library(tidyverse)
library(scales) #For a pretty population Pyramid
PY_DATA <- rbind(readRDS("Data/Cleaned_Data/Demographic_Sex_Age_Data/RDS/Kemmerer_Diamondville_Demographics.Rds"),readRDS("Data/Cleaned_Data/Demographic_Sex_Age_Data/RDS/Other_Lincoln_Demographics.Rds"))
PY_DATA <- PY_DATA %>% pivot_longer(cols=c("Num_Female","Num_Male"),names_to="Sex",values_to="Population") %>% mutate(Sex=ifelse(Sex=="Num_Female","Female","Male"))

PY_DATA <- PY_DATA %>% group_by(Year,Region) %>% mutate('Percent of Population'=Population/sum(Population)) %>% ungroup
PY_DATA <- PY_DATA %>% mutate(Age_Numeric=Age,Age=as.character(Age))
PY_DATA[PY_DATA$Age=='85',"Age"] <- "+85"
ORD <- PY_DATA[,c("Age_Numeric","Age")] %>% unique %>% arrange(Age_Numeric) %>% pull(Age)
PY_DATA$Age <- factor(PY_DATA$Age,levels=ORD)
PY_DATA <- PY_DATA %>% mutate(`Percent of Population`=ifelse(Region=='Kemmerer & Diamondville',`Percent of Population`,-`Percent of Population`))
PY_DATA$Region <- ifelse(PY_DATA$Region=='Lincoln_Other','Rest of Lincoln County',PY_DATA$Region)

PY_DATA_NO_SEX <- PY_DATA %>% group_by(County,Region,Year,Age,Age_Numeric) %>% summarize(Population=sum(Population)) %>% ungroup %>% group_by(Region,Year) %>% mutate('Percent of Population'=Population/sum(Population)) %>% ungroup
PY_DATA_NO_SEX <- PY_DATA_NO_SEX %>% mutate(`Percent of Population`=ifelse(Region=='Kemmerer & Diamondville',`Percent of Population`,-`Percent of Population`))



PY_2023 <- PY_DATA %>% filter(Year==2023)

PY_2023_KEM <- PY_DATA %>% filter(Year==2023,Region=='Kemmerer & Diamondville')  %>% mutate(`Percent of Population`=ifelse(Sex=='Male',`Percent of Population`,-`Percent of Population`))
PY_NO_SEX_2023 <- PY_DATA_NO_SEX %>% filter(Year==2023)
PY_NO_SEX_2009 <- PY_DATA_NO_SEX %>% filter(Year==2009)
PY_KEM_SHIFT <- rbind(PY_NO_SEX_2023,PY_NO_SEX_2009) %>% filter(Region=='Kemmerer & Diamondville') %>% mutate(Year=factor(Year,levels=c(2009,2023)),`Percent of Population`=ifelse(Year==2023,`Percent of Population`,-`Percent of Population`))
PY_KEM_SHIFT <- rbind(PY_NO_SEX_2023,PY_NO_SEX_2009) %>% filter(Region=='Kemmerer & Diamondville') %>% mutate(Year=factor(Year,levels=c(2009,2023)),`Percent of Population`=ifelse(Year==2023,`Percent of Population`,-`Percent of Population`))
PY_OTHER_SHIFT <- rbind(PY_NO_SEX_2023,PY_NO_SEX_2009) %>% filter(Region!='Kemmerer & Diamondville') %>% mutate(Year=factor(Year,levels=c(2009,2023)),`Percent of Population`=ifelse(Year==2023,`Percent of Population`,-`Percent of Population`))


RANGE <- c(pretty(range(PY_2023$`Percent of Population`),n=6),0.02)
LAB <- 	percent(abs(RANGE),accuracy=0.1 )

RANGE_NO_SEX <- c(pretty(range(PY_NO_SEX_2023$`Percent of Population`),n=8))
LAB_NO_SEX <- 	percent(abs(RANGE_NO_SEX),accuracy=0.1 )

  if(!exists("SAVE_PY_LOC")){SAVE_PY_LOC <- "./Results/Population_Pyramids/"}
 dir.create(SAVE_PY_LOC , recursive = TRUE, showWarnings = FALSE)


png(paste0(SAVE_PY_LOC,"Kemmerer_Lincoln_Age_by_Sex_2023_Population_Pyramid.png"), res = 600, width = 8.27, height = 11, units = "in")
	ggplot(PY_2023,aes(y=Age,x=`Percent of Population`,fill=Region))+geom_col() +scale_x_continuous(breaks  = RANGE,labels = LAB,limits=c(-0.02,0.02))+facet_grid(~Sex)+ylab("Age")+ theme_bw()+ theme(axis.text = element_text(size = 10),legend.position = "top",legend.text=element_text(size=14),legend.title = element_blank(),axis.title=element_text(size=18),strip.text = element_text(size = 14))+ scale_fill_discrete(guide = guide_legend(reverse = TRUE))+scale_y_discrete(breaks=c(0,seq(5,80,by=5),"+85"))
dev.off()
png(paste0(SAVE_PY_LOC,"Kemmerer_Age_by_Sex_2023_Population_Pyramid.png"), res = 600, width = 8.27, height = 11, units = "in")
	ggplot(PY_2023_KEM ,aes(y=Age,x=`Percent of Population`,fill=Sex))+geom_col() +scale_x_continuous(breaks  = RANGE,labels = LAB,limits=c(-0.015,0.015))+ theme_bw() + theme(axis.text = element_text(size = 10),legend.position = "top",legend.text=element_text(size=14),legend.title = element_blank(),axis.title=element_text(size=18),strip.text = element_text(size = 14))+ scale_fill_discrete(guide = guide_legend(reverse = TRUE))+ scale_fill_manual(values = c( "mediumpurple2","aquamarine3"))+scale_y_discrete(breaks=c(0,seq(5,80,by=5),"+85"))
dev.off()

png(paste0(SAVE_PY_LOC,"Kemmerer_Lincoln_Age_2023_Population_Pyramid.png"), res = 600, width = 8.27, height = 11, units = "in")
	ggplot(PY_NO_SEX_2023,aes(y=Age,x=`Percent of Population`,fill=Region))+geom_col() +scale_x_continuous(breaks  = RANGE_NO_SEX,labels = LAB_NO_SEX)+ theme_bw()+ theme(axis.text = element_text(size = 10),legend.position = "top",legend.text=element_text(size=14),legend.title = element_blank(),axis.title=element_text(size=18),strip.text = element_text(size = 14))+  guides(fill= guide_legend(reverse = TRUE))+scale_x_continuous(breaks  = RANGE_NO_SEX,labels = LAB_NO_SEX,limits=c(-0.025,0.025))+scale_y_discrete(breaks=c(0,seq(5,80,by=5),"+85"))
dev.off()

png(paste0(SAVE_PY_LOC,"Kemmerer_2009_to_2023_Age_Population_Pyramid.png"), res = 600, width = 8.27, height = 11, units = "in")
	ggplot(PY_KEM_SHIFT,aes(y=Age,x=`Percent of Population`,fill=Year))+geom_col() + theme_bw()+ theme(axis.text = element_text(size = 10),legend.position = "top",legend.text=element_text(size=14),legend.title = element_blank(),axis.title=element_text(size=18),strip.text = element_text(size = 14))+scale_y_discrete(breaks=c(0,seq(5,80,by=5),"+85"))+ scale_fill_manual(values = c("indianred2", "cornflowerblue", "magenta", "yellow"))+scale_x_continuous(breaks  = RANGE_NO_SEX,labels = LAB_NO_SEX,limits=c(-0.025,0.025))
dev.off()


POP_DATA <- PY_KEM_SHIFT %>%  pivot_wider(values_from=`Percent of Population`,names_from=Year,names_prefix="Year_") %>% group_by(Age) %>% summarize(Year_2023=abs(sum(Year_2023,na.rm=TRUE)),Year_2009=abs(sum(Year_2009,na.rm=TRUE)),Shift=Year_2023-Year_2009) 
#Make clean labels of Lollipop graph
POP_RANGE <- c(pretty(range(POP_DATA$Shift),n=8))
POP_LABEL <- 	percent(abs(POP_RANGE),accuracy=0.1 )

POP_OTHER_DATA <- PY_OTHER_SHIFT %>%  pivot_wider(values_from=`Percent of Population`,names_from=Year,names_prefix="Year_") %>% group_by(Age) %>% summarize(Year_2023=abs(sum(Year_2023,na.rm=TRUE)),Year_2009=abs(sum(Year_2009,na.rm=TRUE)),Shift=Year_2023-Year_2009) 

png(paste0(SAVE_PY_LOC,"Kemmerer_2009_to_2023_Age_Changes_Lollipop.png"), res = 600, width = 8.27, height = 11, units = "in")
	ggplot(POP_DATA,aes(y=Age,x=Shift))+ geom_segment( aes(x=0, xend=Shift, y=Age, yend=Age),size=1.5, color="darkgrey")+geom_point( color="darkorange2", size=3.5) + theme_bw()+ theme(axis.text = element_text(size = 10),legend.position = "top",legend.text=element_text(size=14),legend.title = element_blank(),axis.title=element_text(size=18),strip.text = element_text(size = 14))+scale_y_discrete(breaks=c(0,seq(5,80,by=5),"+85"))+ scale_fill_manual(values = c("indianred2", "cornflowerblue", "magenta", "yellow"))+xlab("Share of Population Change (2009 to 2023)")+ scale_x_continuous(breaks  = POP_RANGE,labels = POP_LABEL,limits=c(-0.020,0.020))
dev.off()

png(paste0(SAVE_PY_LOC,"Other_Lincoln_County_2009_to_2023_Age_Changes_Lollipop.png"), res = 600, width = 8.27, height = 11, units = "in")
	ggplot(POP_OTHER_DATA,aes(y=Age,x=Shift))+ geom_segment( aes(x=0, xend=Shift, y=Age, yend=Age),size=1.5, color="darkgrey")+geom_point( color="firebrick3", size=3.5) + theme_bw()+ theme(axis.text = element_text(size = 10),legend.position = "top",legend.text=element_text(size=14),legend.title = element_blank(),axis.title=element_text(size=18),strip.text = element_text(size = 14))+scale_y_discrete(breaks=c(0,seq(5,80,by=5),"+85"))+ scale_fill_manual(values = c("indianred2", "cornflowerblue", "magenta", "yellow"))+xlab("Share of Population Change (2009 to 2023)")+ xlim(-0.02, 0.02)+scale_x_continuous(breaks  = POP_RANGE,labels = POP_LABEL,limits=c(-0.020,0.020))
dev.off()

GRAPH_DATA <- rbind(readRDS("Data/Cleaned_Data/Demographic_Sex_Age_Data/RDS/Kemmerer_Diamondville_Demographics.Rds"),readRDS("Data/Cleaned_Data/Demographic_Sex_Age_Data/RDS/Other_Lincoln_Demographics.Rds"))
AVG_AGE <- GRAPH_DATA %>% mutate(Population=Num_Female+Num_Male) %>% group_by(Region,Year) %>% summarize(Average_Age=sum(Age*Population)/sum(Population)) %>% ungroup
NUM_CHILDREN <- GRAPH_DATA %>% mutate(Population=Num_Female+Num_Male) %>% filter(Age<=18) %>% group_by(Region,Year) %>% summarize(Children=sum(Population)) %>% ungroup
NUM_ADULT <- GRAPH_DATA %>% mutate(Population=Num_Female+Num_Male) %>% filter(Age>=18,Age<31) %>% group_by(Region,Year) %>% summarize('18-30'=sum(Population)) %>% ungroup
NUM_WORKING_ADULT <- GRAPH_DATA %>% mutate(Population=Num_Female+Num_Male) %>% filter(Age>=31,Age<55) %>% group_by(Region,Year) %>% summarize('31-54'=sum(Population)) %>% ungroup
NUM_RETIRED <- GRAPH_DATA %>% mutate(Population=Num_Female+Num_Male) %>% filter(Age>=55) %>% group_by(Region,Year) %>% summarize('55+'=sum(Population)) %>% ungroup



MEDIAN_AGE <- GRAPH_DATA %>% mutate(Population=Num_Female+Num_Male) %>% group_by(Region,Year) %>% mutate(ROLLSUM=cumsum(Population),MID_POINT=ROLLSUM>=sum(Population)/2) %>% filter(MID_POINT) %>% filter(Age==min(Age)) %>% select(County,Region,Med_Age=Age) %>% ungroup
GRAPH_DATA <- AVG_AGE %>% left_join(MEDIAN_AGE) %>% left_join(NUM_CHILDREN ) %>% left_join(NUM_ADULT ) %>% left_join(NUM_WORKING_ADULT)%>% left_join(NUM_RETIRED)
GRAPH_DATA <- GRAPH_DATA  %>% pivot_longer(cols=c('Children','18-30','31-54','55+'),names_to='Age Category',values_to='Population')
readRDS("Data/Cleaned_Data/Population_Data/RDS/All_Wyoming_City_Populations.Rds")  %>% pull(City) %>% unique
readRDS("Data/Cleaned_Data/Population_Data/RDS/All_Wyoming_County_Populations.Rds")

readRDS("Data/Cleaned_Data/Demographic_Sex_Age_Data/RDS/Kemmerer_Diamondville_Demographics.Rds") %>% group_by(Year) %>% summarize(Demo_Population=sum(Num_Male)+sum(Num_Female)) %>% left_join(readRDS("Data/Cleaned_Data/Population_Data/RDS/All_Wyoming_City_Populations.Rds") %>% filter(City=='Kemmerer'| City=='Diamondville') %>% group_by(Year) %>% summarize(City_Population=sum(Population))) %>% mutate(Demo_Population/City_Population)

readRDS("Data/Cleaned_Data/Demographic_Sex_Age_Data/RDS/Full_Lincoln_County_Demographics.Rds") %>% group_by(Year)%>% summarize(Demo_Population=sum(Num_Male)+sum(Num_Female)) %>% left_join(readRDS("Data/Cleaned_Data/Population_Data/RDS/All_Wyoming_County_Populations.Rds") %>% filter(County=='Lincoln') %>% group_by(Year) %>% summarize(County_Population=sum(Population))) %>% tail
%>% mutate(Demo_Population/City_Population

getwd()
%>% group_by(Year) %>% summarize(Demo_Population=sum(Num_Male)+sum(Num_Female))

%>% filter(County='Lincoln') %>% group_by(Year) %>% summarize(Population=sum(Population))
GRAPH_DATA
GRAPH_DATA %>% filter(Region=='Kemmerer & Diamondville') %>% group_by(Year) %>% summarize(POP_DEMO=sum(Population)) %>% left_join(TEST)

rbind(readRDS("Data/Cleaned_Data/Demographic_Sex_Age_Data/RDS/Kemmerer_Diamondville_Demographics.Rds")) %>% mutate(Population=Num_Female+Num_Male) %>% group_by(Year) %>% summarize(Pop_Demo=sum(Population)) %>% left_join(TEST)

GRAPH_DATA 

ggplot(GRAPH_DATA,aes(x=Year,y=Population,color=Region)) +facet_grid( ~`Age Category`)+geom_line()
ggplot(GRAPH_DATA,aes(x=Year,color=Region)) + geom_line(aes(y=Average_Age),linetype=1,size=1.5)
ggplot(GRAPH_DATA,aes(x=Year,color=Region)) + geom_line(aes(y=Children),linetype=1,size=1.5)+ geom_line(aes(y=`18-30`),linetype=2,size=1.5)+ geom_line(aes(y=`31-54`),linetype=3,size=1.5)+ geom_line(aes(y=`55+`),linetype=4,size=1.5)







PY_DATA <- GRAPH_DATA %>% pivot_longer(cols=c("Num_Female","Num_Male"),names_to="Sex",values_to="Population") %>% mutate(Sex=ifelse(Sex=="Num_Female","Female","Male"))