61 lines
2.7 KiB
R
61 lines
2.7 KiB
R
library(tidyverse)
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#R script to quickly find IMPLAN inputs or adjustment factors
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GET_INPUTS_GAS <- function(YEAR){
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FILES <- list.files("./Raw_Output/Detailed_Economic_Indicators/Prelim-Run")
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FILE <- paste0("./Raw_Output/Detailed_Economic_Indicators/Prelim-Run/",FILES[grep(paste0("-",YEAR),FILES)])
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#Dollar value added to the RNG in addition to the main production
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RNG_ADD_YEARS <- c(0,380102,3097001,5943721,11224037,18050255,18050255)
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RNG_ADD <- RNG_ADD_YEARS[YEAR-2023]
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DF <- read_csv(FILE) %>% filter(ImpactType=="Direct",EventName=="Gas Production (Campbell)")
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DF <- DF %>% mutate(IntermediateInputs=Output-EmployeeCompensation-ProprietorIncome-OtherPropertyIncome-TaxesOnProductionAndImports)%>% select(WageAndSalaryEmployment,EmployeeCompensation,Output,ProprietorEmployment,OtherPropertyIncome,Employment,IntermediateInputs)%>% mutate(Output=Output+RNG_ADD)
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return(DF)
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}
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#Find the input values for the beet factories
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GET_INPUTS_BEET <- function(YEAR,OP_COST=75){
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MAX_WASH_EMP <- 8
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MAX_GOSHEN_EMP <-15
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GOSHEN_WAGE <- 53007.10
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WASH_WAGE <- 71017.25
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WASH_PROD <- c(0,0,0,35000,37500,50000,50000)
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GOSHEN_PROD <- c(0,0,0,52500,112500,150000,150000)
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GOSHEN_OP_COST <- OP_COST*GOSHEN_PROD
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WASH_OP_COST <- OP_COST*WASH_PROD
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GOSHEN_TAX_CRED <- GOSHEN_PROD*0.62*90
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WASH_TAX_CRED <- WASH_PROD*0.62*90
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WASH_EMP <- MAX_WASH_EMP*(WASH_PROD)/max(WASH_PROD)
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GOSHEN_EMP <- MAX_GOSHEN_EMP*(GOSHEN_PROD)/max(GOSHEN_PROD)
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TOPI <- GOSHEN_EMP*0
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OTH_PROP <- GOSHEN_EMP*0
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GOSHEN_COMP <- GOSHEN_WAGE*GOSHEN_EMP
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WASH_COMP <- WASH_WAGE*WASH_EMP
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WASH_RETURN <- WASH_TAX_CRED-WASH_COMP-WASH_OP_COST
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GOSHEN_RETURN <- GOSHEN_TAX_CRED-GOSHEN_COMP-GOSHEN_OP_COST
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RES <- rbind(as.numeric(cbind(WASH_EMP,WASH_COMP,WASH_RETURN,TOPI,OTH_PROP,WASH_OP_COST)[YEAR-2023,]),as.numeric(cbind(GOSHEN_EMP,GOSHEN_COMP,GOSHEN_RETURN,TOPI,OTH_PROP,GOSHEN_OP_COST)[YEAR-2023,])) %>% as_tibble
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colnames(RES) <- c("Wage_Emp","Compensation","Proprietor_Income","TOPI","OTHER_PROP","Inter_Inputs")
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RES$County <- c("Washakie","Goshen")
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RES <- RES %>% select(County,everything())
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return(RES)
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}
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#Pull the truck and rail transportation induced, to remove it from IMPLAN results. Those costs are explicitly modeled.
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GET_ADJ <- function(YEAR){
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FILES <- list.files("./Raw_Output/Detailed_Economic_Indicators/Prelim-Run/")
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FILE <- paste0("./Raw_Output/Detailed_Economic_Indicators/Prelim-Run/",FILES[grep(paste0("-",YEAR),FILES)])
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DF <- read_csv(FILE) %>% filter(ImpactType=="Direct",IndustryCode %in% c(397,399),TagName=="beet purchase")
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DF$County <- gsub(" County, WY \\(2023\\)","",DF$DestinationRegion )
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DF <- DF %>% select(County, Industry=IndustryDescription,Output) %>% mutate(Output=-Output)
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return(DF)
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}
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GET_INPUTS_GAS(2028)
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GET_INPUTS_BEET(2029,10)
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GET_ADJ(2027) |