diff --git a/Create_Tables.r b/Create_Tables.r
index 0f53fb3..b9175c6 100644
--- a/Create_Tables.r
+++ b/Create_Tables.r
@@ -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")