diff --git a/IMPLAN_Visuals.r b/IMPLAN_Visuals.r
index 4401858..bcb3760 100644
--- a/IMPLAN_Visuals.r
+++ b/IMPLAN_Visuals.r
@@ -1,4 +1,5 @@
 library(tidyverse)
+library(scales)
 GET_DATA <- function(){
 	GET_FILE_US <- function(YEAR){read_csv(paste0("Model_Outputs/IMPLAN/US/US_",YEAR,"/economic_indicators_by_impact.csv")) %>% mutate(year=YEAR) %>% filter(!is.na(Impact))%>% mutate(Region="US") }
 	GET_FILE_WY <- function(YEAR){read_csv(paste0("Model_Outputs/IMPLAN/Wyoming/WY_",YEAR,"/economic_indicators_by_impact.csv")) %>% mutate(year=YEAR) %>% filter(!is.na(Impact))%>% mutate(Region="WY") }
@@ -23,8 +24,27 @@ DATA$Type <-  factor(DATA$Type,levels=c("Direct","Indirect","Induced"))
 DATA
 EMP_WY <- DATA %>% filter(Impact=="Employment",Region=="WY")
 DOL_WY <- DATA %>% filter(Impact!="Employment",Region=="WY")
-ggplot(EMP_WY, aes(x = year, y = value, fill = Type)) +  geom_area(stat = "identity", position = "stack") +  theme_bw()+scale_x_continuous(breaks=2026:2060)+scale_y_continuous(breaks=seq(0,10000,by=250))+ylab("Wyoming Employment Additions\n(Full-time Equivalent)")
-																	    ?scale_y_continuous
-ggplot(DOL_WY, aes(x = year, y = value/10^9, fill = Type)) +  geom_bar(stat = "identity", position = "stack") +  theme_minimal()+facet_wrap(~Impact)
+EMP <- DATA %>% filter(Impact=="Employment")
+
+ALL_EMP <- EMP %>% group_by(Region,year) %>% summarize(value=sum(value)) %>%  ungroup()
+ALL_EMP <- ALL_EMP %>% group_by(year) %>% mutate(value=ifelse(Region=='US',value-min(value),value))
+
+DOLLAR <-DATA %>% filter(Impact!="Employment")
+
+ALL_DOLLAR <-DOLLAR  %>% group_by(Region,year,Impact) %>% summarize(value=sum(value))
+ALL_DOLLAR <- ALL_DOLLAR %>% group_by(year,Impact) %>% mutate(value=ifelse(Region=='US',value-min(value),value))
+
+
+ggplot(EMP, aes(x = year, y = value, fill = Type)) +  geom_area(stat = "identity", position = "stack") +  theme_bw()+scale_x_continuous(breaks=2026:2060)+scale_y_continuous(breaks=seq(0,10000,by=250))+ylab("Wyoming Employment Additions\n(Full-time Equivalent)")+facet_wrap(~Region)
+
+
+WY_US_EMP_PLOT <- ggplot(ALL_EMP, aes(x = year, y = value, fill = Region)) +  geom_area(stat = "identity", position = "stack") +  theme_bw()+scale_x_continuous(breaks=seq(2026,2060,by=2))+scale_y_continuous(breaks=seq(0,100000,by=1000),labels = label_comma())+ylab("Employment Additions (FTE)")+scale_fill_manual(values=c("#0A3161","#FFC425"))+theme(legend.position = "top",text = element_text(size = 20))
+ggsave(filename = "Employment_Plot.png",  plot = WY_US_EMP_PLOT,  width = 2*6.5,  height = 2*4.0,  units = "in", dpi = 600 )
+ALL_DOLLAR$Impact <- factor(ALL_DOLLAR$Impact,levels=c("Output","Value Added","Income"))
+WY_US_VALUE_ADD <- ggplot(ALL_DOLLAR, aes(x = year, y = value/10^9, fill = Region)) +  geom_area(stat = "identity", position = "stack") +  theme_bw()+scale_x_continuous(breaks=seq(2026,2060,by=2))+scale_y_continuous(breaks=seq(0,10000,by=0.5),labels = label_comma())+ylab("Billion Dollars (2026 USD)")+scale_fill_manual(values=c("#0A3161","#FFC425"))+theme(legend.position = "top",text = element_text(size = 20))+facet_wrap(~Impact,ncol=1)
+
+
+ggsave(filename = "Impact_Plot.png",  plot = WY_US_VALUE_ADD,  width = 2*6.5,  height = 6*4.0,  units = "in", dpi = 600 )
+