diff --git a/Data_Center_Visuals.r b/Data_Center_Visuals.r
index cf4388b..51a99f7 100644
--- a/Data_Center_Visuals.r
+++ b/Data_Center_Visuals.r
@@ -120,24 +120,26 @@ TAX_PLOT <- ggplot(IMPLAN_TAX_FIG_DATA, aes(x=Year,y=value/10^6,fill=Impact,grou
 #############Dollar values table
 NPV <- IMPLAN_DOLLAR %>% mutate(value=value/10^9,Discount_0=value,Discount_2=value/(1+0.02)^(Year-2026),Discount_5=value/(1+0.05)^(Year-2026),Discount_10=value/(1+0.1)^(Year-2026)) %>% group_by(Impact,Region) %>% summarize(Discount_0=sum(Discount_0),Discount_2=sum(Discount_2),Discount_5=sum(Discount_5),Discount_10=sum(Discount_10)) %>% ungroup
 NPV[,3:6] <-round(NPV[,3:6],2)
-NPV
 WY_NPV <- cbind(c("Discount","0%","2%","5%","10%"),NPV %>% filter(Region=='Wyoming') %>% select(-Region) %>% t) %>% as_tibble
 colnames(WY_NPV ) <- WY_NPV[1,]
 WY_NPV <- WY_NPV[-1,] 
 WY_NPV <- WY_NPV %>%  mutate_if(is.character, str_trim)
 WY_NPV[,-1] <- lapply(	lapply(WY_NPV[,-1] ,as.numeric) %>% as_tibble,dollar) %>% as_tibble
-WY_TAX <- WY_NPV[,c(1,5:7)]
-WY_NPV <- WY_NPV[,c(1:4)]
+WY_TAX <- WY_NPV %>% select(Discount,Federal,`Wyoming State Taxes`,`Wyoming County Taxes`,`Sub County General`)
+WY_NPV <- WY_NPV %>% select(Discount,,Output,`Value Added`,Income)
 
 
 #US
 US_NPV <- cbind(c("Discount","0%","2%","5%","10%"),NPV %>% filter(Region!='Wyoming') %>% select(-Region) %>% t) %>% as_tibble
 colnames(US_NPV ) <- US_NPV[1,]
 US_NPV <- US_NPV[-1,] 
+
 US_NPV <- US_NPV %>%  mutate_if(is.character, str_trim)
 US_NPV[,-1] <- lapply(	lapply(US_NPV[,-1] ,as.numeric) %>% as_tibble,dollar) %>% as_tibble
-US_TAX <- US_NPV[,c(1,5:6)]
-US_NPV <- US_NPV[,c(1:4)]
+US_TAX <- US_NPV %>% select(Discount,Federal,`Other States`,`Sub County General`)
+US_NPV <- US_NPV %>% select(Discount,Output,`Value Added`,Income)
+
+
 ALL_TAX <- US_TAX
 ALL_TAX[,2] <- dollar(parse_number(as.character(US_TAX[,2] %>% t))+ parse_number(as.character(WY_TAX[,2] %>% t)))
 ALL_TAX <- cbind(ALL_TAX,WY_TAX[,-1:-2])
@@ -227,7 +229,6 @@ RANK  <- EMP_SUMMARY %>% select(Occupation,Period,Rank)    %>% ungroup
 PEAK_WY <- RES %>% mutate(Period=ifelse(Year>=2030,"Operations","Construction")) %>% left_join(RANK) %>% mutate(Occupation=ifelse(Rank==16,"Other",Occupation)) %>% group_by(Occupation,Year,Period,Rank) %>% summarize(Peak_Employment=max(Employment,na.rm=TRUE)) %>% group_by(Period,Occupation,Rank)  %>% summarize(Wy_Peak_Employment=max(Peak_Employment,na.rm=TRUE)) %>% ungroup %>% select(Occupation,Period,Wy_Peak_Employment)
 
 PEAK_US <- 	RES_US %>% select(Occupation,Year,US_Employment=Employment) %>% left_join(RES %>% mutate(Wy_Employment=Employment)) %>% mutate(Employment=US_Employment-Wy_Employment)%>% select(-US_Employment,-Wages) %>% mutate(Period=ifelse(Year>=2030,"Operations","Construction")) %>% left_join(RANK) %>% mutate(Occupation=ifelse(Rank==16,"Other",Occupation)) %>% group_by(Occupation,Year,Period,Rank) %>% summarize(Peak_Employment=max(Employment,na.rm=TRUE)) %>% group_by(Period,Occupation,Rank)  %>% summarize(US_Peak_Employment=max(Peak_Employment,na.rm=TRUE)) %>% ungroup %>% select(Occupation,Period,US_Peak_Employment)
-rbind(RES_US,RES) %>% group_by(Year) %>% summarize(Employment=max(Employment)) %>% plot
 PEAK_ALL <-	rbind(RES_US,RES) %>% group_by(Occupation,Year) %>% summarize(Employment=max(Employment)) %>% mutate(Period=ifelse(Year>=2030,"Operations","Construction")) %>% left_join(RANK) %>% mutate(Occupation=ifelse(Rank==16,"Other",Occupation)) %>% group_by(Occupation,Year,Period,Rank) %>% summarize(Peak_Employment=max(Employment,na.rm=TRUE)) %>% group_by(Occupation,Period) %>% summarize(Total_Peak_Employment=max(Peak_Employment)) %>% ungroup
 
 #PEAK_ALL <- 	rbind(US ,WY) %>% group_by(Occupation,Year,Period) %>% summarize(Employment=sum(Employment,na.rm=TRUE)) %>% left_join(RANK) %>% mutate(Occupation=ifelse(Rank==16,"Other",Occupation)) %>% group_by(Occupation,Period) %>% summarize(Total_Peak_Employment=max(Employment,na.rm=TRUE)) %>% ungroup