diff --git a/Data_Center_Visuals.r b/Data_Center_Visuals.r
index 2bfaffe..0c6d2fa 100644
--- a/Data_Center_Visuals.r
+++ b/Data_Center_Visuals.r
@@ -25,20 +25,91 @@ return(INPUT_DATA )
 
 WY_DATA <- rbind(GET_DATA_CENTER_RES("Computer_Purchase_Mill_Dollars",'COMPUTERS'),GET_DATA_CENTER_RES("Construction_Mill_Dollars",'BUILDING'),GET_DATA_CENTER_RES("Data_Center_Employment_100_People",'DATA_CENTER_EMP'),GET_DATA_CENTER_RES("Other_Purchases_Mill_Dollars","OTHER"),GET_DATA_CENTER_RES("Pre-Development_Million_Dollars","PRE_DEV"),GET_DATA_CENTER_RES("Utlities_Electricity_Million_USD","UTILITIES"))
 US_DATA <- rbind(GET_DATA_CENTER_RES("Computer_Purchase_Mill_Dollars",'COMPUTERS','US'),GET_DATA_CENTER_RES("Construction_Mill_Dollars",'BUILDING','US'),GET_DATA_CENTER_RES("Data_Center_Employment_100_People",'DATA_CENTER_EMP','US'),GET_DATA_CENTER_RES("Other_Purchases_Mill_Dollars","OTHER",'US'),GET_DATA_CENTER_RES("Pre-Development_Million_Dollars","PRE_DEV",'US'),GET_DATA_CENTER_RES("Utlities_Electricity_Million_USD","UTILITIES",'US'))
-GET_DATA_CENTER_RES("Computer_Purchase_Mill_Dollars",'COMPUTERS','US')
-GET_DATA_CENTER_RES("Data_Center_Employment_100_People",'DATA_CENTER_EMP')
-ALL_DATA <- rbind(WY_DATA,US_DATA)
-GET_DATA_CENTER_RES("Data_Center_Employment_100_People",'DATA_CENTER_EMP')
-ALL_DATA <- ALL_DATA %>% group_by(Year,Region,Impact,Event) %>% summarize(value=sum(value)) %>% ungroup
-US_DATA <- ALL_DATA %>% group_by(Year,Impact,Event) %>% summarize(value=max(value)-min(value)) %>% ungroup %>% mutate(Region='US')
-ALL_DATA <- ALL_DATA %>% filter(Region=='Wyoming') %>% full_join(US_DATA)
-ALL_DATA %>% filter(Year==2030,value>0)
+DATA <-rbind(US_DATA,WY_DATA) %>% group_by(Year,Type,Region,Impact,Source) %>% summarize(value=sum(value)) %>% ungroup
+
+######################
+GET_DATA_CENTER_TAX <- function(DIR,EVENT,REGION='Wyoming'){
+#	DIR<-"Data_Center_Employment_100_People"
+#	EVENT<-'DATA_CENTER_EMP'
+	PATH <- paste0("Model_Outputs/IMPLAN/Data_Center/",REGION,"/",DIR,"/tax_results.csv")
+	INPUT_DATA <- read_csv(PATH)  %>% mutate(`Sub County General`=parse_number(`Sub County General`),`Sub County Special Districts`=parse_number(`Sub County Special Districts`),County=parse_number(County),County=County+`Sub County General`+`Sub County Special Districts`,State=parse_number(State),Federal=parse_number(Federal)) %>% select(-Total,`Sub County General`,`Sub County Special Districts`)  %>% mutate(Region=REGION,Event=EVENT)
+	INPUT_DATA$Impact <- gsub("3 - ","",gsub("2 - ","",gsub("1 - ","",INPUT_DATA$Impact)))
+	INPUT_DATA <-  INPUT_DATA %>% pivot_longer(-c(Impact,Region,Event)) %>% rename(Type=Impact,Impact=name)
+	INPUT_DATA$Impact <- gsub("3 - ","",gsub("2 - ","",gsub("1 - ","",INPUT_DATA$Impact)))
+	INPUT_DATA <- INPUT_DATA %>% mutate(Source='IMPLAN') %>% select(Type,Region,Impact,Event,Source,value)
+	INPUT_DATA <- INPUT_DATA %>% filter(!is.na(Type))
+	INPUT_DATA$value <- if(EVENT=='DATA_CENTER_EMP'){INPUT_DATA$value <- INPUT_DATA$value/100}else{INPUT_DATA$value/10^6} #shift to impact per dollar from impact per million dollar
+INPUT_DATA <-	INPUT_DATA %>% rename('marginal'=value)
+INPUT_DATA <- INPUT_DATA   %>% left_join(DATA_CENTER) %>% mutate(value=marginal*total)
+INPUT_DATA <- INPUT_DATA %>% group_by(Type,Year,Region,Impact,Source,Event) %>% summarize(value=sum(value)) %>% ungroup
+return(INPUT_DATA )
+}
+
+WY_DATA_TAX <- rbind(GET_DATA_CENTER_TAX("Computer_Purchase_Mill_Dollars",'COMPUTERS'),GET_DATA_CENTER_TAX("Construction_Mill_Dollars",'BUILDING'),GET_DATA_CENTER_TAX("Data_Center_Employment_100_People",'DATA_CENTER_EMP'),GET_DATA_CENTER_TAX("Other_Purchases_Mill_Dollars","OTHER"),GET_DATA_CENTER_TAX("Pre-Development_Million_Dollars","PRE_DEV"),GET_DATA_CENTER_TAX("Utlities_Electricity_Million_USD","UTILITIES"))
+US_DATA_TAX <- rbind(GET_DATA_CENTER_TAX("Computer_Purchase_Mill_Dollars",'COMPUTERS','US'),GET_DATA_CENTER_TAX("Construction_Mill_Dollars",'BUILDING','US'),GET_DATA_CENTER_TAX("Data_Center_Employment_100_People",'DATA_CENTER_EMP','US'),GET_DATA_CENTER_TAX("Other_Purchases_Mill_Dollars","OTHER",'US'),GET_DATA_CENTER_TAX("Pre-Development_Million_Dollars","PRE_DEV",'US'),GET_DATA_CENTER_TAX("Utlities_Electricity_Million_USD","UTILITIES",'US'))
+TAX <-rbind(US_DATA_TAX,WY_DATA_TAX) %>% group_by(Year,Type,Region,Impact,Source) %>% summarize(value=sum(value)) %>% ungroup
+IMPLAN <- rbind(TAX,DATA ) %>% select(Type,Year,Region,Impact,Source,value)
+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
 
 
-GRAPH_DATA <- 	ALL_DATA %>%  mutate(value=ifelse(Impact!='Employment',value,value)) %>% group_by(Year,Region,Impact) %>% summarize(value=sum(value)) %>% ungroup
-GRAPH_DATA  %>% filter(Year==2040)
+TEMP <- IMPLAN_DOLLAR %>% filter(Year==2028)  %>% mutate(value=0)
+IMPLAN_DOLLAR <- rbind(IMPLAN_DOLLAR,rbind(TEMP %>% mutate(Year=2025),TEMP %>% mutate(Year=2026),TEMP %>% mutate(Year=2027)))
 
-ggplot(GRAPH_DATA %>% filter(Impact=='Employment'),aes(x=Year,y=value,fill=Region))+geom_area()+facet_grid(~Impact)
-ggplot(GRAPH_DATA ,aes(x=Year,y=value,fill=Region))+geom_area()+facet_grid(~Impact)
+	TAX_STATE_BASE <- 500*(10^6*0.115*((62.835-12)/1000)) #State
+	TAX_COUNTY_BASE <-500*(10^6*0.115*((12)/1000)) #County
+	IMPLAN_DOLLAR[IMPLAN_DOLLAR$Impact %in% c("County") & IMPLAN_DOLLAR$Type=='Direct'& IMPLAN_DOLLAR$Region=='WY',]
+	DIRECT_TAX_FLOW <- (c(seq(0,1,by=1/3),rev(seq(0,1,by=1/40))))[1:28]
+	IMPLAN_DOLLAR[IMPLAN_DOLLAR$Impact %in% c("County") & IMPLAN_DOLLAR$Type=='Direct'& IMPLAN_DOLLAR$Region=='WY','value']	<- DIRECT_TAX_FLOW *TAX_COUNTY_BASE 
+	IMPLAN_DOLLAR[IMPLAN_DOLLAR$Impact %in% c("State") & IMPLAN_DOLLAR$Type=='Direct'& IMPLAN_DOLLAR$Region=='WY','value'] <- DIRECT_TAX_FLOW *TAX_STATE_BASE 
 
 
+IMPLAN_DOLLAR[IMPLAN_DOLLAR$Impact %in% "State" & IMPLAN_DOLLAR$Type=='Direct'& IMPLAN_DOLLAR$Region=='WY',]
+IMPLAN_DOLLAR %>% pull(Year) %>% unique %>% length 
+
+IMPLAN_DOLLAR$Impact <- factor(IMPLAN_DOLLAR$Impact,levels=c('Output','Value Added','Income','Federal','State','County'))
+IMPLAN_DOLLAR$Region <- factor(ifelse(IMPLAN_DOLLAR$Region=="WY",'Wyoming',"Rest of United States"),levels=rev(c("Wyoming","Rest of United States")))
+IMPLAN_DOLLAR_REGION <- IMPLAN_DOLLAR %>% filter(Impact %in% c('Income','Output','Value Added')) %>% group_by(Year,Region,Impact) %>% summarize(value=sum(value)) %>% ungroup
+
+########################Plot of total impact in US and in Wyoming
+IMPLAN_REGION_DOLLAR_PLOT <- ggplot(IMPLAN_DOLLAR_REGION ,aes(x=Year,y=value/10^9,fill=Region,group=Region))+geom_area()+facet_wrap(~Impact,ncol=1)+ylab("Billion Dollars")+theme_bw()+ theme(plot.title = element_text(size = 22, face = "bold"), axis.title = element_text(size = 18),axis.text  = element_text(size = 16),legend.text = element_text(size = 14),strip.text = element_text(size = 14),legend.position="top")+  theme(legend.title=element_blank())+scale_fill_manual(values=c("#3C3B6E","#FFC425"))+scale_x_continuous(breaks=seq(2025,2060,by=5))+scale_y_continuous(breaks=seq(0,5000,by=1))
+ggsave( filename = "./Results/Data Center: Dollar Values of Model.png",  plot = IMPLAN_REGION_DOLLAR_PLOT,  width = 8.5,  height = 11*(3./4),  units = "in", dpi = 300)
+########################Taxes
+IMPLAN_TAX <- IMPLAN_DOLLAR %>% filter(Impact %in% c("County","State","Federal"))
+OLD <- IMPLAN_TAX 
+IMPLAN_TAX <- IMPLAN_TAX %>% group_by(Type,Year,Impact,Source) %>% mutate(value=ifelse(Region=='Rest of United States',value-min(value),value) ) %>% ungroup
+IMPLAN_TAX  %>% left_join(OLD %>% rename(OLD=value))
+
+#IMPLAN_TAX <- IMPLAN_TAX[!(IMPLAN_TAX$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$Impact=='Federal'),] 
+IMPLAN_DOLLAR <- IMPLAN_DOLLAR %>% filter(!(Impact %in% c("County","State","Federal"))) %>% rbind(IMPLAN_TAX)
+
+IMPLAN_TAX_FIG_DATA <- IMPLAN_TAX  %>% group_by(Year,Impact) %>% summarize(value=sum(value))
+TAX_PLOT <- ggplot(IMPLAN_TAX_FIG_DATA, aes(x=Year,y=value/10^6,fill=Impact,group=Impact))+geom_area()+ylab("Million Dollars")+theme_bw()+ theme(plot.title = element_text(size = 22, face = "bold"), axis.title = element_text(size = 18),axis.text  = element_text(size = 16),legend.text = element_text(size = 14),strip.text = element_text(size = 14),legend.position="top")+  theme(legend.title=element_blank())+scale_fill_manual(values=c("#3C3B6E","#FFC425","#492F24"))+scale_x_continuous(breaks=seq(2025,2060,by=5))+scale_y_continuous(breaks=seq(0,5000,by=50))
+TAX_PLOT 
+ggsave( filename = "./Results/Data Center: Tax Model.png",  plot = TAX_PLOT,  width = 8.5,  height = 11*(3./4),  units = "in", dpi = 300)
+#############Dollar values table
+NPV <- IMPLAN_DOLLAR %>% mutate(value=value/10^9,Discount_0=value,Discount_2=value/(1+0.02)^(Year-2028),Discount_5=value/(1+0.05)^(Year-2028),Discount_10=value/(1+0.1)^(Year-2028)) %>% group_by(Impact,Region) %>% summarize(Discount_0=sum(Discount_0),Discount_2=sum(Discount_2),Discount_5=sum(Discount_5),Discount_10=sum(Discount_10)) %>% ungroup
+NPV[,3:6] <-round(NPV[,3:6],2)
+write.csv(NPV,"./Results/Data_Center_NPV_Values.csv",row.names=FALSE)
+
+
+############Employment
+IMPLAN_EMPLOY <- IMPLAN %>% filter(Impact=='Employment',Source=='IMPLAN') %>% group_by(Year,Region,Impact) %>% summarize(value=sum(value)) %>% ungroup
+
+
+IMPLAN_EMPLOY <-  IMPLAN_EMPLOY  %>% group_by(Year,Impact) %>% mutate(value=ifelse(Region=='US',value-min(value),value) ) %>% ungroup
+#TEMP <- IMPLAN_EMPLOY %>% filter(Year==2028)  %>% mutate(value=0)
+#IMPLAN_EMPLOY <- rbind(IMPLAN_EMPLOY,rbind(TEMP %>% mutate(Year=2025),TEMP %>% mutate(Year=2026),TEMP %>% mutate(Year=2027)))
+
+
+IMPLAN_EMPLOY$Region <- factor(ifelse(IMPLAN_EMPLOY$Region=="WY",'Wyoming',"Rest of United States"),levels=rev(c("Wyoming","Rest of United States")))
+ IMPLAN_EMPLOYMENT_PLOT <- ggplot(IMPLAN_EMPLOY ,aes(x=Year,y=value,fill=Region,group=Region))+geom_area()+ylab("Employment (Full-Time Equivalent)")+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=c(seq(0,50000,by=1000)),labels = label_comma())
+IMPLAN_EMPLOYMENT_PLOT 
+ggsave( filename = "./Results/Data Center: Employment.png",  plot = IMPLAN_EMPLOYMENT_PLOT ,  width = 8.5,  height = 11*(2/4),  units = "in", dpi = 600)
+
diff --git a/Model_Outputs/REMI/Data_Center/Direct, Indirect, and Induced - Personal Income.csv b/Model_Outputs/REMI/Data_Center/Direct, Indirect, and Induced - Personal Income.csv
new file mode 100644
index 0000000..4444fbc
--- /dev/null
+++ b/Model_Outputs/REMI/Data_Center/Direct, Indirect, and Induced - Personal Income.csv	
@@ -0,0 +1,10 @@
+"Personal Income"
+,,,
+"Region","Comparison Type","Forecast","Comparison Forecast"
+"All Regions","Differences","Peabody Data Center","Standard Regional Control"
+,"Year",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Category","Units",2025,2026,2027,2028,2029,2030,2031,2032,2033,2034,2035,2036,2037,2038,2039,2040,2041,2042,2043,2044,2045,2046,2047,2048,2049,2050,2051,2052,2053,2054,2055,2056,2057,2058,2059,2060
+"Total Personal Income","Billions of Fixed Local (2025) Dollars",0.000,0.003,0.040,0.396,0.178,0.033,0.023,0.011,0.004,0.001,0.001,0.001,0.002,0.003,0.005,0.006,0.006,0.007,0.007,0.007,0.007,0.007,0.006,0.006,0.006,0.007,0.007,0.007,0.008,0.009,0.009,0.010,0.011,0.012,0.013,0.015
+"Direct Personal Income","Billions of Fixed Local (2025) Dollars",0.000,0.002,0.018,0.273,0.115,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.005,0.005,0.005,0.005,0.005
+"Indirect Personal Income","Billions of Fixed Local (2025) Dollars",0.000,0.000,0.004,0.026,0.010,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000
+"Induced Personal Income","Billions of Fixed Local (2025) Dollars",0.000,0.001,0.018,0.097,0.053,0.029,0.020,0.008,0.001,-0.002,-0.003,-0.002,-0.002,-0.000,0.001,0.002,0.002,0.003,0.003,0.003,0.003,0.002,0.002,0.002,0.002,0.002,0.002,0.003,0.003,0.004,0.005,0.005,0.006,0.007,0.008,0.009
\ No newline at end of file
diff --git a/Model_Outputs/REMI/Data_Center/Direct, Indirect, and Induced - Value-Added.csv b/Model_Outputs/REMI/Data_Center/Direct, Indirect, and Induced - Value-Added.csv
new file mode 100644
index 0000000..0f884ca
--- /dev/null
+++ b/Model_Outputs/REMI/Data_Center/Direct, Indirect, and Induced - Value-Added.csv	
@@ -0,0 +1,10 @@
+"Value-Added"
+,,,,
+"Region","Industry","Comparison Type","Forecast","Comparison Forecast"
+"All Regions","All Industries","Differences","Peabody Data Center","Standard Regional Control"
+,"Year",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Category","Units",2025,2026,2027,2028,2029,2030,2031,2032,2033,2034,2035,2036,2037,2038,2039,2040,2041,2042,2043,2044,2045,2046,2047,2048,2049,2050,2051,2052,2053,2054,2055,2056,2057,2058,2059,2060
+"Total Value-Added","Billions of Fixed (2025) Dollars",0.000,0.005,0.100,0.745,0.312,0.010,-0.011,-0.022,-0.026,-0.025,-0.021,-0.017,-0.012,-0.008,-0.004,-0.001,0.001,0.002,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.004,0.004,0.004,0.005,0.005,0.005,0.006,0.006
+"Direct Value-Added","Billions of Fixed (2025) Dollars",0.000,0.003,0.057,0.464,0.185,0.002,0.002,0.002,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.004,0.004,0.004,0.004,0.004,0.004
+"Indirect Value-Added","Billions of Fixed (2025) Dollars",0.000,0.000,0.010,0.069,0.028,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001
+"Induced Value-Added","Billions of Fixed (2025) Dollars",0.000,0.002,0.034,0.211,0.099,0.007,-0.013,-0.025,-0.029,-0.028,-0.024,-0.020,-0.015,-0.011,-0.007,-0.005,-0.003,-0.001,-0.001,-0.000,-0.000,-0.001,-0.001,-0.001,-0.001,-0.001,-0.001,-0.001,-0.000,-0.000,0.000,0.000,0.001,0.001,0.001,0.002
\ No newline at end of file
diff --git a/Model_Outputs/REMI/Data_Center/Direct, Indirect, and Induced -Employment.csv b/Model_Outputs/REMI/Data_Center/Direct, Indirect, and Induced -Employment.csv
new file mode 100644
index 0000000..5e3403d
--- /dev/null
+++ b/Model_Outputs/REMI/Data_Center/Direct, Indirect, and Induced -Employment.csv	
@@ -0,0 +1,12 @@
+"Employment"
+,,,
+"Region","Comparison Type","Forecast","Comparison Forecast"
+"All Regions","Differences","Peabody Data Center","Standard Regional Control"
+,"Year",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Category","Units",2025,2026,2027,2028,2029,2030,2031,2032,2033,2034,2035,2036,2037,2038,2039,2040,2041,2042,2043,2044,2045,2046,2047,2048,2049,2050,2051,2052,2053,2054,2055,2056,2057,2058,2059,2060
+"Total Employment","Thousands (Jobs)",0.000,0.053,0.530,5.735,2.657,0.303,0.089,-0.040,-0.094,-0.104,-0.088,-0.060,-0.030,-0.002,0.022,0.040,0.054,0.062,0.066,0.067,0.067,0.065,0.063,0.059,0.058,0.058,0.058,0.059,0.062,0.065,0.066,0.068,0.070,0.072,0.075,0.077
+"Direct Employment","Thousands (Jobs)",0.000,0.033,0.161,3.274,1.400,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053,0.053
+"Indirect Employment","Thousands (Jobs)",0.000,0.004,0.055,0.465,0.187,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.004,0.005,0.005
+"Induced Employment","Thousands (Jobs)",0.000,0.016,0.314,1.996,1.070,0.246,0.032,-0.097,-0.151,-0.160,-0.144,-0.117,-0.087,-0.058,-0.034,-0.016,-0.003,0.005,0.010,0.011,0.010,0.008,0.006,0.002,0.001,0.001,0.001,0.002,0.006,0.008,0.009,0.011,0.013,0.015,0.018,0.020
+"Type I Employment Multiplier","Proportion",0.000,1.119,1.343,1.142,1.134,1.076,1.076,1.076,1.076,1.076,1.076,1.077,1.077,1.077,1.078,1.078,1.078,1.078,1.078,1.079,1.079,1.079,1.080,1.080,1.081,1.081,1.082,1.082,1.083,1.083,1.084,1.084,1.085,1.085,1.086,1.087
+"Type II Employment Multiplier","Proportion",0.000,1.596,3.299,1.752,1.898,5.768,1.687,-0.769,-1.800,-1.972,-1.672,-1.146,-0.576,-0.036,0.423,0.767,1.020,1.177,1.262,1.282,1.270,1.231,1.191,1.123,1.096,1.098,1.107,1.128,1.189,1.229,1.260,1.294,1.332,1.375,1.423,1.472
\ No newline at end of file
diff --git a/Model_Outputs/REMI/Data_Center/Direct, Indirect, and Induced -Output.csv b/Model_Outputs/REMI/Data_Center/Direct, Indirect, and Induced -Output.csv
new file mode 100644
index 0000000..891b8f6
--- /dev/null
+++ b/Model_Outputs/REMI/Data_Center/Direct, Indirect, and Induced -Output.csv	
@@ -0,0 +1,12 @@
+"Output"
+,,,
+"Region","Comparison Type","Forecast","Comparison Forecast"
+"All Regions","Differences","Peabody Data Center","Standard Regional Control"
+,"Year",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Category","Units",2025,2026,2027,2028,2029,2030,2031,2032,2033,2034,2035,2036,2037,2038,2039,2040,2041,2042,2043,2044,2045,2046,2047,2048,2049,2050,2051,2052,2053,2054,2055,2056,2057,2058,2059,2060
+"Total Output","Billions of Fixed (2025) Dollars",0.000,0.009,0.167,1.281,0.535,0.014,-0.021,-0.041,-0.047,-0.044,-0.038,-0.030,-0.021,-0.014,-0.008,-0.003,0.001,0.003,0.005,0.006,0.006,0.006,0.005,0.005,0.005,0.005,0.005,0.006,0.007,0.007,0.008,0.009,0.009,0.010,0.011,0.012
+"Direct Output","Billions of Fixed (2025) Dollars",0.000,0.005,0.093,0.817,0.328,0.005,0.005,0.005,0.005,0.005,0.005,0.005,0.005,0.005,0.005,0.005,0.005,0.006,0.006,0.006,0.006,0.006,0.006,0.006,0.006,0.006,0.006,0.007,0.007,0.007,0.007,0.007,0.007,0.007,0.008,0.008
+"Indirect Output","Billions of Fixed (2025) Dollars",0.000,0.001,0.016,0.115,0.046,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001
+"Induced Output","Billions of Fixed (2025) Dollars",0.000,0.003,0.058,0.349,0.161,0.008,-0.027,-0.046,-0.052,-0.050,-0.044,-0.035,-0.027,-0.020,-0.014,-0.009,-0.005,-0.003,-0.002,-0.001,-0.001,-0.001,-0.002,-0.002,-0.002,-0.002,-0.002,-0.002,-0.001,-0.000,0.000,0.000,0.001,0.002,0.002,0.003
+"Type I Output Multiplier","Proportion",0.000,1.123,1.169,1.145,1.143,1.120,1.121,1.120,1.120,1.120,1.121,1.121,1.121,1.121,1.121,1.121,1.121,1.121,1.121,1.121,1.121,1.121,1.121,1.121,1.122,1.122,1.122,1.122,1.123,1.123,1.124,1.124,1.124,1.125,1.125,1.126
+"Type II Output Multiplier","Proportion",0.000,1.579,1.753,1.578,1.641,2.476,-3.048,-6.036,-6.822,-6.369,-5.302,-4.009,-2.760,-1.650,-0.741,-0.064,0.431,0.747,0.932,1.005,1.019,0.989,0.951,0.869,0.847,0.864,0.890,0.930,1.027,1.088,1.134,1.184,1.237,1.296,1.360,1.426
\ No newline at end of file
diff --git a/Scripts/Load_IMPLAN.r b/Scripts/Load_IMPLAN.r
index 2e36494..b9039c4 100644
--- a/Scripts/Load_IMPLAN.r
+++ b/Scripts/Load_IMPLAN.r
@@ -4,10 +4,12 @@ library(janitor)
 library(paletteer)
 
 #Function to load IMPLAN Yearly data.
-GET_IMPLAN_DATA <- function(){
-#	YEAR <- 2028
-	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") %>% left_join(read_csv(paste0("Model_Outputs/IMPLAN/US/US_",YEAR,"/tax_results.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") %>% left_join(read_csv(paste0("Model_Outputs/IMPLAN/Wyoming/WY_",YEAR,"/tax_results.csv")) %>% mutate(Year=YEAR) %>% filter(!is.na(Impact))%>% mutate(Region="WY")) }
+GET_IMPLAN_DATA <- function(SINGLE=FALSE){
+	#Is this a single phase?
+STR_ROOT <- ifelse(!SINGLE,"Model_Outputs/IMPLAN/","Model_Outputs/IMPLAN/Single_Phase/")
+
+	GET_FILE_US <- function(YEAR){read_csv(paste0(STR_ROOT,"US/US_",YEAR,"/economic_indicators_by_impact.csv")) %>% mutate(Year=YEAR) %>% filter(!is.na(Impact))%>% mutate(Region="US") %>% left_join(read_csv(paste0(STR_ROOT,"US/US_",YEAR,"/tax_results.csv")) %>% mutate(Year=YEAR) %>% filter(!is.na(Impact))%>% mutate(Region="US")) }
+	GET_FILE_WY <- function(YEAR){read_csv(paste0(STR_ROOT,"Wyoming/WY_",YEAR,"/economic_indicators_by_impact.csv")) %>% mutate(Year=YEAR) %>% filter(!is.na(Impact))%>% mutate(Region="WY") %>% left_join(read_csv(paste0(STR_ROOT,"Wyoming/WY_",YEAR,"/tax_results.csv")) %>% mutate(Year=YEAR) %>% filter(!is.na(Impact))%>% mutate(Region="WY")) }
 #	GET_FILE_TAX_US <- function(YEAR){read_csv(paste0("Model_Outputs/IMPLAN/US/US_",YEAR,"/tax_results.csv")) %>% mutate(Year=YEAR) %>% filter(!is.na(Impact))%>% mutate(Region="US")}
 #	GET_FILE_TAX_WY <- function(YEAR){read_csv(paste0("Model_Outputs/IMPLAN/Wyoming/WY_",YEAR,"/tax_results.csv")) %>% mutate(Year=YEAR) %>% filter(!is.na(Impact))%>% mutate(Region="US")}
 #	GET_FILE_US(2028) %>% left_join(GET_FILE_TAX_US(2028))
@@ -31,4 +33,3 @@ DATA <- DATA %>% mutate(`Sub County General`=parse_number(`Sub County General`),
 	DATA <- DATA %>% full_join(RES) %>% unique
 return(DATA)
 }
-
diff --git a/Visuals.r b/Visuals.r
index 6b19535..5ef23d9 100644
--- a/Visuals.r
+++ b/Visuals.r
@@ -4,10 +4,13 @@ library(janitor)
 library(paletteer)
 dir.create("Results", showWarnings = FALSE)
 source("Scripts/Load_IMPLAN.r")
+SINGLE <- FALSE
+
+	IMPLAN <- GET_IMPLAN_DATA(SINGLE)
 
-	IMPLAN <- GET_IMPLAN_DATA()
 
 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
 TEMP <- IMPLAN_DOLLAR %>% filter(Year==2028)  %>% mutate(value=0)
 IMPLAN_DOLLAR <- rbind(IMPLAN_DOLLAR,rbind(TEMP %>% mutate(Year=2025),TEMP %>% mutate(Year=2026),TEMP %>% mutate(Year=2027)))
@@ -17,39 +20,36 @@ IMPLAN_DOLLAR_REGION <- IMPLAN_DOLLAR %>% filter(Impact %in% c('Income','Output'
 
 ########################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))
-IMPLAN_REGION_DOLLAR_PLOT 
 ggsave( filename = "./Results/Dollar Values of Model.png",  plot = IMPLAN_REGION_DOLLAR_PLOT,  width = 8.5,  height = 11*(3./4),  units = "in", dpi = 300)
 ########################Taxes
+IMPLAN_TAX <- IMPLAN_DOLLAR %>% filter(Impact %in% c("County","State","Federal"))
+OLD <- IMPLAN_TAX 
+IMPLAN_TAX <- IMPLAN_TAX %>% group_by(Type,Year,Impact,Source) %>% mutate(value=ifelse(Region=='Rest of United States',value-min(value),value) ) %>% ungroup
+IMPLAN_TAX  %>% left_join(OLD %>% rename(OLD=value))
 
-#########################Post 2035 Values to show smaller scale
-IMPLAN_DOLLAR_PROD_PLOT <- ggplot(IMPLAN_DOLLAR %>% filter(Year>2035) ,aes(x=Year,y=value/10^6,fill=Region,group=Region))+geom_area()+facet_wrap(~Impact,ncol=1)+ylab("Million 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=100))
+#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)
 
-ggsave( filename = "./Results/Dollar Values of Model in Production.png",  plot = IMPLAN_DOLLAR_PROD_PLOT ,  width = 8.5,  height = 11*(3./4),  units = "in", dpi = 300)
+IMPLAN_TAX_FIG_DATA <- IMPLAN_TAX  %>% group_by(Year,Impact) %>% summarize(value=sum(value))
+TAX_PLOT <- ggplot(IMPLAN_TAX_FIG_DATA, aes(x=Year,y=value/10^6,fill=Impact,group=Impact))+geom_area()+ylab("Million 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","#492F24"))+scale_x_continuous(breaks=seq(2025,2060,by=5))+scale_y_continuous(breaks=seq(0,5000,by=50))
+TAX_PLOT 
+ggsave( filename = "./Results/Tax Model.png",  plot = TAX_PLOT,  width = 8.5,  height = 11*(3./4),  units = "in", dpi = 300)
 #############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)
 write.csv(NPV,"./Results/NPV_Values.csv",row.names=FALSE)
+
+
 ############Employment
-IMPLAN_EMPLOY <- DATA %>% filter(Impact=='Employment',Source=='IMPLAN') %>% group_by(Year,Region,Impact) %>% summarize(value=sum(value)) %>% ungroup
-IMPLAN_EMPLOY <- rbind(IMPLAN_EMPLOY, IMPLAN_EMPLOY  %>% left_join(ADJUST) %>% mutate(value=value*RATIO,Region='US') %>% select(-RATIO))
+IMPLAN_EMPLOY <- IMPLAN %>% filter(Impact=='Employment',Source=='IMPLAN') %>% group_by(Year,Region,Impact) %>% summarize(value=sum(value)) %>% ungroup
+
+IMPLAN_EMPLOY <-  IMPLAN_EMPLOY  %>% group_by(Year,Impact) %>% mutate(value=ifelse(Region=='US',value-min(value),value) ) %>% ungroup
+TEMP <- IMPLAN_EMPLOY %>% filter(Year==2028)  %>% mutate(value=0)
+IMPLAN_EMPLOY <- rbind(IMPLAN_EMPLOY,rbind(TEMP %>% mutate(Year=2025),TEMP %>% mutate(Year=2026),TEMP %>% mutate(Year=2027)))
+
+
 IMPLAN_EMPLOY$Region <- factor(ifelse(IMPLAN_EMPLOY$Region=="WY",'Wyoming',"Rest of United States"),levels=rev(c("Wyoming","Rest of United States")))
- IMPLAN_EMPLOYMENT_PLOT <- ggplot(IMPLAN_EMPLOY ,aes(x=Year,y=value,fill=Region,group=Region))+geom_area()+ylab("Employment (Full-Time Equivalent)")+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=c(seq(0,50000,by=1500),13000),labels = label_comma())
+ IMPLAN_EMPLOYMENT_PLOT <- ggplot(IMPLAN_EMPLOY ,aes(x=Year,y=value,fill=Region,group=Region))+geom_area()+ylab("Employment (Full-Time Equivalent)")+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=c(seq(0,50000,by=1000)),labels = label_comma())
+IMPLAN_EMPLOYMENT_PLOT 
 ggsave( filename = "./Results/Employment.png",  plot = IMPLAN_EMPLOYMENT_PLOT ,  width = 8.5,  height = 11*(2/4),  units = "in", dpi = 600)
-################IMPLAN
-IMPLAN$Year %>% unique
-IMPLAN <- rbind(IMPLAN,      rbind(IMPLAN %>% filter(Year==2030) %>% mutate(value=0,Year=2025),IMPLAN %>% filter(Year==2030) %>% mutate(value=0,Year=2026),IMPLAN %>% filter(Year==2030) %>% mutate(value=0,Year=2027)))
-
-IMPLAN <- GET_IMPLAN_DATA()
-DATA <- rbind(IMPLAN,IMPLAN)
-DATA
-ggplot(DATA %>% filter(Region=='WY'),aes(x=Year,y=value,color=Type,linetype=Source))+geom_line()+facet_wrap(~Impact)
-DATA
-TOTAL_DATA <- DATA %>% filter(Region=='WY',Impact!='Employment') %>% group_by(Year,Impact,Source) %>% summarize(value=sum(value)) %>% ungroup
-
-TOTAL_DATA$Impact <- factor(TOTAL_DATA$Impact,levels=c('Output','Value Added','Income'))
-IMPLAN_IMPLAN <- ggplot(TOTAL_DATA ,aes(x=Year,y=value/10^9,color=Source,linetype=Source))+geom_line()+facet_wrap(~Impact,ncol=1)+theme_bw()+ theme(plot.title = element_text(size = 22, face = "bold"), axis.title = element_text(size = 18),axis.text  = element_text(size = 16),legend.text = element_text(size = 14),strip.text = element_text(size = 14),legend.position="top")+  theme(legend.title=element_blank())+scale_fill_manual(values=c("#3C3B6E","#FFC425"))+scale_x_continuous(breaks=seq(2025,2060,by=5))+scale_y_continuous(breaks=seq(0,5000,by=0.5))+ylab("Billion USD")
-
-ggsave( filename = "./Results/IMPLAN_IMPLAN_COMP.png",  plot = IMPLAN_IMPLAN ,  width = 8.5,  height = 11*(2/4),  units = "in", dpi = 600)
-
-
-
diff --git a/Visuals_Single_Phase.r b/Visuals_Single_Phase.r
new file mode 100644
index 0000000..4b061ad
--- /dev/null
+++ b/Visuals_Single_Phase.r
@@ -0,0 +1,56 @@
+library(tidyverse)
+library(scales)
+library(janitor)
+library(paletteer)
+dir.create("Results", showWarnings = FALSE)
+source("Scripts/Load_IMPLAN.r")
+SINGLE <- TRUE 
+
+	IMPLAN <- GET_IMPLAN_DATA(SINGLE)
+
+
+IMPLAN_DOLLAR <- IMPLAN %>% filter(Impact!='Employment')
+
+IMPLAN_DOLLAR <-  IMPLAN_DOLLAR  %>% group_by(Type,Year,Impact,Source) %>% mutate(value=ifelse(Region=='US',value-min(value),value) ) %>% ungroup
+TEMP <- IMPLAN_DOLLAR %>% filter(Year==2028)  %>% mutate(value=0)
+IMPLAN_DOLLAR <- rbind(IMPLAN_DOLLAR,rbind(TEMP %>% mutate(Year=2025),TEMP %>% mutate(Year=2026),TEMP %>% mutate(Year=2027)))
+IMPLAN_DOLLAR$Impact <- factor(IMPLAN_DOLLAR$Impact,levels=c('Output','Value Added','Income','Federal','State','County'))
+IMPLAN_DOLLAR$Region <- factor(ifelse(IMPLAN_DOLLAR$Region=="WY",'Wyoming',"Rest of United States"),levels=rev(c("Wyoming","Rest of United States")))
+IMPLAN_DOLLAR_REGION <- IMPLAN_DOLLAR %>% filter(Impact %in% c('Income','Output','Value Added')) %>% group_by(Year,Region,Impact) %>% summarize(value=sum(value)) %>% ungroup
+
+########################Plot of total impact in US and in Wyoming
+IMPLAN_REGION_DOLLAR_PLOT <- ggplot(IMPLAN_DOLLAR_REGION ,aes(x=Year,y=value/10^9,fill=Region,group=Region))+geom_area()+facet_wrap(~Impact,ncol=1)+ylab("Billion Dollars")+theme_bw()+ theme(plot.title = element_text(size = 22, face = "bold"), axis.title = element_text(size = 18),axis.text  = element_text(size = 16),legend.text = element_text(size = 14),strip.text = element_text(size = 14),legend.position="top")+  theme(legend.title=element_blank())+scale_fill_manual(values=c("#3C3B6E","#FFC425"))+scale_x_continuous(breaks=seq(2025,2060,by=5))+scale_y_continuous(breaks=seq(0,5000,by=1))
+IMPLAN_REGION_DOLLAR_PLOT 
+ggsave( filename = "./Results/Single Phase: Dollar Values of Model.png",  plot = IMPLAN_REGION_DOLLAR_PLOT,  width = 8.5,  height = 11*(3./4),  units = "in", dpi = 300)
+########################Taxes
+IMPLAN_TAX <- IMPLAN_DOLLAR %>% filter(Impact %in% c("County","State","Federal"))
+OLD <- IMPLAN_TAX 
+IMPLAN_TAX <- IMPLAN_TAX %>% group_by(Type,Year,Impact,Source) %>% mutate(value=ifelse(Region=='Rest of United States',value-min(value),value) ) %>% ungroup
+IMPLAN_TAX  %>% left_join(OLD %>% rename(OLD=value))
+
+#IMPLAN_TAX <- IMPLAN_TAX[!(IMPLAN_TAX$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$Impact=='Federal'),] 
+IMPLAN_DOLLAR <- IMPLAN_DOLLAR %>% filter(!(Impact %in% c("County","State","Federal"))) %>% rbind(IMPLAN_TAX)
+
+IMPLAN_TAX_FIG_DATA <- IMPLAN_TAX  %>% group_by(Year,Impact) %>% summarize(value=sum(value))
+TAX_PLOT <- ggplot(IMPLAN_TAX_FIG_DATA, aes(x=Year,y=value/10^6,fill=Impact,group=Impact))+geom_area()+ylab("Million Dollars")+theme_bw()+ theme(plot.title = element_text(size = 22, face = "bold"), axis.title = element_text(size = 18),axis.text  = element_text(size = 16),legend.text = element_text(size = 14),strip.text = element_text(size = 14),legend.position="top")+  theme(legend.title=element_blank())+scale_fill_manual(values=c("#3C3B6E","#FFC425","#492F24"))+scale_x_continuous(breaks=seq(2025,2060,by=5))+scale_y_continuous(breaks=seq(0,5000,by=50))
+TAX_PLOT 
+ggsave( filename = "./Results/Single Phase: Tax Model.png",  plot = TAX_PLOT,  width = 8.5,  height = 11*(3./4),  units = "in", dpi = 300)
+#############Dollar values table
+NPV <- IMPLAN_DOLLAR %>% mutate(value=value/10^9,Discount_0=value,Discount_2=value/(1+0.02)^(Year-2028),Discount_5=value/(1+0.05)^(Year-2028),Discount_10=value/(1+0.1)^(Year-2028)) %>% group_by(Impact,Region) %>% summarize(Discount_0=sum(Discount_0),Discount_2=sum(Discount_2),Discount_5=sum(Discount_5),Discount_10=sum(Discount_10)) %>% ungroup
+NPV[,3:6] <-round(NPV[,3:6],2)
+write.csv(NPV,"./Results/Singe_Phase_NPV_Values.csv",row.names=FALSE)
+
+
+############Employment
+IMPLAN_EMPLOY <- IMPLAN %>% filter(Impact=='Employment',Source=='IMPLAN') %>% group_by(Year,Region,Impact) %>% summarize(value=sum(value)) %>% ungroup
+
+IMPLAN_EMPLOY <-  IMPLAN_EMPLOY  %>% group_by(Year,Impact) %>% mutate(value=ifelse(Region=='US',value-min(value),value) ) %>% ungroup
+TEMP <- IMPLAN_EMPLOY %>% filter(Year==2028)  %>% mutate(value=0)
+IMPLAN_EMPLOY <- rbind(IMPLAN_EMPLOY,rbind(TEMP %>% mutate(Year=2025),TEMP %>% mutate(Year=2026),TEMP %>% mutate(Year=2027)))
+
+
+IMPLAN_EMPLOY$Region <- factor(ifelse(IMPLAN_EMPLOY$Region=="WY",'Wyoming',"Rest of United States"),levels=rev(c("Wyoming","Rest of United States")))
+ IMPLAN_EMPLOYMENT_PLOT <- ggplot(IMPLAN_EMPLOY ,aes(x=Year,y=value,fill=Region,group=Region))+geom_area()+ylab("Employment (Full-Time Equivalent)")+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=c(seq(0,50000,by=1000)),labels = label_comma())
+IMPLAN_EMPLOYMENT_PLOT 
+ggsave( filename = "./Results/Single Phase: Employment.png",  plot = IMPLAN_EMPLOYMENT_PLOT ,  width = 8.5,  height = 11*(2/4),  units = "in", dpi = 600)