diff --git a/Visuals.r b/Visuals.r
index c69478c..5d56c40 100644
--- a/Visuals.r
+++ b/Visuals.r
@@ -135,6 +135,7 @@ TAXES$revenue[!grepl("Sales",TAXES$revenue)] <- 'Other Taxes'
 TAXES <- TAXES %>% rename(Revenue='Taxes','Tax'=revenue,'Year'=year) %>% select(Year,Tax,Revenue)
 TAXES <- TAXES %>% group_by(Year,Tax) %>% summarize(Revenue=sum(Revenue))
 TOTAL_TAXES <- TAXES %>% group_by(Year) %>% summarize('Million (USD)'=sum(Revenue)/10^6,Metric='Wyoming Taxes') %>% mutate(Year=as.numeric(Year))
+TOTAL_TAXES 
 
 
 
@@ -146,17 +147,24 @@ DOLLAR_VALUES <- rbind(OUTPUT_NUM %>% filter(Category=='Total') %>% select(-Cate
 GDP_TOTAL %>% mutate(Metric='GDP') %>% rename('Million (USD)'=GDP),
 TOTAL_TAXES,
 TOTAL_PERSONAL_INCOME %>% mutate(Metric='Wages Paid') %>% rename('Million (USD)'=Income))
-
 FACET_INDICATORS_PLOT <- ggplot(DOLLAR_VALUES, aes(x=Year,y=`Million (USD)`,fill=Metric))+geom_area()+facet_wrap(.~Metric,nrow=2)+theme_bw()+scale_x_continuous(breaks=c(2029,seq(2025,2060,by=2)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=50))+theme(legend.position = "top",text=element_text(size=16))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus") )
 DOLLAR_VALUES
-FACET_INDICATORS_PLOT <- ggplot(DOLLAR_VALUES %>% filter(!(Metric %in% c('Wyoming Taxes'))), aes(x=Year,y=`Million (USD)`,fill=Metric))+geom_area()+facet_wrap(.~Metric,nrow=3)+theme_bw()+scale_x_continuous(breaks=c(2029,seq(2025,2060,by=2)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=50))+theme(legend.position = "top",text=element_text(size=16))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus") )
+
+DIRECT_TAX <- c(10.408,22.355,24.647,58.221,55.835,53.449,51.062,48.676,46.290,43.904,41.518,39.132,37.318,35.766,34.214,32.662,31.110,29.557,28.005,26.453,24.901,23.349,21.797,20.244,18.692,17.140,15.588,14.036,12.484,10.931,9.379)
+DIRECT_TAX <- cbind(2030:2060,DIRECT_TAX ) %>% as_tibble
+
+colnames(DIRECT_TAX) <- c("Year","Tax")
+DOLLAR_VALUES 
+DOLLAR_VALUES[DOLLAR_VALUES$Metric=='Wyoming Taxes','Million (USD)'] <- 	DOLLAR_VALUES[DOLLAR_VALUES$Metric=='Wyoming Taxes','Million (USD)'] +	c(0,DIRECT_TAX$Tax)
+TAX_PLOT <- ggplot(DOLLAR_VALUES %>% filter(Metric %in% c('Wyoming Taxes')), aes(x=Year,y=`Million (USD)`,fill=Metric))+geom_area()+theme_bw()+scale_x_continuous(breaks=c(2029,seq(2025,2060,by=2)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=5))+theme(legend.position = "top",text=element_text(size=16))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus")[4] )
+png(paste0(OUTPUT_DIR,"Total_Tax_Plot.png"), units = "in", width = 1.5*11, height = 1.5*10, res = 600)
+TAX_PLOT 
+dev.off()
+
+FACET_INDICATORS_PLOT <- ggplot(DOLLAR_VALUES , aes(x=Year,y=`Million (USD)`,fill=Metric))+geom_area()+facet_wrap(.~Metric,nrow=2)+theme_bw()+scale_x_continuous(breaks=c(2029,seq(2025,2060,by=2)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=50))+theme(legend.position = "top",text=element_text(size=16))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus") )
 FACET_INDICATORS_PLOT 
 
 
-
-TAX_PLOT <- ggplot(DOLLAR_VALUES %>% filter(Metric %in% c('Wyoming Taxes')), aes(x=Year,y=`Million (USD)`,fill=Metric))+geom_area()+facet_wrap(.~Metric,nrow=2)+theme_bw()+scale_x_continuous(breaks=c(2029,seq(2025,2060,by=2)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=0.5))+theme(legend.position = "top",text=element_text(size=16))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus")[4] )
-TAX_PLOT 
-
 FACET_INDICATORS_PLOT 
 
 png(paste0(OUTPUT_DIR,"Facet_Indicator_Plot.png"), units = "in", width = 1.5*11, height = 1.5*10, res = 600)
@@ -171,15 +179,25 @@ Period_Indicator_Summary[,c(3:4)] <- round(Period_Indicator_Summary[,c(3:4)],0)
 Period_Indicator_Summary
 
 write_csv(Period_Indicator_Summary,paste0(OUTPUT_DIR,"Economic_Summary_Indicator.csv"  ),col_names=TRUE)
-
 GDP_SUMMARY <- GDP %>% group_by(Year) %>% summarize(GDP=sum(GDP),NPV_3=(GDP/((1+0.03)^(Year-2029))),NPV_5=(GDP/((1+0.05)^(Year-2029))),NPV_7=(GDP/((1+0.07)^(Year-2029)))) %>% ungroup
 NPV_3 <- GDP_SUMMARY %>% pull(NPV_3) %>% sum
 NPV_5 <-  GDP_SUMMARY %>% pull(NPV_5) %>% sum
 NPV_7 <-  GDP_SUMMARY %>% pull(NPV_7) %>% sum
 NPV_SUMMARY <- round(t(c(NPV_3,NPV_5,NPV_7)),0) %>% as_tibble
+NPV_SUMMARY 
 colnames(NPV_SUMMARY) <- c('3%','5%','7%')
 write_csv(NPV_SUMMARY,paste0(OUTPUT_DIR,"GDP_Net_Present_Value.csv"  ))
-NPV_SUMMARY 
-
+
+#######################
+TAX <- DOLLAR_VALUES %>% filter(Metric=='Wyoming Taxes') %>% select(Year,Tax=`Million (USD)`)
+
+TAX_SUMMARY <- TAX %>% group_by(Year) %>% summarize(Tax=sum(Tax),NPV_3=(Tax/((1+0.03)^(Year-2029))),NPV_5=(Tax/((1+0.05)^(Year-2029))),NPV_7=(Tax/((1+0.07)^(Year-2029)))) %>% ungroup
+NPV_3 <- TAX_SUMMARY %>% pull(NPV_3) %>% sum
+NPV_5 <-  TAX_SUMMARY %>% pull(NPV_5) %>% sum
+NPV_7 <-  TAX_SUMMARY %>% pull(NPV_7) %>% sum
+NPV_TAX_SUMMARY <- round(t(c(NPV_3,NPV_5,NPV_7)),0) %>% as_tibble
+NPV_TAX_SUMMARY 
+colnames(NPV_TAX_SUMMARY) <- c('3%','5%','7%')
+write_csv(NPV_TAX_SUMMARY,paste0(OUTPUT_DIR,"Tax_Net_Present_Value.csv"  ))