From c4382073bc33cc98458d201e025f4edeb0ad0262 Mon Sep 17 00:00:00 2001 From: Will Curran-Groome Date: Thu, 5 Feb 2026 17:21:12 -0500 Subject: [PATCH] econ recovery factsheet draft --- DESCRIPTION | 1 + NAMESPACE | 1 + R/get_business_patterns.R | 62 +- R/get_government_finances.R | 278 +- R/get_nfip_residential_penetration.R | 59 + R/utilities.R | 3 +- _pkgdown.yml | 1 + man/get_business_patterns.Rd | 10 +- man/get_government_finances.Rd | 76 +- man/get_naics_codes.Rd | 6 +- man/get_nfip_residential_penetration.Rd | 39 + renv.lock | 12952 ++++++++++---------- vignettes/CLAUDE.md | 7 + vignettes/economic_recovery_factsheet.Rmd | 671 + vignettes/get_sheldus.Rmd | 7 +- 15 files changed, 7572 insertions(+), 6601 deletions(-) create mode 100644 R/get_nfip_residential_penetration.R create mode 100644 man/get_nfip_residential_penetration.Rd create mode 100644 vignettes/CLAUDE.md create mode 100644 vignettes/economic_recovery_factsheet.Rmd diff --git a/DESCRIPTION b/DESCRIPTION index f202f20..6997f6d 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -59,6 +59,7 @@ Remotes: ropensci/rfema, yonghah/esri2sf, UI-Research/urbnindicators, + UI-Research/crosswalk, UrbanInstitute/urbnthemes URL: https://ui-research.github.io/climateapi/ Suggests: diff --git a/NAMESPACE b/NAMESPACE index cf1dc3d..28858de 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -17,6 +17,7 @@ export(get_lodes) export(get_naics_codes) export(get_nfip_claims) export(get_nfip_policies) +export(get_nfip_residential_penetration) export(get_preliminary_damage_assessments) export(get_public_assistance) export(get_sba_loans) diff --git a/R/get_business_patterns.R b/R/get_business_patterns.R index d2c9c9b..ee3691f 100644 --- a/R/get_business_patterns.R +++ b/R/get_business_patterns.R @@ -7,7 +7,7 @@ #' `censusapi::listCensusMetadata(name = "cbp")`. #' #' @param year The vintage year for NAICS codes. Data are available from 1986 through 2023. -#' Default is 2022. +#' Default is 2023. #' @param digits The number of digits for desired NAICS codes. Must be between 2 and 6. #' Default is 3. Two-digit codes represent broad industry sectors (20 codes), #' while six-digit codes represent detailed industries. @@ -24,7 +24,7 @@ #' @examples #' \dontrun{ #' # Get all 2-digit NAICS codes -#' get_naics_codes(year = 2022, digits = 2) +#' get_naics_codes(year = 2023, digits = 2) #' #' # Get all 3-digit NAICS codes (default) #' get_naics_codes(year = 2022) @@ -32,7 +32,7 @@ #' # Get 4-digit NAICS codes for a specific year #' get_naics_codes(year = 2020, digits = 4) #' } -get_naics_codes <- function(year = 2022, digits = 3) { +get_naics_codes <- function(year = 2023, digits = 3) { if (year < 1986) { stop("Year must be 1986 or later.") } if (year > 2023) { stop("Most recent year for data is 2023.") } if (!digits %in% 2:6) { stop("`digits` must be between 2 and 6.") } @@ -57,9 +57,9 @@ get_naics_codes <- function(year = 2022, digits = 3) { #' @title Obtain County Business Patterns (CBP) Estimates per County #' -#' @param 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. +#' @param year The vintage of CBP data desired. Data are available from 2008-2023. +#' Earlier years use different NAICS classification systems that are not currently supported. +#' Default is 2023. #' @param 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). @@ -128,7 +128,7 @@ get_naics_codes <- function(year = 2022, digits = 3) { #' @examples #' \dontrun{ #' get_business_patterns( -#' year = 2022, +#' year = 2023, #' naics_code_digits = 3) #' #' get_business_patterns( @@ -136,22 +136,34 @@ get_naics_codes <- function(year = 2022, digits = 3) { #' naics_codes = c(221111, 221112)) #' } -get_business_patterns = function(year = 2022, geo = "county", naics_code_digits = 2, naics_codes = NULL) { - if (year < 1986) { stop("Year must be 1986 or later.") } +get_business_patterns = function(year = 2023, geo = "county", naics_code_digits = 2, naics_codes = NULL) { + if (year < 2008) { stop("Year must be 2008 or later. Earlier years use different data structures not currently supported.") } if (year > 2023) { stop("Most recent year for data is 2023.") } if (! geo %in% c("county", "zipcode")) { stop("`geo` must be one of 'county' or 'zipcode'.") } if (! naics_code_digits %in% c(2, 3)) { stop("`naics_code_digits` must be one of c(2, 3). For more detailed codes, explicitly pass desired codes to the `naics_codes` parameter.") } + # Determine NAICS classification system based on year + # Census uses different NAICS vintages for different data years + + naics_version <- dplyr::case_when( + year >= 2017 ~ "NAICS2017", + year >= 2012 ~ "NAICS2012", + year >= 2008 ~ "NAICS2007", + TRUE ~ "NAICS2007" + ) + naics_label_var <- paste0(naics_version, "_LABEL") + naics_codes_metadata = censusapi::listCensusMetadata( name = "cbp", - vintage = "2022", + vintage = as.character(year), type = "variables", include_values = TRUE) %>% #filter out codes 92 and 95 which do not appear to have data associated and #don't appear on the census list of naics codes at #https://www2.census.gov/programs-surveys/cbp/technical-documentation/reference/naics-descriptions/naics2017.txt - dplyr::filter(!stringr::str_starts(values_code, "92|95")) + dplyr::filter(!stringr::str_starts(values_code, "92|95")) %>% + tibble::as_tibble() if (!is.null(naics_codes)) { naics_code_check = naics_codes_metadata %>% @@ -185,20 +197,21 @@ get_business_patterns = function(year = 2022, geo = "county", naics_code_digits ## error with our query--there's no data for these codes on data.census.gov ## either ~ tryCatch({ - censusapi::getCensus( + # Build the API call dynamically based on NAICS version + api_args <- list( name = "cbp", vintage = year, - vars = c( - "EMP", - "YEAR", - "ESTAB", - "PAYANN", - "EMPSZES", - "NAICS2017_LABEL"), - region = paste0(geo, ":*"), - NAICS2017 = .x) %>% - dplyr::mutate(naics_code = .x)}, + vars = c("EMP", "YEAR", "ESTAB", "PAYANN", "EMPSZES", naics_label_var), + region = paste0(geo, ":*") + ) + # Add the NAICS filter with the correct variable name + api_args[[naics_version]] <- .x + + do.call(censusapi::getCensus, api_args) %>% + dplyr::mutate(naics_code = .x)}, error = function(e) { return(tibble::tibble()) })) %>% + # Rename the NAICS label column to a standard name + dplyr::rename_with(~ "NAICS_LABEL", dplyr::matches("NAICS[0-9]+_LABEL")) %>% dplyr::mutate( # state, # county, @@ -206,7 +219,7 @@ get_business_patterns = function(year = 2022, geo = "county", naics_code_digits employers = ESTAB, annual_payroll = PAYANN, employee_size_range = EMPSZES, - industry = NAICS2017_LABEL, + industry = NAICS_LABEL, naics_code) %>% dplyr::mutate( industry = industry %>% @@ -287,6 +300,7 @@ get_business_patterns = function(year = 2022, geo = "county", naics_code_digits } utils::globalVariables( - c("EMP", "EMPSZES", "ESTAB", "NAICS2017_LABEL", "PAYANN", "annual_payroll", + c("EMP", "EMPSZES", "ESTAB", "NAICS2017_LABEL", "NAICS2012_LABEL", "NAICS2007_LABEL", + "NAICS_LABEL", "PAYANN", "annual_payroll", "employee_size_range", "employee_size_range_code", "employee_size_range_label", "employees", "employers", "industry", "values_code", "values_label", "naics_code")) diff --git a/R/get_government_finances.R b/R/get_government_finances.R index de9ca11..21aa24b 100644 --- a/R/get_government_finances.R +++ b/R/get_government_finances.R @@ -1,112 +1,198 @@ -#' Get government unit-level expenses from the Census of Governments +#' Get county government revenues and expenditures #' -#' @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. +#' @description Retrieves county government revenues and expenditures over time. #' #' @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}. +#' Government Finances and Census of Governments, compiled per: Pierson K., Hand M., +#' and Thompson F. (2015). The Government Finance Database: A Common Resource for +#' Quantitative Research in Public Financial Analysis. PLoS ONE doi: +#' 10.1371/journal.pone.0130119. See \url{https://my.willamette.edu/site/mba/public-datasets}. +#' +#' This is a survey in most years--i.e., only a subset of the full population of governments +#' comprises the sampling frame--but is a true census with relatively high response rates (90%%+) +#' in years ending in 2 and 7. +#' +#' Definitions of key constructs: +#' +#' **Revenues** +#' - General revenue: All revenue not arising from utilities, liquor stores, or social insurance. +#' - Intergovernmental revenue: Transfers from federal, state, or other local governments. +#' - Current charges: Fees collected for providing services. #' -#' @return A dataframe containing government unit-level expenses for the specified year. -#' Columns include: +#' **Expenditures** +#' - General expenditure: All spending except utilities, liquor stores, or social insurance. +#' - Capital outlay: Spending on construction and equipment. +#' +#' @return A dataframe containing county government-level revenues and expenditures by year. +#' All monetary values are in thousands of dollars. #' \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{year}{Year of the financial data.} +#' \item{GEOID}{Five-digit county FIPS code.} #' \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.} +#' \item{revenue_total}{Total county revenue.} +#' \item{revenue_general}{General revenue (excludes utilities, liquor stores, social insurance).} +#' \item{revenue_tax_total}{Total tax revenue.} +#' \item{revenue_tax_property}{Property tax revenue.} +#' \item{revenue_tax_sales}{Sales and gross receipts tax revenue.} +#' \item{revenue_tax_income}{Income tax revenue.} +#' \item{revenue_intergovernmental_total}{Total intergovernmental transfers received.} +#' \item{revenue_intergovernmental_federal}{Federal intergovernmental transfers.} +#' \item{revenue_intergovernmental_state}{State intergovernmental transfers.} +#' \item{revenue_charges_total}{Total charges and miscellaneous general revenue.} +#' \item{revenue_utility_total}{Utility revenue.} +#' \item{expenditure_total}{Total county expenditures.} +#' \item{expenditure_general}{General expenditures.} +#' \item{expenditure_capital_outlay}{Capital outlay spending.} +#' \item{expenditure_construction}{Construction spending.} +#' \item{expenditure_salaries_wages}{Salaries and wages.} +#' \item{expenditure_intergovernmental}{Intergovernmental transfers paid.} +#' \item{expenditure_education_total}{Education spending.} +#' \item{expenditure_public_safety}{Police protection spending.} +#' \item{expenditure_health_hospitals}{Health spending.} +#' \item{expenditure_highways}{Highway spending.} +#' \item{expenditure_public_welfare}{Public welfare spending.} +#' \item{expenditure_utility_total}{Utility expenditures.} #' } #' @export -get_government_finances = function(year = 2022) { - - file_path_base = file.path(get_box_path(), "sociodemographics", "census-of-governments") +get_government_finances = function() { - year = stringr::str_sub(year, 1, 2) + file_path_base = file.path(get_box_path(), "government-units", "government-finance-database", "gfd_2026_02_04.csv") + if (!file.exists(file_path_base)) { stop("File does not exist at specified path.") } - ## this contains expense descriptions that map to the expense codes that are - ## included in the raw dataset - item_crosswalk = readr::read_csv( - file.path(file_path_base, "item_crosswalk.csv")) - - ## this contains the names and other metadata for each unit of government - government_unit_crosswalk = readr::read_fwf( - file = file.path(file_path_base, "Fin_PID_2022.txt"), - col_positions = readr::fwf_widths( - widths = c(2, 1, 3, 6, 64, 35, 5, 9, 2, 7, 2, 2, 2, 4, 2), - col_names = c( - "state_code", "government_type", "county_code", "unit_id", "unit_name", - "county_name", "place_code", "population", "population_year", - "enrollment", "enrollment_year", "function_code", "school_level_code", - "fiscal_year_ending", "data_year"))) %>% - dplyr::select(-c(government_type, data_year)) - - ## each row corresponds to a finance item (generally, a class of expenditures) - ## for a single government unit in a single year - data = readr::read_fwf( - file = file.path(file_path_base, "2022FinEstDAT_09202024modp_pu.txt"), - col_positions = readr::fwf_widths( - widths = c(2, 1, 3, 6, 3, 12, 4, 1), - col_names = c( - "state_code", "government_type", "county_code", "unit_id", "item", "amount_thousands", - "year_data", "imputation_type"))) %>% - dplyr::select(-c(state_code, county_code)) %>% - dplyr::mutate( - government_type = dplyr::case_when( - government_type == 0 ~ "State", - government_type == 1 ~ "County", - government_type == 2 ~ "City", - government_type == 3 ~ "Township", - government_type == 4 ~ "Special District", - government_type == 5 ~ "School District/Educational Service Agency"), - imputation_type = dplyr::case_when( - imputation_type == "A" ~ "Analyst correction", - imputation_type == "I" ~ "Imputed", - imputation_type == "M" ~ "Unknown", - imputation_type == "N" ~ "Not applicable", - imputation_type == "R" ~ "Reported", - imputation_type == "S" ~ "Alternative source")) %>% - dplyr::left_join(item_crosswalk, by = c("item" = "code")) %>% - dplyr::select(-item,cexpense_description = description) - - data_by_unit = data %>% - dplyr::group_by(unit_id, year_data, imputation_type) %>% - dplyr::summarize( - count = dplyr::n(), - amount_thousands = sum(amount_thousands, na.rm = TRUE), - dplyr::across(.cols = c(dplyr::matches("code"), dplyr::matches("name"), government_type), dplyr::first), - .groups = "drop") %>% - tidyr::pivot_wider( - names_from = imputation_type, - values_from = count, - values_fill = 0, - names_prefix = "imputation_") %>% + df1 = readr::read_csv(file_path_base) %>% janitor::clean_names() %>% - dplyr::mutate( - data_quality = imputation_reported / - (imputation_reported + imputation_imputed + imputation_analyst_correction + imputation_unknown + imputation_alternative_source)) %>% - dplyr::select(-dplyr::matches("imputation")) %>% - dplyr::left_join(government_unit_crosswalk, by = "unit_id") %>% - dplyr::mutate(amount_per_capita = dplyr::if_else( - is.na(enrollment), - amount_thousands * 1000 / population, - amount_thousands * 1000 / enrollment)) + tibble::as_tibble() %>% + ## Remove duplicate construct of total_ltd_out + dplyr::select(-dplyr::any_of("total_long_term_debt_out")) - return(data_by_unit) + df_renamed = df1 %>% + ## Step 1: Expand common abbreviations across all columns + dplyr::rename_with( + .cols = dplyr::everything(), + .fn = ~ .x %>% + stringr::str_replace_all(c( + "^tot_" = "total_", + "_tot_" = "_total_", + "_tot$" = "_total", + "_igr_" = "_intergovernmental_", + "_igr$" = "_intergovernmental", + "^igr_" = "intergovernmental_", + "_ig_" = "_intergovernmental_", + "_ig$" = "_intergovernmental", + "^ig_" = "intergovernmental_", + "_trans_" = "_transit_", + "_educ_" = "_education_", + "_educ$" = "_education", + "^educ_" = "education_", + "^elem_" = "elementary_", + "_elem_" = "_elementary_", + "_elec_" = "_electric_", + "_chg_" = "_charges_", + "_chg$" = "_charges", + "_ret_" = "_retirement_", + "_prop_" = "_property_", + "_nec_" = "_not_elsewhere_classified_", + "_nec$" = "_not_elsewhere_classified", + "_ltd_" = "_long_term_debt_", + "_ltd$" = "_long_term_debt", + "_unemp_" = "_unemployment_", + "^emp_" = "employee_", + "_emp_" = "_employee_", + "_ffc_" = "_full_faith_credit_", + "_ffc$" = "_full_faith_credit", + "_ng_" = "_nonguaranteed_", + "_ng$" = "_nonguaranteed", + "_util_" = "_utility_", + "_util$" = "_utility", + "_iss_" = "_issued_", + "_iss$" = "_issued", + "_fed_" = "_federal_", + "_fed$" = "_federal", + "^fed_" = "federal_", + "_lic_" = "_license_", + "_lic$" = "_license", + "_misc_" = "_miscellaneous_", + "_misc$" = "_miscellaneous", + "_gen_" = "_general_", + "_gen$" = "_general", + "_cur_" = "_current_", + "_cur$" = "_current", + "_cap_" = "_capital_", + "_cap$" = "_capital", + "_out_" = "_outstanding_", + "_out$" = "_outstanding", + "welf_" = "welfare_", + "__+" = "_"))) %>% + ## Step 2: Prefix revenue columns + dplyr::rename_with( + .cols = dplyr::matches("_rev_|_rev$|^total_revenue"), + .fn = ~ stringr::str_c("revenue_", .x) %>% + stringr::str_remove_all("_rev_|_rev$") %>% + stringr::str_replace("revenue_total_revenue", "revenue_total")) %>% + ## Step 3: Prefix tax columns (subset of revenue) + dplyr::rename_with( + .cols = dplyr::matches("_tax$|_taxes$|^total_taxes"), + .fn = ~ stringr::str_c("revenue_tax_", .x) %>% + stringr::str_remove_all("_tax$|_taxes$") %>% + stringr::str_replace("revenue_tax_total_taxes", "revenue_tax_total")) %>% + ## Step 4: Prefix expenditure columns + dplyr::rename_with( + .cols = dplyr::matches("_exp_|_exp$|_expend$|^total_expend"), + .fn = ~ stringr::str_c("expenditure_", .x) %>% + stringr::str_remove_all("_exp_|_exp$|_expend$|_expend_") %>% + stringr::str_replace("expenditure_total_expenditure", "expenditure_total")) %>% + ## Step 5: Clean up any double underscores or trailing underscores + dplyr::rename_with( + .cols = dplyr::everything(), + .fn = ~ .x %>% + stringr::str_replace_all("__+", "_") %>% + stringr::str_remove("_$")) + + df_final = df_renamed %>% + dplyr::transmute( + year = year4, + GEOID = stringr::str_c(fips_code_state, fips_county), + county_name = name, + ## Total revenues and major subtotals + revenue_total, + revenue_general = general_revenue, + revenue_tax_total, + revenue_tax_property, + revenue_tax_sales = revenue_tax_total_sales_gr_rec, + revenue_tax_income = revenue_tax_total_income, + revenue_intergovernmental_total = total_intergovernmental_revenue, + revenue_intergovernmental_federal = total_federal_intergovernmental_revenue, + revenue_intergovernmental_state = total_state_intergovernmental_revenue, + ## Total expenditures and major subtotals + expenditure_total, + expenditure_general = general_expenditure#, + # expenditure_capital_outlay = expenditure_total_capital_outlay, + # expenditure_construction = expenditure_total_construction, + # expenditure_salaries_wages = expenditure_total_salaries_wages, + # expenditure_intergovernmental = expenditure_total_intergovernmental, + # expenditure_education_total = expenditure_total_education, + # expenditure_public_safety = expenditure_total_police_protection, + # expenditure_health_hospitals = expenditure_total_health, + # expenditure_highways = expenditure_total_highways, + # expenditure_public_welfare = expenditure_total_public_welfare, + # expenditure_utility_total = expenditure_total_utility + ) + + return(df_final) } utils::globalVariables(c( - "amount_thousands", "data_year", "description", "enrollment", "government_type", - "imputation_alternative_source", "imputation_analyst_correction", "imputation_imputed", - "imputation_reported", "imputation_type", "imputation_unknown", "item", "population", - "unit_id", "year_data")) + "year4", "fips_code_state", "fips_county", "name", + "revenue_total", "revenue_total_general", "revenue_tax_total", "revenue_tax_property", + + "revenue_tax_total_sales_gross_receipts", "revenue_tax_total_income", + "revenue_total_intergovernmental", "revenue_total_federal_intergovernmental", + "revenue_total_state_intergovernmental", "revenue_total_charges_miscellaneous_general", + "revenue_total_utility", + "expenditure_total", "expenditure_total_general", "expenditure_total_capital_outlay", + "expenditure_total_construction", "expenditure_total_salaries_wages", + "expenditure_total_intergovernmental", "expenditure_total_education", + "expenditure_total_police_protection", "expenditure_total_health", + "expenditure_total_highways", "expenditure_total_public_welfare", + "expenditure_total_utility")) diff --git a/R/get_nfip_residential_penetration.R b/R/get_nfip_residential_penetration.R new file mode 100644 index 0000000..007999a --- /dev/null +++ b/R/get_nfip_residential_penetration.R @@ -0,0 +1,59 @@ +#' Get the share of residential structures covered by NFIP +#' +#' @param states NULL by default. +#' @param file_name The name (not full path) of the raw dataset. +#' +#' @return A tibble with the following columns: +#' \describe{ +#' \item{state_name}{State name} +#' \item{couty_name}{County name} +#' \item{GEOID}{Five digits county FIPS code} +#' \item{year_data}{The year of the data} +#' \item{penetration_rate}{Share of all residential structures insured by NFIP} +#' \item{penetration_rate_sfha}{Share of all residential structures in the Special Flood Hazard Area insured by NFIP} +#' \item{contracts_in_force}{Residential NFIP contracts currently in effect ("in force")} +#' \item{contracts_in_force_sfha}{Residential NFIP contracts in the Special Flood Hazard Area currently in effect ("in force")} +#' \item{residential_structures}{The number of residential structures, derived from the 2022 National Structure Inventory} +#' \item{residential_structures_sfha}{The number of residential structures in the Special Flood Hazard Area, derived from the 2022 National Structure Inventory} +#' } +#' +#' @export +#' @examples +#' \dontrun{ +#' get_nfip_residential_penetration() +#' } +get_nfip_residential_penetration <- function( + states = NULL, + file_name = "nfip_residential_penetration_rates_12_12_2025.csv") { + + inpath <- file.path( + get_box_path(), "hazards", "fema", "national-flood-insurance-program", "raw", file_name) + + if (!file.exists(inpath)) { stop("The provided `file_name` is invalid.") } + + df1 <- read.csv(file = inpath) %>% + janitor::clean_names() %>% + dplyr::transmute( + state_name = state, + county_name = county, + GEOID = stringr::str_pad(fips_code, side = "left", pad = "0", width = 5), + year_data = lubridate::year(as_of_date), + dplyr::across( + .cols = dplyr::matches("res"), + .fns = ~ .x)) %>% + dplyr::rename_with( + .cols = dplyr::matches("res"), + .fn = ~ .x %>% stringr::str_replace_all(c( + "res_penetration" = "penetration", + "res_contracts" = "contracts", + "res_structures" = "residential_structures", + "total_" = ""))) %>% + tibble::as_tibble() + + # Apply filter only when states is not NULL + if (!is.null(states)) { + df1 <- df1 %>% + dplyr::filter(state %in% states) } + + return(df1) +} \ No newline at end of file diff --git a/R/utilities.R b/R/utilities.R index ea00ea2..a40e9c4 100644 --- a/R/utilities.R +++ b/R/utilities.R @@ -413,8 +413,7 @@ inflation_adjust = function( names_suffix = NULL, base_year = 2024) { - inflation_data = readr::read_csv(here::here( - "data", "data-raw", "pce_index_annual_fred_2025_03_27.csv")) %>% + inflation_data = readr::read_csv(file.path(get_box_path(), "utilities", "pce_index_annual_fred_2025_03_27.csv")) %>% dplyr::transmute( inflation_year_ = lubridate::year(observation_date) %>% as.numeric(), pce_index = DPCERG3A086NBEA) diff --git a/_pkgdown.yml b/_pkgdown.yml index 422d2ab..dcea51e 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -21,6 +21,7 @@ reference: - get_ihp_registrations - get_nfip_policies - get_nfip_claims + - get_nfip_residential_penetration - get_public_assistance - get_sheldus - get_preliminary_damage_assessments diff --git a/man/get_business_patterns.Rd b/man/get_business_patterns.Rd index 6794aaa..0ab0d62 100644 --- a/man/get_business_patterns.Rd +++ b/man/get_business_patterns.Rd @@ -5,16 +5,16 @@ \title{Obtain County Business Patterns (CBP) Estimates per County} \usage{ get_business_patterns( - year = 2022, + year = 2023, geo = "county", naics_code_digits = 2, naics_codes = NULL ) } \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 2008-2023. +Earlier years use different NAICS classification systems that are not currently supported. +Default is 2023.} \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, @@ -89,7 +89,7 @@ and Public Administration (NAICS 92) \examples{ \dontrun{ get_business_patterns( - year = 2022, + year = 2023, naics_code_digits = 3) get_business_patterns( diff --git a/man/get_government_finances.Rd b/man/get_government_finances.Rd index 4117f1a..0db9da0 100644 --- a/man/get_government_finances.Rd +++ b/man/get_government_finances.Rd @@ -2,38 +2,68 @@ % Please edit documentation in R/get_government_finances.R \name{get_government_finances} \alias{get_government_finances} -\title{Get government unit-level expenses from the Census of Governments} +\title{Get county government revenues and expenditures} \usage{ -get_government_finances(year = 2022) -} -\arguments{ -\item{year}{A four-digit year. The default is 2022.} +get_government_finances() } \value{ -A dataframe containing government unit-level expenses for the specified year. -Columns include: +A dataframe containing county government-level revenues and expenditures by year. +All monetary values are in thousands of dollars. \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{year}{Year of the financial data.} +\item{GEOID}{Five-digit county FIPS code.} \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.} +\item{revenue_total}{Total county revenue.} +\item{revenue_general}{General revenue (excludes utilities, liquor stores, social insurance).} +\item{revenue_tax_total}{Total tax revenue.} +\item{revenue_tax_property}{Property tax revenue.} +\item{revenue_tax_sales}{Sales and gross receipts tax revenue.} +\item{revenue_tax_income}{Income tax revenue.} +\item{revenue_intergovernmental_total}{Total intergovernmental transfers received.} +\item{revenue_intergovernmental_federal}{Federal intergovernmental transfers.} +\item{revenue_intergovernmental_state}{State intergovernmental transfers.} +\item{revenue_charges_total}{Total charges and miscellaneous general revenue.} +\item{revenue_utility_total}{Utility revenue.} +\item{expenditure_total}{Total county expenditures.} +\item{expenditure_general}{General expenditures.} +\item{expenditure_capital_outlay}{Capital outlay spending.} +\item{expenditure_construction}{Construction spending.} +\item{expenditure_salaries_wages}{Salaries and wages.} +\item{expenditure_intergovernmental}{Intergovernmental transfers paid.} +\item{expenditure_education_total}{Education spending.} +\item{expenditure_public_safety}{Police protection spending.} +\item{expenditure_health_hospitals}{Health spending.} +\item{expenditure_highways}{Highway spending.} +\item{expenditure_public_welfare}{Public welfare spending.} +\item{expenditure_utility_total}{Utility expenditures.} } } \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. +Retrieves county government revenues and expenditures over time. } \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}. +Government Finances and Census of Governments, compiled per: Pierson K., Hand M., +and Thompson F. (2015). The Government Finance Database: A Common Resource for +Quantitative Research in Public Financial Analysis. PLoS ONE doi: +10.1371/journal.pone.0130119. See \url{https://my.willamette.edu/site/mba/public-datasets}. + +This is a survey in most years--i.e., only a subset of the full population of governments +comprises the sampling frame--but is a true census with relatively high response rates (90\%\%+) +in years ending in 2 and 7. + +Definitions of key constructs: + +\strong{Revenues} +\itemize{ +\item General revenue: All revenue not arising from utilities, liquor stores, or social insurance. +\item Intergovernmental revenue: Transfers from federal, state, or other local governments. +\item Current charges: Fees collected for providing services. +} + +\strong{Expenditures} +\itemize{ +\item General expenditure: All spending except utilities, liquor stores, or social insurance. +\item Capital outlay: Spending on construction and equipment. +} } diff --git a/man/get_naics_codes.Rd b/man/get_naics_codes.Rd index 839c4d0..0220214 100644 --- a/man/get_naics_codes.Rd +++ b/man/get_naics_codes.Rd @@ -4,11 +4,11 @@ \alias{get_naics_codes} \title{Get NAICS Codes for County Business Patterns} \usage{ -get_naics_codes(year = 2022, digits = 3) +get_naics_codes(year = 2023, digits = 3) } \arguments{ \item{year}{The vintage year for NAICS codes. Data are available from 1986 through 2023. -Default is 2022.} +Default is 2023.} \item{digits}{The number of digits for desired NAICS codes. Must be between 2 and 6. Default is 3. Two-digit codes represent broad industry sectors (20 codes), @@ -30,7 +30,7 @@ codes for use with \code{get_business_patterns()}. This is a wrapper around \examples{ \dontrun{ # Get all 2-digit NAICS codes -get_naics_codes(year = 2022, digits = 2) +get_naics_codes(year = 2023, digits = 2) # Get all 3-digit NAICS codes (default) get_naics_codes(year = 2022) diff --git a/man/get_nfip_residential_penetration.Rd b/man/get_nfip_residential_penetration.Rd new file mode 100644 index 0000000..717ee91 --- /dev/null +++ b/man/get_nfip_residential_penetration.Rd @@ -0,0 +1,39 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/get_nfip_residential_penetration.R +\name{get_nfip_residential_penetration} +\alias{get_nfip_residential_penetration} +\title{Get the share of residential structures covered by NFIP} +\usage{ +get_nfip_residential_penetration( + states = NULL, + file_name = "nfip_residential_penetration_rates_12_12_2025.csv" +) +} +\arguments{ +\item{states}{NULL by default.} + +\item{file_name}{The name (not full path) of the raw dataset.} +} +\value{ +A tibble with the following columns: +\describe{ +\item{state_name}{State name} +\item{couty_name}{County name} +\item{GEOID}{Five digits county FIPS code} +\item{year_data}{The year of the data} +\item{penetration_rate}{Share of all residential structures insured by NFIP} +\item{penetration_rate_sfha}{Share of all residential structures in the Special Flood Hazard Area insured by NFIP} +\item{contracts_in_force}{Residential NFIP contracts currently in effect ("in force")} +\item{contracts_in_force_sfha}{Residential NFIP contracts in the Special Flood Hazard Area currently in effect ("in force")} +\item{residential_structures}{The number of residential structures, derived from the 2022 National Structure Inventory} +\item{residential_structures_sfha}{The number of residential structures in the Special Flood Hazard Area, derived from the 2022 National Structure Inventory} +} +} +\description{ +Get the share of residential structures covered by NFIP +} +\examples{ +\dontrun{ +get_nfip_residential_penetration() +} +} diff --git a/renv.lock b/renv.lock index ff8b217..be82b41 100644 --- a/renv.lock +++ b/renv.lock @@ -1,6446 +1,6506 @@ -{ - "R": { - "Version": "4.4.0", - "Repositories": [ - { - "Name": "CRAN", - "URL": "https://cloud.r-project.org" - } - ] - }, - "Packages": { - "BH": { - "Package": "BH", - "Version": "1.87.0-1", - "Source": "Repository", - "Type": "Package", - "Title": "Boost C++ Header Files", - "Date": "2024-12-17", - "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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It support ANSI colors and text styles as well.", - "License": "MIT + file LICENSE", - "URL": "https://cli.r-lib.org, https://github.com/r-lib/cli", - "BugReports": "https://github.com/r-lib/cli/issues", - "Depends": [ - "R (>= 3.4)" - ], - "Imports": [ - "utils" - ], - "Suggests": [ - "callr", - "covr", - "crayon", - "digest", - "glue (>= 1.6.0)", - "grDevices", - "htmltools", - "htmlwidgets", - "knitr", - "methods", - "processx", - "ps (>= 1.3.4.9000)", - "rlang (>= 1.0.2.9003)", - "rmarkdown", - "rprojroot", - "rstudioapi", - "testthat (>= 3.2.0)", - "tibble", - "whoami", - "withr" - ], - "Config/Needs/website": "r-lib/asciicast, bench, brio, cpp11, decor, desc, fansi, prettyunits, sessioninfo, tidyverse/tidytemplate, usethis, vctrs", - "Config/testthat/edition": "3", - "Encoding": "UTF-8", - "RoxygenNote": "7.3.2", - "NeedsCompilation": "yes", - "Author": "Gábor Csárdi [aut, cre], Hadley Wickham [ctb], Kirill Müller [ctb], Salim Brüggemann [ctb] (), Posit Software, PBC [cph, fnd]", - "Maintainer": "Gábor Csárdi ", - "Repository": "https://packagemanager.posit.co/cran/latest" - }, - "clipr": { - "Package": "clipr", - "Version": "0.8.0", - "Source": "Repository", - "Type": "Package", - "Title": "Read and Write from the System Clipboard", - "Authors@R": "c( person(\"Matthew\", \"Lincoln\", , \"matthew.d.lincoln@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-4387-3384\")), person(\"Louis\", \"Maddox\", role = \"ctb\"), person(\"Steve\", \"Simpson\", role = \"ctb\"), person(\"Jennifer\", \"Bryan\", role = \"ctb\") )", - "Description": "Simple utility functions to read from and write to the Windows, OS X, and X11 clipboards.", - "License": "GPL-3", - "URL": "https://github.com/mdlincoln/clipr, http://matthewlincoln.net/clipr/", - "BugReports": "https://github.com/mdlincoln/clipr/issues", - "Imports": [ - "utils" - ], - "Suggests": [ - "covr", - "knitr", - "rmarkdown", - "rstudioapi (>= 0.5)", - "testthat (>= 2.0.0)" - ], - "VignetteBuilder": "knitr", - "Encoding": "UTF-8", - "Language": "en-US", - "RoxygenNote": "7.1.2", - "SystemRequirements": "xclip (https://github.com/astrand/xclip) or xsel (http://www.vergenet.net/~conrad/software/xsel/) for accessing the X11 clipboard, or wl-clipboard (https://github.com/bugaevc/wl-clipboard) for systems using Wayland.", - "NeedsCompilation": "no", - "Author": "Matthew Lincoln [aut, cre] (), Louis Maddox [ctb], Steve Simpson [ctb], Jennifer Bryan [ctb]", - "Maintainer": "Matthew Lincoln ", - "Repository": "CRAN" - }, - "conflicted": { - "Package": "conflicted", - "Version": "1.2.0", - "Source": "Repository", - "Title": "An Alternative Conflict Resolution Strategy", - "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@rstudio.com\", role = c(\"aut\", \"cre\")), person(\"RStudio\", role = c(\"cph\", \"fnd\")) )", - "Description": "R's default conflict management system gives the most recently loaded package precedence. This can make it hard to detect conflicts, particularly when they arise because a package update creates ambiguity that did not previously exist. 'conflicted' takes a different approach, making every conflict an error and forcing you to choose which function to use.", - "License": "MIT + file LICENSE", - "URL": "https://conflicted.r-lib.org/, https://github.com/r-lib/conflicted", - "BugReports": "https://github.com/r-lib/conflicted/issues", - "Depends": [ - "R (>= 3.2)" - ], - "Imports": [ - "cli (>= 3.4.0)", - "memoise", - "rlang (>= 1.0.0)" - ], - "Suggests": [ - "callr", - "covr", - "dplyr", - "Matrix", - "methods", - "pkgload", - "testthat (>= 3.0.0)", - "withr" - ], - "Config/Needs/website": "tidyverse/tidytemplate", - "Config/testthat/edition": "3", - "Encoding": "UTF-8", - "RoxygenNote": "7.2.3", - "NeedsCompilation": "no", - "Author": "Hadley Wickham [aut, cre], RStudio [cph, fnd]", - "Maintainer": "Hadley Wickham ", - "Repository": "CRAN" - }, - "coro": { - "Package": "coro", - "Version": "1.1.0", - "Source": "Repository", - "Title": "'Coroutines' for R", - "Authors@R": "c( person(\"Lionel\", \"Henry\", , \"lionel@posit.co\", role = c(\"aut\", \"cre\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", - "Description": "Provides 'coroutines' for R, a family of functions that can be suspended and resumed later on. This includes 'async' functions (which await) and generators (which yield). 'Async' functions are based on the concurrency framework of the 'promises' package. Generators are based on a dependency free iteration protocol defined in 'coro' and are compatible with iterators from the 'reticulate' package.", - "License": "MIT + file LICENSE", - "URL": "https://github.com/r-lib/coro, https://coro.r-lib.org/", - "BugReports": "https://github.com/r-lib/coro/issues", - "Depends": [ - "R (>= 3.5.0)" - ], - "Imports": [ - "rlang (>= 0.4.12)" - ], - "Suggests": [ - "knitr", - "later (>= 1.1.0)", - "magrittr (>= 2.0.0)", - "promises", - "reticulate", - "rmarkdown", - "testthat (>= 3.0.0)" - ], - "VignetteBuilder": "knitr", - "Config/Needs/website": "tidyverse/tidytemplate", - "Config/testthat/edition": "3", - "Encoding": "UTF-8", - "RoxygenNote": "7.3.2", - "NeedsCompilation": "no", - "Author": "Lionel Henry [aut, cre], Posit Software, PBC [cph, fnd]", - "Maintainer": "Lionel Henry ", - "Repository": "https://packagemanager.posit.co/cran/latest" - }, - "cpp11": { - "Package": "cpp11", - "Version": "0.5.2", - "Source": "Repository", - "Title": "A C++11 Interface for R's C Interface", - "Authors@R": "c( person(\"Davis\", \"Vaughan\", email = \"davis@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-4777-038X\")), person(\"Jim\",\"Hester\", role = \"aut\", comment = c(ORCID = \"0000-0002-2739-7082\")), person(\"Romain\", \"François\", role = \"aut\", comment = c(ORCID = \"0000-0002-2444-4226\")), person(\"Benjamin\", \"Kietzman\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", - "Description": "Provides a header only, C++11 interface to R's C interface. Compared to other approaches 'cpp11' strives to be safe against long jumps from the C API as well as C++ exceptions, conform to normal R function semantics and supports interaction with 'ALTREP' vectors.", - "License": "MIT + file LICENSE", - "URL": "https://cpp11.r-lib.org, https://github.com/r-lib/cpp11", - "BugReports": "https://github.com/r-lib/cpp11/issues", - "Depends": [ - "R (>= 4.0.0)" - ], - "Suggests": [ - "bench", - "brio", - "callr", - "cli", - "covr", - "decor", - "desc", - "ggplot2", - "glue", - "knitr", - "lobstr", - "mockery", - "progress", - "rmarkdown", - "scales", - "Rcpp", - "testthat (>= 3.2.0)", - "tibble", - "utils", - "vctrs", - "withr" - ], - "VignetteBuilder": "knitr", - "Config/Needs/website": "tidyverse/tidytemplate", - "Config/testthat/edition": "3", - "Config/Needs/cpp11/cpp_register": "brio, cli, decor, desc, glue, tibble, vctrs", - "Encoding": "UTF-8", - "RoxygenNote": "7.3.2", - "NeedsCompilation": "no", - "Author": "Davis Vaughan [aut, cre] (), Jim Hester [aut] (), Romain François [aut] (), Benjamin Kietzman [ctb], Posit Software, PBC [cph, fnd]", - "Maintainer": "Davis Vaughan ", - "Repository": "CRAN" - }, - "crayon": { - "Package": "crayon", - "Version": "1.5.3", - "Source": "Repository", - "Title": "Colored Terminal Output", - "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Brodie\", \"Gaslam\", , \"brodie.gaslam@yahoo.com\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", - "Description": "The crayon package is now superseded. Please use the 'cli' package for new projects. Colored terminal output on terminals that support 'ANSI' color and highlight codes. It also works in 'Emacs' 'ESS'. 'ANSI' color support is automatically detected. Colors and highlighting can be combined and nested. New styles can also be created easily. This package was inspired by the 'chalk' 'JavaScript' project.", - "License": "MIT + file LICENSE", - "URL": "https://r-lib.github.io/crayon/, https://github.com/r-lib/crayon", - "BugReports": "https://github.com/r-lib/crayon/issues", - "Imports": [ - "grDevices", - "methods", - "utils" - ], - "Suggests": [ - "mockery", - "rstudioapi", - "testthat", - "withr" - ], - "Config/Needs/website": "tidyverse/tidytemplate", - "Encoding": "UTF-8", - "RoxygenNote": "7.3.1", - "Collate": "'aaa-rstudio-detect.R' 'aaaa-rematch2.R' 'aab-num-ansi-colors.R' 'aac-num-ansi-colors.R' 'ansi-256.R' 'ansi-palette.R' 'combine.R' 'string.R' 'utils.R' 'crayon-package.R' 'disposable.R' 'enc-utils.R' 'has_ansi.R' 'has_color.R' 'link.R' 'styles.R' 'machinery.R' 'parts.R' 'print.R' 'style-var.R' 'show.R' 'string_operations.R'", - "NeedsCompilation": "no", - "Author": "Gábor Csárdi [aut, cre], Brodie Gaslam [ctb], Posit Software, PBC [cph, fnd]", - "Maintainer": "Gábor Csárdi ", - "Repository": "CRAN" - }, - "curl": { - "Package": "curl", - "Version": "6.3.0", - "Source": "Repository", - "Type": "Package", - "Title": "A Modern and Flexible Web Client for R", - "Authors@R": "c( person(\"Jeroen\", \"Ooms\", role = c(\"aut\", \"cre\"), email = \"jeroenooms@gmail.com\", comment = c(ORCID = \"0000-0002-4035-0289\")), person(\"Hadley\", \"Wickham\", role = \"ctb\"), person(\"Posit Software, PBC\", role = \"cph\"))", - "Description": "Bindings to 'libcurl' for performing fully configurable HTTP/FTP requests where responses can be processed in memory, on disk, or streaming via the callback or connection interfaces. 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. Offers a natural and flexible syntax, for faster development.", - "License": "MPL-2.0 | file LICENSE", - "URL": "https://r-datatable.com, https://Rdatatable.gitlab.io/data.table, https://github.com/Rdatatable/data.table", - "BugReports": "https://github.com/Rdatatable/data.table/issues", - "VignetteBuilder": "knitr", - "Encoding": "UTF-8", - "ByteCompile": "TRUE", - "Authors@R": "c( person(\"Tyson\",\"Barrett\", role=c(\"aut\",\"cre\"), email=\"t.barrett88@gmail.com\", comment = c(ORCID=\"0000-0002-2137-1391\")), person(\"Matt\",\"Dowle\", role=\"aut\", email=\"mattjdowle@gmail.com\"), person(\"Arun\",\"Srinivasan\", role=\"aut\", email=\"asrini@pm.me\"), person(\"Jan\",\"Gorecki\", role=\"aut\"), person(\"Michael\",\"Chirico\", role=\"aut\", comment = c(ORCID=\"0000-0003-0787-087X\")), person(\"Toby\",\"Hocking\", role=\"aut\", comment = c(ORCID=\"0000-0002-3146-0865\")), person(\"Benjamin\",\"Schwendinger\",role=\"aut\", comment = c(ORCID=\"0000-0003-3315-8114\")), person(\"Ivan\", \"Krylov\", role=\"aut\", email=\"ikrylov@disroot.org\", comment = c(ORCID=\"0000-0002-0172-3812\")), person(\"Pasha\",\"Stetsenko\", role=\"ctb\"), person(\"Tom\",\"Short\", role=\"ctb\"), person(\"Steve\",\"Lianoglou\", role=\"ctb\"), person(\"Eduard\",\"Antonyan\", role=\"ctb\"), person(\"Markus\",\"Bonsch\", role=\"ctb\"), person(\"Hugh\",\"Parsonage\", role=\"ctb\"), person(\"Scott\",\"Ritchie\", role=\"ctb\"), person(\"Kun\",\"Ren\", role=\"ctb\"), person(\"Xianying\",\"Tan\", role=\"ctb\"), person(\"Rick\",\"Saporta\", role=\"ctb\"), person(\"Otto\",\"Seiskari\", role=\"ctb\"), person(\"Xianghui\",\"Dong\", role=\"ctb\"), person(\"Michel\",\"Lang\", role=\"ctb\"), person(\"Watal\",\"Iwasaki\", role=\"ctb\"), person(\"Seth\",\"Wenchel\", role=\"ctb\"), person(\"Karl\",\"Broman\", role=\"ctb\"), person(\"Tobias\",\"Schmidt\", role=\"ctb\"), person(\"David\",\"Arenburg\", role=\"ctb\"), person(\"Ethan\",\"Smith\", role=\"ctb\"), person(\"Francois\",\"Cocquemas\", role=\"ctb\"), person(\"Matthieu\",\"Gomez\", role=\"ctb\"), person(\"Philippe\",\"Chataignon\", role=\"ctb\"), person(\"Nello\",\"Blaser\", role=\"ctb\"), person(\"Dmitry\",\"Selivanov\", role=\"ctb\"), person(\"Andrey\",\"Riabushenko\", role=\"ctb\"), person(\"Cheng\",\"Lee\", role=\"ctb\"), person(\"Declan\",\"Groves\", role=\"ctb\"), person(\"Daniel\",\"Possenriede\", role=\"ctb\"), person(\"Felipe\",\"Parages\", role=\"ctb\"), person(\"Denes\",\"Toth\", role=\"ctb\"), person(\"Mus\",\"Yaramaz-David\", role=\"ctb\"), person(\"Ayappan\",\"Perumal\", role=\"ctb\"), person(\"James\",\"Sams\", role=\"ctb\"), person(\"Martin\",\"Morgan\", role=\"ctb\"), person(\"Michael\",\"Quinn\", role=\"ctb\"), person(\"@javrucebo\",\"\", role=\"ctb\"), person(\"@marc-outins\",\"\", role=\"ctb\"), person(\"Roy\",\"Storey\", role=\"ctb\"), person(\"Manish\",\"Saraswat\", role=\"ctb\"), person(\"Morgan\",\"Jacob\", role=\"ctb\"), person(\"Michael\",\"Schubmehl\", role=\"ctb\"), person(\"Davis\",\"Vaughan\", role=\"ctb\"), person(\"Leonardo\",\"Silvestri\", role=\"ctb\"), person(\"Jim\",\"Hester\", role=\"ctb\"), person(\"Anthony\",\"Damico\", role=\"ctb\"), person(\"Sebastian\",\"Freundt\", role=\"ctb\"), person(\"David\",\"Simons\", role=\"ctb\"), person(\"Elliott\",\"Sales de Andrade\", role=\"ctb\"), person(\"Cole\",\"Miller\", role=\"ctb\"), person(\"Jens Peder\",\"Meldgaard\", role=\"ctb\"), person(\"Vaclav\",\"Tlapak\", role=\"ctb\"), person(\"Kevin\",\"Ushey\", role=\"ctb\"), person(\"Dirk\",\"Eddelbuettel\", role=\"ctb\"), person(\"Tony\",\"Fischetti\", role=\"ctb\"), person(\"Ofek\",\"Shilon\", role=\"ctb\"), person(\"Vadim\",\"Khotilovich\", role=\"ctb\"), person(\"Hadley\",\"Wickham\", role=\"ctb\"), person(\"Bennet\",\"Becker\", role=\"ctb\"), person(\"Kyle\",\"Haynes\", role=\"ctb\"), person(\"Boniface Christian\",\"Kamgang\", role=\"ctb\"), person(\"Olivier\",\"Delmarcell\", role=\"ctb\"), person(\"Josh\",\"O'Brien\", role=\"ctb\"), person(\"Dereck\",\"de Mezquita\", role=\"ctb\"), person(\"Michael\",\"Czekanski\", role=\"ctb\"), person(\"Dmitry\", \"Shemetov\", role=\"ctb\"), person(\"Nitish\", \"Jha\", role=\"ctb\"), person(\"Joshua\", \"Wu\", role=\"ctb\"), person(\"Iago\", \"Giné-Vázquez\", role=\"ctb\"), person(\"Anirban\", \"Chetia\", role=\"ctb\"), person(\"Doris\", \"Amoakohene\", role=\"ctb\"), person(\"Angel\", \"Feliz\", role=\"ctb\"), person(\"Michael\",\"Young\", role=\"ctb\"), person(\"Mark\", \"Seeto\", role=\"ctb\"), person(\"Philippe\", \"Grosjean\", role=\"ctb\"), person(\"Vincent\", \"Runge\", role=\"ctb\"), person(\"Christian\", \"Wia\", role=\"ctb\"), person(\"Elise\", \"Maigné\", role=\"ctb\"), person(\"Vincent\", \"Rocher\", role=\"ctb\"), person(\"Vijay\", \"Lulla\", role=\"ctb\"), person(\"Aljaž\", \"Sluga\", role=\"ctb\"), person(\"Bill\", \"Evans\", role=\"ctb\") )", - 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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. 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'Parquet' files are an open-source, column-oriented data storage format from Apache (), now popular across programming languages. This implementation converts simple feature list geometries into well-known binary format for use by 'arrow', and coordinate reference system information is maintained in a standard metadata format.", - "License": "MIT + file LICENSE", - "URL": "https://github.com/wcjochem/sfarrow, https://wcjochem.github.io/sfarrow/", - "BugReports": "https://github.com/wcjochem/sfarrow/issues", - "Encoding": "UTF-8", - "RoxygenNote": "7.1.1", - "Imports": [ - "sf", - "arrow", - "jsonlite", - "dplyr" - ], - "Suggests": [ - "knitr", - "rmarkdown" - ], - "VignetteBuilder": "knitr", - "NeedsCompilation": "no", - "Author": "Chris Jochem [aut, cre] ()", - "Maintainer": "Chris Jochem ", - "Repository": "CRAN" - }, - "snakecase": { - "Package": "snakecase", - "Version": "0.11.1", - "Source": "Repository", - "Date": "2023-08-27", - "Title": "Convert Strings into any Case", - "Description": "A consistent, flexible and easy to use tool to parse and convert strings into cases like snake or camel among others.", - "Authors@R": "c( person(\"Malte\", \"Grosser\", , \"malte.grosser@gmail.com\", role = c(\"aut\", \"cre\")))", - "Maintainer": "Malte Grosser ", - "Depends": [ - "R (>= 3.2)" - ], - "Imports": [ - "stringr", - "stringi" - ], - "Suggests": [ - "testthat", - "covr", - "tibble", - "purrrlyr", - "knitr", - "rmarkdown", - "magrittr" - ], - "URL": "https://github.com/Tazinho/snakecase", - "BugReports": "https://github.com/Tazinho/snakecase/issues", - "Encoding": "UTF-8", - "License": "GPL-3", - "RoxygenNote": "6.1.1", - "VignetteBuilder": "knitr", - "NeedsCompilation": "no", - "Author": "Malte Grosser [aut, cre]", - "Repository": "CRAN" - }, - "stringi": { - "Package": "stringi", - "Version": "1.8.7", - "Source": "Repository", - "Date": "2025-03-27", - "Title": "Fast and Portable Character String Processing Facilities", - "Description": "A collection of character string/text/natural language processing tools for pattern searching (e.g., with 'Java'-like regular expressions or the 'Unicode' collation algorithm), random string generation, case mapping, string transliteration, concatenation, sorting, padding, wrapping, Unicode normalisation, date-time formatting and parsing, and many more. They are fast, consistent, convenient, and - thanks to 'ICU' (International Components for Unicode) - portable across all locales and platforms. 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Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. 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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 (>= 4.1)" + ], + "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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Gray [ctb], Shabbh E. Banks [ctb], Serina Robinson [ctb], Roger Bivand [ctb], Riinu Ots [ctb], Nicholas Williams [ctb], Nina Jakobsen [ctb], Michael Weylandt [ctb], Lisa Lendway [ctb], Karl Hailperin [ctb], Josue Rodriguez [ctb], Jenny Bryan [ctb], Chris Jarvis [ctb], Greg Macfarlane [ctb], Brian Mannakee [ctb], Drew Tyre [ctb], Shreyas Singh [ctb], Laurens Geffert [ctb], Hong Ooi [ctb], Henrik Bengtsson [ctb], Eduard Szocs [ctb], David Hugh-Jones [ctb], Matthieu Stigler [ctb], Hugo Tavares [ctb] (ORCID: ), R. Willem Vervoort [ctb], Brenton M. Wiernik [ctb], Josh Yamamoto [ctb], Jasme Lee [ctb], Taren Sanders [ctb] (ORCID: ), Ilaria Prosdocimi [ctb] (ORCID: ), Daniel D. Sjoberg [ctb] (ORCID: ), Alex Reinhart [ctb] (ORCID: )", + "Maintainer": "Emil Hvitfeldt ", + "Repository": "https://packagemanager.posit.co/cran/latest" + }, + "bslib": { + "Package": "bslib", + "Version": "0.9.0", + "Source": "Repository", + "Title": "Custom 'Bootstrap' 'Sass' Themes for 'shiny' and 'rmarkdown'", + "Authors@R": "c( person(\"Carson\", \"Sievert\", , \"carson@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-4958-2844\")), person(\"Joe\", \"Cheng\", , \"joe@posit.co\", role = \"aut\"), person(\"Garrick\", \"Aden-Buie\", , \"garrick@posit.co\", role = \"aut\", comment = c(ORCID = \"0000-0002-7111-0077\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")), person(, \"Bootstrap contributors\", role = \"ctb\", comment = \"Bootstrap library\"), person(, \"Twitter, Inc\", role = \"cph\", comment = \"Bootstrap library\"), person(\"Javi\", \"Aguilar\", role = c(\"ctb\", \"cph\"), comment = \"Bootstrap colorpicker library\"), person(\"Thomas\", \"Park\", role = c(\"ctb\", \"cph\"), comment = \"Bootswatch library\"), person(, \"PayPal\", role = c(\"ctb\", \"cph\"), comment = \"Bootstrap accessibility plugin\") )", + "Description": "Simplifies custom 'CSS' styling of both 'shiny' and 'rmarkdown' via 'Bootstrap' 'Sass'. 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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. 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": "CRAN" + }, + "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": "CRAN" + }, + "knitr": { + "Package": "knitr", + "Version": "1.51", + "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\", 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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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High-performance constructors and exporters provide high compatibility with existing spatial packages, transformers construct new geometries from existing geometries, predicates provide a means to select geometries based on spatial relationships, and accessors extract information about geometries.", + "License": "Apache License (== 2.0)", + "Encoding": "UTF-8", + "LazyData": "true", + "RoxygenNote": "7.3.2", + "SystemRequirements": "cmake, OpenSSL >= 1.0.1, Abseil >= 20230802.0", + "LinkingTo": [ + "Rcpp", + "wk" + ], + "Imports": [ + "Rcpp", + "wk (>= 0.6.0)" + ], + "Suggests": [ + "bit64", + "testthat (>= 3.0.0)", + "vctrs" + ], + "URL": "https://r-spatial.github.io/s2/, https://github.com/r-spatial/s2, http://s2geometry.io/", + "BugReports": "https://github.com/r-spatial/s2/issues", + "Depends": [ + "R (>= 3.0.0)" + ], + "Config/testthat/edition": "3", + "NeedsCompilation": "yes", + "Author": "Dewey Dunnington [aut] (ORCID: ), Edzer Pebesma [aut, cre] (ORCID: ), Ege Rubak [aut], Jeroen Ooms [ctb] (configure script), Google, Inc. 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+ "VignetteBuilder": "knitr", + "Config/testthat/edition": "3", + "NeedsCompilation": "yes", + "Author": "Joe Cheng [aut], Timothy Mastny [aut], Richard Iannone [aut] (), Barret Schloerke [aut] (), Carson Sievert [aut, cre] (), Christophe Dervieux [ctb] (), RStudio [cph, fnd], Sass Open Source Foundation [ctb, cph] (LibSass library), Greter Marcel [ctb, cph] (LibSass library), Mifsud Michael [ctb, cph] (LibSass library), Hampton Catlin [ctb, cph] (LibSass library), Natalie Weizenbaum [ctb, cph] (LibSass library), Chris Eppstein [ctb, cph] (LibSass library), Adams Joseph [ctb, cph] (json.cpp), Trifunovic Nemanja [ctb, cph] (utf8.h)", + "Maintainer": "Carson Sievert ", + "Repository": "CRAN" + }, + "scales": { + "Package": "scales", + "Version": "1.4.0", + "Source": "Repository", + "Title": "Scale Functions for Visualization", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = \"aut\"), person(\"Thomas Lin\", \"Pedersen\", , \"thomas.pedersen@posit.co\", 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an equivalent XPath expression. This allows us to use CSS selectors when working with the XML package as it can only evaluate XPath expressions. Also provided are convenience functions useful for using CSS selectors on XML nodes. 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": "1.0-24", + "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. Binds to 'GDAL' for reading and writing data, to 'GEOS' for geometrical operations, and to 'PROJ' for projection conversions and datum transformations. Uses by default the 's2' package for geometry operations on geodetic (long/lat degree) coordinates.", + "License": "GPL-2 | MIT + file LICENSE", + "URL": "https://r-spatial.github.io/sf/, https://github.com/r-spatial/sf", + "BugReports": "https://github.com/r-spatial/sf/issues", + "Depends": [ + "methods", + "R (>= 3.3.0)" + ], + "Imports": [ + "classInt (>= 0.4-1)", + "DBI (>= 0.8)", + "graphics", + "grDevices", + "grid", + "magrittr", + "s2 (>= 1.1.0)", + "stats", + "tools", + "units (>= 0.7-0)", + "utils" + ], + "Suggests": [ + "blob", + "nanoarrow", + "covr", + "dplyr (>= 1.0.0)", + "ggplot2", + "knitr", + "lwgeom (>= 0.2-14)", + "maps", + "mapview", + "Matrix", + "microbenchmark", + "odbc", + "pbapply", + "pillar", + "pool", + "raster", + "rlang", + "rmarkdown", + "RPostgres (>= 1.1.0)", + "RPostgreSQL", + "RSQLite", + "sp (>= 1.2-4)", + "spatstat (>= 2.0-1)", + "spatstat.geom", + "spatstat.random", + "spatstat.linnet", + "spatstat.utils", + "stars (>= 0.6-0)", + "terra", + "testthat (>= 3.0.0)", + "tibble (>= 1.4.1)", + "tidyr (>= 1.2.0)", + "tidyselect (>= 1.0.0)", + "tmap (>= 2.0)", + "vctrs", + "wk (>= 0.9.0)" + ], + "LinkingTo": [ + "Rcpp" + ], + "VignetteBuilder": "knitr", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "Config/testthat/edition": "2", + "Config/needs/coverage": "XML", + "SystemRequirements": "GDAL (>= 2.0.1), GEOS (>= 3.4.0), PROJ (>= 4.8.0), sqlite3", + "Collate": "'RcppExports.R' 'init.R' 'import-standalone-s3-register.R' 'crs.R' 'bbox.R' 'read.R' 'db.R' 'sfc.R' 'sfg.R' 'sf.R' 'bind.R' 'wkb.R' 'wkt.R' 'plot.R' 'geom-measures.R' 'geom-predicates.R' 'geom-transformers.R' 'transform.R' 'proj.R' 'sp.R' 'grid.R' 'arith.R' 'tidyverse.R' 'tidyverse-vctrs.R' 'cast_sfg.R' 'cast_sfc.R' 'graticule.R' 'datasets.R' 'aggregate.R' 'agr.R' 'maps.R' 'join.R' 'sample.R' 'valid.R' 'collection_extract.R' 'jitter.R' 'sgbp.R' 'spatstat.R' 'stars.R' 'crop.R' 'gdal_utils.R' 'nearest.R' 'normalize.R' 'sf-package.R' 'defunct.R' 'z_range.R' 'm_range.R' 'shift_longitude.R' 'make_grid.R' 's2.R' 'terra.R' 'geos-overlayng.R' 'break_antimeridian.R'", + "NeedsCompilation": "yes", + "Author": "Edzer Pebesma [aut, cre] (ORCID: ), Roger Bivand [ctb] (ORCID: ), Etienne Racine [ctb], Michael Sumner [ctb], Ian Cook [ctb], Tim Keitt [ctb], Robin Lovelace [ctb], Hadley Wickham [ctb], Jeroen Ooms [ctb] (ORCID: ), Kirill Müller [ctb], Thomas Lin Pedersen [ctb], Dan Baston [ctb], Dewey Dunnington [ctb] (ORCID: )", + "Maintainer": "Edzer Pebesma ", + "Repository": "CRAN" + }, + "sfarrow": { + "Package": "sfarrow", + "Version": "0.4.1", + "Source": "Repository", + "Title": "Read/Write Simple Feature Objects ('sf') with 'Apache' 'Arrow'", + "Date": "2021-10-25", + "Authors@R": "person(given = \"Chris\", family = \"Jochem\", role = c(\"aut\", \"cre\"), email = \"w.c.jochem@soton.ac.uk\", comment = c(ORCID = \"0000-0003-2192-5988\"))", + "Description": "Support for reading/writing simple feature ('sf') spatial objects from/to 'Parquet' files. 'Parquet' files are an open-source, column-oriented data storage format from Apache (), now popular across programming languages. This implementation converts simple feature list geometries into well-known binary format for use by 'arrow', and coordinate reference system information is maintained in a standard metadata format.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/wcjochem/sfarrow, https://wcjochem.github.io/sfarrow/", + "BugReports": "https://github.com/wcjochem/sfarrow/issues", + "Encoding": "UTF-8", + "RoxygenNote": "7.1.1", + "Imports": [ + "sf", + "arrow", + "jsonlite", + "dplyr" + ], + "Suggests": [ + "knitr", + "rmarkdown" + ], + "VignetteBuilder": "knitr", + "NeedsCompilation": "no", + "Author": "Chris Jochem [aut, cre] ()", + "Maintainer": "Chris Jochem ", + "Repository": "CRAN" + }, + "skimr": { + "Package": "skimr", + "Version": "2.2.2", + "Source": "Repository", + "Title": "Compact and Flexible Summaries of Data", + "Authors@R": "c(person(given = \"Elin\", family = \"Waring\", role = c(\"cre\", \"aut\"), email = \"elin.waring@gmail.com\"), person(given = \"Michael\", family = \"Quinn\", role = \"aut\", email = \"msquinn@google.com\"), person(given = \"Amelia\", family = \"McNamara\", role = \"aut\", email = \"amcnamara@smith.edu\"), person(given = \"Eduardo\", family = \"Arino de la Rubia\", role = \"aut\", email = \"earino@gmail.com\"), person(given = \"Hao\", family = \"Zhu\", role = \"aut\", email = \"haozhu233@gmail.com\"), person(given = \"Julia\", family = \"Lowndes\", role = \"ctb\", email = \"lowndes@nceas.ucsb.edu\"), person(given = \"Shannon\", family = \"Ellis\", role = \"aut\", email = \"sellis18@jhmi.edu\"), person(given = \"Hope\", family = \"McLeod\", role = \"ctb\", email = \"hmgit2@gmail.com\"), person(given = \"Hadley\", family = \"Wickham\", role = \"ctb\", email = \"hadley@rstudio.com\"), person(given = \"Kirill\", family = \"Müller\", role = \"ctb\", email = \"krlmlr+r@mailbox.org\"), person(family = \"RStudio, Inc.\", role = \"cph\", comment = \"Spark functions\"), person(given = \"Connor\", family = \"Kirkpatrick\", role = \"ctb\", email = \"hello@connorkirkpatrick.com\"), person(given = \"Scott\", family = \"Brenstuhl\", role = \"ctb\", email = \"brenstsr@miamioh.edu\"), person(given = \"Patrick\", family = \"Schratz\", role = \"ctb\", email = \"patrick.schratz@gmail.com\"), person(given = \"lbusett\", role = \"ctb\", email = \"lbusett@gmail.com\"), person(given = \"Mikko\", family = \"Korpela\", role = \"ctb\", email = \"mvkorpel@iki.fi\"), person(given = \"Jennifer\", family = \"Thompson\", role = \"ctb\", email = \"thompson.jennifer@gmail.com\"), person(given = \"Harris\", family = \"McGehee\", role = \"ctb\", email = \"mcgehee.harris@gmail.com\"), person(given = \"Mark\", family = \"Roepke\", role = \"ctb\", email = \"mroepke5@gmail.com\"), person(given = \"Patrick\", family = \"Kennedy\", role = \"ctb\", email = \"pkqstr@protonmail.com\"), person(given = \"Daniel\", family = \"Possenriede\", role = \"ctb\", email = \"possenriede@gmail.com\"), person(given = \"David\", family = \"Zimmermann\", role = \"ctb\", email = \"david_j_zimmermann@hotmail.com\"), person(given = \"Kyle\", family = \"Butts\", role =\"ctb\", email = \"buttskyle96@gmail.com\"), person(given = \"Bastian\", family = \"Torges\", role =\"ctb\", email = \"bastian.torges@gmail.com\"), person(given = \"Rick\", family = \"Saporta\", role = \"ctb\", email = \"Rick@TheFarmersDog.com\"), person(given = \"Henry\", family = \"Morgan Stewart\", role = \"ctb\", email = \"henry.morganstewart@gmail.com\"), person(given = \"Olivier\", family = \"Roy\", role = \"ctb\") )", + "Description": "A simple to use summary function that can be used with pipes and displays nicely in the console. 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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": "https://packagemanager.posit.co/cran/latest" + }, + "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": "https://packagemanager.posit.co/cran/latest" + } + } +} diff --git a/vignettes/CLAUDE.md b/vignettes/CLAUDE.md new file mode 100644 index 0000000..adfdcb1 --- /dev/null +++ b/vignettes/CLAUDE.md @@ -0,0 +1,7 @@ + +# Recent Activity + + + +*No recent activity* + \ No newline at end of file diff --git a/vignettes/economic_recovery_factsheet.Rmd b/vignettes/economic_recovery_factsheet.Rmd new file mode 100644 index 0000000..009e827 --- /dev/null +++ b/vignettes/economic_recovery_factsheet.Rmd @@ -0,0 +1,671 @@ +--- +title: "Economic Recovery Post-Disaster" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Economic Recovery Post-Disaster} + %\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 = 1000, + eval = TRUE, + echo = TRUE) + +options(scipen = 999) +``` + +## Overview + +Because post-event data for the affected area will not be available for months or years, this approach identifies historical comparison—counties that experienced similar disasters and had similar pre-disaster characteristics—in the past—to highlight various recovery trajectories. + +Contents: + +1. The affected county's pre-disaster economic baseline +2. Employment, business, and local government finance trajectories using an event-study framework +4. Typical PA/IHP/SBA flows from federal programs (post-disaster period only) + +## Notes + +Critical to this approach is an observable pre-period, especially for the disaster-impacted county (which has no post-period data). But because of data lags, the pre-period will never run up through 2026 (current year at time of writing)--at best, it will go through ~2024 (ACS) or in most cases only 2023 (County Business Patterns, government finances). + +Accordingly, we want our pre-period to be long--say, 8 years preceding the disaster, if possible--so that we have multiple years of observation, even for the disaster-affected county. But for our comparison counties, we are hemmed in on the other side--we want to have post-period observations for these, so the comparison disaster ideally occurs between 2018-2020, giving us 3-5 post-period years. However, this requires a pre-period dating back to as early as 2010... which is not covered in many datasets (e.g., ACS). Thus, we have a tension in identifying overlapping pre-period timelines for our comparison and affected +counties due to historical data coverage. + +## Setup + +```{r setup} +library(climateapi) +library(tidyverse) +library(urbnthemes) +library(crosswalk) + +set_urbn_defaults(style = "print") +``` + +## Parameters + +Define the affected county and disaster characteristics. These would be updated for each new event. +```{r parameters} +# Affected county +affected_county_fips <- "12087" +affected_county_name <- "Monroe County, FL" +affected_state <- "FL" + +# Disaster characteristics +disaster_type <- "Hurricane" +disaster_year <- 2026 + +# Event-study window +years_before <- 6 +years_after <- 4 +``` + +### Preceding Disasters +```{r} +disasters = get_fema_disaster_declarations(api = FALSE) +disasters_affected = disasters %>% + filter(GEOID %in% affected_county_fips, incidents_natural_hazard > 0) +``` + +--- + +# Section 1: The Affected County Baseline Profile + +Establish the pre-disaster economic conditions of the affected county. + +## Sociodemographic Characteristics + +```{r} +acs_df_2023 = arrow::read_parquet(file.path(get_box_path(), "sociodemographics", "acs", "acs_county_2023.parquet")) + +acs_matching_variables = c( + "median_household_income_universe_allraces", + "race_personofcolor_percent", + "population_density_land_sq_kilometer", + "total_population_universe", + "educational_attainment_degree_bachelors_percent") +``` + + +## Hazard Damages over Time + +```{r} +sheldus_df = get_sheldus() %>% + summarize(.by = c(GEOID, year), damage_property_millions = sum(damage_property, na.rm = TRUE) / 1000000) + +sheldus_df %>% + filter(GEOID %in% affected_county_fips) %>% + ggplot(aes(x = year, y = damage_property_millions)) + + geom_line() + + geom_point() + + scale_y_continuous(labels = scales::dollar_format(suffix = "M")) + + labs(x = "Year", y = "Property damage (millions)") +``` + + +## Industry Composition over Time + +Identify sectors that may be particularly vulnerable to this disaster type. + +```{r baseline-industry} +cbp_df = cache_it( + cbp_df, + file_name = "cbp_2016_2023", + path = file.path(get_box_path(), "employment"), read = TRUE) + +cbp_affected = cbp_df %>% + mutate( + county_fips = str_c(state, county), + industry_label = industry %>% str_replace_all("_", " ") %>% str_to_sentence()) %>% + filter(county_fips %in% affected_county_fips) + +top_8_industries = cbp_affected %>% + filter(year == 2023, industry != "total") %>% + arrange(desc(employees)) %>% + slice(1:8) %>% + pull(industry) + +cbp_prior_disaster_years = disasters_affected %>% + filter(year_declared %in% (cbp_df$year %>% unique())) %>% + distinct(year_declared) + +cbp_affected %>% + filter(employees > 0, industry %in% top_8_industries) %>% + arrange(desc(employees)) %>% + ggplot(aes(x = year, y = employees, color = industry_label, group = industry_label)) + + geom_line() + + geom_vline(data = cbp_prior_disaster_years, color = "grey", linetype = "dashed", aes(xintercept = year_declared)) + + scale_y_continuous(labels = scales::comma) + + scale_x_continuous(n.breaks = length((cbp_df$year %>% unique()))) + + labs(y = "Employees (n)", x = "") +``` + +## County Fiscal Capacity Over Time + +```{r baseline-fiscal} +county_expenses = get_government_finances() +finances_years = county_expenses %>% pull(year) %>% unique() + +finances_prior_disaster_years = disasters_affected %>% + filter(year_declared %in% finances_years) %>% + distinct(year_declared) + +county_expenses %>% + filter(GEOID %in% affected_county_fips) %>% + mutate(across(matches("revenue|expenditure"), ~ .x / 1000)) %>% + pivot_longer(-c(year, GEOID, county_name)) %>% + filter(name %in% c("revenue_total", "expenditure_total")) %>% + mutate(name = if_else(str_detect(name, "expenditure"), "Expenditures", "Revenues")) %>% + inflation_adjust(year_variable = "year", dollar_variables = "value", base_year = 2024, names_suffix = "_adjusted") %>% + ggplot(aes(x = year, y = value, color = name, group = name)) + + geom_line() + + geom_point() + + geom_vline(data = finances_prior_disaster_years, color = "grey", linetype = "dashed", aes(xintercept = year_declared)) + + scale_y_continuous(labels = scales::dollar_format(suffix = "M")) + + labs(x = "Year", y = "USD (Millions)") +``` + +--- + +# Section 2: Selecting Comparison Counties + +Identify historical analogs: counties that experienced similar disasters 5+ years ago. + +## Assemble Matching Dataset + +Build a county-level dataset with all variables needed for matching. + +```{r matching-disasters} +# Disaster history: count and binary indicators by hazard type in past 5 years +disaster_lookback_years <- 5 +reference_year <- disaster_year - 1 # Use year before disaster for matching + +disaster_history <- disasters %>% + filter( + year_declared >= (reference_year - disaster_lookback_years), + year_declared <= reference_year) %>% + tidytable::summarise( + .by = GEOID, + n_disasters_prior_5yr = sum(incidents_natural_hazard, na.rm = TRUE), + fire_prior_5yr = as.integer(sum(incidents_fire, na.rm = TRUE) > 0), + flood_prior_5yr = as.integer(sum(incidents_flood, na.rm = TRUE) > 0), + hurricane_prior_5yr = as.integer(sum(incidents_hurricane, na.rm = TRUE) > 0), + severe_storm_prior_5yr = as.integer(sum(incidents_severe_storm, na.rm = TRUE) > 0), + tornado_prior_5yr = as.integer(sum(incidents_tornado, na.rm = TRUE) > 0), + winter_storm_prior_5yr = as.integer(sum(incidents_winter_storm, na.rm = TRUE) > 0), + drought_prior_5yr = as.integer(sum(incidents_drought, na.rm = TRUE) > 0)) %>% + as_tibble() +``` + +```{r matching-sheldus} +sheldus_reference_year = if_else(reference_year < 2023, reference_year, 2023) + +sheldus_matching = sheldus_df %>% + filter(year == sheldus_reference_year) +``` + +```{r matching-industry} +# Industry employment shares from County Business Patterns +# Use 2-digit NAICS codes for broad sector shares +cbp_reference_year = if_else(reference_year < 2023, reference_year, 2023) + +cbp_matching <- cbp_df %>% + ## filter out the employers by employee size records + filter(year == cbp_reference_year, employees > 0) %>% + mutate(GEOID = str_c(state, county)) %>% + select(GEOID, industry, employees) %>% + as_tibble() + +# Calculate total employment per county +cbp_totals <- cbp_matching %>% + filter(industry == "total") %>% + select(GEOID, total_employees = employees) + +# Calculate employment share by industry +industry_shares <- cbp_matching %>% + filter(industry != "total") %>% + tidylog::left_join(cbp_totals, by = "GEOID") %>% + arrange(GEOID) %>% + mutate( + share_employees_ = employees / total_employees, + industry_var = str_c("share_employees_", industry)) %>% + select(GEOID, industry_var, share_employees_) %>% + pivot_wider(names_from = industry_var, values_from = share_employees_, values_fill = 0) + +# Add total employment +## NOTE: the total employee count is always greater than or equal to +## the sum of individual industries' employment countes because CBP omits +## some industry categories +industry_matching <- industry_shares %>% + left_join(cbp_totals, by = "GEOID") + +industry_matching %>% + filter(GEOID %in% affected_county_fips) +``` + +```{r matching-nfip} +# NFIP residential coverage rate by county +nfip_matching <- get_nfip_residential_penetration() %>% + mutate(share_residential_structures_sfha = residential_structures_sfha / residential_structures) %>% + select(GEOID, share_residential_structures_sfha, penetration_rate_sfha) + +nfip_matching %>% + filter(GEOID %in% affected_county_fips) +``` + +```{r matching-fiscal} +finance_reference_year = if_else(reference_year < 2023, reference_year, 2022) + +# Total county government expenses +county_finances_matching = county_expenses %>% + filter(year == finance_reference_year) + +county_finances_matching %>% + filter(GEOID %in% affected_county_fips) +``` + +```{r matching-acs} +# Sociodemographic characteristics from ACS +acs_matching <- acs_df_2023 %>% + select(GEOID, all_of(acs_matching_variables)) +``` + +```{r matching-combine} +# Combine all matching variables into single dataset +matching_data <- acs_matching %>% + left_join(disaster_history, by = "GEOID") %>% + left_join(sheldus_matching, by = "GEOID") %>% + left_join(industry_matching, by = "GEOID") %>% + left_join(nfip_matching, by = "GEOID") %>% + left_join(county_finances_matching, by = "GEOID") %>% + # Replace NAs with 0 for disaster counts (counties with no disasters) + mutate(across(ends_with("_prior_5yr"), ~ replace_na(.x, 0))) %>% + # Drop counties with missing core variables + filter(!is.na(total_population_universe), !is.na(total_employees)) +``` + +## Select Comparison Counties + +```{r comparison-selection} +# Hard filter: same disaster type, disaster occurred 5+ years ago +comparison_pool <- disasters %>% + filter( + year_declared <= 2021, # Enough post-period + year_declared >= 2018) %>% + slice(.by = GEOID, 1) %>% + distinct(GEOID, year_declared) %>% + rename(comparison_disaster_year = year_declared) + +# Get affected county's characteristics +affected_county_chars <- matching_data %>% + filter(GEOID == affected_county_fips) + +# Join matching data +comparison_candidates <- comparison_pool %>% + inner_join(matching_data, by = "GEOID") %>% + filter( + total_population_universe > affected_county_chars$total_population_universe * .75, + total_population_universe < affected_county_chars$total_population_universe * 1.25, + median_household_income_universe_allraces > affected_county_chars$median_household_income_universe_allraces * .75, + median_household_income_universe_allraces < affected_county_chars$median_household_income_universe_allraces * 1.25, + GEOID != affected_county_fips) # Exclude affected county +``` + +```{r comparison-distance} +# Calculate Mahalanobis distance to affected county + +# Select numeric variables for distance calculation +matching_vars <- c( + acs_matching_variables, + "n_disasters_prior_5yr", + "total_employees", + "penetration_rate_sfha", + "revenue_total", + "expenditure_total", + "damage_property_millions") + +# Prepare matrices +matrix_candidates <- comparison_candidates %>% + select(all_of(matching_vars)) %>% + as.matrix() + +matrix_affected <- affected_county_chars %>% + select(all_of(matching_vars)) %>% + as.matrix() + +# Calculate covariance matrix and Mahalanobis distance +covariance_matrix <- cov(matrix_candidates, use = "pairwise.complete.obs") +distances <- mahalanobis(matrix_candidates, center = matrix_affected, cov = covariance_matrix, tol=1e-30) + +# Select top k nearest neighbors +k_neighbors <- 10 + +comparison_counties <- comparison_candidates %>% + mutate(mahalanobis_distance = distances) %>% + slice_min(mahalanobis_distance, n = k_neighbors) %>% + select(GEOID, comparison_disaster_year, mahalanobis_distance) %>% + left_join(tidycensus::fips_codes %>% transmute(county, GEOID = str_c(state_code, county_code))) %>% + slice(1:5) +``` + +--- + +# Section 3: Event-Study Data Preparation + +Align all outcome datasets to event time (t=0 is disaster year). + +```{r event-study-reference} +# Create reference table: affected county + comparison counties with disaster years +county_reference <- bind_rows( + # Affected county + tibble( + GEOID = affected_county_fips, + disaster_year_event = disaster_year, + county_type = "affected"), + # Comparison counties + comparison_counties %>% + transmute( + GEOID, + disaster_year_event = comparison_disaster_year, + county_type = "comparison")) + +# Define event window +event_window <- c(-5, 4) +``` + +```{r event-study-employment} +# Align to event time +cbp_event_aligned <- cbp_df %>% + mutate(GEOID = str_c(state, county)) %>% + rename(calendar_year = year) %>% + inner_join(county_reference, by = "GEOID") %>% + mutate(event_time = calendar_year - disaster_year_event) %>% + filter(event_time >= event_window[1], event_time <= event_window[2]) %>% + select(GEOID, county_type, disaster_year_event, calendar_year, event_time, + industry, employees, employers, annual_payroll) +``` + +```{r event-study-fiscal} +# Align to event time +fiscal_event_aligned <- county_expenses %>% + rename(calendar_year = year) %>% + inner_join(county_reference, by = "GEOID") %>% + mutate(event_time = calendar_year - disaster_year_event) %>% + filter(event_time >= event_window[1], event_time <= event_window[2]) %>% + select(GEOID, county_type, disaster_year_event, calendar_year, event_time, + expenditure_total, revenue_total) +``` + +```{r event-study-sba} +# SBA disaster loans +sba_raw <- get_sba_loans() + +sba_crosswalk = get_crosswalk( + source_geography = "zcta", + target_geography = "county") + +sba_simple = sba_raw %>% + select( + source_geoid = damaged_property_zip_code, + sba_loan_amount = approved_amount_total, + loan_type, + fiscal_year) + +warning("The crosswalking is imperfect and fiscal and calendar years are not aligned.") +sba_county = sba_simple %>% + tidylog::left_join(sba_crosswalk$crosswalks$step_1, by = "source_geoid") %>% + mutate(target_geoid = str_c(state_fips, target_geoid)) %>% + tidytable::summarize( + .by = c(target_geoid, loan_type, fiscal_year), + sba_loan_amount = sum(sba_loan_amount * allocation_factor_source_to_target)) %>% + filter(!is.na(target_geoid)) %>% + mutate( + GEOID = target_geoid, + calendar_year = fiscal_year %>% as.numeric) %>% + pivot_wider(names_from = loan_type, values_from = sba_loan_amount) %>% + rename(business_loan = business, residential_loan = residential) %>% + mutate(across(matches("loan"), ~ if_else(is.na(.x), 0, .x))) + +# Align to event time +sba_event_aligned <- sba_county %>% + inner_join(county_reference, by = "GEOID") %>% + mutate(event_time = calendar_year - disaster_year_event) %>% + filter(event_time >= event_window[1], event_time <= event_window[2]) %>% + select(GEOID, county_type, disaster_year_event, calendar_year, event_time, + matches("loan")) +``` + +```{r event-study-pa} +# FEMA Public Assistance +pa_raw <- get_public_assistance() + +# Aggregate to county-year level +pa_county_year <- pa_raw %>% + mutate( + GEOID = county_fips, + calendar_year = declaration_year) %>% + summarise( + .by = c(GEOID, calendar_year), + across(.cols = matches("split"), ~ sum(.x, na.rm = TRUE))) + +# Align to event time +pa_event_aligned <- pa_county_year %>% + inner_join(county_reference, by = "GEOID") %>% + mutate(event_time = calendar_year - disaster_year_event) %>% + filter(event_time >= event_window[1], event_time <= event_window[2]) %>% + select(GEOID, county_type, disaster_year_event, calendar_year, event_time, + pa_federal_funding_obligated_split) +``` + +```{r event-study-ihp} +# FEMA Individual and Households Program +# ihp_raw <- get_ihp_registrations() + +# # Aggregate to county-year level +# ihp_county_year <- ihp_raw %>% +# mutate(calendar_year = lubridate::year(declaration_date)) %>% +# summarise( +# .by = c(GEOID, calendar_year), +# ihp_registrations_n = n(), +# ihp_approved_n = sum(ihp_eligible, na.rm = TRUE), +# ihp_amount_total = sum(ihp_amount, na.rm = TRUE), +# ihp_ha_amount = sum(ha_amount, na.rm = TRUE), +# ihp_ona_amount = sum(ona_amount, na.rm = TRUE)) + +# # Align to event time +# ihp_event_aligned <- ihp_county_year %>% +# inner_join(county_reference, by = "GEOID") %>% +# mutate(event_time = calendar_year - disaster_year_event) %>% +# filter(event_time >= event_window[1], event_time <= event_window[2]) %>% +# select(GEOID, county_type, disaster_year_event, calendar_year, event_time, +# ihp_registrations_n, ihp_approved_n, ihp_amount_total, ihp_ha_amount, ihp_ona_amount) +``` + +--- + +# Section 4: Employment Trajectories (Event-Study Style) + +Plot employment trends with time relative to disaster (t=0). + +- **Affected county**: t-6 through t=0 (present) +- **Comparison counties**: full t-6 through t+6 window, aligned to their disaster year + +```{r employment-total} +# Total employment over event time +cbp_event_aligned %>% + filter(industry == "total", employees > 0) %>% + tibble::as_tibble() %>% + ggplot(aes(x = event_time, y = employees, group = GEOID, color = county_type, linetype = county_type)) + + geom_line() + + geom_point() + + geom_vline(xintercept = 0, linetype = "dashed", color = "grey40") + + scale_y_continuous(labels = scales::comma) + + scale_color_manual(values = c("affected" = "#1696d2", "comparison" = "#d2d2d2")) + + labs( + x = "Years Relative to Disaster (t=0)", + y = "Total Employees", + title = "Employment Trajectory: Affected vs. Comparison Counties") + + theme(legend.position = "none") +``` + +--- + +# Section 5: Local Government Fiscal Trajectories (Event-Study Style) + +Examine how local government finances evolved in comparison counties post-disaster. + +```{r fiscal-expenses} +# Total county expenses over event time +fiscal_event_aligned %>% + mutate(county_type = if_else(str_detect(county_type, "affected"), "Impacted", "Comparison")) %>% + ggplot(aes(x = event_time, y = expenditure_total / 1000, group = GEOID, color = county_type)) + + geom_line() + + geom_point() + + geom_vline(xintercept = 0, linetype = "dashed", color = "grey40") + + scale_y_continuous(labels = scales::dollar_format(suffix = "M")) + + scale_color_manual(values = c("Impacted" = "#1696d2", "Comparison" = "#d2d2d2")) + + labs( + x = "Years Relative to Disaster (t=0)", + y = "Total County Expenses (Millions)", + title = "County Government Expenses: Affected vs. Comparison Counties") + + theme(legend.position = "none") +``` + +```{r fiscal-revenue} +# Total county expenses over event time +fiscal_event_aligned %>% + mutate(county_type = if_else(str_detect(county_type, "affected"), "Impacted", "Comparison")) %>% + ggplot(aes(x = event_time, y = revenue_total / 1000, group = GEOID, color = county_type)) + + geom_line() + + geom_point() + + geom_vline(xintercept = 0, linetype = "dashed", color = "grey40") + + scale_y_continuous(labels = scales::dollar_format(suffix = "M")) + + scale_color_manual(values = c("Impacted" = "#1696d2", "Comparison" = "#d2d2d2")) + + labs( + x = "Years Relative to Disaster (t=0)", + y = "Total County Revenues (Millions)", + title = "County Government Revenues: Affected vs. Comparison Counties") + + theme(legend.position = "none") +``` + +--- + +# Section 6: Recovery Resources in Comparison Cases + +Describe typical federal assistance flows based on historical analogs. Since the affected county's disaster just occurred, we show comparison counties' post-disaster resource flows as projections. + +## SBA Disaster Loans + +```{r recovery-sba} +# SBA loans over event time (comparison counties only for post-period) + +sba_event_aligned %>% + arrange(GEOID, event_time) %>% + mutate( + .by = GEOID, + across(matches("loan"), ~ cumsum(.x))) %>% + mutate(county_type = if_else(str_detect(county_type, "affected"), "Impacted", "Comparison")) %>% + ggplot(aes(x = event_time, y = residential_loan / 1000000, group = GEOID, color = county_type)) + + geom_line() + + geom_vline(xintercept = 0, linetype = "dashed", color = "grey40") + + scale_y_continuous(labels = scales::dollar_format(suffix = "M")) + + scale_color_manual(values = c("Impacted" = "#1696d2", "Comparison" = "#d2d2d2")) + + labs( + x = "Years relative to disaster (t=0)", + y = "SBA loans (cumulative)", + title = "Cumulative SBA Loans") + + theme(legend.position = "none") + +sba_event_aligned %>% + arrange(GEOID, event_time) %>% + mutate( + .by = GEOID, + across(matches("loan"), ~ cumsum(.x))) %>% + ggplot(aes(x = event_time, y = business_loan / 1000000, group = GEOID, color = county_type)) + + geom_line() + + geom_vline(xintercept = 0, linetype = "dashed", color = "grey40") + + scale_y_continuous(labels = scales::dollar_format(suffix = "M")) + + scale_color_manual(values = c("affected" = "#1696d2", "comparison" = "#d2d2d2")) + + labs( + x = "Years relative to disaster (t=0)", + y = "SBA loans (cumulative)", + title = "Cumulative SBA Loans") + + theme(legend.position = "none") + + +``` + +## FEMA Public Assistance + +```{r recovery-pa} +# PA funding over event time (comparison counties only for post-period) +pa_event_aligned %>% + mutate(county_type = if_else(str_detect(county_type, "affected"), "Impacted", "Comparison")) %>% + ggplot(aes(x = event_time, y = pa_federal_funding_obligated_split / 1e6, group = GEOID, color = county_type)) + + geom_line() + + geom_vline(xintercept = 0, linetype = "dashed", color = "grey40") + + scale_y_continuous(labels = scales::dollar_format(suffix = "M")) + + scale_color_manual(values = c("Impacted" = "#1696d2", "Comparison" = "#d2d2d2")) + + labs( + x = "Years Relative to Disaster (t=0)", + y = "FEMA PA Federal Share Obligated (Millions)", + title = "FEMA Public Assistance in Comparison Counties") +``` + +## Individual and Households Program + +```{r recovery-ihp} +# # IHP funding over event time (comparison counties only for post-period) +# ihp_event_aligned %>% +# ggplot(aes(x = event_time, y = ihp_amount_total / 1e6, group = GEOID)) + +# geom_line(alpha = 0.4, color = "#d2d2d2") + +# stat_summary(aes(group = 1), fun = median, geom = "line", color = "#1696d2", linewidth = 1.2) + +# geom_vline(xintercept = 0, linetype = "dashed", color = "grey40") + +# scale_y_continuous(labels = scales::dollar_format(suffix = "M")) + +# labs( +# x = "Years Relative to Disaster (t=0)", +# y = "IHP Total Amount (Millions)", +# title = "FEMA Individual & Households Program in Comparison Counties", +# subtitle = "Blue line = median across comparison counties" +# ) + +``` + +--- + +# Section 7: Key Takeaways + +```{r takeaways} +# Programmatically generate summary statistics for the factsheet: +# - Pre-disaster vulnerability indicators for affected county +# - Median/range of recovery trajectories from comparison counties +# - Typical federal resource flows +``` + +## Summary Table + +```{r summary-table} +# Create summary table of key indicators +``` + +--- + +## Technical Notes + +### Comparison County Selection Criteria +- Similar population size and income (within 25% of affected county in either direction) + +### Data Availability Notes +- LODES data typically lag 2 years +- County Business Patterns lag 2 years +- Government finances lag 2 +- FEMA assistance data are near real-time + diff --git a/vignettes/get_sheldus.Rmd b/vignettes/get_sheldus.Rmd index 23f4968..d3a6340 100644 --- a/vignettes/get_sheldus.Rmd +++ b/vignettes/get_sheldus.Rmd @@ -13,10 +13,13 @@ knitr::opts_chunk$set( comment = "#>", warning = FALSE, message = FALSE, - echo = TRUE, fig.width = 8, fig.height = 6, - dpi = 1000) + dpi = 1000, + eval = TRUE, + echo = TRUE) + +options(scipen = 999) ``` ## Overview