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Updated summary script for all data

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Alex Gebben Work 2025-10-20 16:21:34 -06:00
parent c41f41e6a3
commit da4c52fd79
1 changed files with 11 additions and 8 deletions
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19
Create_Tables.r
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@ -23,7 +23,8 @@ GET_SUMMARY <- function(COL_NUM,DATA){
RES <- RES[,c(1,5,2:4)]
return(RES)
}
TOTAL_IMPACT_SUMMARY <- DATA
TOTAL_IMPACT_SUMMARY <- DATA %>% group_by(Event_Name,Impact) %>% summarize(Employment=sum(Employment),Labor_Income=sum(Labor_Income),Value_Added=sum(Value_Added),Output=sum(Output))
DIRECT_SPLIT_SUMMARY <- rbind(GET_SUMMARY(3,DATA),GET_SUMMARY(4,DATA),GET_SUMMARY(5,DATA),GET_SUMMARY(6,DATA)) %>% arrange(Event_Name)
write_csv(DIRECT_SPLIT_SUMMARY, "Results/Event_Impact_Summary.csv")
@ -38,15 +39,17 @@ TAX_SUMMARY <- TAX %>% group_by(Event_Name,Impact) %>% summarize(County=sum(Cou
TAX_SUMMARY[TAX_SUMMARY$Impact=='Direct',-1:-2] <- 0.25*TAX_SUMMARY[TAX_SUMMARY$Impact=='Direct',-1:-2]
GET_SUMMARY(6,TAX_SUMMARY)
DIRECT_SPLIT_SUMMARY <- rbind(GET_SUMMARY(3,DATA),GET_SUMMARY(4,DATA),GET_SUMMARY(5,DATA),GET_SUMMARY(6,DATA)) %>% arrange(Event_Name)
DIRECT_SPLIT_SUMMARY
TAX_SUMMARY
DIRECT_SPLIT_SUMMARY <- rbind(DIRECT_SPLIT_SUMMARY,GET_SUMMARY(6,TAX_SUMMARY) %>% mutate(Type='Wyoming Total Taxes')) %>% arrange(Event_Name)
DIRECT_SPLIT_SUMMARY <- rbind(DIRECT_SPLIT_SUMMARY,GET_SUMMARY(6,TAX_SUMMARY) %>% mutate(Type='Wyoming Total Taxes')) %>% arrange(Event_Name) %>% unique
write_csv(DIRECT_SPLIT_SUMMARY, "Results/Impact_Ratio_Summary.csv")
DIRECT_SPLIT_SUMMARY
TOTAL_IMPACT_SUMMARY
colnames(TAX_SUMMARY)[-1:-2] <- paste0(colnames(TAX_SUMMARY)[-1:-2],"_Taxes")
TOTAL_IMPACT_SUMMARY <- left_join(TOTAL_IMPACT_SUMMARY ,TAX_SUMMARY) %>% arrange(Event_Name)
TOTAL_IMPACT_SUMMARY <- left_join(TOTAL_IMPACT_SUMMARY ,TAX_SUMMARY) %>% arrange(Event_Name) %>% select(-Federal)
TOTAL_IMPACT_SUMMARY <- full_join(TOTAL_IMPACT_SUMMARY, TOTAL_IMPACT_SUMMARY %>% group_by(Event_Name) %>% summarize(Impact='Total',Employment=sum(Employment),Labor_Income=sum(Labor_Income),Value_Added=sum(Value_Added),Output=sum(Output),Wyoming_Total=sum(Wyoming_Total),State=sum(State),County=sum(County)) %>% arrange(Event_Name,Impact)) %>% select(Event_Name,Impact,Employment,Labor_Income,Value_Added,Output,Wyoming_Total,State,County)
TOTAL_IMPACT_SUMMARY[,3] <- round(TOTAL_IMPACT_SUMMARY[,3])
TOTAL_IMPACT_SUMMARY[4:9] <- round(TOTAL_IMPACT_SUMMARY[4:9]/10^6,2)
colnames(TOTAL_IMPACT_SUMMARY)[7:9] <- paste0(colnames(TOTAL_IMPACT_SUMMARY[,7:9]),"_Taxes")
colnames(TOTAL_IMPACT_SUMMARY)
TOTAL_IMPACT_SUMMARY %>% arrange(Event_Name,Impact)
write_csv(TOTAL_IMPACT_SUMMARY, "Results/Event_Impact_Summary.csv")