies = function( policy_premium_total_cost = total_insurance_premium_of_the_policy, policy_date_termination = policy_termination_date, policy_date_effective = policy_effective_date, - building_occupancy_type = dplyr::case_when(occupancy_type %in% c(1, 11) ~ "single family", - occupancy_type %in% c(2, 12, 3, 13, 16, 15) ~ "multi-family", - occupancy_type %in% c(14) ~ "mobile/manufactured home", - occupancy_type %in% c(4, 6, 17, 18, 19) ~ "non-residential"), + building_occupancy_type = dplyr::case_when( + occupancy_type %in% c(1, 11) ~ "single family", + occupancy_type %in% c(2, 12, 3, 13, 16, 15) ~ "multi-family", + occupancy_type %in% c(14) ~ "mobile/manufactured home", + occupancy_type %in% c(4, 6, 17, 18, 19) ~ "non-residential"), building_replacement_cost = building_replacement_cost) message(" diff --git a/R/get_preliminary_damage_assessments.R b/R/get_preliminary_damage_assessments.R index fefd94e..a4c81b5 100644 --- a/R/get_preliminary_damage_assessments.R +++ b/R/get_preliminary_damage_assessments.R @@ -248,34 +248,40 @@ extract_pda_attributes = function(path) { #' Get Data from Preliminary Damage Assessments Submitted to FEMA for Disaster Declarations #' -#' These data reflect extracted attributes from PDF preliminary damage assessments -#' hosted on FEMA's website at: https://www.fema.gov/disaster/how-declared/preliminary-damage-assessments/reports. -#' Owing to the unstructured nature of the source documents, some fields may be incorrect -#' in the data returned by the function, though significant quality checks have been -#' implemented in an effort to produce a high-quality dataset. +#' @description Retrieves data extracted from PDF preliminary damage assessment (PDA) +#' reports submitted to FEMA for disaster declarations. #' -#' @param file_path The file path to the cached dataset, or if there is no cache, the path at which to cache the resulting data. -#' @param directory_path The path to the directory where PDA PDFs are stored. Use `scrape_pda_pdfs` to generate these files. -#' @param use_cache Boolean. Read the existing dataset stored at `file_path`? If FALSE, data will be generated anew. Else, if a file exists at `file_path`, this file will be returned. +#' @details Data are extracted from PDF reports hosted at +#' \url{https://www.fema.gov/disaster/how-declared/preliminary-damage-assessments/reports}. +#' Owing to the unstructured nature of the source documents, some fields may be incorrect +#' in the data returned by the function, though significant quality checks have been +#' implemented in an effort to produce a high-quality dataset. #' -#' @return A tibble containing parsed data from FEMA Preliminary Damage Assessment (PDA) PDF reports. Each row represents one disaster declaration request. The returned object includes: -#' \describe{ -#' \item{disaster_number}{Character. Four-digit FEMA disaster number.} -#' \item{event_type}{Character. Declaration outcome: "approved", "denial", "appeal_approved", or "appeal_denial".} -#' \item{event_title}{Character. Title/description of the disaster event from the PDA.} -#' \item{event_date_determined}{Date. Date the declaration determination was made.} -#' \item{event_native_flag}{Integer. 1 if this is a tribal declaration, 0 otherwise.} -#' \item{ia_requested}{Integer. 1 if Individual Assistance was requested, 0 otherwise.} -#' \item{ia_residences_impacted, ia_residences_destroyed, ia_residences_major_damage, ia_residences_minor_damage, ia_residences_affected}{Numeric. Counts of affected residences by damage category.} -#' \item{ia_residences_insured_total_percent, ia_residences_insured_flood_percent}{Numeric. Insurance coverage percentages.} -#' \item{ia_cost_estimate_total}{Numeric. Total estimated Individual Assistance cost.} -#' \item{pa_requested}{Integer. 1 if Public Assistance was requested, 0 otherwise.} -#' \item{pa_cost_estimate_total}{Numeric. Total estimated Public Assistance cost.} -#' \item{pa_per_capita_impact_statewide, pa_per_capita_impact_countywide_max, pa_per_capita_impact_countywide_min}{Numeric. Per capita impact metrics.} -#' \item{pa_per_capita_impact_indicator_statewide, pa_per_capita_impact_indicator_countywide}{Character. Per capita impact indicator values.} -#' \item{text}{Character. Full extracted text from the PDA for reference.} -#' } -#' Note: Due to the unstructured nature of PDF source documents, some extracted values may be inaccurate and should be verified. +#' @param file_path The file path to the cached dataset, or if there is no cache, the path +#' at which to cache the resulting data. +#' @param directory_path The path to the directory where PDA PDFs are stored. Use +#' `scrape_pda_pdfs` to generate these files. +#' @param use_cache Boolean. Read the existing dataset stored at `file_path`? If FALSE, +#' data will be generated anew. Else, if a file exists at `file_path`, this file will be returned. +#' +#' @return A dataframe of preliminary damage assessment reports. Key columns include: +#' \describe{ +#' \item{disaster_number}{FEMA disaster number.} +#' \item{event_type}{Type of decision: "approved", "denial", "appeal_approved", or "appeal_denial".} +#' \item{event_title}{Title/description of the disaster event.} +#' \item{event_date_determined}{Date the PDA determination was made.} +#' \item{event_native_flag}{1 if tribal request, 0 otherwise.} +#' \item{ia_requested}{1 if Individual Assistance was requested, 0 otherwise.} +#' \item{ia_residences_impacted}{Total residences impacted.} +#' \item{ia_residences_destroyed}{Number of residences destroyed.} +#' \item{ia_residences_major_damage}{Number of residences with major damage.} +#' \item{ia_residences_minor_damage}{Number of residences with minor damage.} +#' \item{ia_cost_estimate_total}{Estimated total Individual Assistance cost.} +#' \item{pa_requested}{1 if Public Assistance was requested, 0 otherwise.} +#' \item{pa_cost_estimate_total}{Estimated total Public Assistance cost.} +#' \item{pa_per_capita_impact_statewide}{Statewide per capita impact amount.} +#' \item{pa_per_capita_impact_indicator_statewide}{Met/Not Met indicator for statewide threshold.} +#' } #' @export #' #' @examples diff --git a/R/get_public_assistance.R b/R/get_public_assistance.R index 2dec622..0bf8b9d 100644 --- a/R/get_public_assistance.R +++ b/R/get_public_assistance.R @@ -2,24 +2,28 @@ #' Get FEMA Public Assistance (PA) funding #' -#' Project- and county-level data on PA funding over time +#' @description Retrieves FEMA Public Assistance (PA) project funding data, +#' crosswalked to the county level for geographic analysis. #' -#' @param state_abbreviations A character vector of state abbreviations. NULL by default, which returns records for all 51 states. Only the 51 states are supported at this time. +#' @param state_abbreviations A character vector of state abbreviations. NULL by default, +#' which returns records for all 51 states. Only the 51 states are supported at this time. #' @param file_path The file path to the raw data contained in a .parquet file. #' -#' @details +#' @details Data are from FEMA's OpenFEMA API. See +#' \url{https://www.fema.gov/openfema-data-page/public-assistance-funded-projects-details-v2}. +#' #' These data have been crosswalked so that estimates can be aggregated at the county level. #' This is necessary (for county-level estimates) because many projects are statewide #' projects and do not have county-level observations in the data. #' #' Analysts thus have two options for working with these data: #' (1) De-select the variables suffixed with `_split` and then run `distinct(df)`. -#' This will provide unique observations for projects; projects are both county-level -#' and statewide. These data can be aggregated to the state level but cannot be +#' This will provide unique observations for projects; projects can be either county-level +#' or statewide. These data can be aggregated to the state level but cannot be #' comprehensively aggregated to the county level. #' (2) Group the data at the county level and summarize to produce county-level #' characterizations of PA projects and funding, using the `_split`-suffixed -#' variables to calculate funding totals. For example, this might look like: +#' variables to calculate funding totals. #' #' The attribution of statewide projects to the county level occurs by proportionally attributing #' project costs based on county-level populations. For example, in a fictional state with two diff --git a/R/get_sba_loans.R b/R/get_sba_loans.R index 37b2210..5a7d0a0 100644 --- a/R/get_sba_loans.R +++ b/R/get_sba_loans.R @@ -3,21 +3,27 @@ #' @importFrom magrittr %>% #' @title Access SBA data on disaster loans - -#' @return A tibble containing SBA disaster loan data at the city/zip level, combining both home and business loan records. The returned object includes: -#' \describe{ -#' \item{disaster_number_fema}{Character. The FEMA disaster number associated with the loan.} -#' \item{disaster_number_sba_physical}{Character. SBA physical disaster declaration number.} -#' \item{disaster_number_sba_eidl}{Character. SBA Economic Injury Disaster Loan (EIDL) declaration number.} -#' \item{damaged_property_zip_code}{Character. ZIP code of the damaged property.} -#' \item{damaged_property_city_name}{Character. City name of the damaged property.} -#' \item{damaged_property_state_code}{Character. State code of the damaged property.} -#' \item{verified_loss_total}{Numeric. Total verified loss amount in dollars.} -#' \item{approved_amount_total}{Numeric. Total approved loan amount in dollars.} -#' \item{approved_amount_real_estate}{Numeric. Approved loan amount for real estate in dollars.} -#' \item{fiscal_year}{Character. Fiscal year of the loan (format: "20XX").} -#' \item{loan_type}{Character. Either "business" or "residential" indicating the loan category.} -#' } +#' @description Retrieves Small Business Administration (SBA) disaster loan data +#' for both home and business loans at the city and zip code level. +#' +#' @details Data are sourced from the SBA's disaster loan reports. See +#' \url{https://www.sba.gov/funding-programs/disaster-assistance}. +#' +#' @returns A dataframe comprising city- and zip-level data on SBA loanmaking. +#' Columns include: +#' \describe{ +#' \item{fiscal_year}{The federal fiscal year of the loan.} +#' \item{disaster_number_fema}{FEMA disaster number associated with the loan.} +#' \item{disaster_number_sba_physical}{SBA physical disaster declaration number.} +#' \item{disaster_number_sba_eidl}{SBA Economic Injury Disaster Loan (EIDL) declaration number.} +#' \item{damaged_property_zip_code}{ZIP code of the damaged property.} +#' \item{damaged_property_city_name}{City name of the damaged property.} +#' \item{damaged_property_state_code}{Two-letter state abbreviation.} +#' \item{verified_loss_total}{Total verified loss amount in dollars.} +#' \item{approved_amount_total}{Total approved loan amount in dollars.} +#' \item{approved_amount_real_estate}{Approved loan amount for real estate in dollars.} +#' \item{loan_type}{Type of loan: "residential" or "business".} +#' } #' @export #' #' @examples diff --git a/R/get_sheldus.R b/R/get_sheldus.R index a0d5644..02a0516 100644 --- a/R/get_sheldus.R +++ b/R/get_sheldus.R @@ -1,22 +1,30 @@ -#' @title Access temporal county-level SHELDUS hazard damage data. +#' @title Access temporal county-level SHELDUS hazard damage data +#' +#' @description Retrieves county-level hazard event data from the Spatial Hazard Events +#' and Losses Database for the United States (SHELDUS), including property damage, +#' crop damage, fatalities, and injuries. +#' #' @param file_path The path to the raw SHELDUS data. #' -#' @return A tibble containing SHELDUS (Spatial Hazard Events and Losses Database for the United States) data at the county-year-month-hazard level. The returned object includes: -#' \describe{ -#' \item{unique_id}{Character. A UUID uniquely identifying each observation.} -#' \item{GEOID}{Character. Five-digit FIPS county code. Connecticut counties are crosswalked to 2022 planning regions using population-weighted allocation factors.} -#' \item{state_name}{Character. State name (sentence case).} -#' \item{county_name}{Character. County name.} -#' \item{year}{Numeric. Year of the hazard event.} -#' \item{month}{Numeric. Month of the hazard event (1-12).} -#' \item{hazard}{Character. Type of hazard event.} -#' \item{damage_property}{Numeric. Property damage in 2023 inflation-adjusted dollars.} -#' \item{damage_crop}{Numeric. Crop damage in 2023 inflation-adjusted dollars.} -#' \item{injuries}{Numeric. Number of injuries.} -#' \item{fatalities}{Numeric. Number of fatalities.} -#' \item{records}{Numeric. Number of individual event records aggregated into this observation.} -#' } -#' Note: Only counties that existed in either 2010 or 2022 are included. +#' @details Data are from Arizona State University's SHELDUS database. Access requires +#' a subscription. See \url{https://cemhs.asu.edu/sheldus}. +#' +#' @returns A dataframe comprising hazard x month x year x county observations of hazard events. +#' Columns include: +#' \describe{ +#' \item{unique_id}{Unique identifier for each observation.} +#' \item{GEOID}{Five-digit county FIPS code.} +#' \item{state_name}{Full state name (sentence case).} +#' \item{county_name}{County name.} +#' \item{year}{Year of the hazard event(s).} +#' \item{month}{Month of the hazard event(s).} +#' \item{hazard}{Type of hazard (e.g., "Flooding", "Hurricane/Tropical Storm").} +#' \item{damage_property}{Property damage in 2023 inflation-adjusted dollars.} +#' \item{damage_crop}{Crop damage in 2023 inflation-adjusted dollars.} +#' \item{fatalities}{Number of fatalities.} +#' \item{injuries}{Number of injuries.} +#' \item{records}{Number of individual events aggregated into this observation.} +#' } #' @export #' #' @examples diff --git a/R/get_structures.R b/R/get_structures.R index d50a647..46f83a0 100644 --- a/R/get_structures.R +++ b/R/get_structures.R @@ -1,6 +1,8 @@ #' @importFrom magrittr %>% #' @title Estimate counts of hazard-impacted structures by structure type +#' @description Retrieves building footprint data from the USA Structures dataset and +#' summarizes structure counts by type at the tract or county level. #' @param geography The desired geography of the results. One of "tract" or "county". #' @param boundaries A POLYGON or MULTIPOLYGON object, or an sf::st_bbox()-style bbox. #' @param keep_structures Logical. If TRUE, the raw structure data will be returned alongside the summarized data. diff --git a/R/get_wildfire_burn_zones.R b/R/get_wildfire_burn_zones.R new file mode 100644 index 0000000..a4f7e0f --- /dev/null +++ b/R/get_wildfire_burn_zones.R @@ -0,0 +1,97 @@ +#' @title Get wildfire burn zones +#' +#' @description Returns spatial data on wildfire burn zones in the US from 2000-2025. +#' This dataset harmonizes six wildfire datasets (FIRED, MTBS, NIFC, ICS-209, RedBook, and FEMA) +#' to identify wildfires that burned near communities and resulted in civilian fatalities, +#' destroyed structures, or received federal disaster relief. +#' +#' @param file_path The path to the geojson file containing the raw data. Defaults to a +#' path within Box. +#' +#' @details Data are from a harmonized wildfire burn zone disaster dataset combining +#' FIRED, MTBS, NIFC, ICS-209, RedBook, and FEMA data sources. Geometries are in +#' NAD83 / Conus Albers (EPSG:5070). +#' +#' @returns An sf dataframe comprising wildfire burn zone disasters. Each row represents a +#' single wildfire event, with polygon geometries representing burn zones. +#' Columns include: +#' \describe{ +#' \item{wildfire_id}{Unique identifier for the wildfire event.} +#' \item{id_fema}{FEMA disaster identifier (if applicable).} +#' \item{year}{Year of the wildfire.} +#' \item{wildfire_name}{Name of the wildfire or fire complex.} +#' \item{county_fips}{Pipe-delimited string of five-digit county FIPS codes for all +#' counties affected by the wildfire.} +#' \item{county_name}{Pipe-delimited string of county names for all counties +#' affected by the wildfire.} +#' \item{area_sq_km}{Burned area in square kilometers.} +#' \item{wildfire_complex_binary}{Whether the fire is a complex (multiple fires).} +#' \item{date_start}{Ignition date.} +#' \item{date_containment}{Containment date.} +#' \item{fatalities_total}{Total fatalities.} +#' \item{injuries_total}{Total injuries.} +#' \item{structures_destroyed}{Number of structures destroyed.} +#' \item{structures_threatened}{Number of structures threatened.} +#' \item{evacuation_total}{Total evacuations.} +#' \item{wui_type}{Wildland-urban interface type.} +#' \item{density_people_sq_km_wildfire_buffer}{Population density in wildfire buffer area.} +#' \item{geometry}{Burn zone polygon geometry.} +#' } +#' @export +#' @examples +#' \dontrun{ +#' burn_zones <- get_wildfire_burn_zones() +#' } +get_wildfire_burn_zones <- function( + file_path = file.path( + get_box_path(), "hazards", "other-sources", "wildfire-burn-zones", + "wfbz_disasters_2000-2025.geojson")) { + + if (!file.exists(file_path)) { + stop(stringr::str_c( + "The path to the dataset does not point to a valid file. ", + "Please ensure there is a file located at this path: ", file_path, ".")) + } + + burn_zones1 <- sf::st_read(file_path, quiet = TRUE) |> + janitor::clean_names() |> + sf::st_transform(5070) + + burn_zones2 <- burn_zones1 |> + dplyr::transmute( + wildfire_id = wildfire_id, + id_fema = fema_id, + year = as.integer(wildfire_year), + wildfire_name = wildfire_complex_names, + county_fips = wildfire_counties_fips, + county_name = wildfire_counties, + area_sq_km = wildfire_area, + wildfire_complex_binary = wildfire_complex, + date_start = lubridate::as_date(wildfire_ignition_date), + date_containment = lubridate::as_date(wildfire_containment_date), + fatalities_total = as.integer(wildfire_total_fatalities), + injuries_total = as.integer(wildfire_total_injuries), + structures_destroyed = as.integer(wildfire_struct_destroyed), + structures_threatened = as.integer(wildfire_struct_threatened), + evacuation_total = as.integer(wildfire_total_evacuation), + wui_type = wildfire_wui, + ## codebook says this is per square meter, but that must be a typo based on distribution of density values + ## other values are per square kilometer, which makes more sense + density_people_sq_km_wildfire_buffer = wildfire_buffered_avg_pop_den) + + message(stringr::str_c( + "Each observation represents a wildfire burn zone disaster. ", + "Counties affected by each wildfire are stored as pipe-delimited strings in county_fips and county_name columns. ", + "Disasters are defined as wildfires that burned near a community and resulted in ", + "at least one civilian fatality, one destroyed structure, or received federal disaster relief. ", + "Geometries represent burn zone perimeters sourced from FIRED, MTBS, or NIFC datasets.")) + + return(burn_zones2) +} + +utils::globalVariables(c( + "wildfire_id", "fema_id", "wildfire_year", "wildfire_complex_names", "wildfire_counties", + "wildfire_counties_fips", "wildfire_area", "wildfire_complex", "wildfire_ignition_date", + "wildfire_containment_date", "wildfire_total_fatalities", "wildfire_total_injuries", + "wildfire_struct_destroyed", "wildfire_struct_threatened", "wildfire_total_evacuation", + "wildfire_wui", "wildfire_buffered_avg_pop_den")) diff --git a/R/test.R b/R/test.R new file mode 100644 index 0000000..e69de29 diff --git a/_pkgdown.yml b/_pkgdown.yml index def5c1f..44e7cb8 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -31,6 +31,7 @@ reference: - title: Event boundaries contents: - get_current_fire_perimeters + - get_wildfire_burn_zones - title: Land use contents: - estimate_zoning_envelope diff --git a/man/get_business_patterns.Rd b/man/get_business_patterns.Rd index 6794aaa..3ba7e6d 100644 --- a/man/get_business_patterns.Rd +++ b/man/get_business_patterns.Rd @@ -12,47 +12,41 @@ get_business_patterns( ) } \arguments{ -\item{year}{The vintage of CBP data desired. Data are available from 1986, -though this function likely only supports more recent years (it it tested on 2022-vintage data only). -Default is 2022.} +\item{year}{The vintage of CBP data desired. Data are available from 1986, though +this function likely only supports more recent years (it is tested on 2022-vintage +data only). Default is 2022.} -\item{geo}{The level of geography of CBP data desired. Either "county" or "zipcode". Zipcode -level data only The ZIP Code Business Patterns (ZBP) dataset includes the number of establishments, -employment during the week of March 12th, first quarter and annual payroll for NAICS 00 (total for all sectors). -Additionally, the number of establishments (but not employment or payroll) are available by employment -size of the establishment for 2- through 6-digit NAICS.} +\item{naics_code_digits}{One of c(2, 3). Default is 2. NAICS codes range in specificity; +2-digit codes describe the highest groupings of industries, while six-digit codes +are exceedingly detailed. There are 20 2-digit NAICS codes and 196 3-digit codes.} -\item{naics_code_digits}{One of c(2, 3). Default is 2. NAICS codes range in -specificity; 2-digit codes describe the highest groupings of industries, -while six-digit codes are exceedingly detailed. There are 20 2-digit NAICS -codes and 196 3-digit codes. If more specific codes are desired, leave this -argument as NULL and supply the desired codes as the argument to \code{naics_codes}.} - -\item{naics_codes}{A vector of NAICS codes to query. If NULL, the function will -query all available codes with the specified number of digits. If not NULL, -this argument overrides the \code{naics_code_digits} argument.} +\item{naics_codes}{A vector of NAICS codes to query. If NULL, the function will query +all available codes with the specified number of digits. If not NULL, this argument +overrides the \code{naics_code_digits} argument.} } \value{ A tibble with data on county-level employees, employers, and aggregate -annual payrolls by industry and employer size +annual payrolls by industry and employer size. Columns include: \describe{ -\item{year}{the year for which CBP data is pulled from} -\item{state}{A two-digit state identifier.} -\item{county}{A three-digit county identifier.} -\item{employees}{number of individual employees employed in that particular industry -and establishment size combination} -\item{employers}{number of establishments of each employment size} -\item{annual_payroll}{total annual payroll expenditures measured in $1,000's of USD} -\item{industry}{industry classification according to North American Industry Classification System. -Refer to details for additional information} -\item{employee_size_range_label}{range for the employment size of establishments included in each -given grouping} -\item{employee_size_range_code}{three-digit code used to categorize employment sizes} -\item{naics_code}{two to six-digit code used by the NAICS to categorize and sub-categorize industries} +\item{state}{Two-digit state FIPS code.} +\item{county}{Three-digit county FIPS code.} +\item{employees}{Number of employees mid-March of the reference year.} +\item{employers}{Number of establishments.} +\item{annual_payroll}{Annual payroll in thousands of dollars.} +\item{industry}{NAICS industry description (lowercase, underscored).} +\item{employee_size_range_label}{Human-readable employer size category.} +\item{employee_size_range_code}{Census code for employer size range.} } } \description{ -Obtain County Business Patterns (CBP) Estimates per County +Retrieves County Business Patterns data from the Census Bureau, +providing counts of establishments, employees, and payroll by industry and +employer size at the county level. +} +\details{ +Data are from the U.S. Census Bureau's County Business Patterns program. +See \url{https://www.census.gov/programs-surveys/cbp.html} and +\url{https://www.census.gov/naics/} for NAICS code definitions. } \details{ County Business Patterns (CBP) is an annual series that provides subnational diff --git a/man/get_current_fire_perimeters.Rd b/man/get_current_fire_perimeters.Rd index 9676d71..7042505 100644 --- a/man/get_current_fire_perimeters.Rd +++ b/man/get_current_fire_perimeters.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/get_current_fire_perimeters.R \name{get_current_fire_perimeters} \alias{get_current_fire_perimeters} -\title{Acquire wildfire perimeters} +\title{Acquire current wildfire perimeters} \usage{ get_current_fire_perimeters( geography = NULL, @@ -21,10 +21,26 @@ get_current_fire_perimeters( \item{api}{Included only for API consistency; this must be TRUE.} } \value{ -A library(sf) enabled dataframe comprising perimeters of current wildfires. +An sf dataframe comprising perimeters of current wildfires. Columns include: +\describe{ +\item{unique_id}{Unique identifier for each observation (generated).} +\item{incident_name}{Name of the fire incident (title case).} +\item{incident_size_acres}{Size of the fire in acres.} +\item{incident_short_description}{Brief description of the incident.} +\item{percent_contained}{Percent of fire contained (0-100).} +\item{identified_date}{Date/time the fire was discovered.} +\item{updated_date}{Date/time the record was last updated.} +\item{geometry}{Polygon geometry of the fire perimeter.} +} } \description{ -Acquire wildfire perimeters +Retrieves current wildfire perimeter data from the NIFC (National +Interagency Fire Center) via the Wildland Fire Interagency Geospatial Services +(WFIGS) API. +} +\details{ +Data are from the NIFC WFIGS service. See +\url{https://data-nifc.opendata.arcgis.com/datasets/nifc::wfigs-interagency-fire-perimeters/about}. } \examples{ \dontrun{ diff --git a/man/get_emergency_management_performance.Rd b/man/get_emergency_management_performance.Rd index a1739b3..a909600 100644 --- a/man/get_emergency_management_performance.Rd +++ b/man/get_emergency_management_performance.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/get_emergency_managerment_performance.R \name{get_emergency_management_performance} \alias{get_emergency_management_performance} -\title{Get EMPG data} +\title{Get Emergency Management Performance Grant (EMPG) data} \usage{ get_emergency_management_performance( file_path = file.path(get_box_path(), "hazards", "FEMA", @@ -14,19 +14,31 @@ get_emergency_management_performance( \arguments{ \item{file_path}{Path to the downloaded dataset on Box.} -\item{api}{Logical indicating whether to use the OpenFEMA API to retrieve the data. Default is TRUE.} +\item{api}{Logical indicating whether to use the OpenFEMA API to retrieve the data. +Default is TRUE.} } \value{ -A tibble containing Emergency Management Performance Grant (EMPG) data with the following columns: +A data frame containing emergency management performance grant (EMPG) data. +Columns include: \describe{ -\item{state_name}{Character. The name of the state receiving the grant (renamed from original "state" column).} -\item{year_project_start}{Numeric. The year the project started, with corrections applied for known data entry errors in the source data.} -\item{state_code}{Character. Two-digit FIPS state code.} -\item{state_abbreviation}{Character. Two-letter USPS state abbreviation.} -\item{...}{Additional columns from the OpenFEMA EMPG dataset, cleaned via \code{janitor::clean_names()}.} +\item{id}{Unique identifier for the grant record.} +\item{state_name}{Full state name.} +\item{state_code}{Two-digit state FIPS code.} +\item{state_abbreviation}{Two-letter state abbreviation.} +\item{year_project_start}{Year the project started.} +\item{project_start_date}{Date the project started.} +\item{project_end_date}{Date the project ended.} +\item{grant_amount}{Total grant amount in dollars.} +\item{federal_share}{Federal portion of the grant in dollars.} +\item{non_federal_share}{Non-federal cost share in dollars.} +\item{program}{EMPG program type.} } -Data are filtered to records with \code{year_project_start > 2012}. A warning is issued noting data completeness concerns for 2024-2025. } \description{ -Get EMPG data +Retrieves Emergency Management Performance Grant (EMPG) award data +from FEMA, which supports state and local emergency management agencies. +} +\details{ +Data are from FEMA's OpenFEMA API. See +\url{https://www.fema.gov/openfema-data-page/emergency-management-performance-grants-v2}. } diff --git a/man/get_fema_disaster_declarations.Rd b/man/get_fema_disaster_declarations.Rd index 18f3f8d..ee0e193 100644 --- a/man/get_fema_disaster_declarations.Rd +++ b/man/get_fema_disaster_declarations.Rd @@ -16,10 +16,28 @@ get_fema_disaster_declarations( \item{api}{If TRUE (default), access data from the API. Else, read locally from \code{file_path}.} } \value{ -A dataframe comprising Major Disaster Declarations by month by year by county. Tribal declarations are stored as an attribute of the primary dataframe called \code{tribal_declarations}. +A dataframe comprising Major Disaster Declarations by month by year by county. +Tribal declarations are stored as an attribute (\code{tribal_declarations}). Columns include: +\describe{ +\item{unique_id}{Unique identifier for each observation.} +\item{GEOID}{Five-digit county FIPS code.} +\item{year_declared}{Year the disaster was declared.} +\item{month_declared}{Month the disaster was declared (1-12).} +\item{declaration_title}{Title(s) of the disaster declaration(s).} +\item{incidents_all}{Total count of disaster declarations in the county-month.} +\item{incidents_natural_hazard}{Count of natural hazard declarations.} +\item{incidents_*}{Additional columns for other incident types, each of which reflects the count of the given incident type.} +} } \description{ -Get major disaster declarations by county +Retrieves FEMA Major Disaster Declarations at the county level, +aggregated by year and month. Tribal declarations are stored separately as +an attribute. +} +\details{ +Data are from FEMA's OpenFEMA API. See +\url{https://www.fema.gov/openfema-data-page/disaster-declarations-summaries-v2}. +Statewide declarations are expanded to all counties in the state. } \examples{ \dontrun{ diff --git a/man/get_government_finances.Rd b/man/get_government_finances.Rd index 0dca1be..4117f1a 100644 --- a/man/get_government_finances.Rd +++ b/man/get_government_finances.Rd @@ -10,21 +10,30 @@ get_government_finances(year = 2022) \item{year}{A four-digit year. The default is 2022.} } \value{ -A tibble containing government unit-level financial data aggregated by unit, with the following columns: +A dataframe containing government unit-level expenses for the specified year. +Columns include: \describe{ -\item{unit_id}{Character. Unique identifier for the government unit.} -\item{year_data}{Numeric. The year of the financial data.} -\item{amount_thousands}{Numeric. Total expenditure amount in thousands of dollars.} -\item{government_type}{Character. Type of government unit: "State", "County", "City", "Township", "Special District", or "School District/Educational Service Agency".} -\item{data_quality}{Numeric. Proportion of records that were reported (vs. imputed or from alternative sources), ranging from 0 to 1.} -\item{unit_name}{Character. Name of the government unit.} -\item{county_name}{Character. County name where the unit is located.} -\item{state_code}{Character. Two-digit state FIPS code.} -\item{population}{Numeric. Population served by the government unit.} -\item{enrollment}{Numeric. Student enrollment (for school districts; NA for other unit types).} -\item{amount_per_capita}{Numeric. Expenditure per capita (or per enrolled student for school districts).} +\item{unit_id}{Unique identifier for the government unit.} +\item{year_data}{Year of the financial data.} +\item{amount_thousands}{Total expenses in thousands of dollars.} +\item{government_type}{Type of government (State, County, City, Township, Special District, School District).} +\item{data_quality}{Proportion of expense items that were reported (vs. imputed).} +\item{state_code}{Two-digit state FIPS code.} +\item{county_code}{Three-digit county FIPS code.} +\item{unit_name}{Name of the government unit.} +\item{county_name}{Name of the county.} +\item{population}{Population served by the unit.} +\item{enrollment}{Student enrollment (for school districts).} +\item{amount_per_capita}{Expenses per capita or per enrolled student.} } } \description{ -Get government unit-level expenses from the Census of Governments +Retrieves government unit-level finance data from the Census of Governments, +including expenses by category for state, county, city, township, special district, +and school district government units. +} +\details{ +Data are from the U.S. Census Bureau's Annual Survey of State and Local +Government Finances and Census of Governments. See +\url{https://www.census.gov/programs-surveys/gov-finances.html}. } diff --git a/man/get_hazard_mitigation_assistance.Rd b/man/get_hazard_mitigation_assistance.Rd index ca8de19..3937063 100644 --- a/man/get_hazard_mitigation_assistance.Rd +++ b/man/get_hazard_mitigation_assistance.Rd @@ -14,17 +14,44 @@ get_hazard_mitigation_assistance( ) } \arguments{ -\item{file_path_old_grant_system}{The file path to raw data for HMA applications from the older grant-reporting system. These data are typically available from: https://www.fema.gov/openfema-data-page/hazard-mitigation-assistance-projects-v4} +\item{file_path_old_grant_system}{The file path to raw data for HMA applications from +the older grant-reporting system. These data are typically available from: +\url{https://www.fema.gov/openfema-data-page/hazard-mitigation-assistance-projects-v4}} -\item{file_path_new_grant_system}{The file path to raw data for HMA applications from the newer (FEMA GO) grant-reporting system. These data are typically available from: https://www.fema.gov/openfema-data-page/hma-subapplications-v2} +\item{file_path_new_grant_system}{The file path to raw data for HMA applications from +the newer (FEMA GO) grant-reporting system. These data are typically available from: +\url{https://www.fema.gov/openfema-data-page/hma-subapplications-v2}} -\item{state_abbreviations}{NULL by default, in which case data are returned for all 51 states. Provide a vector of two-character USPS state abbreviations to obtain data for a sub-selection of states.} +\item{state_abbreviations}{NULL by default, in which case data are returned for all 51 +states. Provide a vector of two-character USPS state abbreviations to obtain data for +a sub-selection of states.} } \value{ -A dataframe of project-county HMA application data, aggregated across both old and new grant reporting systems. +A dataframe of project-county HMA application data. Only \code{project_cost_federal_split} +should be used for county-level aggregations. Columns include: +\describe{ +\item{data_source}{"hma-projects" (legacy) or "hma-subapplications" (FEMA GO).} +\item{project_id}{Unique project identifier.} +\item{disaster_number}{FEMA disaster number (if disaster-related).} +\item{project_program_area}{HMA program: HMGP, BRIC, FMA, or PDM.} +\item{project_fiscal_year}{Fiscal year of the project.} +\item{state_name}{Full state name.} +\item{county_geoid}{Five-digit county FIPS code.} +\item{county_population}{County population used for allocation.} +\item{project_status}{Current project status (e.g., "Closed", "Active").} +\item{project_cost_federal}{Total federal cost at project level.} +\item{project_cost_federal_split}{Federal cost allocated to this county.} +} } \description{ -Get Hazard Mitigation Assistance (HMA) Project Details +Retrieves Hazard Mitigation Assistance project data from both the legacy +HMA Projects dataset and the newer FEMA GO subapplications dataset, harmonized +at the project-county level. +} +\details{ +Data are from FEMA's OpenFEMA API, combining two data sources: the legacy +Hazard Mitigation Assistance Projects (v4) and the newer HMA Subapplications (v2). +Multi-county projects are split across counties based on population proportions. } \examples{ \dontrun{ diff --git a/man/get_ihp_registrations.Rd b/man/get_ihp_registrations.Rd index 0fd2b40..9f859e7 100644 --- a/man/get_ihp_registrations.Rd +++ b/man/get_ihp_registrations.Rd @@ -12,7 +12,8 @@ get_ihp_registrations( ) } \arguments{ -\item{state_fips}{A character vector of two-letter state abbreviations. If NULL (default), return data for all 51 states. Otherwise return data for the specified states.} +\item{state_fips}{A character vector of two-letter state abbreviations. If NULL (default), +return data for all 51 states. Otherwise return data for the specified states.} \item{file_name}{The name (not the full path) of the Box file containing the raw data.} @@ -21,22 +22,37 @@ get_ihp_registrations( \item{outpath}{The path to save the parquet-formatted datafile. Applicable only when \code{api = FALSE}.} } \value{ -A tibble containing Individual and Households Program (IHP) registration data at the household level, joined to county-level geography. Due to ZIP-to-county crosswalking, records may be duplicated across counties (see warning). The returned object includes: +A dataframe comprising IHP registrations. Note that records are duplicated +due to a many-to-many join with a ZCTA-to-county crosswalk; use \code{allocation_factor_zcta_to_county} +to properly aggregate. Columns include: \describe{ -\item{unique_id}{Character. A UUID uniquely identifying each original IHP registration.} -\item{allocation_factor_zcta_to_county}{Numeric. The proportion of the ZCTA's population in this county (0-1). Used to apportion registrations when a ZIP spans multiple counties.} -\item{geoid_county}{Character. Five-digit FIPS county code.} -\item{zcta_code}{Character. Five-digit ZCTA (ZIP Code Tabulation Area) code.} -\item{geoid_tract}{Character. 11-digit Census tract GEOID (may have missingness).} -\item{geoid_block_group}{Character. 12-digit Census block group GEOID (may have missingness).} -\item{disaster_number}{Character. FEMA disaster number associated with the registration.} -\item{amount_individual_housing_program, amount_housing_assistance, amount_other_needs_assistance, amount_rental_assistance, amount_repairs, amount_replacement, amount_personal_property}{Numeric. Various IHP assistance amounts in dollars.} -\item{amount_flood_insurance_premium_paid_by_fema}{Numeric. Flood insurance premium paid by FEMA in dollars.} -\item{state_name, state_abbreviation, state_code}{Character. State identifiers.} +\item{unique_id}{Unique identifier for the original registration.} +\item{allocation_factor_zcta_to_county}{Weight for attributing registration to county.} +\item{geoid_county}{Five-digit county FIPS code.} +\item{zcta_code}{Five-digit ZIP Code Tabulation Area.} +\item{geoid_tract}{11-digit census tract FIPS code.} +\item{geoid_block_group}{12-digit census block group FIPS code.} +\item{disaster_number}{FEMA disaster number.} +\item{amount_individual_housing_program}{Total IHP assistance amount in dollars.} +\item{amount_housing_assistance}{Housing assistance amount in dollars.} +\item{amount_other_needs_assistance}{Other needs assistance amount in dollars.} +\item{amount_rental_assistance}{Rental assistance amount in dollars.} +\item{amount_repairs}{Repair assistance amount in dollars.} +\item{amount_replacement}{Replacement assistance amount in dollars.} +\item{amount_personal_property}{Personal property assistance amount in dollars.} +\item{amount_flood_insurance_premium_paid_by_fema}{FEMA-paid flood insurance premium.} +\item{state_name}{Full state name.} +\item{state_abbreviation}{Two-letter state abbreviation.} +\item{state_code}{Two-digit state FIPS code.} } } \description{ -Get Individuals and Households Program (IHP) registrations +Retrieves FEMA Individual and Households Program (IHP) registration data, +which captures applications for disaster assistance from individuals and households. +} +\details{ +Data are from FEMA's OpenFEMA API. See +\url{https://www.fema.gov/openfema-data-page/individuals-and-households-program-valid-registrations-v2}. } \examples{ \dontrun{ diff --git a/man/get_lodes.Rd b/man/get_lodes.Rd index bdd468a..14ee347 100644 --- a/man/get_lodes.Rd +++ b/man/get_lodes.Rd @@ -2,8 +2,7 @@ % Please edit documentation in R/get_lodes.R \name{get_lodes} \alias{get_lodes} -\title{Get LEHD Origin-Destination Employment Statistics (LODES) data -Returned data are from LODES Version 8, which is enumerated in 2020-vintage geometries.} +\title{Get LEHD Origin-Destination Employment Statistics (LODES) data} \usage{ get_lodes( lodes_type, @@ -15,15 +14,13 @@ get_lodes( ) } \arguments{ -\item{lodes_type}{One of c("rac", "wac", "od"). "rac" = Residence Area -Characteristics, where jobs are associated with employees' residences. -"wac" = Workplace Area Characteristics, where jobs are associated with -employees' workplaces. "od" = Origin-Destination data, where jobs are associated -with both workers' residences and their workplaces.} +\item{lodes_type}{One of c("rac", "wac", "od"). "rac" = Residence Area Characteristics, +where jobs are associated with employees' residences. "wac" = Workplace Area Characteristics, +where jobs are associated with employees' workplaces. "od" = Origin-Destination data, +where jobs are associated with both workers' residences and their workplaces.} -\item{jobs_type}{One of c("all", "primary"). Default is "all", which includes -multiple jobs for workers with multiple jobs. "primary" includes only the -highest-paying job per worker.} +\item{jobs_type}{One of c("all", "primary"). Default is "all", which includes multiple +jobs for workers with multiple jobs. "primary" includes only the highest-paying job per worker.} \item{states}{A vector of state abbreviations.} @@ -32,61 +29,37 @@ highest-paying job per worker.} \item{geography}{One of c("block", "block group", "tract", "county", "state"). Default is "tract".} -\item{state_part}{One of c("main", "aux"). Default is "main", which includes -only workers who reside inside the state where they work. "aux" returns -only workers who work in the specified state but live outside of that state.} +\item{state_part}{One of c("main", "aux"). Default is "main", which includes only workers +who reside inside the state where they work. "aux" returns only workers who work in the +specified state but live outside of that state.} } \value{ -A tibble with one record per geography per year per job type. Attributes -include total jobs and jobs by worker earnings, industry, and demographics; -the origin-destination results have more limited demographics compared to -the "wac" and "rac" results. +A tibble with one record per geography per year per job type. Key columns include: \describe{ -\item{year}{the year for which LODES data is pulled from} -\item{state}{A two-digit state identifier.} -\item{GEOID}{11 digit identifier denoted as either h_GEOID representing the employees' residence census block code or w_GEOID representing the employees' workplace census block code} -\item{job_type}{one of either 'all' jobs or only 'federal' jobs} -\item{total_jobs}{total number of jobs in a given tract} -\item{jobs_workers_age}{number of employees by given age range} -\item{jobs_earnings}{number of employees by given monthly earnings range} -\item{jobs_industry}{number of employees by given industry} -\item{jobs_workers_race}{number of employees by given race, inclusive of hispanic or latino; only available in 'wac' and 'rac' datasets} -\item{jobs_workers_ethnicity}{number of employess by hispanic or latino status, regardless of race; only available in 'wac' and 'rac' datasets} -\item{jobs_workers_educational_attainment}{number of employees by highest level of education attained; only available in 'wac' and 'rac' datasets} -\item{jobs_workers_sex}{number of employees by sex; only available in 'wac' and 'rac' datasets} -\item{jobs_firm_age}{number of employees by the age of employing firm; only available in 'wac' datasets} -\item{jobs_firm_size}{number of employees for a given range in employer size; only available in 'wac' datasets} +\item{year}{Year of the data.} +\item{state}{Two-letter state abbreviation.} +\item{job_type}{"all" or "federal" indicating the job category.} +\item{h_GEOID / w_GEOID}{Home (h) or work (w) GEOID depending on lodes_type.} +\item{jobs}{Total number of jobs.} +\item{jobs_workers_age_29_or_younger}{Jobs for workers age 29 or younger.} +\item{jobs_workers_age_30_to_54}{Jobs for workers age 30 to 54.} +\item{jobs_workers_age_55_or_older}{Jobs for workers age 55 or older.} +\item{jobs_earnings_1250_month_or_less}{Jobs with earnings $1250/month or less.} +\item{jobs_earnings_1251_month_to_3333_month}{Jobs with earnings $1251-$3333/month.} +\item{jobs_earnings_greater_than_3333_month}{Jobs with earnings >$3333/month.} +\item{jobs_industry_\emph{}{Jobs by NAICS industry sector (20 sectors for WAC/RAC).} +\item{jobs_workers_race_}}{Jobs by worker race (WAC/RAC only).} +\item{jobs_workers_ethnicity_\emph{}{Jobs by worker ethnicity (WAC/RAC only).} +\item{jobs_workers_educational_attainment_}}{Jobs by education level (WAC/RAC only).} +\item{jobs_workers_sex_*}{Jobs by worker sex (WAC/RAC only).} } } \description{ -Get LEHD Origin-Destination Employment Statistics (LODES) data +Retrieves LODES employment data at various geographic levels. Returned data are from LODES Version 8, which is enumerated in 2020-vintage geometries. } \details{ -The Longitudinal Employer-Household Dynamics (LEHD) data at the U.S. Census Bureau -is a quarterly database of linked employer-employee data covering over 95\% of employment -in the United States. The LEHD data are generated by merging previously collected survey -and administrative data on jobs, businesses, and workers. - -LEHD Origin-Destination Employment Statistics (LODES)is a partially synthetic dataset -that describes geographic patterns of jobs by their employment locations and residential -locations as well as the connections between the two locations. The microdata link employee -and employer data by combining administrative state unemployment insurance wage records -with other administrative and survey data. The source data are aggregated and adjusted -to protect confidentiality. - -LODES data includes three datasets: -Residence Area Characteristics (RAC): -This file lists the total number of jobs by the census block where the employee lives. - -Workplace Area Characteristics (WAC): -This file lists the total number of jobs by the census block where the employee works. - -Origin-Destination (OD): -This file lists job totals by both the census block where the employee lives and the -census block where the employee works - -While similar to County Business Patterns (CBP) data in it's coverage of employment -statistics, LODES differs mainly due to its more granular geographies (tract vs. county) and -focus on framing the statistics at the individual/job level found in LODES data. +Data are from the Longitudinal Employer-Household Dynamics (LEHD) program. +See \url{https://lehd.ces.census.gov/data/} and the technical documentation at +\url{https://lehd.ces.census.gov/data/lodes/LODES8/LODESTechDoc8.0.pdf}. } diff --git a/man/get_nfip_claims.Rd b/man/get_nfip_claims.Rd index d480d0f..69672b2 100644 --- a/man/get_nfip_claims.Rd +++ b/man/get_nfip_claims.Rd @@ -11,7 +11,8 @@ get_nfip_claims( ) } \arguments{ -\item{county_geoids}{A character vector of five-digit county codes. NULL by default; must be non-NULL if \code{api = TRUE}.} +\item{county_geoids}{A character vector of five-digit county codes. NULL by default; +must be non-NULL if \code{api = TRUE}.} \item{file_name}{The name (not the full path) of the Box file containing the raw data.} @@ -44,10 +45,12 @@ A data frame comprising county-level data on current NFIP policies } } \description{ -Access county-level data on NFIP claims +Retrieves National Flood Insurance Program (NFIP) claims data at +the county level, including damage amounts, payments, and building characteristics. } \details{ -These data are from: https://www.fema.gov/openfema-data-page/fima-nfip-redacted-claims-v2. +Data are from FEMA's OpenFEMA API. See +\url{https://www.fema.gov/openfema-data-page/fima-nfip-redacted-claims-v2}. Per FEMA: This data set represents more than 2,000,000 NFIP claims transactions. It is derived from the NFIP system of record, staged in the NFIP reporting platform and redacted to protect policy holder personally identifiable information. The diff --git a/man/get_nfip_policies.Rd b/man/get_nfip_policies.Rd index bf86511..8ed550e 100644 --- a/man/get_nfip_policies.Rd +++ b/man/get_nfip_policies.Rd @@ -41,9 +41,13 @@ either because it was cancelled or lapsed.} } } \description{ -Access county-level data on NFIP policies +Retrieves National Flood Insurance Program (NFIP) policy data at +the county level, including both current and historical policies. } \details{ +Data are from FEMA's OpenFEMA API. See +\url{https://www.fema.gov/openfema-data-page/fima-nfip-redacted-policies-v2}. + The following dataset houses information on NFIP policies (both historic and current). In order to filter to current policies, the analyst will need to filter on the policy_date_termination and policy_date_effective columns. diff --git a/man/get_preliminary_damage_assessments.Rd b/man/get_preliminary_damage_assessments.Rd index a165dde..563e5f3 100644 --- a/man/get_preliminary_damage_assessments.Rd +++ b/man/get_preliminary_damage_assessments.Rd @@ -13,35 +13,42 @@ get_preliminary_damage_assessments( ) } \arguments{ -\item{file_path}{The file path to the cached dataset, or if there is no cache, the path at which to cache the resulting data.} +\item{file_path}{The file path to the cached dataset, or if there is no cache, the path +at which to cache the resulting data.} -\item{directory_path}{The path to the directory where PDA PDFs are stored. Use \code{scrape_pda_pdfs} to generate these files.} +\item{directory_path}{The path to the directory where PDA PDFs are stored. Use +\code{scrape_pda_pdfs} to generate these files.} -\item{use_cache}{Boolean. Read the existing dataset stored at \code{file_path}? If FALSE, data will be generated anew. Else, if a file exists at \code{file_path}, this file will be returned.} +\item{use_cache}{Boolean. Read the existing dataset stored at \code{file_path}? If FALSE, +data will be generated anew. Else, if a file exists at \code{file_path}, this file will be returned.} } \value{ -A tibble containing parsed data from FEMA Preliminary Damage Assessment (PDA) PDF reports. Each row represents one disaster declaration request. The returned object includes: +A dataframe of preliminary damage assessment reports. Key columns include: \describe{ -\item{disaster_number}{Character. Four-digit FEMA disaster number.} -\item{event_type}{Character. Declaration outcome: "approved", "denial", "appeal_approved", or "appeal_denial".} -\item{event_title}{Character. Title/description of the disaster event from the PDA.} -\item{event_date_determined}{Date. Date the declaration determination was made.} -\item{event_native_flag}{Integer. 1 if this is a tribal declaration, 0 otherwise.} -\item{ia_requested}{Integer. 1 if Individual Assistance was requested, 0 otherwise.} -\item{ia_residences_impacted, ia_residences_destroyed, ia_residences_major_damage, ia_residences_minor_damage, ia_residences_affected}{Numeric. Counts of affected residences by damage category.} -\item{ia_residences_insured_total_percent, ia_residences_insured_flood_percent}{Numeric. Insurance coverage percentages.} -\item{ia_cost_estimate_total}{Numeric. Total estimated Individual Assistance cost.} -\item{pa_requested}{Integer. 1 if Public Assistance was requested, 0 otherwise.} -\item{pa_cost_estimate_total}{Numeric. Total estimated Public Assistance cost.} -\item{pa_per_capita_impact_statewide, pa_per_capita_impact_countywide_max, pa_per_capita_impact_countywide_min}{Numeric. Per capita impact metrics.} -\item{pa_per_capita_impact_indicator_statewide, pa_per_capita_impact_indicator_countywide}{Character. Per capita impact indicator values.} -\item{text}{Character. Full extracted text from the PDA for reference.} -} -Note: Due to the unstructured nature of PDF source documents, some extracted values may be inaccurate and should be verified. +\item{disaster_number}{FEMA disaster number.} +\item{event_type}{Type of decision: "approved", "denial", "appeal_approved", or "appeal_denial".} +\item{event_title}{Title/description of the disaster event.} +\item{event_date_determined}{Date the PDA determination was made.} +\item{event_native_flag}{1 if tribal request, 0 otherwise.} +\item{ia_requested}{1 if Individual Assistance was requested, 0 otherwise.} +\item{ia_residences_impacted}{Total residences impacted.} +\item{ia_residences_destroyed}{Number of residences destroyed.} +\item{ia_residences_major_damage}{Number of residences with major damage.} +\item{ia_residences_minor_damage}{Number of residences with minor damage.} +\item{ia_cost_estimate_total}{Estimated total Individual Assistance cost.} +\item{pa_requested}{1 if Public Assistance was requested, 0 otherwise.} +\item{pa_cost_estimate_total}{Estimated total Public Assistance cost.} +\item{pa_per_capita_impact_statewide}{Statewide per capita impact amount.} +\item{pa_per_capita_impact_indicator_statewide}{Met/Not Met indicator for statewide threshold.} +} } \description{ -These data reflect extracted attributes from PDF preliminary damage assessments -hosted on FEMA's website at: https://www.fema.gov/disaster/how-declared/preliminary-damage-assessments/reports. +Retrieves data extracted from PDF preliminary damage assessment (PDA) +reports submitted to FEMA for disaster declarations. +} +\details{ +Data are extracted from PDF reports hosted at +\url{https://www.fema.gov/disaster/how-declared/preliminary-damage-assessments/reports}. Owing to the unstructured nature of the source documents, some fields may be incorrect in the data returned by the function, though significant quality checks have been implemented in an effort to produce a high-quality dataset. diff --git a/man/get_public_assistance.Rd b/man/get_public_assistance.Rd index a5f5c8c..311a5b5 100644 --- a/man/get_public_assistance.Rd +++ b/man/get_public_assistance.Rd @@ -13,7 +13,8 @@ get_public_assistance( \arguments{ \item{file_path}{The file path to the raw data contained in a .parquet file.} -\item{state_abbreviations}{A character vector of state abbreviations. NULL by default, which returns records for all 51 states. Only the 51 states are supported at this time.} +\item{state_abbreviations}{A character vector of state abbreviations. NULL by default, +which returns records for all 51 states. Only the 51 states are supported at this time.} } \value{ A dataframe of project-level funding requests and awards, along with variables that can be aggregated to the county level. @@ -40,21 +41,25 @@ to the \code{id}-by-county level. Refer to the details for additional informatio } } \description{ -Project- and county-level data on PA funding over time +Retrieves FEMA Public Assistance (PA) project funding data, +crosswalked to the county level for geographic analysis. } \details{ +Data are from FEMA's OpenFEMA API. See +\url{https://www.fema.gov/openfema-data-page/public-assistance-funded-projects-details-v2}. + These data have been crosswalked so that estimates can be aggregated at the county level. This is necessary (for county-level estimates) because many projects are statewide projects and do not have county-level observations in the data. Analysts thus have two options for working with these data: (1) De-select the variables suffixed with \verb{_split} and then run \code{distinct(df)}. -This will provide unique observations for projects; projects are both county-level -and statewide. These data can be aggregated to the state level but cannot be +This will provide unique observations for projects; projects can be either county-level +or statewide. These data can be aggregated to the state level but cannot be comprehensively aggregated to the county level. (2) Group the data at the county level and summarize to produce county-level characterizations of PA projects and funding, using the \verb{_split}-suffixed -variables to calculate funding totals. For example, this might look like: +variables to calculate funding totals. The attribution of statewide projects to the county level occurs by proportionally attributing project costs based on county-level populations. For example, in a fictional state with two diff --git a/man/get_sba_loans.Rd b/man/get_sba_loans.Rd index 08b782c..51531e6 100644 --- a/man/get_sba_loans.Rd +++ b/man/get_sba_loans.Rd @@ -7,23 +7,29 @@ get_sba_loans() } \value{ -A tibble containing SBA disaster loan data at the city/zip level, combining both home and business loan records. The returned object includes: +A dataframe comprising city- and zip-level data on SBA loanmaking. +Columns include: \describe{ -\item{disaster_number_fema}{Character. The FEMA disaster number associated with the loan.} -\item{disaster_number_sba_physical}{Character. SBA physical disaster declaration number.} -\item{disaster_number_sba_eidl}{Character. SBA Economic Injury Disaster Loan (EIDL) declaration number.} -\item{damaged_property_zip_code}{Character. ZIP code of the damaged property.} -\item{damaged_property_city_name}{Character. City name of the damaged property.} -\item{damaged_property_state_code}{Character. State code of the damaged property.} -\item{verified_loss_total}{Numeric. Total verified loss amount in dollars.} -\item{approved_amount_total}{Numeric. Total approved loan amount in dollars.} -\item{approved_amount_real_estate}{Numeric. Approved loan amount for real estate in dollars.} -\item{fiscal_year}{Character. Fiscal year of the loan (format: "20XX").} -\item{loan_type}{Character. Either "business" or "residential" indicating the loan category.} +\item{fiscal_year}{The federal fiscal year of the loan.} +\item{disaster_number_fema}{FEMA disaster number associated with the loan.} +\item{disaster_number_sba_physical}{SBA physical disaster declaration number.} +\item{disaster_number_sba_eidl}{SBA Economic Injury Disaster Loan (EIDL) declaration number.} +\item{damaged_property_zip_code}{ZIP code of the damaged property.} +\item{damaged_property_city_name}{City name of the damaged property.} +\item{damaged_property_state_code}{Two-letter state abbreviation.} +\item{verified_loss_total}{Total verified loss amount in dollars.} +\item{approved_amount_total}{Total approved loan amount in dollars.} +\item{approved_amount_real_estate}{Approved loan amount for real estate in dollars.} +\item{loan_type}{Type of loan: "residential" or "business".} } } \description{ -Access SBA data on disaster loans +Retrieves Small Business Administration (SBA) disaster loan data +for both home and business loans at the city and zip code level. +} +\details{ +Data are sourced from the SBA's disaster loan reports. See +\url{https://www.sba.gov/funding-programs/disaster-assistance}. } \examples{ \dontrun{ diff --git a/man/get_sheldus.Rd b/man/get_sheldus.Rd index 7db61f0..76caf3f 100644 --- a/man/get_sheldus.Rd +++ b/man/get_sheldus.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/get_sheldus.R \name{get_sheldus} \alias{get_sheldus} -\title{Access temporal county-level SHELDUS hazard damage data.} +\title{Access temporal county-level SHELDUS hazard damage data} \usage{ get_sheldus( file_path = file.path(get_box_path(), "hazards", "sheldus", @@ -14,25 +14,31 @@ get_sheldus( \item{file_path}{The path to the raw SHELDUS data.} } \value{ -A tibble containing SHELDUS (Spatial Hazard Events and Losses Database for the United States) data at the county-year-month-hazard level. The returned object includes: +A dataframe comprising hazard x month x year x county observations of hazard events. +Columns include: \describe{ -\item{unique_id}{Character. A UUID uniquely identifying each observation.} -\item{GEOID}{Character. Five-digit FIPS county code. Connecticut counties are crosswalked to 2022 planning regions using population-weighted allocation factors.} -\item{state_name}{Character. State name (sentence case).} -\item{county_name}{Character. County name.} -\item{year}{Numeric. Year of the hazard event.} -\item{month}{Numeric. Month of the hazard event (1-12).} -\item{hazard}{Character. Type of hazard event.} -\item{damage_property}{Numeric. Property damage in 2023 inflation-adjusted dollars.} -\item{damage_crop}{Numeric. Crop damage in 2023 inflation-adjusted dollars.} -\item{injuries}{Numeric. Number of injuries.} -\item{fatalities}{Numeric. Number of fatalities.} -\item{records}{Numeric. Number of individual event records aggregated into this observation.} -} -Note: Only counties that existed in either 2010 or 2022 are included. +\item{unique_id}{Unique identifier for each observation.} +\item{GEOID}{Five-digit county FIPS code.} +\item{state_name}{Full state name (sentence case).} +\item{county_name}{County name.} +\item{year}{Year of the hazard event(s).} +\item{month}{Month of the hazard event(s).} +\item{hazard}{Type of hazard (e.g., "Flooding", "Hurricane/Tropical Storm").} +\item{damage_property}{Property damage in 2023 inflation-adjusted dollars.} +\item{damage_crop}{Crop damage in 2023 inflation-adjusted dollars.} +\item{fatalities}{Number of fatalities.} +\item{injuries}{Number of injuries.} +\item{records}{Number of individual events aggregated into this observation.} +} } \description{ -Access temporal county-level SHELDUS hazard damage data. +Retrieves county-level hazard event data from the Spatial Hazard Events +and Losses Database for the United States (SHELDUS), including property damage, +crop damage, fatalities, and injuries. +} +\details{ +Data are from Arizona State University's SHELDUS database. Access requires +a subscription. See \url{https://cemhs.asu.edu/sheldus}. } \examples{ \dontrun{ diff --git a/man/get_structures.Rd b/man/get_structures.Rd index 027e22c..729fda8 100644 --- a/man/get_structures.Rd +++ b/man/get_structures.Rd @@ -14,24 +14,24 @@ get_structures(boundaries, geography = "county", keep_structures = FALSE) \item{keep_structures}{Logical. If TRUE, the raw structure data will be returned alongside the summarized data.} } \value{ -Depends on the \code{keep_structures} parameter: - -\strong{When \code{keep_structures = FALSE} (default):} A tibble containing structure counts aggregated by geography and occupancy type, with columns: +A dataframe comprising estimated counts of each structure type, at the +specified \code{geography}, for all such geographic units intersecting the \code{boundaries} +object. If keep_structure = TRUE, returns a list with two elements: the summarized +data and the raw structure data. Columns include: \describe{ -\item{GEOID}{Character. Census geography identifier (county FIPS or tract GEOID depending on \code{geography} parameter).} -\item{primary_occupancy}{Character. The primary occupancy classification of the structures (e.g., "Single Family Dwelling", "Multi - Family Dwelling").} -\item{occupancy_class}{Character. Broad occupancy classification (e.g., "Residential", "Commercial").} -\item{count}{Integer. Number of structures of this occupancy type in the geography.} -} - -\strong{When \code{keep_structures = TRUE}:} A named list with two elements: -\describe{ -\item{structures_summarized}{The aggregated tibble described above.} -\item{structures_raw}{An \code{sf} object (POINT geometry) containing individual structure records with columns: \code{unique_id} (building ID), \code{occupancy_class}, \code{primary_occupancy}, \code{county_fips}, and geometry.} +\item{GEOID}{Census tract (11-digit) or county (5-digit) FIPS code.} +\item{primary_occupancy}{The primary use of the structure (e.g., "Residential", "Commercial").} +\item{occupancy_class}{Broader classification of occupancy type.} +\item{count}{Number of structures of this type in the geographic unit.} } } \description{ -Estimate counts of hazard-impacted structures by structure type +Retrieves building footprint data from the USA Structures dataset and +summarizes structure counts by type at the tract or county level. +} +\details{ +Data are sourced from the USA Structures dataset maintained by the +Department of Homeland Security. See \url{https://geoplatform.gov/metadata/9d4a3ae3-8637-4707-92a7-b7d67b769a6b}. } \examples{ \dontrun{ diff --git a/man/get_wildfire_burn_zones.Rd b/man/get_wildfire_burn_zones.Rd new file mode 100644 index 0000000..29c119f --- /dev/null +++ b/man/get_wildfire_burn_zones.Rd @@ -0,0 +1,58 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/get_wildfire_burn_zones.R +\name{get_wildfire_burn_zones} +\alias{get_wildfire_burn_zones} +\title{Get wildfire burn zones} +\usage{ +get_wildfire_burn_zones( + file_path = file.path(get_box_path(), "hazards", "other-sources", + "wildfire-burn-zones", "wfbz_disasters_2000-2025.geojson") +) +} +\arguments{ +\item{file_path}{The path to the geojson file containing the raw data. Defaults to a +path within Box.} +} +\value{ +An sf dataframe comprising wildfire burn zone disasters. Each row represents a +single wildfire event, with polygon geometries representing burn zones. +Columns include: +\describe{ +\item{wildfire_id}{Unique identifier for the wildfire event.} +\item{id_fema}{FEMA disaster identifier (if applicable).} +\item{year}{Year of the wildfire.} +\item{wildfire_name}{Name of the wildfire or fire complex.} +\item{county_fips}{Pipe-delimited string of five-digit county FIPS codes for all +counties affected by the wildfire.} +\item{county_name}{Pipe-delimited string of county names for all counties +affected by the wildfire.} +\item{area_sq_km}{Burned area in square kilometers.} +\item{wildfire_complex_binary}{Whether the fire is a complex (multiple fires).} +\item{date_start}{Ignition date.} +\item{date_containment}{Containment date.} +\item{fatalities_total}{Total fatalities.} +\item{injuries_total}{Total injuries.} +\item{structures_destroyed}{Number of structures destroyed.} +\item{structures_threatened}{Number of structures threatened.} +\item{evacuation_total}{Total evacuations.} +\item{wui_type}{Wildland-urban interface type.} +\item{density_people_sq_km_wildfire_buffer}{Population density in wildfire buffer area.} +\item{geometry}{Burn zone polygon geometry.} +} +} +\description{ +Returns spatial data on wildfire burn zones in the US from 2000-2025. +This dataset harmonizes six wildfire datasets (FIRED, MTBS, NIFC, ICS-209, RedBook, and FEMA) +to identify wildfires that burned near communities and resulted in civilian fatalities, +destroyed structures, or received federal disaster relief. +} +\details{ +Data are from a harmonized wildfire burn zone disaster dataset combining +FIRED, MTBS, NIFC, ICS-209, RedBook, and FEMA data sources. Geometries are in +NAD83 / Conus Albers (EPSG:5070). +} +\examples{ +\dontrun{ +burn_zones <- get_wildfire_burn_zones() +} +} diff --git a/renv.lock b/renv.lock index ce91522..ff8b217 100644 --- a/renv.lock +++ b/renv.lock @@ -1,6372 +1,6446 @@ -{ - "R": { - "Version": "4.5.1", - "Repositories": [ - { - "Name": "CRAN", - "URL": "https://repo.miserver.it.umich.edu/cran" - } - ] - }, - "Packages": { - "BH": { - "Package": "BH", - "Version": "1.90.0-1", - "Source": "Repository", - "Type": "Package", - "Title": "Boost C++ Header Files", - "Date": "2025-12-13", - "Authors@R": "c(person(\"Dirk\", \"Eddelbuettel\", role = c(\"aut\", \"cre\"), email = \"edd@debian.org\", comment = c(ORCID = \"0000-0001-6419-907X\")), person(\"John W.\", \"Emerson\", role = \"aut\"), person(\"Michael J.\", \"Kane\", role = \"aut\", comment = c(ORCID = \"0000-0003-1899-6662\")))", - "Description": "Boost provides free peer-reviewed portable C++ source libraries. 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Wilke [aut] (Original author, ORCID: ), Thomas Lin Pedersen [aut, cre] (ORCID: ), Posit, PBC [cph, fnd] (ROR: )", - "Maintainer": "Thomas Lin Pedersen ", - "Repository": "https://packagemanager.posit.co/cran/latest" - }, - "janitor": { - "Package": "janitor", - "Version": "2.2.1", - "Source": "Repository", - "Title": "Simple Tools for Examining and Cleaning Dirty Data", - "Authors@R": "c(person(\"Sam\", \"Firke\", email = \"samuel.firke@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Bill\", \"Denney\", email = \"wdenney@humanpredictions.com\", role = \"ctb\"), person(\"Chris\", \"Haid\", email = \"chrishaid@gmail.com\", role = \"ctb\"), person(\"Ryan\", \"Knight\", email = \"ryangknight@gmail.com\", role = \"ctb\"), person(\"Malte\", \"Grosser\", email = \"malte.grosser@gmail.com\", role = \"ctb\"), person(\"Jonathan\", \"Zadra\", email = \"jonathan.zadra@sorensonimpact.com\", role = \"ctb\"))", - "Description": "The main janitor functions can: perfectly format data.frame column names; provide quick counts of variable combinations (i.e., frequency tables and crosstabs); and explore duplicate records. 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The function rst2pdf() requires rst2pdf (https://github.com/rst2pdf/rst2pdf).", - "Collate": "'block.R' 'cache.R' 'citation.R' 'hooks-html.R' 'plot.R' 'utils.R' 'defaults.R' 'concordance.R' 'engine.R' 'highlight.R' 'themes.R' 'header.R' 'hooks-asciidoc.R' 'hooks-chunk.R' 'hooks-extra.R' 'hooks-latex.R' 'hooks-md.R' 'hooks-rst.R' 'hooks-textile.R' 'hooks.R' 'otel.R' 'output.R' 'package.R' 'pandoc.R' 'params.R' 'parser.R' 'pattern.R' 'rocco.R' 'spin.R' 'table.R' 'template.R' 'utils-conversion.R' 'utils-rd2html.R' 'utils-string.R' 'utils-sweave.R' 'utils-upload.R' 'utils-vignettes.R' 'zzz.R'", - "RoxygenNote": "7.3.3", - "NeedsCompilation": "no", - "Author": "Yihui Xie [aut, cre] (ORCID: , URL: https://yihui.org), Abhraneel Sarma [ctb], Adam Vogt [ctb], Alastair Andrew [ctb], Alex Zvoleff [ctb], Amar Al-Zubaidi [ctb], Andre Simon [ctb] (the CSS files under inst/themes/ were derived from the Highlight package http://www.andre-simon.de), Aron Atkins [ctb], Aaron Wolen [ctb], Ashley Manton [ctb], Atsushi Yasumoto [ctb] (ORCID: ), Ben Baumer [ctb], Brian Diggs [ctb], Brian Zhang [ctb], Bulat Yapparov [ctb], Cassio Pereira [ctb], Christophe Dervieux [ctb], David Hall [ctb], David Hugh-Jones [ctb], David Robinson [ctb], Doug Hemken [ctb], Duncan Murdoch [ctb], Elio Campitelli [ctb], Ellis Hughes [ctb], Emily Riederer [ctb], Fabian Hirschmann [ctb], Fitch Simeon [ctb], Forest Fang [ctb], Frank E Harrell Jr [ctb] (the Sweavel package at inst/misc/Sweavel.sty), Garrick Aden-Buie [ctb], Gregoire Detrez [ctb], Hadley Wickham [ctb], Hao Zhu [ctb], Heewon Jeon [ctb], Henrik Bengtsson [ctb], Hiroaki Yutani [ctb], Ian Lyttle [ctb], Hodges Daniel [ctb], Jacob Bien [ctb], Jake Burkhead [ctb], James Manton [ctb], Jared Lander [ctb], Jason Punyon [ctb], Javier Luraschi [ctb], Jeff Arnold [ctb], Jenny Bryan [ctb], Jeremy Ashkenas [ctb, cph] (the CSS file at inst/misc/docco-classic.css), Jeremy Stephens [ctb], Jim Hester [ctb], Joe Cheng [ctb], Johannes Ranke [ctb], John Honaker [ctb], John Muschelli [ctb], Jonathan Keane [ctb], JJ Allaire [ctb], Johan Toloe [ctb], Jonathan Sidi [ctb], Joseph Larmarange [ctb], Julien Barnier [ctb], Kaiyin Zhong [ctb], Kamil Slowikowski [ctb], Karl Forner [ctb], Kevin K. Smith [ctb], Kirill Mueller [ctb], Kohske Takahashi [ctb], Lorenz Walthert [ctb], Lucas Gallindo [ctb], Marius Hofert [ctb], Martin Modrák [ctb], Michael Chirico [ctb], Michael Friendly [ctb], Michal Bojanowski [ctb], Michel Kuhlmann [ctb], Miller Patrick [ctb], Nacho Caballero [ctb], Nick Salkowski [ctb], Niels Richard Hansen [ctb], Noam Ross [ctb], Obada Mahdi [ctb], Pavel N. Krivitsky [ctb] (ORCID: ), Pedro Faria [ctb], Qiang Li [ctb], Ramnath Vaidyanathan [ctb], Richard Cotton [ctb], Robert Krzyzanowski [ctb], Rodrigo Copetti [ctb], Romain Francois [ctb], Ruaridh Williamson [ctb], Sagiru Mati [ctb] (ORCID: ), Scott Kostyshak [ctb], Sebastian Meyer [ctb], Sietse Brouwer [ctb], Simon de Bernard [ctb], Sylvain Rousseau [ctb], Taiyun Wei [ctb], Thibaut Assus [ctb], Thibaut Lamadon [ctb], Thomas Leeper [ctb], Tim Mastny [ctb], Tom Torsney-Weir [ctb], Trevor Davis [ctb], Viktoras Veitas [ctb], Weicheng Zhu [ctb], Wush Wu [ctb], Zachary Foster [ctb], Zhian N. 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"Description": "A simple to use summary function that can be used with pipes and displays nicely in the console. 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This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.", + "License": "MIT + file LICENSE", + "URL": "https://broom.tidymodels.org/, https://github.com/tidymodels/broom", + "BugReports": "https://github.com/tidymodels/broom/issues", + "Depends": [ + "R (>= 3.5)" + ], + "Imports": [ + "backports", + "cli", + "dplyr (>= 1.0.0)", + "generics (>= 0.0.2)", + "glue", + "lifecycle", + "purrr", + "rlang (>= 1.1.0)", + "stringr", + "tibble (>= 3.0.0)", + "tidyr (>= 1.0.0)" + ], + "Suggests": [ + "AER", + "AUC", + "bbmle", + "betareg (>= 3.2-1)", + "biglm", + "binGroup", + "boot", + "btergm (>= 1.10.6)", + "car (>= 3.1-2)", + "carData", + "caret", + 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Some knowledge of 'libcurl' is recommended; for a more-user-friendly web client see the 'httr2' package which builds on this package with http specific tools and logic.", + "License": "MIT + file LICENSE", + "SystemRequirements": "libcurl (>= 7.62): libcurl-devel (rpm) or libcurl4-openssl-dev (deb)", + "URL": "https://jeroen.r-universe.dev/curl", + "BugReports": "https://github.com/jeroen/curl/issues", + "Suggests": [ + "spelling", + "testthat (>= 1.0.0)", + "knitr", + "jsonlite", + "later", + "rmarkdown", + "httpuv (>= 1.4.4)", + "webutils" + ], + "VignetteBuilder": "knitr", + "Depends": [ + "R (>= 3.0.0)" + ], + "RoxygenNote": "7.3.2.9000", + "Encoding": "UTF-8", + "Language": "en-US", + "NeedsCompilation": "yes", + "Author": "Jeroen Ooms [aut, cre] (ORCID: ), Hadley Wickham [ctb], Posit Software, PBC [cph]", + "Maintainer": "Jeroen Ooms ", + "Repository": "CRAN" + }, + "data.table": { + "Package": "data.table", + "Version": "1.17.4", + "Source": "Repository", + "Title": "Extension of `data.frame`", + "Depends": [ + "R (>= 3.3.0)" + ], + "Imports": [ + "methods" + ], + "Suggests": [ + "bit64 (>= 4.0.0)", + "bit (>= 4.0.4)", + "R.utils", + "xts", + "zoo (>= 1.8-1)", + "yaml", + "knitr", + "markdown" + ], + "Description": "Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. 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Wilke [aut] (Original author, ), Thomas Lin Pedersen [aut] ()", + "Maintainer": "Hadley Wickham ", + "Repository": "CRAN" + }, + "janitor": { + "Package": "janitor", + "Version": "2.2.1", + "Source": "Repository", + "Title": "Simple Tools for Examining and Cleaning Dirty Data", + "Authors@R": "c(person(\"Sam\", \"Firke\", email = \"samuel.firke@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Bill\", \"Denney\", email = \"wdenney@humanpredictions.com\", role = \"ctb\"), person(\"Chris\", \"Haid\", email = \"chrishaid@gmail.com\", role = \"ctb\"), person(\"Ryan\", \"Knight\", email = \"ryangknight@gmail.com\", role = \"ctb\"), person(\"Malte\", \"Grosser\", email = \"malte.grosser@gmail.com\", role = \"ctb\"), person(\"Jonathan\", \"Zadra\", email = \"jonathan.zadra@sorensonimpact.com\", role = \"ctb\"))", + "Description": "The main janitor functions can: perfectly format data.frame column names; provide quick counts of variable combinations (i.e., frequency tables and crosstabs); and explore duplicate records. Other janitor functions nicely format the tabulation results. These tabulate-and-report functions approximate popular features of SPSS and Microsoft Excel. This package follows the principles of the \"tidyverse\" and works well with the pipe function %>%. janitor was built with beginning-to-intermediate R users in mind and is optimized for user-friendliness.", + "URL": "https://github.com/sfirke/janitor, https://sfirke.github.io/janitor/", + "BugReports": "https://github.com/sfirke/janitor/issues", + "Depends": [ + "R (>= 3.1.2)" + ], + "Imports": [ + "dplyr (>= 1.0.0)", + "hms", + "lifecycle", + "lubridate", + "magrittr", + "purrr", + "rlang", + "stringi", + "stringr", + "snakecase (>= 0.9.2)", + "tidyselect (>= 1.0.0)", + "tidyr (>= 0.7.0)" + ], + "License": "MIT + file LICENSE", + "RoxygenNote": "7.2.3", + "Suggests": [ + "dbplyr", + "knitr", + "rmarkdown", + "RSQLite", + "sf", + "testthat (>= 3.0.0)", + "tibble", + "tidygraph" + ], + "VignetteBuilder": "knitr", + "Encoding": "UTF-8", + "Config/testthat/edition": "3", + "NeedsCompilation": "no", + "Author": "Sam Firke [aut, cre], Bill Denney [ctb], Chris Haid [ctb], Ryan Knight [ctb], Malte Grosser [ctb], Jonathan Zadra [ctb]", + "Maintainer": "Sam Firke ", + "Repository": "https://packagemanager.posit.co/cran/latest" + }, + "jquerylib": { + "Package": "jquerylib", + "Version": "0.1.4", + "Source": "Repository", + "Title": "Obtain 'jQuery' as an HTML Dependency Object", + "Authors@R": "c( person(\"Carson\", \"Sievert\", role = c(\"aut\", \"cre\"), email = \"carson@rstudio.com\", comment = c(ORCID = \"0000-0002-4958-2844\")), person(\"Joe\", \"Cheng\", role = \"aut\", email = \"joe@rstudio.com\"), person(family = \"RStudio\", role = \"cph\"), person(family = \"jQuery Foundation\", role = \"cph\", comment = \"jQuery library and jQuery UI library\"), person(family = \"jQuery contributors\", role = c(\"ctb\", \"cph\"), comment = \"jQuery library; authors listed in inst/lib/jquery-AUTHORS.txt\") )", + "Description": "Obtain any major version of 'jQuery' () and use it in any webpage generated by 'htmltools' (e.g. 'shiny', 'htmlwidgets', and 'rmarkdown'). Most R users don't need to use this package directly, but other R packages (e.g. 'shiny', 'rmarkdown', etc.) depend on this package to avoid bundling redundant copies of 'jQuery'.", + "License": "MIT + file LICENSE", + "Encoding": "UTF-8", + "Config/testthat/edition": "3", + "RoxygenNote": "7.0.2", + "Imports": [ + "htmltools" + ], + "Suggests": [ + "testthat" + ], + "NeedsCompilation": "no", + "Author": "Carson Sievert [aut, cre] (), Joe Cheng [aut], RStudio [cph], jQuery Foundation [cph] (jQuery library and jQuery UI library), jQuery contributors [ctb, cph] (jQuery library; authors listed in inst/lib/jquery-AUTHORS.txt)", + "Maintainer": "Carson Sievert ", + "Repository": "CRAN" + }, + "jsonlite": { + "Package": "jsonlite", + "Version": "2.0.0", + "Source": "Repository", + "Title": "A Simple and Robust JSON Parser and Generator for R", + "License": "MIT + file LICENSE", + "Depends": [ + "methods" + ], + "Authors@R": "c( person(\"Jeroen\", \"Ooms\", role = c(\"aut\", \"cre\"), email = \"jeroenooms@gmail.com\", comment = c(ORCID = \"0000-0002-4035-0289\")), person(\"Duncan\", \"Temple Lang\", role = \"ctb\"), person(\"Lloyd\", \"Hilaiel\", role = \"cph\", comment=\"author of bundled libyajl\"))", + "URL": "https://jeroen.r-universe.dev/jsonlite https://arxiv.org/abs/1403.2805", + "BugReports": "https://github.com/jeroen/jsonlite/issues", + "Maintainer": "Jeroen Ooms ", + "VignetteBuilder": "knitr, R.rsp", + "Description": "A reasonably fast JSON parser and generator, optimized for statistical data and the web. Offers simple, flexible tools for working with JSON in R, and is particularly powerful for building pipelines and interacting with a web API. The implementation is based on the mapping described in the vignette (Ooms, 2014). In addition to converting JSON data from/to R objects, 'jsonlite' contains functions to stream, validate, and prettify JSON data. The unit tests included with the package verify that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications.", + "Suggests": [ + "httr", + "vctrs", + "testthat", + "knitr", + "rmarkdown", + "R.rsp", + "sf" + ], + "RoxygenNote": "7.3.2", + "Encoding": "UTF-8", + "NeedsCompilation": "yes", + "Author": "Jeroen Ooms [aut, cre] (), Duncan Temple Lang [ctb], Lloyd Hilaiel [cph] (author of bundled libyajl)", + "Repository": "https://packagemanager.posit.co/cran/latest" + }, + "knitr": { + "Package": "knitr", + "Version": "1.50", + "Source": "Repository", + "Type": "Package", + "Title": "A General-Purpose Package for Dynamic Report Generation in R", + "Authors@R": "c( person(\"Yihui\", \"Xie\", role = c(\"aut\", \"cre\"), email = \"xie@yihui.name\", comment = c(ORCID = \"0000-0003-0645-5666\", URL = \"https://yihui.org\")), person(\"Abhraneel\", \"Sarma\", role = \"ctb\"), person(\"Adam\", \"Vogt\", role = \"ctb\"), person(\"Alastair\", \"Andrew\", role = \"ctb\"), person(\"Alex\", \"Zvoleff\", role = \"ctb\"), person(\"Amar\", \"Al-Zubaidi\", role = \"ctb\"), person(\"Andre\", \"Simon\", role = \"ctb\", comment = \"the CSS files under inst/themes/ were derived from the Highlight package http://www.andre-simon.de\"), person(\"Aron\", \"Atkins\", role = \"ctb\"), person(\"Aaron\", \"Wolen\", role = \"ctb\"), person(\"Ashley\", \"Manton\", role = \"ctb\"), person(\"Atsushi\", \"Yasumoto\", role = \"ctb\", comment = c(ORCID = \"0000-0002-8335-495X\")), person(\"Ben\", \"Baumer\", role = \"ctb\"), person(\"Brian\", \"Diggs\", role = \"ctb\"), person(\"Brian\", \"Zhang\", role = \"ctb\"), person(\"Bulat\", \"Yapparov\", role = \"ctb\"), person(\"Cassio\", \"Pereira\", role = \"ctb\"), person(\"Christophe\", \"Dervieux\", role = \"ctb\"), person(\"David\", \"Hall\", role = \"ctb\"), person(\"David\", \"Hugh-Jones\", role = \"ctb\"), person(\"David\", \"Robinson\", role = \"ctb\"), person(\"Doug\", \"Hemken\", role = \"ctb\"), person(\"Duncan\", \"Murdoch\", role = \"ctb\"), person(\"Elio\", \"Campitelli\", role = \"ctb\"), person(\"Ellis\", \"Hughes\", role = \"ctb\"), person(\"Emily\", \"Riederer\", role = \"ctb\"), person(\"Fabian\", \"Hirschmann\", role = \"ctb\"), person(\"Fitch\", \"Simeon\", role = \"ctb\"), person(\"Forest\", \"Fang\", role = \"ctb\"), person(c(\"Frank\", \"E\", \"Harrell\", \"Jr\"), role = \"ctb\", comment = \"the Sweavel package at inst/misc/Sweavel.sty\"), person(\"Garrick\", \"Aden-Buie\", role = \"ctb\"), person(\"Gregoire\", \"Detrez\", role = \"ctb\"), person(\"Hadley\", \"Wickham\", role = \"ctb\"), person(\"Hao\", \"Zhu\", role = \"ctb\"), person(\"Heewon\", \"Jeon\", role = \"ctb\"), person(\"Henrik\", \"Bengtsson\", role = \"ctb\"), person(\"Hiroaki\", \"Yutani\", role = \"ctb\"), person(\"Ian\", \"Lyttle\", role = \"ctb\"), person(\"Hodges\", \"Daniel\", role = \"ctb\"), person(\"Jacob\", \"Bien\", role = \"ctb\"), person(\"Jake\", \"Burkhead\", role = \"ctb\"), person(\"James\", \"Manton\", role = \"ctb\"), person(\"Jared\", \"Lander\", role = \"ctb\"), person(\"Jason\", \"Punyon\", role = \"ctb\"), person(\"Javier\", \"Luraschi\", role = \"ctb\"), person(\"Jeff\", \"Arnold\", role = \"ctb\"), person(\"Jenny\", \"Bryan\", role = \"ctb\"), person(\"Jeremy\", \"Ashkenas\", role = c(\"ctb\", \"cph\"), comment = \"the CSS file at inst/misc/docco-classic.css\"), person(\"Jeremy\", \"Stephens\", role = \"ctb\"), person(\"Jim\", \"Hester\", role = \"ctb\"), person(\"Joe\", \"Cheng\", role = \"ctb\"), person(\"Johannes\", \"Ranke\", role = \"ctb\"), person(\"John\", \"Honaker\", role = \"ctb\"), person(\"John\", \"Muschelli\", role = \"ctb\"), person(\"Jonathan\", \"Keane\", role = \"ctb\"), person(\"JJ\", \"Allaire\", role = \"ctb\"), person(\"Johan\", \"Toloe\", role = \"ctb\"), person(\"Jonathan\", \"Sidi\", role = \"ctb\"), person(\"Joseph\", \"Larmarange\", role = \"ctb\"), person(\"Julien\", \"Barnier\", role = \"ctb\"), person(\"Kaiyin\", \"Zhong\", role = \"ctb\"), person(\"Kamil\", \"Slowikowski\", role = \"ctb\"), person(\"Karl\", \"Forner\", role = \"ctb\"), person(c(\"Kevin\", \"K.\"), \"Smith\", role = \"ctb\"), person(\"Kirill\", \"Mueller\", role = \"ctb\"), person(\"Kohske\", \"Takahashi\", role = \"ctb\"), person(\"Lorenz\", \"Walthert\", role = \"ctb\"), person(\"Lucas\", \"Gallindo\", role = \"ctb\"), person(\"Marius\", \"Hofert\", role = \"ctb\"), person(\"Martin\", \"Modrák\", role = \"ctb\"), person(\"Michael\", \"Chirico\", role = \"ctb\"), person(\"Michael\", \"Friendly\", role = \"ctb\"), person(\"Michal\", \"Bojanowski\", role = \"ctb\"), person(\"Michel\", \"Kuhlmann\", role = \"ctb\"), person(\"Miller\", \"Patrick\", role = \"ctb\"), person(\"Nacho\", \"Caballero\", role = \"ctb\"), person(\"Nick\", \"Salkowski\", role = \"ctb\"), person(\"Niels Richard\", \"Hansen\", role = \"ctb\"), person(\"Noam\", \"Ross\", role = \"ctb\"), person(\"Obada\", \"Mahdi\", role = \"ctb\"), person(\"Pavel N.\", \"Krivitsky\", role = \"ctb\", comment=c(ORCID = \"0000-0002-9101-3362\")), person(\"Pedro\", \"Faria\", role = \"ctb\"), person(\"Qiang\", \"Li\", role = \"ctb\"), person(\"Ramnath\", \"Vaidyanathan\", role = \"ctb\"), person(\"Richard\", \"Cotton\", role = \"ctb\"), person(\"Robert\", \"Krzyzanowski\", role = \"ctb\"), person(\"Rodrigo\", \"Copetti\", role = \"ctb\"), person(\"Romain\", \"Francois\", role = \"ctb\"), person(\"Ruaridh\", \"Williamson\", role = \"ctb\"), person(\"Sagiru\", \"Mati\", role = \"ctb\", comment = c(ORCID = \"0000-0003-1413-3974\")), person(\"Scott\", \"Kostyshak\", role = \"ctb\"), person(\"Sebastian\", \"Meyer\", role = \"ctb\"), person(\"Sietse\", \"Brouwer\", role = \"ctb\"), person(c(\"Simon\", \"de\"), \"Bernard\", role = \"ctb\"), person(\"Sylvain\", \"Rousseau\", role = \"ctb\"), person(\"Taiyun\", \"Wei\", role = \"ctb\"), person(\"Thibaut\", \"Assus\", role = \"ctb\"), person(\"Thibaut\", \"Lamadon\", role = \"ctb\"), person(\"Thomas\", \"Leeper\", role = \"ctb\"), person(\"Tim\", \"Mastny\", role = \"ctb\"), person(\"Tom\", \"Torsney-Weir\", role = \"ctb\"), person(\"Trevor\", \"Davis\", role = \"ctb\"), person(\"Viktoras\", \"Veitas\", role = \"ctb\"), person(\"Weicheng\", \"Zhu\", role = \"ctb\"), person(\"Wush\", \"Wu\", role = \"ctb\"), person(\"Zachary\", \"Foster\", role = \"ctb\"), person(\"Zhian N.\", \"Kamvar\", role = \"ctb\", comment = c(ORCID = \"0000-0003-1458-7108\")), person(given = \"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", + "Description": "Provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques.", + "Depends": [ + "R (>= 3.6.0)" + ], + "Imports": [ + "evaluate (>= 0.15)", + "highr (>= 0.11)", + "methods", + "tools", + "xfun (>= 0.51)", + "yaml (>= 2.1.19)" + ], + "Suggests": [ + "bslib", + "codetools", + "DBI (>= 0.4-1)", + "digest", + "formatR", + "gifski", + "gridSVG", + "htmlwidgets (>= 0.7)", + "jpeg", + "JuliaCall (>= 0.11.1)", + "magick", + "litedown", + "markdown (>= 1.3)", + "png", + "ragg", + "reticulate (>= 1.4)", + "rgl (>= 0.95.1201)", + "rlang", + "rmarkdown", + "sass", + "showtext", + "styler (>= 1.2.0)", + "targets (>= 0.6.0)", + "testit", + "tibble", + "tikzDevice (>= 0.10)", + "tinytex (>= 0.56)", + "webshot", + "rstudioapi", + "svglite" + ], + "License": "GPL", + "URL": "https://yihui.org/knitr/", + "BugReports": "https://github.com/yihui/knitr/issues", + "Encoding": "UTF-8", + "VignetteBuilder": "litedown, knitr", + "SystemRequirements": "Package vignettes based on R Markdown v2 or reStructuredText require Pandoc (http://pandoc.org). The function rst2pdf() requires rst2pdf (https://github.com/rst2pdf/rst2pdf).", + "Collate": "'block.R' 'cache.R' 'citation.R' 'hooks-html.R' 'plot.R' 'utils.R' 'defaults.R' 'concordance.R' 'engine.R' 'highlight.R' 'themes.R' 'header.R' 'hooks-asciidoc.R' 'hooks-chunk.R' 'hooks-extra.R' 'hooks-latex.R' 'hooks-md.R' 'hooks-rst.R' 'hooks-textile.R' 'hooks.R' 'output.R' 'package.R' 'pandoc.R' 'params.R' 'parser.R' 'pattern.R' 'rocco.R' 'spin.R' 'table.R' 'template.R' 'utils-conversion.R' 'utils-rd2html.R' 'utils-string.R' 'utils-sweave.R' 'utils-upload.R' 'utils-vignettes.R' 'zzz.R'", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "no", + "Author": "Yihui Xie [aut, cre] (, https://yihui.org), Abhraneel Sarma [ctb], Adam Vogt [ctb], Alastair Andrew [ctb], Alex Zvoleff [ctb], Amar Al-Zubaidi [ctb], Andre Simon [ctb] (the CSS files under inst/themes/ were derived from the Highlight package http://www.andre-simon.de), Aron Atkins [ctb], Aaron Wolen [ctb], Ashley Manton [ctb], Atsushi Yasumoto [ctb] (), Ben Baumer [ctb], Brian Diggs [ctb], Brian Zhang [ctb], Bulat Yapparov [ctb], Cassio Pereira [ctb], Christophe Dervieux [ctb], David Hall [ctb], David Hugh-Jones [ctb], David Robinson [ctb], Doug Hemken [ctb], Duncan Murdoch [ctb], Elio Campitelli [ctb], Ellis Hughes [ctb], Emily Riederer [ctb], Fabian Hirschmann [ctb], Fitch Simeon [ctb], Forest Fang [ctb], Frank E Harrell Jr [ctb] (the Sweavel package at inst/misc/Sweavel.sty), Garrick Aden-Buie [ctb], Gregoire Detrez [ctb], Hadley Wickham [ctb], Hao Zhu [ctb], Heewon Jeon [ctb], Henrik Bengtsson [ctb], Hiroaki Yutani [ctb], Ian Lyttle [ctb], Hodges Daniel [ctb], Jacob Bien [ctb], Jake Burkhead [ctb], James Manton [ctb], Jared Lander [ctb], Jason Punyon [ctb], Javier Luraschi [ctb], Jeff Arnold [ctb], Jenny Bryan [ctb], Jeremy Ashkenas [ctb, cph] (the CSS file at inst/misc/docco-classic.css), Jeremy Stephens [ctb], Jim Hester [ctb], Joe Cheng [ctb], Johannes Ranke [ctb], John Honaker [ctb], John Muschelli [ctb], Jonathan Keane [ctb], JJ Allaire [ctb], Johan Toloe [ctb], Jonathan Sidi [ctb], Joseph Larmarange [ctb], Julien Barnier [ctb], Kaiyin Zhong [ctb], Kamil Slowikowski [ctb], Karl Forner [ctb], Kevin K. Smith [ctb], Kirill Mueller [ctb], Kohske Takahashi [ctb], Lorenz Walthert [ctb], Lucas Gallindo [ctb], Marius Hofert [ctb], Martin Modrák [ctb], Michael Chirico [ctb], Michael Friendly [ctb], Michal Bojanowski [ctb], Michel Kuhlmann [ctb], Miller Patrick [ctb], Nacho Caballero [ctb], Nick Salkowski [ctb], Niels Richard Hansen [ctb], Noam Ross [ctb], Obada Mahdi [ctb], Pavel N. Krivitsky [ctb] (), Pedro Faria [ctb], Qiang Li [ctb], Ramnath Vaidyanathan [ctb], Richard Cotton [ctb], Robert Krzyzanowski [ctb], Rodrigo Copetti [ctb], Romain Francois [ctb], Ruaridh Williamson [ctb], Sagiru Mati [ctb] (), Scott Kostyshak [ctb], Sebastian Meyer [ctb], Sietse Brouwer [ctb], Simon de Bernard [ctb], Sylvain Rousseau [ctb], Taiyun Wei [ctb], Thibaut Assus [ctb], Thibaut Lamadon [ctb], Thomas Leeper [ctb], Tim Mastny [ctb], Tom Torsney-Weir [ctb], Trevor Davis [ctb], Viktoras Veitas [ctb], Weicheng Zhu [ctb], Wush Wu [ctb], Zachary Foster [ctb], Zhian N. Kamvar [ctb] (), Posit Software, PBC [cph, fnd]", + "Maintainer": "Yihui Xie ", + "Repository": "https://packagemanager.posit.co/cran/latest" + }, + "labeling": { + "Package": "labeling", + "Version": "0.4.3", + "Source": "Repository", + "Type": "Package", + "Title": "Axis Labeling", + "Date": "2023-08-29", + "Author": "Justin Talbot,", + "Maintainer": "Nuno Sempere ", + "Description": "Functions which provide a range of axis labeling algorithms.", + "License": "MIT + file LICENSE | Unlimited", + "Collate": "'labeling.R'", + "NeedsCompilation": "no", + "Imports": [ + "stats", + "graphics" + ], + "Repository": "CRAN" + }, + "later": { + "Package": "later", + "Version": "1.4.2", + "Source": "Repository", + "Type": "Package", + "Title": "Utilities for Scheduling Functions to Execute Later with Event Loops", + "Authors@R": "c( person(\"Winston\", \"Chang\", role = c(\"aut\", \"cre\"), email = \"winston@posit.co\"), person(\"Joe\", \"Cheng\", role = c(\"aut\"), email = \"joe@posit.co\"), person(\"Charlie\", \"Gao\", role = c(\"aut\"), email = \"charlie.gao@shikokuchuo.net\", comment = c(ORCID = \"0000-0002-0750-061X\")), person(family = \"Posit Software, PBC\", role = \"cph\"), person(\"Marcus\", \"Geelnard\", role = c(\"ctb\", \"cph\"), comment = \"TinyCThread library, https://tinycthread.github.io/\"), person(\"Evan\", \"Nemerson\", role = c(\"ctb\", \"cph\"), comment = \"TinyCThread library, https://tinycthread.github.io/\") )", + "Description": "Executes arbitrary R or C functions some time after the current time, after the R execution stack has emptied. The functions are scheduled in an event loop.", + "URL": "https://r-lib.github.io/later/, https://github.com/r-lib/later", + "BugReports": "https://github.com/r-lib/later/issues", + "License": "MIT + file LICENSE", + "Imports": [ + "Rcpp (>= 0.12.9)", + "rlang" + ], + "LinkingTo": [ + "Rcpp" + ], + "RoxygenNote": "7.3.2", + "Suggests": [ + "knitr", + "nanonext", + "R6", + "rmarkdown", + "testthat (>= 2.1.0)" + ], + "VignetteBuilder": "knitr", + "Encoding": "UTF-8", + "NeedsCompilation": "yes", + "Author": "Winston Chang [aut, cre], Joe Cheng [aut], Charlie Gao [aut] (), Posit Software, PBC [cph], Marcus Geelnard [ctb, cph] (TinyCThread library, https://tinycthread.github.io/), Evan Nemerson [ctb, cph] (TinyCThread library, https://tinycthread.github.io/)", + "Maintainer": "Winston Chang ", + "Repository": "https://packagemanager.posit.co/cran/latest" + }, + "lattice": { + "Package": "lattice", + "Version": "0.22-7", + "Source": "Repository", + "Date": "2025-03-31", + "Priority": "recommended", + "Title": "Trellis Graphics for R", + "Authors@R": "c(person(\"Deepayan\", \"Sarkar\", role = c(\"aut\", \"cre\"), email = \"deepayan.sarkar@r-project.org\", comment = c(ORCID = \"0000-0003-4107-1553\")), person(\"Felix\", \"Andrews\", role = \"ctb\"), person(\"Kevin\", \"Wright\", role = \"ctb\", comment = \"documentation\"), person(\"Neil\", \"Klepeis\", role = \"ctb\"), person(\"Johan\", \"Larsson\", role = \"ctb\", comment = \"miscellaneous improvements\"), person(\"Zhijian (Jason)\", \"Wen\", role = \"cph\", comment = \"filled contour code\"), person(\"Paul\", \"Murrell\", role = \"ctb\", email = \"paul@stat.auckland.ac.nz\"), person(\"Stefan\", \"Eng\", role = \"ctb\", comment = \"violin plot improvements\"), person(\"Achim\", \"Zeileis\", role = \"ctb\", comment = \"modern colors\"), person(\"Alexandre\", \"Courtiol\", role = \"ctb\", comment = \"generics for larrows, lpolygon, lrect and lsegments\") )", + "Description": "A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. 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This package is a port of the Python package 'cssselect' ().", + "NeedsCompilation": "no", + "Author": "Simon Potter [aut, trl, cre], Simon Sapin [aut], Ian Bicking [aut]", + "Maintainer": "Simon Potter ", + "Repository": "CRAN" + }, + "sf": { + "Package": "sf", + "Version": "1.0-21", + "Source": "Repository", + "Title": "Simple Features for R", + "Authors@R": "c(person(given = \"Edzer\", family = \"Pebesma\", role = c(\"aut\", \"cre\"), email = \"edzer.pebesma@uni-muenster.de\", comment = c(ORCID = \"0000-0001-8049-7069\")), person(given = \"Roger\", family = \"Bivand\", role = \"ctb\", comment = c(ORCID = \"0000-0003-2392-6140\")), person(given = \"Etienne\", family = \"Racine\", role = \"ctb\"), person(given = \"Michael\", family = \"Sumner\", role = \"ctb\"), person(given = \"Ian\", family = \"Cook\", role = \"ctb\"), person(given = \"Tim\", family = \"Keitt\", role = \"ctb\"), person(given = \"Robin\", family = \"Lovelace\", role = \"ctb\"), person(given = \"Hadley\", family = \"Wickham\", role = \"ctb\"), person(given = \"Jeroen\", family = \"Ooms\", role = \"ctb\", comment = c(ORCID = \"0000-0002-4035-0289\")), person(given = \"Kirill\", family = \"M\\u00fcller\", role = \"ctb\"), person(given = \"Thomas Lin\", family = \"Pedersen\", role = \"ctb\"), person(given = \"Dan\", family = \"Baston\", role = \"ctb\"), person(given = \"Dewey\", family = \"Dunnington\", role = \"ctb\", comment = c(ORCID = \"0000-0002-9415-4582\")) )", + "Description": "Support for simple feature access, a standardized way to encode and analyze spatial vector data. 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They are fast, consistent, convenient, and - thanks to 'ICU' (International Components for Unicode) - portable across all locales and platforms. 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Many of these functions were originally a part of the 'devtools' package, this provides a simple package with limited dependencies to provide access to these functions.", + "License": "MIT + file LICENSE", + "URL": "https://withr.r-lib.org, https://github.com/r-lib/withr#readme", + "BugReports": "https://github.com/r-lib/withr/issues", + "Depends": [ + "R (>= 3.6.0)" + ], + "Imports": [ + "graphics", + "grDevices" + ], + "Suggests": [ + "callr", + "DBI", + "knitr", + "methods", + "rlang", + "rmarkdown (>= 2.12)", + "RSQLite", + "testthat (>= 3.0.0)" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "Collate": "'aaa.R' 'collate.R' 'connection.R' 'db.R' 'defer-exit.R' 'standalone-defer.R' 'defer.R' 'devices.R' 'local_.R' 'with_.R' 'dir.R' 'env.R' 'file.R' 'language.R' 'libpaths.R' 'locale.R' 'makevars.R' 'namespace.R' 'options.R' 'par.R' 'path.R' 'rng.R' 'seed.R' 'wrap.R' 'sink.R' 'tempfile.R' 'timezone.R' 'torture.R' 'utils.R' 'with.R'", + "NeedsCompilation": "no", + "Author": "Jim Hester [aut], Lionel Henry [aut, cre], Kirill Müller [aut], Kevin Ushey [aut], Hadley Wickham [aut], Winston Chang [aut], Jennifer Bryan [ctb], Richard Cotton [ctb], Posit Software, PBC [cph, fnd]", + "Maintainer": "Lionel Henry ", + "Repository": "https://packagemanager.posit.co/cran/latest" + }, + "wk": { + "Package": "wk", + "Version": "0.9.4", + "Source": "Repository", + "Title": "Lightweight Well-Known Geometry Parsing", + "Authors@R": "c( person(given = \"Dewey\", family = \"Dunnington\", role = c(\"aut\", \"cre\"), email = \"dewey@fishandwhistle.net\", comment = c(ORCID = \"0000-0002-9415-4582\")), person(given = \"Edzer\", family = \"Pebesma\", role = c(\"aut\"), email = \"edzer.pebesma@uni-muenster.de\", comment = c(ORCID = \"0000-0001-8049-7069\")), person(given = \"Anthony\", family = \"North\", email = \"anthony.jl.north@gmail.com\", role = c(\"ctb\")) )", + "Maintainer": "Dewey Dunnington ", + "Description": "Provides a minimal R and C++ API for parsing well-known binary and well-known text representation of geometries to and from R-native formats. Well-known binary is compact and fast to parse; well-known text is human-readable and is useful for writing tests. These formats are useful in R only if the information they contain can be accessed in R, for which high-performance functions are provided here.", + "License": "MIT + file LICENSE", + "Encoding": "UTF-8", + "RoxygenNote": "7.2.3", + "Suggests": [ + "testthat (>= 3.0.0)", + "vctrs (>= 0.3.0)", + "sf", + "tibble", + "readr" + ], + "URL": "https://paleolimbot.github.io/wk/, https://github.com/paleolimbot/wk", + "BugReports": "https://github.com/paleolimbot/wk/issues", + "Config/testthat/edition": "3", + "Depends": [ + "R (>= 2.10)" + ], + "LazyData": "true", + "NeedsCompilation": "yes", + "Author": "Dewey Dunnington [aut, cre] (), Edzer Pebesma [aut] (), Anthony North [ctb]", + "Repository": "https://packagemanager.posit.co/cran/latest" + }, + "xfun": { + "Package": "xfun", + "Version": "0.52", + "Source": "Repository", + "Type": "Package", + "Title": "Supporting Functions for Packages Maintained by 'Yihui Xie'", + "Authors@R": "c( person(\"Yihui\", \"Xie\", role = c(\"aut\", \"cre\", \"cph\"), email = \"xie@yihui.name\", comment = c(ORCID = \"0000-0003-0645-5666\", URL = \"https://yihui.org\")), person(\"Wush\", \"Wu\", role = \"ctb\"), person(\"Daijiang\", \"Li\", role = \"ctb\"), person(\"Xianying\", \"Tan\", role = \"ctb\"), person(\"Salim\", \"Brüggemann\", role = \"ctb\", email = \"salim-b@pm.me\", comment = c(ORCID = \"0000-0002-5329-5987\")), person(\"Christophe\", \"Dervieux\", role = \"ctb\"), person() )", + "Description": "Miscellaneous functions commonly used in other packages maintained by 'Yihui Xie'.", + "Depends": [ + "R (>= 3.2.0)" + ], + "Imports": [ + "grDevices", + "stats", + "tools" + ], + "Suggests": [ + "testit", + "parallel", + "codetools", + "methods", + "rstudioapi", + "tinytex (>= 0.30)", + "mime", + "litedown (>= 0.4)", + "commonmark", + "knitr (>= 1.50)", + "remotes", + "pak", + "curl", + "xml2", + "jsonlite", + "magick", + "yaml", + "qs" + ], + "License": "MIT + file LICENSE", + "URL": "https://github.com/yihui/xfun", + "BugReports": "https://github.com/yihui/xfun/issues", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "VignetteBuilder": "litedown", + "NeedsCompilation": "yes", + "Author": "Yihui Xie [aut, cre, cph] (, https://yihui.org), Wush Wu [ctb], Daijiang Li [ctb], Xianying Tan [ctb], Salim Brüggemann [ctb] (), Christophe Dervieux [ctb]", + "Maintainer": "Yihui Xie ", + "Repository": "https://packagemanager.posit.co/cran/latest" + }, + "xml2": { + "Package": "xml2", + "Version": "1.3.8", + "Source": "Repository", + "Title": "Parse XML", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", role = \"aut\"), person(\"Jim\", \"Hester\", role = \"aut\"), person(\"Jeroen\", \"Ooms\", email = \"jeroenooms@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")), person(\"R Foundation\", role = \"ctb\", comment = \"Copy of R-project homepage cached as example\") )", + "Description": "Bindings to 'libxml2' for working with XML data using a simple, consistent interface based on 'XPath' expressions. Also supports XML schema validation; for 'XSLT' transformations see the 'xslt' package.", + "License": "MIT + file LICENSE", + "URL": "https://xml2.r-lib.org, https://r-lib.r-universe.dev/xml2", + "BugReports": "https://github.com/r-lib/xml2/issues", + "Depends": [ + "R (>= 3.6.0)" + ], + "Imports": [ + "cli", + "methods", + "rlang (>= 1.1.0)" + ], + "Suggests": [ + "covr", + "curl", + "httr", + "knitr", + "magrittr", + "mockery", + "rmarkdown", + "testthat (>= 3.2.0)", + "xslt" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Encoding": "UTF-8", + "RoxygenNote": "7.2.3", + "SystemRequirements": "libxml2: libxml2-dev (deb), libxml2-devel (rpm)", + "Collate": "'S4.R' 'as_list.R' 'xml_parse.R' 'as_xml_document.R' 'classes.R' 'format.R' 'import-standalone-obj-type.R' 'import-standalone-purrr.R' 'import-standalone-types-check.R' 'init.R' 'nodeset_apply.R' 'paths.R' 'utils.R' 'xml2-package.R' 'xml_attr.R' 'xml_children.R' 'xml_document.R' 'xml_find.R' 'xml_missing.R' 'xml_modify.R' 'xml_name.R' 'xml_namespaces.R' 'xml_node.R' 'xml_nodeset.R' 'xml_path.R' 'xml_schema.R' 'xml_serialize.R' 'xml_structure.R' 'xml_text.R' 'xml_type.R' 'xml_url.R' 'xml_write.R' 'zzz.R'", + "Config/testthat/edition": "3", + "NeedsCompilation": "yes", + "Author": "Hadley Wickham [aut], Jim Hester [aut], Jeroen Ooms [aut, cre], Posit Software, PBC [cph, fnd], R Foundation [ctb] (Copy of R-project homepage cached as example)", + "Maintainer": "Jeroen Ooms ", + "Repository": "CRAN" + }, + "yaml": { + "Package": "yaml", + "Version": "2.3.10", + "Source": "Repository", + "Type": "Package", + "Title": "Methods to Convert R Data to YAML and Back", + "Date": "2024-07-22", + "Suggests": [ + "RUnit" + ], + "Author": "Shawn P Garbett [aut], Jeremy Stephens [aut, cre], Kirill Simonov [aut], Yihui Xie [ctb], Zhuoer Dong [ctb], Hadley Wickham [ctb], Jeffrey Horner [ctb], reikoch [ctb], Will Beasley [ctb], Brendan O'Connor [ctb], Gregory R. Warnes [ctb], Michael Quinn [ctb], Zhian N. Kamvar [ctb], Charlie Gao [ctb]", + "Maintainer": "Shawn Garbett ", + "License": "BSD_3_clause + file LICENSE", + "Description": "Implements the 'libyaml' 'YAML' 1.1 parser and emitter () for R.", + "URL": "https://github.com/vubiostat/r-yaml/", + "BugReports": "https://github.com/vubiostat/r-yaml/issues", + "NeedsCompilation": "yes", + "Repository": "CRAN" + }, + "zeallot": { + "Package": "zeallot", + "Version": "0.2.0", + "Source": "Repository", + "Type": "Package", + "Title": "Multiple, Unpacking, and Destructuring Assignment", + "Authors@R": "c( person(\"Nathan\", \"Teetor\", email = \"nate@haufin.ch\", role = c(\"aut\", \"cre\")), person(\"Paul\", \"Teetor\", role = \"ctb\"))", + "Description": "Provides a %<-% operator to perform multiple, unpacking, and destructuring assignment in R. The operator unpacks the right-hand side of an assignment into multiple values and assigns these values to variables on the left-hand side of the assignment.", + "URL": "https://github.com/r-lib/zeallot", + "BugReports": "https://github.com/r-lib/zeallot/issues", + "License": "MIT + file LICENSE", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "VignetteBuilder": "knitr", + "Depends": [ + "R (>= 3.2)" + ], + "Suggests": [ + "codetools", + "testthat", + "knitr", + "rmarkdown", + "purrr" + ], + "NeedsCompilation": "no", + "Author": "Nathan Teetor [aut, cre], Paul Teetor [ctb]", + "Maintainer": "Nathan Teetor ", + "Repository": "CRAN" + }, + "zip": { + "Package": "zip", + "Version": "2.3.3", + "Source": "Repository", + "Title": "Cross-Platform 'zip' Compression", + "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Kuba\", \"Podgórski\", role = \"ctb\"), person(\"Rich\", \"Geldreich\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )", + "Description": "Cross-Platform 'zip' Compression Library. A replacement for the 'zip' function, that does not require any additional external tools on any platform.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/r-lib/zip, https://r-lib.github.io/zip/", + "BugReports": "https://github.com/r-lib/zip/issues", + "Suggests": [ + "covr", + "pillar", + "processx", + "R6", + "testthat", + "withr" + ], + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-05-07", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2.9000", + "NeedsCompilation": "yes", + "Author": "Gábor Csárdi [aut, cre], Kuba Podgórski [ctb], Rich Geldreich [ctb], Posit Software, PBC [cph, fnd] (ROR: )", + "Maintainer": "Gábor Csárdi ", + "Repository": "CRAN" + } + } +} diff --git a/tests/testthat.R b/tests/testthat.R index 0fb3335..17ae5ff 100644 --- a/tests/testthat.R +++ b/tests/testthat.R @@ -1,11 +1,3 @@ -# This file is part of the standard setup for testthat. -# It is recommended that you do not modify it. -# -# Where should you do additional test configuration? -# Learn more about the roles of various files in: -# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview -# * https://testthat.r-lib.org/articles/special-files.html - library(testthat) library(climateapi) diff --git a/tests/testthat/test-get_wildfire_burn_zones.R b/tests/testthat/test-get_wildfire_burn_zones.R new file mode 100644 index 0000000..c9daa44 --- /dev/null +++ b/tests/testthat/test-get_wildfire_burn_zones.R @@ -0,0 +1,137 @@ +test_that("get_wildfire_burn_zones errors when file path does not exist", { + expect_error( + get_wildfire_burn_zones(file_path = "/nonexistent/path/to/file.geojson"), + "does not point to a valid file" + ) +}) + +test_that("get_wildfire_burn_zones returns expected structure", { + skip_if_not( + file.exists(file.path( + get_box_path(), "hazards", "other-sources", "wildfire-burn-zones", + "wfbz_disasters_2000-2025.geojson")), + message = "Wildfire burn zones data file not available" + ) + + result <- suppressMessages(get_wildfire_burn_zones()) + + expect_s3_class(result, "sf") + + expected_columns <- c( + "wildfire_id", "id_fema", "year", "wildfire_name", + "state_fips", "county_fips", "county_name", + "area_sq_km", "wildfire_complex_binary", + "date_start", "date_containment", + "fatalities_total", "injuries_total", + "structures_destroyed", "structures_threatened", + "evacuation_total", "wui_type", + "density_people_sq_km_wildfire_buffer", "geometry" + ) + expect_true(all(expected_columns %in% names(result))) +}) + +test_that("get_wildfire_burn_zones has correct CRS", { + skip_if_not( + file.exists(file.path( + get_box_path(), "hazards", "other-sources", "wildfire-burn-zones", + "wfbz_disasters_2000-2025.geojson")), + message = "Wildfire burn zones data file not available" + ) + + result <- suppressMessages(get_wildfire_burn_zones()) + + expect_equal(sf::st_crs(result)$epsg, 5070) +}) + +test_that("get_wildfire_burn_zones has one county per row", { + skip_if_not( + file.exists(file.path( + get_box_path(), "hazards", "other-sources", "wildfire-burn-zones", + "wfbz_disasters_2000-2025.geojson")), + message = "Wildfire burn zones data file not available" + ) + + result <- suppressMessages(get_wildfire_burn_zones()) + + expect_false(any(stringr::str_detect(result$county_fips, "\\|"), na.rm = TRUE)) + expect_false(any(stringr::str_detect(result$county_name, "\\|"), na.rm = TRUE)) +}) + +test_that("get_wildfire_burn_zones has valid FIPS codes", { + skip_if_not( + file.exists(file.path( + get_box_path(), "hazards", "other-sources", "wildfire-burn-zones", + "wfbz_disasters_2000-2025.geojson")), + message = "Wildfire burn zones data file not available" + ) + + result <- suppressMessages(get_wildfire_burn_zones()) + + non_na_state_fips <- result$state_fips[!is.na(result$state_fips)] + non_na_county_fips <- result$county_fips[!is.na(result$county_fips)] + + expect_true(all(stringr::str_length(non_na_state_fips) == 2)) + expect_true(all(stringr::str_length(non_na_county_fips) == 5)) + expect_true(all(result$state_fips == stringr::str_sub(result$county_fips, 1, 2), na.rm = TRUE)) +}) + +test_that("get_wildfire_burn_zones county names are title case", { + skip_if_not( + file.exists(file.path( + get_box_path(), "hazards", "other-sources", "wildfire-burn-zones", + "wfbz_disasters_2000-2025.geojson")), + message = "Wildfire burn zones data file not available" + ) + + result <- suppressMessages(get_wildfire_burn_zones()) + + non_na_names <- result$county_name[!is.na(result$county_name)] + expect_equal(non_na_names, stringr::str_to_title(non_na_names)) +}) + +test_that("get_wildfire_burn_zones emits expected message", { + skip_if_not( + file.exists(file.path( + get_box_path(), "hazards", "other-sources", "wildfire-burn-zones", + "wfbz_disasters_2000-2025.geojson")), + message = "Wildfire burn zones data file not available" + ) + + expect_message( + get_wildfire_burn_zones(), + stringr::str_c( + "Each observation represents a county affected by a wildfire burn zone disaster. ", + "Wildfires spanning multiple counties appear as multiple rows. ", + "Disasters are defined as wildfires that burned near a community and resulted in ", + "at least one civilian fatality, one destroyed structure, or received federal disaster relief. ", + "Geometries represent burn zone perimeters sourced from FIRED, MTBS, or NIFC datasets.") + ) +}) + +test_that("get_wildfire_burn_zones year column is integer", { + skip_if_not( + file.exists(file.path( + get_box_path(), "hazards", "other-sources", "wildfire-burn-zones", + "wfbz_disasters_2000-2025.geojson")), + message = "Wildfire burn zones data file not available" + ) + + result <- suppressMessages(get_wildfire_burn_zones()) + + expect_type(result$year, "integer") + expect_true(all(result$year >= 2000 & result$year <= 2025, na.rm = TRUE)) +}) + +test_that("get_wildfire_burn_zones date columns are Date class", { + skip_if_not( + file.exists(file.path( + get_box_path(), "hazards", "other-sources", "wildfire-burn-zones", + "wfbz_disasters_2000-2025.geojson")), + message = "Wildfire burn zones data file not available" + ) + + result <- suppressMessages(get_wildfire_burn_zones()) + + expect_s3_class(result$date_start, "Date") + expect_s3_class(result$date_containment, "Date") +}) diff --git a/vignettes/get_sheldus.Rmd b/vignettes/get_sheldus.Rmd new file mode 100644 index 0000000..f8add1b --- /dev/null +++ b/vignettes/get_sheldus.Rmd @@ -0,0 +1,222 @@ +--- +title: "SHELDUS Hazard Data" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{SHELDUS Hazard Data} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + warning = FALSE, + message = FALSE, + fig.width = 8, + fig.height = 6, + dpi = 150 +) +``` + +## Overview + +The `get_sheldus()` function provides access to county-level hazard event data from the Spatial Hazard Events and Losses Database for the United States (SHELDUS). This database tracks property damage, crop damage, fatalities, and injuries from natural hazards across all US counties. + +## Data source + +SHELDUS is maintained by Arizona State University's Center for Emergency Management and Homeland Security. The database compiles hazard event data from multiple sources including NOAA Storm Events, the National Climatic Data Center, and other federal agencies. + +Access to SHELDUS requires a subscription. See [https://cemhs.asu.edu/sheldus](https://cemhs.asu.edu/sheldus) for more information. + +## Loading the data + +```{r setup} +library(climateapi) +library(tidyverse) +library(sf) +library(urbnthemes) + +set_urbn_defaults(style = "print") +``` + +```{r load-data, eval = FALSE} +sheldus <- get_sheldus() +``` + +```{r load-data-hidden, echo = FALSE} +sheldus <- get_sheldus() +``` + +## Data structure + +Each row represents a unique combination of county, year, month, and hazard type. Only county-month-hazard combinations with recorded events are included. + +```{r structure} +glimpse(sheldus) +``` + +Key variables include: + +- `unique_id`: Unique identifier for each observation +- `GEOID`: Five-digit county FIPS code +- `year` and `month`: Temporal identifiers +- `hazard`: Type of hazard event (e.g., "Flooding", "Hurricane/Tropical Storm") +- `damage_property`: Property damage in 2023 inflation-adjusted dollars +- `damage_crop`: Crop damage in 2023 inflation-adjusted dollars +- `fatalities` and `injuries`: Human impacts +- `records`: Number of individual events aggregated into the observation + +## Example analyses + +### Hazard types in the database + +```{r hazard-types} +sheldus |> + distinct(hazard) |> + arrange(hazard) |> + pull(hazard) +``` + +### Annual property damage by hazard type + +```{r annual-damage} +df1 <- sheldus |> + summarize( + .by = c(year, hazard), + total_damage = sum(damage_property, na.rm = TRUE) / 1e9) + +top_hazards <- df1 |> + summarize( + .by = hazard, + total = sum(total_damage, na.rm = TRUE)) |> + slice_max(total, n = 5) |> + pull(hazard) + +df1 |> + filter(hazard %in% top_hazards) |> + ggplot(aes(x = year, y = total_damage, fill = hazard)) + + geom_col() + + scale_fill_manual(values = c( + palette_urbn_main[1:4], palette_urbn_cyan[5])) + + labs( + title = "Annual Property Damage by Hazard Type", + subtitle = "Top 5 hazard types by total damage, 2023 dollars", + x = "", + y = "Property damage (billions)", + fill = "Hazard type") +``` + +### Geographic distribution of flood damage + +```{r flood-damage-map, fig.height = 7} +flood_damage <- sheldus |> + filter(hazard == "Flooding") |> + summarize( + .by = GEOID, + total_damage = sum(damage_property, na.rm = TRUE)) + +counties_sf <- tigris::counties(cb = TRUE, year = 2022, progress_bar = FALSE) |> + st_transform(5070) |> + filter(!STATEFP %in% c("02", "15", "72", "78", "66", "60", "69")) + +flood_map <- counties_sf |> + left_join(flood_damage, by = "GEOID") |> + mutate( + damage_category = case_when( + is.na(total_damage) | total_damage == 0 ~ "No recorded damage", + total_damage < 1e6 ~ "< $1M", + total_damage < 10e6 ~ "$1M - $10M", + total_damage < 100e6 ~ "$10M - $100M", + TRUE ~ "> $100M"), + damage_category = factor( + damage_category, + levels = c("No recorded damage", "< $1M", "$1M - $10M", "$10M - $100M", "> $100M"))) + +ggplot(flood_map) + + geom_sf(aes(fill = damage_category), color = NA) + + scale_fill_manual(values = c( + "No recorded damage" = "grey90", + "< $1M" = palette_urbn_cyan[1], + "$1M - $10M" = palette_urbn_cyan[3], + "$10M - $100M" = palette_urbn_cyan[5], + "> $100M" = palette_urbn_cyan[7])) + + labs( + title = "Cumulative Flood Damage by County", + subtitle = "Total property damage from flooding events, 2023 dollars", + fill = "Total damage") + + theme_urbn_map() +``` + +### Seasonal patterns in hazard events + +```{r seasonal-patterns} +seasonal <- sheldus |> + filter(hazard %in% top_hazards) |> + summarize( + .by = c(month, hazard), + total_events = sum(records, na.rm = TRUE)) + +ggplot(seasonal, aes(x = month, y = total_events, color = hazard)) + + geom_line(linewidth = 1) + + geom_point(size = 2) + + scale_x_continuous(breaks = 1:12, labels = month.abb) + + scale_color_manual(values = c( + palette_urbn_main[1:4], palette_urbn_cyan[5])) + + labs( + title = "Seasonal Distribution of Hazard Events", + subtitle = "Total event count by month across all years", + x = "", + y = "Number of events", + color = "Hazard type") +``` + +### Counties with highest fatalities + +```{r fatalities} +high_fatality_counties <- sheldus |> + summarize( + .by = c(GEOID, state_name, county_name), + total_fatalities = sum(fatalities, na.rm = TRUE)) |> + slice_max(total_fatalities, n = 15) + +high_fatality_counties |> + mutate( + county_label = str_c(county_name, ", ", state_name), + county_label = fct_reorder(county_label, total_fatalities)) |> + ggplot(aes(y = county_label, x = total_fatalities)) + + geom_col() + + labs( + title = "Counties with Highest Hazard-Related Fatalities", + x = "Total fatalities", + y = "") +``` + +### Linking with census data + +The county-level structure makes it straightforward to join with demographic data. + +```{r demographics, eval = FALSE} +county_demographics <- tidycensus::get_acs( + geography = "county", + variables = c( + median_income = "B19013_001", + total_pop = "B01003_001"), + year = 2022, + output = "wide") + +county_hazard_summary <- sheldus |> + filter(year >= 2018) |> + summarize( + .by = GEOID, + total_damage = sum(damage_property, na.rm = TRUE), + total_events = sum(records, na.rm = TRUE)) |> + left_join(county_demographics, by = "GEOID") |> + mutate(damage_per_capita = total_damage / total_popE) +``` + +## See also + +- `get_fema_disaster_declarations()`: FEMA disaster declarations +- `get_nfip_claims()`: National Flood Insurance Program claims data +- `get_wildfire_burn_zones()`: Wildfire burn zone disasters diff --git a/vignettes/get_structures.Rmd b/vignettes/get_structures.Rmd new file mode 100644 index 0000000..1577b18 --- /dev/null +++ b/vignettes/get_structures.Rmd @@ -0,0 +1,229 @@ +--- +title: "USA Structures Data" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{USA Structures Data} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + warning = FALSE, + message = FALSE, + fig.width = 8, + fig.height = 6, + dpi = 150 +) +``` + +## Overview + +The `get_structures()` function retrieves building footprint data from the USA Structures dataset and summarizes structure counts by type at the tract or county level. This is useful for estimating the number and types of buildings within areas affected by natural hazards. + +## Data source + +Data are sourced from the USA Structures dataset maintained by the Department of Homeland Security. The dataset contains building footprints derived from high-resolution imagery for structures across the United States. + +See [https://geoplatform.gov/metadata/9d4a3ae3-8637-4707-92a7-b7d67b769a6b](https://geoplatform.gov/metadata/9d4a3ae3-8637-4707-92a7-b7d67b769a6b) for more information. + +## Loading the data + +```{r setup} +library(climateapi) +library(tidyverse) +library(sf) +library(urbnthemes) + +set_urbn_defaults(style = "print") +``` + +The function requires a `boundaries` argument specifying the geographic area of interest. This must be a spatial polygon object with a defined coordinate reference system. + +```{r load-data, eval = FALSE} +# Example: Get structures in Washington, DC +dc_boundary <- tigris::states(cb = TRUE) |> + filter(STUSPS == "DC") + +dc_structures <- get_structures( + boundaries = dc_boundary, + geography = "tract") +``` + +```{r load-data-hidden, echo = FALSE} +dc_boundary <- tigris::states(cb = TRUE) |> + filter(STUSPS == "DC") + +dc_structures <- get_structures( + boundaries = dc_boundary, + geography = "tract") +``` + +## Function parameters + +- `boundaries`: A POLYGON or MULTIPOLYGON sf object, or an `sf::st_bbox()`-style bounding box. Must have a defined CRS. +- `geography`: The desired output geography. One of `"tract"` or `"county"`. +- `keep_structures`: If `TRUE`, returns both the summarized counts and the raw point-level structure data. + +## Data structure + +Each row represents a unique combination of geographic unit (tract or county) and structure type. + +```{r structure} +glimpse(dc_structures) +``` + +Key variables include: + +- `GEOID`: Census tract (11-digit) or county (5-digit) FIPS code +- `primary_occupancy`: The primary use of the structure (e.g., "Residential", "Commercial") +- `occupancy_class`: Broader classification of occupancy type +- `count`: Number of structures of this type in the geographic unit + +### Occupancy types + +```{r occupancy-types} +dc_structures |> + distinct(occupancy_class, primary_occupancy) |> + arrange(occupancy_class, primary_occupancy) +``` + +## Example analyses + +### Structure composition by tract + +```{r tract-composition} +dc_summary <- dc_structures |> + summarize( + .by = GEOID, + total_structures = sum(count, na.rm = TRUE), + residential = sum(count[occupancy_class == "Residential"], na.rm = TRUE), + commercial = sum(count[occupancy_class == "Commercial"], na.rm = TRUE)) |> + mutate( + residential_share = residential / total_structures) + +dc_summary |> + ggplot(aes(x = total_structures, y = residential_share)) + + geom_point(alpha = 0.7) + + scale_y_continuous(labels = scales::percent) + + labs( + title = "Residential Share vs. Total Structures by Tract", + subtitle = "Washington, DC census tracts", + x = "Total structures", + y = "Share residential") +``` + +### Mapping structure density + +```{r map-structures, fig.height = 8} +dc_tracts <- tigris::tracts(state = "DC", cb = TRUE, year = 2023, progress_bar = FALSE) |> + st_transform(5070) + +dc_tract_totals <- dc_structures |> + summarize( + .by = GEOID, + total_structures = sum(count, na.rm = TRUE)) + +dc_map <- dc_tracts |> + left_join(dc_tract_totals, by = "GEOID") + +ggplot(dc_map) + + geom_sf(aes(fill = total_structures), color = "white", linewidth = 0.2) + + scale_fill_gradientn( + colors = c(palette_urbn_cyan[1], palette_urbn_cyan[4], palette_urbn_cyan[7]), + labels = scales::comma) + + labs( + title = "Structure Count by Census Tract", + subtitle = "Washington, DC", + fill = "Structures") + + theme_urbn_map() +``` + +### Analyzing structures in a disaster area + +A common use case is estimating structures within a hazard-affected area. This example shows how to combine structure data with wildfire burn zones. + +```{r disaster-example, eval = FALSE} +# Get a specific wildfire's burn zone +burn_zones <- get_wildfire_burn_zones() + +camp_fire <- burn_zones |> + filter(str_detect(wildfire_name, "CAMP")) + +# Get structures within the burn zone +camp_fire_structures <- get_structures( + + boundaries = camp_fire, + geography = "tract", + keep_structures = TRUE) + +# Summarized counts +camp_fire_structures$structures_summarized |> + summarize( + .by = occupancy_class, + total = sum(count, na.rm = TRUE)) |> + arrange(desc(total)) +``` + +### Working with raw structure data + +Setting `keep_structures = TRUE` returns both the summarized data and the raw point-level structure data. + +```{r raw-structures, eval = FALSE} +dc_full <- get_structures( + boundaries = dc_boundary, + geography = "county", + keep_structures = TRUE) + +# Access the raw point data +raw_structures <- dc_full$structures_raw + +# Access the summarized data +summary_structures <- dc_full$structures_summarized + +# Map individual structures +ggplot() + + geom_sf(data = dc_boundary, fill = "grey95") + + geom_sf( + data = raw_structures |> filter(primary_occupancy == "Commercial"), + size = 0.1, + alpha = 0.5) + + labs(title = "Commercial Structures in Washington, DC") + + theme_urbn_map() +``` + +### County-level analysis + +For larger areas, county-level aggregation provides a useful summary. + +```{r county-example, eval = FALSE} +# Get structures for multiple states +southeast_boundary <- tigris::states(cb = TRUE) |> + filter(STUSPS %in% c("FL", "GA", "AL", "SC")) + +southeast_structures <- get_structures( + boundaries = southeast_boundary, + geography = "county") + +# Summarize by county +county_totals <- southeast_structures |> + summarize( + .by = GEOID, + total_structures = sum(count, na.rm = TRUE)) +``` + +## Performance considerations + +The raw building footprint data files are large. Processing can be slow, especially for multi-state analyses. Consider: + +- Starting with a small geographic area to test your workflow +- Using county-level aggregation when tract-level detail is not required +- Caching results for repeated analyses + +## See also + +- `get_wildfire_burn_zones()`: Wildfire burn zone disasters for use as boundaries +- `get_current_fire_perimeters()`: Active wildfire perimeters for use as boundaries +- `get_fema_disaster_declarations()`: FEMA disaster declarations diff --git a/vignettes/get_wildfire_burn_zones.Rmd b/vignettes/get_wildfire_burn_zones.Rmd new file mode 100644 index 0000000..0d5bdd4 --- /dev/null +++ b/vignettes/get_wildfire_burn_zones.Rmd @@ -0,0 +1,215 @@ +--- +title: "Wildfire Burn Zones" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Wildfire Burn Zones} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + warning = FALSE, + message = FALSE, + fig.width = 8, + fig.height = 6, + dpi = 150 +) +``` + +## Overview + +The `get_wildfire_burn_zones()` function provides access to a harmonized dataset of wildfire burn zone disasters in the United States from 2000-2025. + +## Data sources and methodology + +This dataset combines six authoritative wildfire data sources to identify wildfires that burned near communities and resulted in civilian fatalities, destroyed structures, or received federal disaster relief. These sources are: + +- **FIRED (Fire Event Delineation)**: Satellite-derived fire perimeters +- **MTBS (Monitoring Trends in Burn Severity)**: Burn severity and perimeters for large fires +- **NIFC (National Interagency Fire Center)**: Official fire incident data +- **ICS-209 (Incident Status Summary)**: Incident management records +- **RedBook**: Historical wildfire statistics +- **FEMA**: Federal disaster declarations + +Wildfires are classified as "disasters" if they: + +1. Burned near a community AND +2. Resulted in at least one of: + - Civilian fatality + - Destroyed structure + - Federal disaster relief + +These data are described and provided in association with the journal article: Wilner, L.B., Piepmeier, L., Gordon, M. et al. Two and a half decades of United States wildfire burn zone disaster data, 2000-2025. Sci Data 12, 1948 (2025). https://doi.org/10.1038/s41597-025-06226-8. + +## Loading the data + +```{r setup} +library(climateapi) +library(tidyverse) +library(sf) +library(urbnthemes) + +set_urbn_defaults(style = "print") +``` + +```{r load-data, eval = FALSE} +burn_zones <- get_wildfire_burn_zones() +``` + +```{r load-data-hidden, echo = FALSE} +# For vignette building, we'll create example output structure +# In practice, users would run the above code +burn_zones <- get_wildfire_burn_zones() +``` + +## Data structure + +Each row in the dataset represents a single wildfire burn zone disaster. + +```{r structure} +glimpse(burn_zones) +``` + +Key variables include: + +- `wildfire_id`: Unique identifier for each wildfire event +- `year`: Year the wildfire occurred (2000-2025) +- `wildfire_name`: Name of the wildfire or fire complex +- `county_fips`: Pipe-delimited string of county FIPS codes for all affected counties +- `county_name`: Pipe-delimited string of county names for all affected counties +- `area_sq_km`: Burned area in square kilometers +- `fatalities_total` and `injuries_total`: Human impacts +- `structures_destroyed` and `structures_threatened`: Built environment impacts +- `geometry`: Burn zone polygon boundaries + +## Example analyses + +### Annual trends in wildfire disasters + +```{r annual-trends} +# Extract state FIPS from the first county in the pipe-delimited list +df1 = burn_zones |> + st_drop_geometry() |> + mutate(state_fips = str_sub(county_fips, 1, 2)) |> + summarize( + .by = c(year, state_fips), + n_wildfires = n(), + total_area_sq_km = sum(area_sq_km, na.rm = TRUE), + total_structures_destroyed = sum(structures_destroyed, na.rm = TRUE)) |> + left_join( + tigris::fips_codes %>% distinct(state, state_code), + by = c("state_fips" = "state_code")) + +top_five_states = df1 %>% + arrange(desc(n_wildfires)) %>% + distinct(state) %>% + slice(1:5) + +df1 %>% + filter(state %in% top_five_states$state) %>% + mutate(state = factor(state, levels = top_five_states %>% pull(state), ordered = TRUE)) %>% + ggplot(aes(x = year, y = n_wildfires)) + + geom_col() + + labs( + title = "Many states frequently experience more than 50 wildfires per year", + subtitle = "Disasters defined as wildfires causing fatalities, structure loss, or federal relief", + x = "", + y = "Number of wildfires") + + facet_wrap(~state) +``` + +### Geographic distribution of impacts + +```{r state-impacts} +state_impacts <- burn_zones |> + st_drop_geometry() |> + mutate(state_fips = str_sub(county_fips, 1, 2)) |> + summarize( + .by = state_fips, + n_wildfires = n_distinct(wildfire_id), + total_structures_destroyed = sum(structures_destroyed, na.rm = TRUE), + total_fatalities = sum(fatalities_total, na.rm = TRUE) + ) |> + left_join( + tidycensus::fips_codes |> + distinct(state_code, state_name), + by = c("state_fips" = "state_code") + ) |> + filter(!is.na(state_name)) + +state_impacts |> + slice_max(n_wildfires, n = 10) |> + mutate(state_name = fct_reorder(state_name, n_wildfires)) |> + ggplot(aes(y = state_name, x = n_wildfires)) + + geom_col() + + labs( + title = "States with Most Wildfire Burn Zone Disasters (2000-2025)", + x = "Number of distinct wildfires", + y = "" + ) +``` + +### Mapping wildfire burn zones + +```{r map-example, fig.height = 8} +# Get wildfires from a recent year in California +ca_2020_fires <- burn_zones |> + filter(str_detect(county_fips, "^06"), year == 2020) + +# Get California counties for context +ca_counties <- tigris::counties(state = "CA", cb = TRUE, year = 2022, progress_bar = FALSE) |> + st_transform(5070) + +ggplot() + + geom_sf(data = ca_counties, fill = "grey95", color = "grey70") + + geom_sf(data = ca_2020_fires, aes(fill = structures_destroyed), alpha = 0.8) + + scale_fill_continuous(trans = "reverse") + + labs( + title = "California Wildfire Burn Zone Disasters (2020)", + subtitle = "Burn zone perimeters colored by structures destroyed", + fill = "Structures\ndestroyed") + + theme_urbn_map() +``` + +### Analyzing structure loss severity + +```{r severity-analysis} +burn_zones |> + st_drop_geometry() |> + distinct(wildfire_id, year, wildfire_name, structures_destroyed) |> + filter(!is.na(structures_destroyed), structures_destroyed > 0) |> + mutate( + severity = case_when( + structures_destroyed >= 1000 ~ "1,000+ structures", + structures_destroyed >= 100 ~ "100-999", + structures_destroyed >= 10 ~ "10-99", + TRUE ~ "1-9 structures"), + severity = factor( + severity, + levels = c("1-9 structures", "10-99", "100-999", "1,000+ structures"))) |> + count(year, severity) |> + mutate( + .by = year, + percent = n / sum(n, na.rm = TRUE)) |> + ggplot(aes(x = year, y = percent, fill = severity)) + + geom_col() + + scale_fill_manual(values = c( + "1-9 structures" = palette_urbn_cyan[1], + "10-99" = palette_urbn_cyan[3], + "100-999" = palette_urbn_cyan[5], + "1,000+ structures" = palette_urbn_cyan[7])) + + scale_y_continuous(labels = scales::percent) + + labs( + title = "Most wildifres destroy under 10 structures", + x = "", + y = "") +``` + +## See also + +- `get_current_fire_perimeters()`: Access current/active wildfire perimeters +- `get_fema_disaster_declarations()`: FEMA disaster declarations including fire-related declarations +- `get_structures()`: Estimate structures within geographic boundaries