53 lines
2.8 KiB
R
53 lines
2.8 KiB
R
library(tidyverse)
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RES_PATH <- './Results'
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dir.create(RES_PATH,showWarnings=FALSE)
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source("Scripts/Occupation_Clean.r")
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#Clean all of the saved Occupation files
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Occupation_Summary_Directory()
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SUMMARY_DIR <- "Data/Economic_Indicator_Summary"
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FILE <- list.files(SUMMARY_DIR,pattern="*.csv",full.names=TRUE)
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DATA <- read_csv("Data/Economic_Indicator_Summary/Economic Indicator Summary.csv") %>% mutate_at(6:8,parse_number)
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colnames(DATA) <- gsub(" ","_",colnames(DATA))
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DATA$Impact <- gsub("1|2|3|-| ","",DATA$Impact)
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DATA <- DATA %>% filter(!is.na(Group_Name))
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colnames(DATA )
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OVERALL <- DATA %>% group_by(Event_Name) %>% summarize(Output=sum(Output),Value_Added=sum(Value_Added) ,Employment=sum(Employment),Labor_Income=sum(Labor_Income))
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DATA <- DATA %>% select(-Group_Name,-Region)
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library(scales)
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GET_SUMMARY <- function(COL_NUM,DATA){
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DATA <- DATA[,c(1,2,COL_NUM)]
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ORIG_COL_NAME <- colnames(DATA)[3]
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colnames(DATA)[3] <- "TEMP_COL"
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RES <- DATA %>% group_by(Event_Name)%>% summarize('Direct'=percent(sum(ifelse(Impact=='Direct',TEMP_COL,0))/sum(TEMP_COL)),'Indirect'=percent(sum(ifelse(Impact=='Indirect',TEMP_COL,0))/sum(TEMP_COL)),'Induced'=percent(sum(ifelse(Impact=='Induced',TEMP_COL,0))/sum(TEMP_COL)))
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RES$Type <- ORIG_COL_NAME
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RES <- RES[,c(1,5,2:4)]
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return(RES)
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}
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TOTAL_IMPACT_SUMMARY <- DATA
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DIRECT_SPLIT_SUMMARY <- rbind(GET_SUMMARY(3,DATA),GET_SUMMARY(4,DATA),GET_SUMMARY(5,DATA),GET_SUMMARY(6,DATA)) %>% arrange(Event_Name)
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write_csv(DIRECT_SPLIT_SUMMARY, "Results/Event_Impact_Summary.csv")
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####Taxes
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TAX <- read_csv("Data/Tax_Impacts_Summary/tax_summary.csv") %>% mutate_at(4:10,parse_number)
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colnames(TAX) <- gsub(" ","_",colnames(TAX))
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TAX$Impact <- ifelse(TAX$Impact==1,'Direct',ifelse(TAX$Impact==2,'Indirect' ,'Induced'))
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TAX <- TAX %>% filter(!is.na(Group_Name))
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TAX <- TAX %>% filter(!is.na(Event_Name))
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TAX_SUMMARY <- TAX %>% group_by(Event_Name,Impact) %>% summarize(County=sum(County+Sub_County_General+Sub_County_Special_Districts),State=sum(State) ,Federal=sum(Federal)) %>% mutate(Wyoming_Total=County+State)
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#Reduce to 25% for direct due to manufacturing exemption.
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TAX_SUMMARY[TAX_SUMMARY$Impact=='Direct',-1:-2] <- 0.25*TAX_SUMMARY[TAX_SUMMARY$Impact=='Direct',-1:-2]
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GET_SUMMARY(6,TAX_SUMMARY)
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DIRECT_SPLIT_SUMMARY <- rbind(GET_SUMMARY(3,DATA),GET_SUMMARY(4,DATA),GET_SUMMARY(5,DATA),GET_SUMMARY(6,DATA)) %>% arrange(Event_Name)
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DIRECT_SPLIT_SUMMARY
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TAX_SUMMARY
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DIRECT_SPLIT_SUMMARY <- rbind(DIRECT_SPLIT_SUMMARY,GET_SUMMARY(6,TAX_SUMMARY) %>% mutate(Type='Wyoming Total Taxes')) %>% arrange(Event_Name)
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write_csv(DIRECT_SPLIT_SUMMARY, "Results/Impact_Ratio_Summary.csv")
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DIRECT_SPLIT_SUMMARY
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TOTAL_IMPACT_SUMMARY
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colnames(TAX_SUMMARY)[-1:-2] <- paste0(colnames(TAX_SUMMARY)[-1:-2],"_Taxes")
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TOTAL_IMPACT_SUMMARY <- left_join(TOTAL_IMPACT_SUMMARY ,TAX_SUMMARY) %>% arrange(Event_Name)
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write_csv(TOTAL_IMPACT_SUMMARY, "Results/Event_Impact_Summary.csv")
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