diff --git a/Data_Analysis.r b/Data_Analysis.r
index 08f26de..6e8613a 100644
--- a/Data_Analysis.r
+++ b/Data_Analysis.r
@@ -1,38 +1,47 @@
 library(tidyverse)
+library("RColorBrewer")
+library("knitr")
+library("kableExtra")
+
+
+##########Save all raw data categories
 DATA_PATH <- "./Raw_Output/"
 if(!file.exists("./Results")){dir.create("./Results")}
 source("Scripts/Data_Proc_Script.r")
 EVENT_DATA <- GET_EVENT_DATA(2025:2031)
+  EVENT_DATA <- EVENT_DATA %>% mutate(VA=OPI+TOPI+EMP_COM+PROP_INC)
   write_csv(EVENT_DATA,"./Results/Yearly_Detailed_Event_Data.csv")
 DATA <- GET_SUMMARY_DATA()
   DATA[,c(-2:-6,-8:-10)] <- round(DATA[,c(-2:-6,-8:-10)])
   DATA[,c(3:6,8:10)] <- round(DATA[,c(3:6,8:10)],2)
   DATA[2,3:6] <- NA
+  DATA <- DATA  %>% mutate(VA=OPI+TOPI+EMP_COM+PROP_INC)
+ DATA <- DATA %>% filter(!is.na(MAJOR_EVENT)) %>% ungroup
   write_csv(DATA,"./Results/Yearly_Event_Summary.csv")
-
+DATA_ORIG <- DATA
 
 DETAILED_DATA <- GET_DETAIL_ECON_DATA()
 write_csv(DETAILED_DATA,"./Results/Detailed_Economic_Indicators.csv")
 
 SUMMARY_BY_YEAR <- round(GET_TOTAL_SUMMARY())
 write_csv(SUMMARY_BY_YEAR,"./Results/Yearly_State_Totals.csv")
-
+DATA <- DATA_ORIG
 #Advanced Summary Tables
-EMP_OUTPUTS_CONSTRUCTION <- DETAILED_DATA %>% filter(YEAR<=2029) %>% group_by(IND_DESC)  %>% summarize('Employment'=sum(EMP),'Income'=sum(EMP_COMP+PROP_INC),'Other Profits'=sum(OPI)) %>% arrange(desc(Employment))
+EMP_OUTPUTS_CONSTRUCTION <- DETAILED_DATA %>% filter(YEAR<=2029) %>% rename(Industry=IND_DESC) %>%  group_by(Industry)  %>% summarize('Employment'=sum(EMP),'Income'=sum(EMP_COMP+PROP_INC),'Other Profits'=sum(OPI),'Value Added'=sum(EMP_COMP+PROP_INC+TOPI+OPI),'Economic Output'=sum(OUTPUT)) %>% arrange(desc(Employment))
 TOP_20 <- EMP_OUTPUTS_CONSTRUCTION[1:20,] 
-OTHERS <- TOP_20[1,]
-OTHERS[1,1] <-"Other Industries"
+OTHERS <- EMP_OUTPUTS_CONSTRUCTION[1,] 
+OTHERS[,1] <-"Other Industries"
 OTHERS[,-1] <- t(colSums(EMP_OUTPUTS_CONSTRUCTION[-1-20,-1] ))
 EMP_OUTPUTS_CONSTRUCTION <-rbind(TOP_20,OTHERS)
 EMP_OUTPUTS_CONSTRUCTION[,-1] <- round(EMP_OUTPUTS_CONSTRUCTION[,-1])
 write_csv(EMP_OUTPUTS_CONSTRUCTION,"./Results/Top_20_Employmnet_During_Development_(2029).csv")
-EMP_OUTPUTS_CONSTRUCTION%>% print(n=100)
 
-COUNTY_OUTPUT_CONSTRUCTION <- EVENT_DATA %>% filter(YEAR<=2029) %>% group_by(COUNTY) %>% summarize(EMP=sum(EMP),OUTPUT=sum(OUTPUT),VALUE_ADDED=sum(EMP_COM+PROP_INC+TOPI+OPI),COUNTY_TAX=sum(SUBCOUNTY_TAX+SPECIAL_TAX+COUNTY_TAX),WY_TAX=sum(STATE_TAX)) %>% arrange(desc(EMP)) %>% print(n=200)
+
+COUNTY_OUTPUT_CONSTRUCTION <- EVENT_DATA %>% filter(YEAR<=2029)  %>% rename(County=COUNTY) %>% group_by(County) %>% summarize('Total Economic Output'=sum(OUTPUT),'Economic Value Added'=sum(EMP_COM+PROP_INC+TOPI+OPI),Employment=sum(EMP),'County Taxes'=sum(SUBCOUNTY_TAX+SPECIAL_TAX+COUNTY_TAX),'State Taxes'=sum(STATE_TAX)) %>% arrange(desc(Employment)) 
 COUNTY_OUTPUT_CONSTRUCTION[,-1] <- round(COUNTY_OUTPUT_CONSTRUCTION[,-1])
 write_csv(COUNTY_OUTPUT_CONSTRUCTION,"./Results/County_Outcomes_During_Development_(2029).csv")
 
-EMP_OUTPUTS_OPERATING <- DETAILED_DATA %>% filter(YEAR==2030) %>% group_by(IND_DESC)  %>% summarize('Employment'=sum(EMP),'Income'=sum(EMP_COMP+PROP_INC),'Other Profits'=sum(OPI)) %>% arrange(desc(Employment)) %>% print(n=50)
+EMP_OUTPUTS_OPERATING <- DETAILED_DATA %>% filter(YEAR==2030) %>% rename(Industry=IND_DESC) %>%  group_by(Industry)   %>% summarize('Employment'=sum(EMP),'Income'=sum(EMP_COMP+PROP_INC),'Other Profits'=sum(OPI),'Value Added'=sum(EMP_COMP+PROP_INC+TOPI+OPI),'Economic Output'=sum(OUTPUT)) %>% arrange(desc(Employment)) 
 TOP_20 <- EMP_OUTPUTS_OPERATING[1:20,] 
 OTHERS <- TOP_20[1,]
 OTHERS[1,1] <-"Other Industries"
@@ -41,45 +50,145 @@ EMP_OUTPUTS_OPERATING <-rbind(TOP_20,OTHERS)
 EMP_OUTPUTS_OPERATING[,-1] <- round(EMP_OUTPUTS_OPERATING[,-1])
 write_csv(EMP_OUTPUTS_OPERATING,"./Results/Top_20_Yearly_Employmnet_During_Operation_(2030+).csv")
 
-COUNTY_OUTPUTS_OPERATING<- EVENT_DATA %>% filter(YEAR==2030) %>% group_by(COUNTY) %>% summarize(EMP=sum(EMP),OUTPUT=sum(OUTPUT),VALUE_ADDED=sum(EMP_COM+PROP_INC+TOPI+OPI),COUNTY_TAX=sum(SUBCOUNTY_TAX+SPECIAL_TAX+COUNTY_TAX),WY_TAX=sum(STATE_TAX)) %>% arrange(desc(EMP)) %>% print(n=200) %>% arrange(desc(EMP))
+COUNTY_OUTPUTS_OPERATING<- EVENT_DATA %>% filter(YEAR==2030) %>% rename(County=COUNTY) %>% group_by(County) %>% summarize('Total Economic Output'=sum(OUTPUT),'Economic Value Added'=sum(EMP_COM+PROP_INC+TOPI+OPI),Employment=sum(EMP),'County Taxes'=sum(SUBCOUNTY_TAX+SPECIAL_TAX+COUNTY_TAX),'State Taxes'=sum(STATE_TAX)) %>% arrange(desc(Employment)) 
 COUNTY_OUTPUTS_OPERATING[,-1] <- round(COUNTY_OUTPUTS_OPERATING[,-1])
 write_csv(COUNTY_OUTPUTS_OPERATING,"./Results/County_Yearly_Outcomes_During_Operation_(2030+).csv")
 
+
+STATE_SUMMARY <- EVENT_DATA %>% ungroup() %>% filter(YEAR<=2030) %>% mutate(Period=ifelse(YEAR!=2030,"Development","Operating")) %>% group_by(Period) %>% summarize('Total Economic Output'=sum(OUTPUT),'Economic Value Added'=sum(EMP_COM+PROP_INC+TOPI+OPI),'County Taxes'=sum(SUBCOUNTY_TAX+SPECIAL_TAX+COUNTY_TAX),Employment=sum(EMP),'State Taxes'=sum(STATE_TAX)) %>% arrange(desc(Employment))
+  TEMP <- EVENT_DATA %>% ungroup() %>% filter(YEAR<2030) %>% mutate(Period=ifelse(YEAR!=2030,"Development","Operating")) %>% group_by(Period)  %>% summarize(Employment=sum(EMP)/5,'Total Economic Output'=sum(OUTPUT)/5,'Economic Value Added'=sum(EMP_COM+PROP_INC+TOPI+OPI)/5,'County Taxes'=sum(SUBCOUNTY_TAX+SPECIAL_TAX+COUNTY_TAX)/5,'State Taxes'=sum(STATE_TAX)/5) %>% arrange(desc(Employment))
+  STATE_SUMMARY <- rbind(STATE_SUMMARY[2,],TEMP,STATE_SUMMARY[1,])
+STATE_SUMMARY[,-1] <- round(STATE_SUMMARY[,-1])
+STATE_SUMMARY$Length <- "Average"
+STATE_SUMMARY$Length[3] <- "Total"
+STATE_SUMMARY <- STATE_SUMMARY %>% select(Period,Length,everything())
+write_csv(STATE_SUMMARY,"./Results/State_Summary.csv")
+
+
+
+#Write all tables for publiation
+kable(STATE_SUMMARY, format = "html", caption = "Economic Impact by Project Period",digits = 1, format.args = list(big.mark = ","))  %>%
+  kable_styling(bootstrap_options = c("striped")) %>%
+  column_spec(1:2, bold = TRUE, color = "Black")
+
+kable(COUNTY_OUTPUTS_OPERATING, format = "html", caption = "County Level Yearly Economic Impact During Operation",digits = 1, format.args = list(big.mark = ","))  %>%
+  kable_styling(bootstrap_options = c("striped")) %>%
+  column_spec(1, bold = TRUE, color = "Black")
+
+kable(COUNTY_OUTPUT_CONSTRUCTION, format = "html", caption = "County Level Total Economic Impact During Construction",digits = 1, format.args = list(big.mark = ","))  %>%
+  kable_styling(bootstrap_options = c("striped")) %>%
+  column_spec(1, bold = TRUE, color = "Black")
+
+
+kable(EMP_OUTPUTS_CONSTRUCTION, format = "html", caption = "Sector Level Total Economic Impact During Construction",digits = 1, format.args = list(big.mark = ","))  %>%
+  kable_styling(bootstrap_options = c("striped")) %>%
+  column_spec(1, bold = TRUE, color = "Black")
+
+kable(EMP_OUTPUTS_OPERATING, format = "html", caption = "Sector Level Yearly Economic Impact During Operation",digits = 1, format.args = list(big.mark = ","))  %>%
+  kable_styling(bootstrap_options = c("striped")) %>%
+  column_spec(1, bold = TRUE, color = "Black")
+
+
+#############Make All Figures
 #TAX_FILE <- FILES[grep("Taxes",FILES)]
-ggplot(aes(x=YEAR,y=EMP,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")
-ggplot(aes(x=YEAR,y=WY_TOTAL_TAX/10^6,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")
-EVENT_DATA$MAJOR_EVENT <- ifelse(EVENT_DATA$MAJOR_EVENT=="Income","Royalties and income",EVENT_DATA$MAJOR_EVENT)
-ORD <- EVENT_DATA$MAJOR_EVENT %>% unique
-EVENT_DATA$MAJOR_EVENT <- factor(EVENT_DATA$MAJOR_EVENT,levels=ORD)
-ggplot(aes(x=YEAR,y=WY_TOTAL_TAX/10^6,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")
+             
+              EVENT_DATA$MAJOR_EVENT <- ifelse(EVENT_DATA$MAJOR_EVENT=="Income","Royalties and income",EVENT_DATA$MAJOR_EVENT)
+              DATA$MAJOR_EVENT <- ifelse(DATA$MAJOR_EVENT=="Income","Royalties and income",DATA$MAJOR_EVENT)
+              ORD <- EVENT_DATA$MAJOR_EVENT %>% unique
+              DATA$MAJOR_EVENT %>% unique
+              EVENT_DATA$MAJOR_EVENT <- factor(EVENT_DATA$MAJOR_EVENT,levels=ORD)
+              DATA$MAJOR_EVENT <- factor(DATA$MAJOR_EVENT,levels=ORD)
+         
+TAX_EVENT <- ggplot(aes(x=YEAR,y=WY_TOTAL_TAX/10^6,group=MAJOR_EVENT,fill=MAJOR_EVENT),data=EVENT_DATA)+geom_bar(stat = "identity")+
+                  ylab("Wyoming Taxes (Million USD)")  + 
+                xlab("Year")+
+                labs(fill='Economic Event')+
+                scale_x_continuous(breaks=2025:2031)+
+                theme(legend.position = "top")+
+                theme(text=element_text(size=20))+scale_fill_brewer(palette = "Pastel1")
+EMP_EVENT<- ggplot(aes(x=YEAR,y=EMP,group=MAJOR_EVENT,fill=MAJOR_EVENT),data=EVENT_DATA)+geom_bar(stat = "identity")+
+                ylab("Added Employment (Person-Years)")  + 
+                xlab("Year")+
+                labs(fill='Economic Event')+
+                scale_x_continuous(breaks=2025:2031)+
+                theme(legend.position = "top")+
+                theme(text=element_text(size=20))+scale_fill_brewer(palette = "Set3")
+VA_EVENT <- ggplot(aes(x=YEAR,y=VA/10^6,group=MAJOR_EVENT,fill=MAJOR_EVENT),data=EVENT_DATA)+geom_bar(stat = "identity")+
+                ylab("Value Added (Million USD)")  + 
+                xlab("Year")+
+                labs(fill='Economic Event')+
+                scale_x_continuous(breaks=2025:2031)+
+                theme(legend.position = "top")+
+                theme(text=element_text(size=20))+scale_fill_brewer(palette = "Pastel1")
+OUTPUT_EVENT <- ggplot(aes(x=YEAR,y=OUTPUT/10^6,group=MAJOR_EVENT,fill=MAJOR_EVENT),data=EVENT_DATA)+geom_bar(stat = "identity")+
+                    ylab("Value Added (Million USD)")  + 
+                    xlab("Year")+
+                    labs(fill='Economic Event')+
+                    scale_x_continuous(breaks=2025:2031)+
+                    theme(legend.position = "top")+
+                    theme(text=element_text(size=20))+scale_fill_brewer(palette = "Pastel1")
+TAX_TYPE <- ggplot(aes(x=YEAR,y=WY_TOTAL_TAX/10^6,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")+
+              ylab("Wyoming Taxes (Million USD)")  + 
+              xlab("Year")+
+              labs(fill='Impact Type')+
+              scale_x_continuous(breaks=2025:2031)+
+              theme(legend.position = "top")+
+              theme(text=element_text(size=20))
 
-ggplot(aes(x=YEAR,y=WY_TOTAL_TAX/10^6,group=MAJOR_EVENT,fill=MAJOR_EVENT),data=EVENT_DATA)+geom_bar(stat = "identity")+
-    ylab("Wyoming Taxes (Million USD)")  + 
-  xlab("Year")+
-  labs(fill='Economic Event')+
-  scale_x_continuous(breaks=2025:2031)+
-  theme(legend.position = "top")+
-  theme(text=element_text(size=20))
-ggplot(aes(x=YEAR,y=EMP,group=MAJOR_EVENT,fill=MAJOR_EVENT),data=EVENT_DATA)+geom_bar(stat = "identity")+
-  ylab("Wyoming Added Employment (Person-Years)")  + 
-  xlab("Year")+
-  labs(fill='Economic Event')+
-  scale_x_continuous(breaks=2025:2031)+
-  theme(legend.position = "top")+
-  theme(text=element_text(size=20))
+EMP_TYPE <- ggplot(aes(x=YEAR,y=EMP,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")+
+              ylab("Added Employment (Person-Years)")  + 
+              xlab("Year")+
+              labs(fill='Impact Type')+
+              scale_x_continuous(breaks=2025:2031)+
+              theme(legend.position = "top")+
+              theme(text=element_text(size=20))
 
-ggplot(aes(x=YEAR,y=WY_TOTAL_TAX/10^6,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")+
-  ylab("Wyoming Taxes (Million USD)")  + 
+VA_TYPE <- ggplot(aes(x=YEAR,y=VA/10^6,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")+
+              ylab("Value Added (Million USD)")  + 
+              xlab("Year")+
+              labs(fill='Impact Type')+
+              scale_x_continuous(breaks=2025:2031)+
+              theme(legend.position = "top")+
+              theme(text=element_text(size=20))
+
+OUTPUT_TYPE <- ggplot(aes(x=YEAR,y=OUTPUT/10^6,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")+
+                      ylab("Total Output (Million USD)")  + 
+                      xlab("Year")+
+                      labs(fill='Impact Type')+
+                      scale_x_continuous(breaks=2025:2031)+
+                      theme(legend.position = "top")+
+                      theme(text=element_text(size=20))
+COMBINED_EVENT <- rbind(DATA %>% select(YEAR,MAJOR_EVENT,RES=OUTPUT) %>% mutate(GROUP="Economic Output",AREA="Total"),
+              DATA %>% select(YEAR,MAJOR_EVENT,RES=VA) %>% mutate(GROUP="Economic Value Added",AREA="Total"),
+DATA %>% select(YEAR,MAJOR_EVENT,RES=EMP_COM) %>% mutate(GROUP="Wyoming Salaries Paid",AREA="Wyoming"),
+DATA %>% select(YEAR,MAJOR_EVENT,RES=WY_TOTAL_TAX) %>% mutate(GROUP="Wyoming Taxes",AREA="Wyoming")) %>% filter(YEAR<2032)
+COMBINED_EVENT
+
+
+FACET_PLOT_EVENT <- ggplot(aes(x=YEAR,y=RES/10^6,fill=MAJOR_EVENT,group=GROUP),data=COMBINED_EVENT)+
+  geom_bar(stat = "identity")+facet_wrap(~GROUP,nrow=2)+
+  ylab("Million USD")  + 
   xlab("Year")+
   labs(fill='Impact Type')+
   scale_x_continuous(breaks=2025:2031)+
+  scale_y_continuous(breaks=c(0,5,10,15,seq(20,80,by=10)))+
   theme(legend.position = "top")+
-  theme(text=element_text(size=20))
+  theme(text=element_text(size=20))+scale_fill_brewer(palette = "Set3")
+FACET_PLOT_EVENT
 
-ggplot(aes(x=YEAR,y=EMP,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")+
-  ylab("Wyoming Added Employment (Person-Years)")  + 
+COMBINED_TYPE <- rbind(EVENT_DATA %>% select(YEAR,IMPACT_TYPE,RES=OUTPUT) %>% mutate(GROUP="Economic Output",AREA="Total"),
+                       EVENT_DATA %>% select(YEAR,IMPACT_TYPE,RES=VA) %>% mutate(GROUP="Economic Value Added",AREA="Total"),
+                       EVENT_DATA %>% select(YEAR,IMPACT_TYPE,RES=EMP_COM) %>% mutate(GROUP="Wyoming Salaries Paid",AREA="Wyoming"),
+                       EVENT_DATA %>% select(YEAR,IMPACT_TYPE,RES=WY_TOTAL_TAX) %>% mutate(GROUP="Wyoming Taxes",AREA="Wyoming")) %>% filter(YEAR<2032)
+FACET_PLOT_TYPE <- ggplot(aes(x=YEAR,y=RES/10^6,fill=IMPACT_TYPE,group=IMPACT_TYPE),data=COMBINED_TYPE)+
+  geom_bar(stat = "identity")+facet_wrap(~GROUP,nrow=2)+
+  ylab("Million USD")  + 
   xlab("Year")+
   labs(fill='Impact Type')+
   scale_x_continuous(breaks=2025:2031)+
+  scale_y_continuous(breaks=c(0,5,10,15,seq(20,80,by=10)))+
   theme(legend.position = "top")+
-  theme(text=element_text(size=20))
+  theme(text=element_text(size=20))+scale_fill_brewer(palette = "Pastel1")
+FACET_PLOT_TYPE
+FACET_PLOT_EVENT
+EMP_EVENT
\ No newline at end of file