R/io_calculator.R
In AlexRichardson-NOAA/feusIO2: Runs a Fisheries Input/Output Model Based on Harvests and Implan Multipliers
Defines functions
io_calculator
Documented in
io_calculator
#' Run a Fisheries Input/Output Model
#'
#' This function takes in commercial fisheries catch numbers, IMPLAN multipliers, a GDP deflator, and imports numbers and outputs economic impacts.
#'
#' @param catch A data frame that details catch numbers at the state-species category level for a single year, including variables fips (FIPs number, 0 for US), spec_no (a numeric variable for species category), and base_catch (raw catch numbers in dollars).
#' @param import_numbers A data frame that includes imports numbers in dollars at the state level for a single year, including fips (FIPs number, 0 for US) and imports. Defaults to False for no imports.
#' @param implan_multipliers A data frame that includes 17 multipliers at the state-species category-economic category for a single year. Defaults to David Records numbers.
#' @param deflator A numeric value that adjusts jobs numbers from the current year to the year the multipliers were made; defaults to 0.8734298, which represents 2017 to 2014.
#' @param imports_state_multipliers A data frame that includes multipliers governing the percentages of imports going to each economic category for a single year. Defaults to 2017 numbers. Set to False for no imports.
#' @param manual A binary value indicating whether values were manually input. Defaults to FALSE
#' @importFrom magrittr %>%
#' @export
io_calculator <- function(catch, import_numbers = F, implan_multipliers = multipliers, deflator = 0.8734298, import_state_multipliers = imports_states, manual = FALSE) {
base_catch = catch %>% dplyr::mutate(spec_no = dplyr::case_when(
`Species Category` == "Shrimp" ~ 1,
`Species Category` == "Crab" ~ 2,
`Species Category` == "Lobster" ~ 3,
`Species Category` == "East Coast Groundfish" ~ 4,
`Species Category` == "HMS" ~ 5,
`Species Category` == "Reef Fish" ~ 6,
`Species Category` == "West Coast Groundfish " ~ 7,
`Species Category` == "West Coast Groundfish" ~ 7,
`Species Category` == "West Coast Whiting " ~ 8,
`Species Category` == "West Coast Whiting" ~ 8,
`Species Category` == "Halibut" ~ 9,
`Species Category` == "Menhaden and Industrial" ~ 10,
`Species Category` == "Salmon" ~ 11,
`Species Category` == "Sea Scallop" ~ 12,
`Species Category` == "Pelagic Herring and Mackerel" ~ 13,
`Species Category` == "Surf Clam and Ocean Quahog " ~ 14,
`Species Category` == "Surf Clam and Ocean Quahog" ~ 14,
`Species Category` == "Surf Clam/Ocean Quahog" ~ 14,
`Species Category` == "Other Trawl" ~ 15,
`Species Category` == "All Other Finfish" ~ 16,
`Species Category` == "All Other Shellfish " ~ 17,
`Species Category` == "All Other Shellfish" ~ 17,
`Species Category` == "Freshwater " ~ 18,
`Species Category` == "Freshwater" ~ 18,
`Species Category` == "Inshore and Miscellaneous" ~ 19,
`Species Category` == "Bait" ~ 20,
`Species Category` == "Imports" ~ 0)) %>%
dplyr::select(-`Species Category`) %>%
dplyr::filter(!is.na(spec_no))
if(length(base_catch$base_catch[base_catch$State == "US"])==0) {
US_base_catch = base_catch %>%
dplyr::select(base_catch, spec_no, Year) %>%
dplyr::group_by(spec_no, Year) %>%
dplyr::summarize(base_catch = sum(base_catch)) %>%
dplyr::mutate(Region = "National",
State = "US",
fips = 0)
} else {
US_base_catch = base_catch %>% dplyr::filter(State == "US")
base_catch = base_catch %>% dplyr::filter(State != "US")
}
base_catch = dplyr::bind_rows(US_base_catch, base_catch)
imports = import_numbers
multipliers = implan_multipliers
import_states = import_state_multipliers
############
# Cleaning #
############
if(imports!=F) {
imports = import_states %>%
dplyr::left_join(imports) %>%
dplyr::mutate(imports = imports * value) %>%
dplyr::select(fips, `Economic Category` = name, base_catch = imports) %>%
dplyr::mutate(`Species Category` = "Imports")
} else {
}
if(manual == TRUE){
multipliers = multipliers %>%
dplyr::filter(fips %in% unique(base_catch$fips))
}
multipliers_harvesters = multipliers %>% dplyr::filter(`Economic Category` == "Harvesters")
multipliers_processors = multipliers %>% dplyr::filter(`Economic Category` == "Processors")
multipliers_wholesalers = multipliers %>% dplyr::filter(`Economic Category` == "Wholesalers")
multipliers_grocers = multipliers %>% dplyr::filter(`Economic Category` == "Grocers")
multipliers_restaurants = multipliers %>% dplyr::filter(`Economic Category` == "Restaurants")
multipliers_brokers = multipliers %>% dplyr::filter(state_name == "All jurisdictions") %>%
dplyr::select(-state_abbr, -fips)
##############
# Harvesters #
##############
multipliers_harvesters = multipliers_harvesters %>%
dplyr::mutate(Species.Category = `Species Category`) %>%
dplyr::left_join(base_catch, by = c("spec_no", "fips")) %>%
dplyr::mutate(
PI_Direct_Impact = `Personal Income Direct Impacts` * base_catch,
PI_Indirect_Impact = `Personal Income Indirect Impacts` * `RPC RPC` *
base_catch,
PI_Induced_Impact = `Personal Income Induced Impacts` * `RPC RPC` *
base_catch,
PI_Total = PI_Direct_Impact + PI_Indirect_Impact + PI_Induced_Impact,
TV_Direct_Impact = `Total Value Direct Impacts` * base_catch,
TV_Indirect_Impact = `Total Value Indirect Impacts` * `RPC RPC` *
base_catch,
TV_Induced_Impact = `Total Value Induced Impacts` * `RPC RPC` *
base_catch,
TV_Total = TV_Direct_Impact + TV_Indirect_Impact + TV_Induced_Impact,
O_Direct_Impact = `Output Direct Impacts` * base_catch,
O_Indirect_Impact = `Output Indirect Impacts` * `RPC RPC` * base_catch,
O_Induced_Impact = `Output Induced Impacts` * `RPC RPC` * base_catch,
O_Total = O_Direct_Impact + O_Indirect_Impact + O_Induced_Impact,
E_Direct_Impact = `Employment Direct Impacts` * base_catch * deflator / 1000000,
E_Indirect_Impact = `Employment Indirect Impacts` * `RPC RPC` *
base_catch * deflator / 1000000,
E_Induced_Impact = `Employment Induced Impacts` * `RPC RPC` * base_catch * deflator / 1000000,
E_Total = E_Direct_Impact + E_Indirect_Impact + E_Induced_Impact
)
harvesters_output = multipliers_harvesters %>% dplyr::select(
fips,
`Economic Category`,
`Species Category`,
spec_no,
PI_Direct_Impact,
PI_Indirect_Impact,
PI_Induced_Impact,
PI_Total,
TV_Direct_Impact,
TV_Indirect_Impact,
TV_Induced_Impact,
TV_Total,
O_Direct_Impact,
O_Indirect_Impact,
O_Induced_Impact,
O_Total,
E_Direct_Impact,
E_Indirect_Impact,
E_Induced_Impact,
E_Total
)
##############
# Processors #
##############
multipliers_processors = multipliers_processors %>%
dplyr::mutate(Species.Category = `Species Category`) %>%
dplyr::left_join(base_catch, by = c("spec_no", "fips")) %>%
dplyr::mutate(processor_inputs = base_catch * Harvesters)
if (imports != F) {
for (n in unique(multipliers_processors$fips)) {
multipliers_processors$processor_inputs[multipliers_processors$`Species Category` == "Imports" &
multipliers_processors$fips == n] <-
imports$base_catch[imports$fips == n &
imports$`Economic Category` == "Processors"]
}
}
multipliers_processors = multipliers_processors %>%
dplyr::mutate(processor_markup = processor_inputs * markup) %>%
dplyr::mutate(
PI_Direct_Impact = `Personal Income Direct Impacts` * processor_markup,
PI_Indirect_Impact = `Personal Income Indirect Impacts` * `RPC RPC` *
processor_markup,
PI_Induced_Impact = `Personal Income Induced Impacts` * `RPC RPC` *
processor_markup,
PI_Total = PI_Direct_Impact + PI_Indirect_Impact + PI_Induced_Impact,
TV_Direct_Impact = `Total Value Direct Impacts` * processor_markup,
TV_Indirect_Impact = `Total Value Indirect Impacts` * `RPC RPC` *
processor_markup,
TV_Induced_Impact = `Total Value Induced Impacts` * `RPC RPC` *
processor_markup,
TV_Total = TV_Direct_Impact + TV_Indirect_Impact + TV_Induced_Impact,
O_Direct_Impact = `Output Direct Impacts` * processor_markup,
O_Indirect_Impact = `Output Indirect Impacts` * `RPC RPC` * processor_markup,
O_Induced_Impact = `Output Induced Impacts` * `RPC RPC` * processor_markup,
O_Total = O_Direct_Impact + O_Indirect_Impact + O_Induced_Impact,
E_Direct_Impact = `Employment Direct Impacts` * processor_markup * deflator / 1000000,
E_Indirect_Impact = `Employment Indirect Impacts` * `RPC RPC` *
processor_markup * deflator / 1000000,
E_Induced_Impact = `Employment Induced Impacts` * `RPC RPC` * processor_markup * deflator / 1000000,
E_Total = E_Direct_Impact + E_Indirect_Impact + E_Induced_Impact
)
processors_output = multipliers_processors %>% dplyr::select(
fips,
`Economic Category`,
`Species Category`,
spec_no,
PI_Direct_Impact,
PI_Indirect_Impact,
PI_Induced_Impact,
PI_Total,
TV_Direct_Impact,
TV_Indirect_Impact,
TV_Induced_Impact,
TV_Total,
O_Direct_Impact,
O_Indirect_Impact,
O_Induced_Impact,
O_Total,
E_Direct_Impact,
E_Indirect_Impact,
E_Induced_Impact,
E_Total
)
processor_inputs = multipliers_processors %>% dplyr::select(fips, spec_no, processor_inputs, processor_markup)
###############
# Wholesalers #
###############
multipliers_wholesalers = multipliers_wholesalers %>%
dplyr::mutate(Species.Category = `Species Category`) %>%
dplyr::left_join(base_catch, by = c("spec_no", "fips")) %>%
dplyr::left_join(processor_inputs, by = c("spec_no", "fips")) %>%
dplyr::mutate(wholesaler_inputs = base_catch * Harvesters + Processors * (processor_inputs +
processor_markup))
if(imports != F) {
for (n in unique(multipliers_wholesalers$fips)) {
multipliers_wholesalers$wholesaler_inputs[multipliers_wholesalers$`Species Category` == "Imports" &
multipliers_wholesalers$fips == n] <-
imports$base_catch[imports$fips == n &
imports$`Economic Category` == "Wholesalers"]
}
}
multipliers_wholesalers = multipliers_wholesalers %>%
dplyr::mutate(wholesaler_markup = wholesaler_inputs * markup) %>%
dplyr::mutate(
PI_Direct_Impact = `Personal Income Direct Impacts` * wholesaler_markup,
PI_Indirect_Impact = `Personal Income Indirect Impacts` * `RPC RPC` *
wholesaler_markup,
PI_Induced_Impact = `Personal Income Induced Impacts` * `RPC RPC` *
wholesaler_markup,
PI_Total = PI_Direct_Impact + PI_Indirect_Impact + PI_Induced_Impact,
TV_Direct_Impact = `Total Value Direct Impacts` * wholesaler_markup,
TV_Indirect_Impact = `Total Value Indirect Impacts` * `RPC RPC` *
wholesaler_markup,
TV_Induced_Impact = `Total Value Induced Impacts` * `RPC RPC` *
wholesaler_markup,
TV_Total = TV_Direct_Impact + TV_Indirect_Impact + TV_Induced_Impact,
O_Direct_Impact = `Output Direct Impacts` * wholesaler_markup,
O_Indirect_Impact = `Output Indirect Impacts` * `RPC RPC` * wholesaler_markup,
O_Induced_Impact = `Output Induced Impacts` * `RPC RPC` * wholesaler_markup,
O_Total = O_Direct_Impact + O_Indirect_Impact + O_Induced_Impact,
E_Direct_Impact = `Employment Direct Impacts` * wholesaler_markup * deflator / 1000000,
E_Indirect_Impact = `Employment Indirect Impacts` * `RPC RPC` *
wholesaler_markup * deflator / 1000000,
E_Induced_Impact = `Employment Induced Impacts` * `RPC RPC` * wholesaler_markup * deflator / 1000000,
E_Total = E_Direct_Impact + E_Indirect_Impact + E_Induced_Impact
)
wholesalers_output = multipliers_wholesalers %>% dplyr::select(
fips,
`Economic Category`,
`Species Category`,
spec_no,
PI_Direct_Impact,
PI_Indirect_Impact,
PI_Induced_Impact,
PI_Total,
TV_Direct_Impact,
TV_Indirect_Impact,
TV_Induced_Impact,
TV_Total,
O_Direct_Impact,
O_Indirect_Impact,
O_Induced_Impact,
O_Total,
E_Direct_Impact,
E_Indirect_Impact,
E_Induced_Impact,
E_Total
)
wholesaler_inputs = multipliers_wholesalers %>% dplyr::select(fips,
spec_no,
wholesaler_inputs,
wholesaler_markup)
###########
# Grocers #
###########
multipliers_grocers = multipliers_grocers %>%
dplyr::mutate(Species.Category = `Species Category`) %>%
dplyr::left_join(base_catch, by = c("spec_no", "fips")) %>%
dplyr::left_join(processor_inputs, by = c("spec_no", "fips")) %>%
dplyr::left_join(wholesaler_inputs, by = c("spec_no", "fips")) %>%
dplyr::mutate(
grocer_inputs = base_catch * Harvesters + Processors * (processor_inputs +
processor_markup) + Wholesalers * (wholesaler_inputs + wholesaler_markup)
)
if (imports != F) {
for (n in unique(multipliers_grocers$fips)) {
multipliers_grocers$grocer_inputs[multipliers_grocers$`Species Category` == "Imports" &
multipliers_grocers$fips == n] <-
imports$base_catch[imports$fips == n &
imports$`Economic Category` == "Grocers"]
}
}
multipliers_grocers = multipliers_grocers %>%
dplyr::mutate(grocer_markup = grocer_inputs * markup) %>%
dplyr::mutate(
PI_Direct_Impact = `Personal Income Direct Impacts` * grocer_markup,
PI_Indirect_Impact = `Personal Income Indirect Impacts` * `RPC RPC` *
grocer_markup,
PI_Induced_Impact = `Personal Income Induced Impacts` * `RPC RPC` *
grocer_markup,
PI_Total = PI_Direct_Impact + PI_Indirect_Impact + PI_Induced_Impact,
TV_Direct_Impact = `Total Value Direct Impacts` * grocer_markup,
TV_Indirect_Impact = `Total Value Indirect Impacts` * `RPC RPC` *
grocer_markup,
TV_Induced_Impact = `Total Value Induced Impacts` * `RPC RPC` *
grocer_markup,
TV_Total = TV_Direct_Impact + TV_Indirect_Impact + TV_Induced_Impact,
O_Direct_Impact = `Output Direct Impacts` * grocer_markup,
O_Indirect_Impact = `Output Indirect Impacts` * `RPC RPC` * grocer_markup,
O_Induced_Impact = `Output Induced Impacts` * `RPC RPC` * grocer_markup,
O_Total = O_Direct_Impact + O_Indirect_Impact + O_Induced_Impact,
E_Direct_Impact = `Employment Direct Impacts` * grocer_markup * deflator / 1000000,
E_Indirect_Impact = `Employment Indirect Impacts` * `RPC RPC` *
grocer_markup * deflator / 1000000,
E_Induced_Impact = `Employment Induced Impacts` * `RPC RPC` * grocer_markup * deflator / 1000000,
E_Total = E_Direct_Impact + E_Indirect_Impact + E_Induced_Impact
)
grocers_output = multipliers_grocers %>% dplyr::select(
fips,
`Economic Category`,
`Species Category`,
spec_no,
PI_Direct_Impact,
PI_Indirect_Impact,
PI_Induced_Impact,
PI_Total,
TV_Direct_Impact,
TV_Indirect_Impact,
TV_Induced_Impact,
TV_Total,
O_Direct_Impact,
O_Indirect_Impact,
O_Induced_Impact,
O_Total,
E_Direct_Impact,
E_Indirect_Impact,
E_Induced_Impact,
E_Total
)
grocer_inputs = multipliers_grocers %>% dplyr::select(fips, spec_no, grocer_inputs, grocer_markup)
###############
# Restaurants #
###############
multipliers_restaurants = multipliers_restaurants %>%
dplyr::mutate(Species.Category = `Species Category`) %>%
dplyr::left_join(base_catch, by = c("spec_no", "fips")) %>%
dplyr::left_join(processor_inputs, by = c("spec_no", "fips")) %>%
dplyr::left_join(wholesaler_inputs, by = c("spec_no", "fips")) %>%
dplyr::left_join(grocer_inputs, by = c("spec_no", "fips")) %>%
dplyr::mutate(
restaurant_inputs = base_catch * Harvesters + Processors * (processor_inputs +
processor_markup) + Wholesalers * (wholesaler_inputs + wholesaler_markup) + Grocers *
(grocer_inputs + grocer_markup)
)
if(imports != F) {
for (n in unique(multipliers_restaurants$fips)) {
multipliers_restaurants$restaurant_inputs[multipliers_restaurants$`Species Category` == "Imports" &
multipliers_restaurants$fips == n] <-
imports$base_catch[imports$fips == n &
imports$`Economic Category` == "Restaurants"]
}
}
multipliers_restaurants = multipliers_restaurants %>%
dplyr::mutate(restaurant_markup = restaurant_inputs * markup) %>%
dplyr::mutate(
PI_Direct_Impact = `Personal Income Direct Impacts` * restaurant_markup,
PI_Indirect_Impact = `Personal Income Indirect Impacts` * `RPC RPC` *
restaurant_markup,
PI_Induced_Impact = `Personal Income Induced Impacts` * `RPC RPC` *
restaurant_markup,
PI_Total = PI_Direct_Impact + PI_Indirect_Impact + PI_Induced_Impact,
TV_Direct_Impact = `Total Value Direct Impacts` * restaurant_markup,
TV_Indirect_Impact = `Total Value Indirect Impacts` * `RPC RPC` *
restaurant_markup,
TV_Induced_Impact = `Total Value Induced Impacts` * `RPC RPC` *
restaurant_markup,
TV_Total = TV_Direct_Impact + TV_Indirect_Impact + TV_Induced_Impact,
O_Direct_Impact = `Output Direct Impacts` * restaurant_markup,
O_Indirect_Impact = `Output Indirect Impacts` * `RPC RPC` * restaurant_markup,
O_Induced_Impact = `Output Induced Impacts` * `RPC RPC` * restaurant_markup,
O_Total = O_Direct_Impact + O_Indirect_Impact + O_Induced_Impact,
E_Direct_Impact = `Employment Direct Impacts` * restaurant_markup * deflator / 1000000,
E_Indirect_Impact = `Employment Indirect Impacts` * `RPC RPC` *
restaurant_markup * deflator / 1000000,
E_Induced_Impact = `Employment Induced Impacts` * `RPC RPC` * restaurant_markup * deflator / 1000000,
E_Total = E_Direct_Impact + E_Indirect_Impact + E_Induced_Impact
)
restaurants_output = multipliers_restaurants %>% dplyr::select(
fips,
`Economic Category`,
`Species Category`,
spec_no,
PI_Direct_Impact,
PI_Indirect_Impact,
PI_Induced_Impact,
PI_Total,
TV_Direct_Impact,
TV_Indirect_Impact,
TV_Induced_Impact,
TV_Total,
O_Direct_Impact,
O_Indirect_Impact,
O_Induced_Impact,
O_Total,
E_Direct_Impact,
E_Indirect_Impact,
E_Induced_Impact,
E_Total
)
###########
# Brokers #
###########
if(import_numbers!=F) {
broker_output = import_numbers %>%
dplyr::bind_cols(multipliers_brokers) %>%
dplyr::mutate(broker_markup = (imports) * 1.186554) %>%
dplyr::mutate(
PI_Direct_Impact = `Personal Income Direct Impacts` * broker_markup,
PI_Indirect_Impact = `Personal Income Indirect Impacts` * `RPC RPC` *
broker_markup,
PI_Induced_Impact = `Personal Income Induced Impacts` * `RPC RPC` *
broker_markup,
PI_Total = PI_Direct_Impact + PI_Indirect_Impact + PI_Induced_Impact,
TV_Direct_Impact = `Total Value Direct Impacts` * broker_markup,
TV_Indirect_Impact = `Total Value Indirect Impacts` * `RPC RPC` *
broker_markup,
TV_Induced_Impact = `Total Value Induced Impacts` * `RPC RPC` *
broker_markup,
TV_Total = TV_Direct_Impact + TV_Indirect_Impact + TV_Induced_Impact,
O_Direct_Impact = `Output Direct Impacts` * broker_markup,
O_Indirect_Impact = `Output Indirect Impacts` * `RPC RPC` * broker_markup,
O_Induced_Impact = `Output Induced Impacts` * `RPC RPC` * broker_markup,
O_Total = O_Direct_Impact + O_Indirect_Impact + O_Induced_Impact,
E_Direct_Impact = `Employment Direct Impacts` * broker_markup * deflator / 1000000,
E_Indirect_Impact = `Employment Indirect Impacts` * `RPC RPC` *
broker_markup * deflator / 1000000,
E_Induced_Impact = `Employment Induced Impacts` * `RPC RPC` * broker_markup * deflator / 1000000,
E_Total = E_Direct_Impact + E_Indirect_Impact + E_Induced_Impact
) %>%
dplyr::select(
fips,
`Economic Category`,
`Species Category`,
spec_no,
PI_Direct_Impact,
PI_Indirect_Impact,
PI_Induced_Impact,
PI_Total,
TV_Direct_Impact,
TV_Indirect_Impact,
TV_Induced_Impact,
TV_Total,
O_Direct_Impact,
O_Indirect_Impact,
O_Induced_Impact,
O_Total,
E_Direct_Impact,
E_Indirect_Impact,
E_Induced_Impact,
E_Total
) %>% dplyr::filter(!is.na(fips))
} else {
broker_output = NULL
}
################
# Final Output #
################
final_output = dplyr::bind_rows(
harvesters_output,
processors_output,
wholesalers_output,
grocers_output,
restaurants_output,
broker_output
)
final_output[is.na(final_output)] <- 0
if(imports==F){
final_output = final_output %>%
dplyr::mutate(Imports = "No")
}
if(imports!=F){
final_output = final_output %>%
dplyr::mutate(Imports = "Yes")
}
return(final_output)
}
AlexRichardson-NOAA/feusIO2 documentation built on Dec. 17, 2021, 7:50 a.m.
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Defines functions io_calculator
Documented in io_calculator
#' Run a Fisheries Input/Output Model
#'
#' This function takes in commercial fisheries catch numbers, IMPLAN multipliers, a GDP deflator, and imports numbers and outputs economic impacts.
#'
#' @param catch A data frame that details catch numbers at the state-species category level for a single year, including variables fips (FIPs number, 0 for US), spec_no (a numeric variable for species category), and base_catch (raw catch numbers in dollars).
#' @param import_numbers A data frame that includes imports numbers in dollars at the state level for a single year, including fips (FIPs number, 0 for US) and imports. Defaults to False for no imports.
#' @param implan_multipliers A data frame that includes 17 multipliers at the state-species category-economic category for a single year. Defaults to David Records numbers.
#' @param deflator A numeric value that adjusts jobs numbers from the current year to the year the multipliers were made; defaults to 0.8734298, which represents 2017 to 2014.
#' @param imports_state_multipliers A data frame that includes multipliers governing the percentages of imports going to each economic category for a single year. Defaults to 2017 numbers. Set to False for no imports.
#' @param manual A binary value indicating whether values were manually input. Defaults to FALSE
#' @importFrom magrittr %>%
#' @export
io_calculator <- function(catch, import_numbers = F, implan_multipliers = multipliers, deflator = 0.8734298, import_state_multipliers = imports_states, manual = FALSE) {
base_catch = catch %>% dplyr::mutate(spec_no = dplyr::case_when(
`Species Category` == "Shrimp" ~ 1,
`Species Category` == "Crab" ~ 2,
`Species Category` == "Lobster" ~ 3,
`Species Category` == "East Coast Groundfish" ~ 4,
`Species Category` == "HMS" ~ 5,
`Species Category` == "Reef Fish" ~ 6,
`Species Category` == "West Coast Groundfish " ~ 7,
`Species Category` == "West Coast Groundfish" ~ 7,
`Species Category` == "West Coast Whiting " ~ 8,
`Species Category` == "West Coast Whiting" ~ 8,
`Species Category` == "Halibut" ~ 9,
`Species Category` == "Menhaden and Industrial" ~ 10,
`Species Category` == "Salmon" ~ 11,
`Species Category` == "Sea Scallop" ~ 12,
`Species Category` == "Pelagic Herring and Mackerel" ~ 13,
`Species Category` == "Surf Clam and Ocean Quahog " ~ 14,
`Species Category` == "Surf Clam and Ocean Quahog" ~ 14,
`Species Category` == "Surf Clam/Ocean Quahog" ~ 14,
`Species Category` == "Other Trawl" ~ 15,
`Species Category` == "All Other Finfish" ~ 16,
`Species Category` == "All Other Shellfish " ~ 17,
`Species Category` == "All Other Shellfish" ~ 17,
`Species Category` == "Freshwater " ~ 18,
`Species Category` == "Freshwater" ~ 18,
`Species Category` == "Inshore and Miscellaneous" ~ 19,
`Species Category` == "Bait" ~ 20,
`Species Category` == "Imports" ~ 0)) %>%
dplyr::select(-`Species Category`) %>%
dplyr::filter(!is.na(spec_no))
if(length(base_catch$base_catch[base_catch$State == "US"])==0) {
US_base_catch = base_catch %>%
dplyr::select(base_catch, spec_no, Year) %>%
dplyr::group_by(spec_no, Year) %>%
dplyr::summarize(base_catch = sum(base_catch)) %>%
dplyr::mutate(Region = "National",
State = "US",
fips = 0)
} else {
US_base_catch = base_catch %>% dplyr::filter(State == "US")
base_catch = base_catch %>% dplyr::filter(State != "US")
}
base_catch = dplyr::bind_rows(US_base_catch, base_catch)
imports = import_numbers
multipliers = implan_multipliers
import_states = import_state_multipliers
############
# Cleaning #
############
if(imports!=F) {
imports = import_states %>%
dplyr::left_join(imports) %>%
dplyr::mutate(imports = imports * value) %>%
dplyr::select(fips, `Economic Category` = name, base_catch = imports) %>%
dplyr::mutate(`Species Category` = "Imports")
} else {
}
if(manual == TRUE){
multipliers = multipliers %>%
dplyr::filter(fips %in% unique(base_catch$fips))
}
multipliers_harvesters = multipliers %>% dplyr::filter(`Economic Category` == "Harvesters")
multipliers_processors = multipliers %>% dplyr::filter(`Economic Category` == "Processors")
multipliers_wholesalers = multipliers %>% dplyr::filter(`Economic Category` == "Wholesalers")
multipliers_grocers = multipliers %>% dplyr::filter(`Economic Category` == "Grocers")
multipliers_restaurants = multipliers %>% dplyr::filter(`Economic Category` == "Restaurants")
multipliers_brokers = multipliers %>% dplyr::filter(state_name == "All jurisdictions") %>%
dplyr::select(-state_abbr, -fips)
##############
# Harvesters #
##############
multipliers_harvesters = multipliers_harvesters %>%
dplyr::mutate(Species.Category = `Species Category`) %>%
dplyr::left_join(base_catch, by = c("spec_no", "fips")) %>%
dplyr::mutate(
PI_Direct_Impact = `Personal Income Direct Impacts` * base_catch,
PI_Indirect_Impact = `Personal Income Indirect Impacts` * `RPC RPC` *
base_catch,
PI_Induced_Impact = `Personal Income Induced Impacts` * `RPC RPC` *
base_catch,
PI_Total = PI_Direct_Impact + PI_Indirect_Impact + PI_Induced_Impact,
TV_Direct_Impact = `Total Value Direct Impacts` * base_catch,
TV_Indirect_Impact = `Total Value Indirect Impacts` * `RPC RPC` *
base_catch,
TV_Induced_Impact = `Total Value Induced Impacts` * `RPC RPC` *
base_catch,
TV_Total = TV_Direct_Impact + TV_Indirect_Impact + TV_Induced_Impact,
O_Direct_Impact = `Output Direct Impacts` * base_catch,
O_Indirect_Impact = `Output Indirect Impacts` * `RPC RPC` * base_catch,
O_Induced_Impact = `Output Induced Impacts` * `RPC RPC` * base_catch,
O_Total = O_Direct_Impact + O_Indirect_Impact + O_Induced_Impact,
E_Direct_Impact = `Employment Direct Impacts` * base_catch * deflator / 1000000,
E_Indirect_Impact = `Employment Indirect Impacts` * `RPC RPC` *
base_catch * deflator / 1000000,
E_Induced_Impact = `Employment Induced Impacts` * `RPC RPC` * base_catch * deflator / 1000000,
E_Total = E_Direct_Impact + E_Indirect_Impact + E_Induced_Impact
)
harvesters_output = multipliers_harvesters %>% dplyr::select(
fips,
`Economic Category`,
`Species Category`,
spec_no,
PI_Direct_Impact,
PI_Indirect_Impact,
PI_Induced_Impact,
PI_Total,
TV_Direct_Impact,
TV_Indirect_Impact,
TV_Induced_Impact,
TV_Total,
O_Direct_Impact,
O_Indirect_Impact,
O_Induced_Impact,
O_Total,
E_Direct_Impact,
E_Indirect_Impact,
E_Induced_Impact,
E_Total
)
##############
# Processors #
##############
multipliers_processors = multipliers_processors %>%
dplyr::mutate(Species.Category = `Species Category`) %>%
dplyr::left_join(base_catch, by = c("spec_no", "fips")) %>%
dplyr::mutate(processor_inputs = base_catch * Harvesters)
if (imports != F) {
for (n in unique(multipliers_processors$fips)) {
multipliers_processors$processor_inputs[multipliers_processors$`Species Category` == "Imports" &
multipliers_processors$fips == n] <-
imports$base_catch[imports$fips == n &
imports$`Economic Category` == "Processors"]
}
}
multipliers_processors = multipliers_processors %>%
dplyr::mutate(processor_markup = processor_inputs * markup) %>%
dplyr::mutate(
PI_Direct_Impact = `Personal Income Direct Impacts` * processor_markup,
PI_Indirect_Impact = `Personal Income Indirect Impacts` * `RPC RPC` *
processor_markup,
PI_Induced_Impact = `Personal Income Induced Impacts` * `RPC RPC` *
processor_markup,
PI_Total = PI_Direct_Impact + PI_Indirect_Impact + PI_Induced_Impact,
TV_Direct_Impact = `Total Value Direct Impacts` * processor_markup,
TV_Indirect_Impact = `Total Value Indirect Impacts` * `RPC RPC` *
processor_markup,
TV_Induced_Impact = `Total Value Induced Impacts` * `RPC RPC` *
processor_markup,
TV_Total = TV_Direct_Impact + TV_Indirect_Impact + TV_Induced_Impact,
O_Direct_Impact = `Output Direct Impacts` * processor_markup,
O_Indirect_Impact = `Output Indirect Impacts` * `RPC RPC` * processor_markup,
O_Induced_Impact = `Output Induced Impacts` * `RPC RPC` * processor_markup,
O_Total = O_Direct_Impact + O_Indirect_Impact + O_Induced_Impact,
E_Direct_Impact = `Employment Direct Impacts` * processor_markup * deflator / 1000000,
E_Indirect_Impact = `Employment Indirect Impacts` * `RPC RPC` *
processor_markup * deflator / 1000000,
E_Induced_Impact = `Employment Induced Impacts` * `RPC RPC` * processor_markup * deflator / 1000000,
E_Total = E_Direct_Impact + E_Indirect_Impact + E_Induced_Impact
)
processors_output = multipliers_processors %>% dplyr::select(
fips,
`Economic Category`,
`Species Category`,
spec_no,
PI_Direct_Impact,
PI_Indirect_Impact,
PI_Induced_Impact,
PI_Total,
TV_Direct_Impact,
TV_Indirect_Impact,
TV_Induced_Impact,
TV_Total,
O_Direct_Impact,
O_Indirect_Impact,
O_Induced_Impact,
O_Total,
E_Direct_Impact,
E_Indirect_Impact,
E_Induced_Impact,
E_Total
)
processor_inputs = multipliers_processors %>% dplyr::select(fips, spec_no, processor_inputs, processor_markup)
###############
# Wholesalers #
###############
multipliers_wholesalers = multipliers_wholesalers %>%
dplyr::mutate(Species.Category = `Species Category`) %>%
dplyr::left_join(base_catch, by = c("spec_no", "fips")) %>%
dplyr::left_join(processor_inputs, by = c("spec_no", "fips")) %>%
dplyr::mutate(wholesaler_inputs = base_catch * Harvesters + Processors * (processor_inputs +
processor_markup))
if(imports != F) {
for (n in unique(multipliers_wholesalers$fips)) {
multipliers_wholesalers$wholesaler_inputs[multipliers_wholesalers$`Species Category` == "Imports" &
multipliers_wholesalers$fips == n] <-
imports$base_catch[imports$fips == n &
imports$`Economic Category` == "Wholesalers"]
}
}
multipliers_wholesalers = multipliers_wholesalers %>%
dplyr::mutate(wholesaler_markup = wholesaler_inputs * markup) %>%
dplyr::mutate(
PI_Direct_Impact = `Personal Income Direct Impacts` * wholesaler_markup,
PI_Indirect_Impact = `Personal Income Indirect Impacts` * `RPC RPC` *
wholesaler_markup,
PI_Induced_Impact = `Personal Income Induced Impacts` * `RPC RPC` *
wholesaler_markup,
PI_Total = PI_Direct_Impact + PI_Indirect_Impact + PI_Induced_Impact,
TV_Direct_Impact = `Total Value Direct Impacts` * wholesaler_markup,
TV_Indirect_Impact = `Total Value Indirect Impacts` * `RPC RPC` *
wholesaler_markup,
TV_Induced_Impact = `Total Value Induced Impacts` * `RPC RPC` *
wholesaler_markup,
TV_Total = TV_Direct_Impact + TV_Indirect_Impact + TV_Induced_Impact,
O_Direct_Impact = `Output Direct Impacts` * wholesaler_markup,
O_Indirect_Impact = `Output Indirect Impacts` * `RPC RPC` * wholesaler_markup,
O_Induced_Impact = `Output Induced Impacts` * `RPC RPC` * wholesaler_markup,
O_Total = O_Direct_Impact + O_Indirect_Impact + O_Induced_Impact,
E_Direct_Impact = `Employment Direct Impacts` * wholesaler_markup * deflator / 1000000,
E_Indirect_Impact = `Employment Indirect Impacts` * `RPC RPC` *
wholesaler_markup * deflator / 1000000,
E_Induced_Impact = `Employment Induced Impacts` * `RPC RPC` * wholesaler_markup * deflator / 1000000,
E_Total = E_Direct_Impact + E_Indirect_Impact + E_Induced_Impact
)
wholesalers_output = multipliers_wholesalers %>% dplyr::select(
fips,
`Economic Category`,
`Species Category`,
spec_no,
PI_Direct_Impact,
PI_Indirect_Impact,
PI_Induced_Impact,
PI_Total,
TV_Direct_Impact,
TV_Indirect_Impact,
TV_Induced_Impact,
TV_Total,
O_Direct_Impact,
O_Indirect_Impact,
O_Induced_Impact,
O_Total,
E_Direct_Impact,
E_Indirect_Impact,
E_Induced_Impact,
E_Total
)
wholesaler_inputs = multipliers_wholesalers %>% dplyr::select(fips,
spec_no,
wholesaler_inputs,
wholesaler_markup)
###########
# Grocers #
###########
multipliers_grocers = multipliers_grocers %>%
dplyr::mutate(Species.Category = `Species Category`) %>%
dplyr::left_join(base_catch, by = c("spec_no", "fips")) %>%
dplyr::left_join(processor_inputs, by = c("spec_no", "fips")) %>%
dplyr::left_join(wholesaler_inputs, by = c("spec_no", "fips")) %>%
dplyr::mutate(
grocer_inputs = base_catch * Harvesters + Processors * (processor_inputs +
processor_markup) + Wholesalers * (wholesaler_inputs + wholesaler_markup)
)
if (imports != F) {
for (n in unique(multipliers_grocers$fips)) {
multipliers_grocers$grocer_inputs[multipliers_grocers$`Species Category` == "Imports" &
multipliers_grocers$fips == n] <-
imports$base_catch[imports$fips == n &
imports$`Economic Category` == "Grocers"]
}
}
multipliers_grocers = multipliers_grocers %>%
dplyr::mutate(grocer_markup = grocer_inputs * markup) %>%
dplyr::mutate(
PI_Direct_Impact = `Personal Income Direct Impacts` * grocer_markup,
PI_Indirect_Impact = `Personal Income Indirect Impacts` * `RPC RPC` *
grocer_markup,
PI_Induced_Impact = `Personal Income Induced Impacts` * `RPC RPC` *
grocer_markup,
PI_Total = PI_Direct_Impact + PI_Indirect_Impact + PI_Induced_Impact,
TV_Direct_Impact = `Total Value Direct Impacts` * grocer_markup,
TV_Indirect_Impact = `Total Value Indirect Impacts` * `RPC RPC` *
grocer_markup,
TV_Induced_Impact = `Total Value Induced Impacts` * `RPC RPC` *
grocer_markup,
TV_Total = TV_Direct_Impact + TV_Indirect_Impact + TV_Induced_Impact,
O_Direct_Impact = `Output Direct Impacts` * grocer_markup,
O_Indirect_Impact = `Output Indirect Impacts` * `RPC RPC` * grocer_markup,
O_Induced_Impact = `Output Induced Impacts` * `RPC RPC` * grocer_markup,
O_Total = O_Direct_Impact + O_Indirect_Impact + O_Induced_Impact,
E_Direct_Impact = `Employment Direct Impacts` * grocer_markup * deflator / 1000000,
E_Indirect_Impact = `Employment Indirect Impacts` * `RPC RPC` *
grocer_markup * deflator / 1000000,
E_Induced_Impact = `Employment Induced Impacts` * `RPC RPC` * grocer_markup * deflator / 1000000,
E_Total = E_Direct_Impact + E_Indirect_Impact + E_Induced_Impact
)
grocers_output = multipliers_grocers %>% dplyr::select(
fips,
`Economic Category`,
`Species Category`,
spec_no,
PI_Direct_Impact,
PI_Indirect_Impact,
PI_Induced_Impact,
PI_Total,
TV_Direct_Impact,
TV_Indirect_Impact,
TV_Induced_Impact,
TV_Total,
O_Direct_Impact,
O_Indirect_Impact,
O_Induced_Impact,
O_Total,
E_Direct_Impact,
E_Indirect_Impact,
E_Induced_Impact,
E_Total
)
grocer_inputs = multipliers_grocers %>% dplyr::select(fips, spec_no, grocer_inputs, grocer_markup)
###############
# Restaurants #
###############
multipliers_restaurants = multipliers_restaurants %>%
dplyr::mutate(Species.Category = `Species Category`) %>%
dplyr::left_join(base_catch, by = c("spec_no", "fips")) %>%
dplyr::left_join(processor_inputs, by = c("spec_no", "fips")) %>%
dplyr::left_join(wholesaler_inputs, by = c("spec_no", "fips")) %>%
dplyr::left_join(grocer_inputs, by = c("spec_no", "fips")) %>%
dplyr::mutate(
restaurant_inputs = base_catch * Harvesters + Processors * (processor_inputs +
processor_markup) + Wholesalers * (wholesaler_inputs + wholesaler_markup) + Grocers *
(grocer_inputs + grocer_markup)
)
if(imports != F) {
for (n in unique(multipliers_restaurants$fips)) {
multipliers_restaurants$restaurant_inputs[multipliers_restaurants$`Species Category` == "Imports" &
multipliers_restaurants$fips == n] <-
imports$base_catch[imports$fips == n &
imports$`Economic Category` == "Restaurants"]
}
}
multipliers_restaurants = multipliers_restaurants %>%
dplyr::mutate(restaurant_markup = restaurant_inputs * markup) %>%
dplyr::mutate(
PI_Direct_Impact = `Personal Income Direct Impacts` * restaurant_markup,
PI_Indirect_Impact = `Personal Income Indirect Impacts` * `RPC RPC` *
restaurant_markup,
PI_Induced_Impact = `Personal Income Induced Impacts` * `RPC RPC` *
restaurant_markup,
PI_Total = PI_Direct_Impact + PI_Indirect_Impact + PI_Induced_Impact,
TV_Direct_Impact = `Total Value Direct Impacts` * restaurant_markup,
TV_Indirect_Impact = `Total Value Indirect Impacts` * `RPC RPC` *
restaurant_markup,
TV_Induced_Impact = `Total Value Induced Impacts` * `RPC RPC` *
restaurant_markup,
TV_Total = TV_Direct_Impact + TV_Indirect_Impact + TV_Induced_Impact,
O_Direct_Impact = `Output Direct Impacts` * restaurant_markup,
O_Indirect_Impact = `Output Indirect Impacts` * `RPC RPC` * restaurant_markup,
O_Induced_Impact = `Output Induced Impacts` * `RPC RPC` * restaurant_markup,
O_Total = O_Direct_Impact + O_Indirect_Impact + O_Induced_Impact,
E_Direct_Impact = `Employment Direct Impacts` * restaurant_markup * deflator / 1000000,
E_Indirect_Impact = `Employment Indirect Impacts` * `RPC RPC` *
restaurant_markup * deflator / 1000000,
E_Induced_Impact = `Employment Induced Impacts` * `RPC RPC` * restaurant_markup * deflator / 1000000,
E_Total = E_Direct_Impact + E_Indirect_Impact + E_Induced_Impact
)
restaurants_output = multipliers_restaurants %>% dplyr::select(
fips,
`Economic Category`,
`Species Category`,
spec_no,
PI_Direct_Impact,
PI_Indirect_Impact,
PI_Induced_Impact,
PI_Total,
TV_Direct_Impact,
TV_Indirect_Impact,
TV_Induced_Impact,
TV_Total,
O_Direct_Impact,
O_Indirect_Impact,
O_Induced_Impact,
O_Total,
E_Direct_Impact,
E_Indirect_Impact,
E_Induced_Impact,
E_Total
)
###########
# Brokers #
###########
if(import_numbers!=F) {
broker_output = import_numbers %>%
dplyr::bind_cols(multipliers_brokers) %>%
dplyr::mutate(broker_markup = (imports) * 1.186554) %>%
dplyr::mutate(
PI_Direct_Impact = `Personal Income Direct Impacts` * broker_markup,
PI_Indirect_Impact = `Personal Income Indirect Impacts` * `RPC RPC` *
broker_markup,
PI_Induced_Impact = `Personal Income Induced Impacts` * `RPC RPC` *
broker_markup,
PI_Total = PI_Direct_Impact + PI_Indirect_Impact + PI_Induced_Impact,
TV_Direct_Impact = `Total Value Direct Impacts` * broker_markup,
TV_Indirect_Impact = `Total Value Indirect Impacts` * `RPC RPC` *
broker_markup,
TV_Induced_Impact = `Total Value Induced Impacts` * `RPC RPC` *
broker_markup,
TV_Total = TV_Direct_Impact + TV_Indirect_Impact + TV_Induced_Impact,
O_Direct_Impact = `Output Direct Impacts` * broker_markup,
O_Indirect_Impact = `Output Indirect Impacts` * `RPC RPC` * broker_markup,
O_Induced_Impact = `Output Induced Impacts` * `RPC RPC` * broker_markup,
O_Total = O_Direct_Impact + O_Indirect_Impact + O_Induced_Impact,
E_Direct_Impact = `Employment Direct Impacts` * broker_markup * deflator / 1000000,
E_Indirect_Impact = `Employment Indirect Impacts` * `RPC RPC` *
broker_markup * deflator / 1000000,
E_Induced_Impact = `Employment Induced Impacts` * `RPC RPC` * broker_markup * deflator / 1000000,
E_Total = E_Direct_Impact + E_Indirect_Impact + E_Induced_Impact
) %>%
dplyr::select(
fips,
`Economic Category`,
`Species Category`,
spec_no,
PI_Direct_Impact,
PI_Indirect_Impact,
PI_Induced_Impact,
PI_Total,
TV_Direct_Impact,
TV_Indirect_Impact,
TV_Induced_Impact,
TV_Total,
O_Direct_Impact,
O_Indirect_Impact,
O_Induced_Impact,
O_Total,
E_Direct_Impact,
E_Indirect_Impact,
E_Induced_Impact,
E_Total
) %>% dplyr::filter(!is.na(fips))
} else {
broker_output = NULL
}
################
# Final Output #
################
final_output = dplyr::bind_rows(
harvesters_output,
processors_output,
wholesalers_output,
grocers_output,
restaurants_output,
broker_output
)
final_output[is.na(final_output)] <- 0
if(imports==F){
final_output = final_output %>%
dplyr::mutate(Imports = "No")
}
if(imports!=F){
final_output = final_output %>%
dplyr::mutate(Imports = "Yes")
}
return(final_output)
}
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