library(tidyverse)
library(scales)
if(!exists("SAVE_SPENDING_LOC")){SAVE_SPENDING_LOC <- "./Results/Household_Spending/"}
dir.create(SAVE_SPENDING_LOC , recursive = TRUE, showWarnings = FALSE)


TOTAL_DEMAND_KEM <- read_csv("Data/Raw_Data/IMPLAN_Household_Spending/Household_Demand_Kemmerer.csv") %>% mutate(Demand=parse_number(Demand))
LOCAL_DEMAND_KEM <- read_csv("Data/Raw_Data/IMPLAN_Household_Spending/Local_Household_Demand_Kemmerer.csv")  %>% select(Good=Commodity,Local_Demand=Total) %>% mutate(Local_Demand=parse_number(Local_Demand))
SPENDING_SUMMARY_KEM <- (TOTAL_DEMAND_KEM %>% full_join(LOCAL_DEMAND_KEM) %>% mutate(Import=Demand-Local_Demand,Import_Per=Import/Demand))[,-1:-2] %>% arrange(desc(Import)) %>% filter(!is.na(Good))

SPENDING_SUMMARY_KEM <- (TOTAL_DEMAND_KEM %>% full_join(LOCAL_DEMAND_KEM) %>% mutate(Import=Demand-Local_Demand,Import_Per=Import/Demand)) %>% arrange(desc(Import)) %>% filter(!is.na(Good))
KEM_PER <- sum(sum(SPENDING_SUMMARY_KEM[,"Import"])/sum(SPENDING_SUMMARY_KEM[,"Demand"]))
SPENDING_SUMMARY_KEM <-  SPENDING_SUMMARY_KEM %>% arrange(desc(Demand))  %>% mutate(Rank=rank(-Demand)) %>% mutate(Good=ifelse(Rank>10,"Other",Good)) %>% group_by(Good) %>% summarize(Demand=sum(Demand),Local_Demand=sum(Local_Demand),Import=Demand-Local_Demand,Import_Per=Import/Demand) %>% mutate(Rank=rank(-Demand),Rank=ifelse(Good=='Other',30,Rank)) %>% arrange(Rank) %>% select(-Rank)
	SPENDING_SUMMARY_KEM[nrow(SPENDING_SUMMARY_KEM)+1,2:4] <- t(colSums(SPENDING_SUMMARY_KEM[,2:4]))
SPENDING_SUMMARY_KEM[nrow(SPENDING_SUMMARY_KEM),5] <- KEM_PER 
SPENDING_SUMMARY_KEM[nrow(SPENDING_SUMMARY_KEM),1] <- 'Kemmerer Total'
SPENDING_SUMMARY_KEM[,2:4]<- round(SPENDING_SUMMARY_KEM[,2:4]/10^6,1 )
SPENDING_SUMMARY_KEM$Import_Per <- percent(SPENDING_SUMMARY_KEM$Import_Per,0.1)
SPENDING_SUMMARY_KEM



TOTAL_DEMAND_LIN <- read_csv("Data/Raw_Data/IMPLAN_Household_Spending/Household_Demand_Lincoln.csv") %>% select(Good,Total) %>% mutate(Total=parse_number(Total)) %>% rename(Demand=Total) %>% arrange(Good)
LOCAL_DEMAND_LIN <- read_csv("Data/Raw_Data/IMPLAN_Household_Spending/Local_Household_Demand_Lincoln.csv") %>% select(Good,Total) %>% mutate(Total=parse_number(Total)) %>% rename(Local_Demand=Total) %>% arrange(Good) 
SPENDING_SUMMARY_LIN <- (TOTAL_DEMAND_LIN %>% full_join(LOCAL_DEMAND_LIN) %>% mutate(Import=Demand-Local_Demand,Import_Per=Import/Demand)) %>% arrange(desc(Import)) %>% filter(!is.na(Good))
LIN_PER <- sum(SPENDING_SUMMARY_LIN[,"Import"])/sum(SPENDING_SUMMARY_LIN[,"Demand"])
SPENDING_SUMMARY_LIN <-  SPENDING_SUMMARY_LIN %>% arrange(desc(Demand))  %>% mutate(Rank=rank(-Demand)) %>% mutate(Good=ifelse(Rank>10,"Other",Good)) %>% group_by(Good) %>% summarize(Demand=sum(Demand),Local_Demand=sum(Local_Demand),Import=Demand-Local_Demand,Import_Per=Import/Demand) %>% mutate(Rank=rank(-Demand),Rank=ifelse(Good=='Other',30,Rank)) %>% arrange(Rank) %>% select(-Rank)
	SPENDING_SUMMARY_LIN[nrow(SPENDING_SUMMARY_LIN)+1,2:4] <- t(colSums(SPENDING_SUMMARY_LIN[,2:4]))
SPENDING_SUMMARY_LIN[nrow(SPENDING_SUMMARY_LIN),5] <- LIN_PER 
SPENDING_SUMMARY_LIN[nrow(SPENDING_SUMMARY_LIN),1] <- 'Lincoln Total'
SPENDING_SUMMARY_LIN[,2:4]<- round(SPENDING_SUMMARY_LIN[,2:4]/10^6,1 )
SPENDING_SUMMARY_LIN$Import_Per <- percent(SPENDING_SUMMARY_LIN$Import_Per,0.1)
SPENDING_SUMMARY_LIN

TOTAL_DEMAND_LIN_OTHER <- read_csv("Data/Raw_Data/IMPLAN_Household_Spending/Household_Demand_Lincoln_Other.csv") %>% select(Good,Total) %>% mutate(Total=parse_number(Total)) %>% rename(Demand=Total) %>% arrange(Good)
LOCAL_DEMAND_LIN_OTHER <- read_csv("Data/Raw_Data/IMPLAN_Household_Spending/Local_Household_Demand_Lincoln_Other.csv") %>% select(Good,Total) %>% mutate(Total=parse_number(Total)) %>% rename(Local_Demand=Total) %>% arrange(Good) 
SPENDING_SUMMARY_LIN_OTHER <- (TOTAL_DEMAND_LIN_OTHER %>% full_join(LOCAL_DEMAND_LIN_OTHER) %>% mutate(Import=Demand-Local_Demand,Import_Per=Import/Demand)) %>% arrange(desc(Import)) %>% filter(!is.na(Good))
LIN_PER <- sum(SPENDING_SUMMARY_LIN_OTHER[,"Import"])/sum(SPENDING_SUMMARY_LIN_OTHER[,"Demand"])
SPENDING_SUMMARY_LIN_OTHER <-  SPENDING_SUMMARY_LIN_OTHER %>% arrange(desc(Demand))  %>% mutate(Rank=rank(-Demand)) %>% mutate(Good=ifelse(Rank>10,"Other",Good)) %>% group_by(Good) %>% summarize(Demand=sum(Demand),Local_Demand=sum(Local_Demand),Import=Demand-Local_Demand,Import_Per=Import/Demand) %>% mutate(Rank=rank(-Demand),Rank=ifelse(Good=='Other',30,Rank)) %>% arrange(Rank) %>% select(-Rank)
	SPENDING_SUMMARY_LIN_OTHER[nrow(SPENDING_SUMMARY_LIN_OTHER)+1,2:4] <- t(colSums(SPENDING_SUMMARY_LIN_OTHER[,2:4]))
SPENDING_SUMMARY_LIN_OTHER[nrow(SPENDING_SUMMARY_LIN_OTHER),5] <- LIN_PER 
SPENDING_SUMMARY_LIN_OTHER[nrow(SPENDING_SUMMARY_LIN_OTHER),1] <- 'Lincoln Total'
SPENDING_SUMMARY_LIN_OTHER[,2:4]<- round(SPENDING_SUMMARY_LIN_OTHER[,2:4]/10^6,1 )
SPENDING_SUMMARY_LIN_OTHER$Import_Per <- percent(SPENDING_SUMMARY_LIN_OTHER$Import_Per,0.1)

write_csv(SPENDING_SUMMARY_KEM,paste0(SAVE_SPENDING_LOC,"Household_Spending_Kemmerer_Total.csv"))
write_csv(SPENDING_SUMMARY_LIN,paste0(SAVE_SPENDING_LOC,"Household_Spending_Lincoln_County_Total.csv"))
write_csv(SPENDING_SUMMARY_LIN_OTHER,paste0(SAVE_SPENDING_LOC,"Household_Spending_Lincoln_County_Other_Than_Kemmerer_Total.csv"))