diff --git a/Data_Center_Visuals.r b/Data_Center_Visuals.r
index 0c6d2fa..dc08c43 100644
--- a/Data_Center_Visuals.r
+++ b/Data_Center_Visuals.r
@@ -53,7 +53,6 @@ IMPLAN$Region <- ifelse(IMPLAN$Region=='Wyoming','WY',IMPLAN$Region)
 ########################
 IMPLAN_DOLLAR <- IMPLAN %>% filter(Impact!='Employment')
 IMPLAN_DOLLAR <-  IMPLAN_DOLLAR  %>% group_by(Type,Year,Impact,Source) %>% mutate(value=ifelse(Region=='US',value-min(value),value) ) %>% ungroup
-IMPLAN_DOLLAR
 #State tax write off
 IMPLAN_DOLLAR[IMPLAN_DOLLAR$Impact %in% c("County","State") & IMPLAN_DOLLAR$Type=='Direct'& IMPLAN_DOLLAR$Region=='WY',"value"] <- 0
 
@@ -71,11 +70,14 @@ IMPLAN_DOLLAR <- rbind(IMPLAN_DOLLAR,rbind(TEMP %>% mutate(Year=2025),TEMP %>% m
 
 IMPLAN_DOLLAR[IMPLAN_DOLLAR$Impact %in% "State" & IMPLAN_DOLLAR$Type=='Direct'& IMPLAN_DOLLAR$Region=='WY',]
 IMPLAN_DOLLAR %>% pull(Year) %>% unique %>% length 
+IMPLAN_DOLLAR <- 	IMPLAN_DOLLAR %>% filter(!(Impact %in% c("Sub County Special Districts","Sub County County General")))
 
+IMPLAN_DOLLAR  %>% pull(Impact) %>% unique
+IMPLAN_DOLLAR  %>% filter(is.na(Impact))
 IMPLAN_DOLLAR$Impact <- factor(IMPLAN_DOLLAR$Impact,levels=c('Output','Value Added','Income','Federal','State','County'))
 IMPLAN_DOLLAR$Region <- factor(ifelse(IMPLAN_DOLLAR$Region=="WY",'Wyoming',"Rest of United States"),levels=rev(c("Wyoming","Rest of United States")))
 IMPLAN_DOLLAR_REGION <- IMPLAN_DOLLAR %>% filter(Impact %in% c('Income','Output','Value Added')) %>% group_by(Year,Region,Impact) %>% summarize(value=sum(value)) %>% ungroup
-
+IMPLAN_DOLLAR  %>% filter(is.na(Impact))
 ########################Plot of total impact in US and in Wyoming
 IMPLAN_REGION_DOLLAR_PLOT <- ggplot(IMPLAN_DOLLAR_REGION ,aes(x=Year,y=value/10^9,fill=Region,group=Region))+geom_area()+facet_wrap(~Impact,ncol=1)+ylab("Billion Dollars")+theme_bw()+ theme(plot.title = element_text(size = 22, face = "bold"), axis.title = element_text(size = 18),axis.text  = element_text(size = 16),legend.text = element_text(size = 14),strip.text = element_text(size = 14),legend.position="top")+  theme(legend.title=element_blank())+scale_fill_manual(values=c("#3C3B6E","#FFC425"))+scale_x_continuous(breaks=seq(2025,2060,by=5))+scale_y_continuous(breaks=seq(0,5000,by=1))
 ggsave( filename = "./Results/Data Center: Dollar Values of Model.png",  plot = IMPLAN_REGION_DOLLAR_PLOT,  width = 8.5,  height = 11*(3./4),  units = "in", dpi = 300)
@@ -87,6 +89,7 @@ IMPLAN_TAX  %>% left_join(OLD %>% rename(OLD=value))
 
 #IMPLAN_TAX <- IMPLAN_TAX[!(IMPLAN_TAX$Type=='Direct' & IMPLAN_TAX$Region=='Rest of United States'& IMPLAN_TAX$Impact!='Federal'),] 
 #IMPLAN_TAX <-IMPLAN_TAX[!(IMPLAN_TAX$Type=='Direct' & IMPLAN_TAX$Region=='Wyoming'& IMPLAN_TAX$Impact=='Federal'),] 
+
 IMPLAN_DOLLAR <- IMPLAN_DOLLAR %>% filter(!(Impact %in% c("County","State","Federal"))) %>% rbind(IMPLAN_TAX)
 
 IMPLAN_TAX_FIG_DATA <- IMPLAN_TAX  %>% group_by(Year,Impact) %>% summarize(value=sum(value))
@@ -96,6 +99,7 @@ ggsave( filename = "./Results/Data Center: Tax Model.png",  plot = TAX_PLOT,  wi
 #############Dollar values table
 NPV <- IMPLAN_DOLLAR %>% mutate(value=value/10^9,Discount_0=value,Discount_2=value/(1+0.02)^(Year-2028),Discount_5=value/(1+0.05)^(Year-2028),Discount_10=value/(1+0.1)^(Year-2028)) %>% 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
 write.csv(NPV,"./Results/Data_Center_NPV_Values.csv",row.names=FALSE)