34 lines
1.6 KiB
R
34 lines
1.6 KiB
R
library(tidyverse)
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#R script to quickly find IMPLAN inputs or adjustment factors
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YEAR <- 2027
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#TAX_FILE <- FILES[grep("Taxes",FILES)]
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GET_TRANSPORT_ADJ <- function(YEAR){
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YEAR <- ifelse(YEAR>2030,2030,YEAR)
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HEADER <- paste0('./Raw_Output/Prelim-Runs/')
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FILES <- list.files(HEADER)
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FILE <- paste0(HEADER,FILES[grep(paste0(YEAR,"-"),FILES)])
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DF <- read_csv(FILE)
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DF$County <- gsub(" County, WY \\(2023\\)","",DF$DestinationRegion )
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DF$Impact_County <- gsub(" County, WY Group","",DF$OriginRegion )
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TEMP <- DF %>% filter(ImpactType!="Induced",County==Impact_County,IndustryCode %in% c(397,399),TagName %in% c("Beet Purchase","SBS Purchase")) %>% group_by(County,IndustryCode,IndustryDescription) %>% summarize(Output=-sum(Output)) %>% arrange(Output)
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return(TEMP)
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}
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GET_INCOME_ADJ <- function(YEAR){
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PROFIT <- c(0,-1568776,3691597,9769730,26627999,52383510,55929633,56121706,56175248,56188791,56190869)
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TOTAL_PROFIT <- (PROFIT[YEAR-2023]-179917)
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YEAR <- ifelse(YEAR>2030,2030,YEAR)
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HEADER <- paste0('./Raw_Output/Prelim-Runs/')
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FILES <- list.files(HEADER)
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FILE <- paste0(HEADER,FILES[grep(paste0(YEAR,"-"),FILES)])
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DF <- read_csv(FILE)
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DF$County <- gsub(" County, WY \\(2023\\)","",DF$DestinationRegion )
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DF$Impact_County <- gsub(" County, WY Group","",DF$OriginRegion )
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REPORTED_PROFIT <- DF %>% filter(EventName=="Natural Gas Prod (Campbell)",ImpactType=="Direct",IndustryCode==20) %>% group_by(IndustryDescription) %>% summarize(TOTAL=OtherPropertyIncome+ProprietorIncome) %>% pull(TOTAL)
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PROFIT_ADJ <- TOTAL_PROFIT-REPORTED_PROFIT
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return(PROFIT_ADJ)
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}
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CYEAR <- 2030
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GET_INCOME_ADJ(CYEAR)
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GET_TRANSPORT_ADJ(CYEAR)
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