library(rvest)
library(tidyverse)


########County, Death, Birth and Migration Data
#Data found on the page http://eadiv.state.wy.us/pop/
PAGE <- read_html("http://eadiv.state.wy.us/pop/BirthDeathMig.htm") 
NODE <- html_element(PAGE ,"table")
TBL <- html_table(NODE)

ST <- which(toupper(TBL$X1)=="ALBANY")
END <- which(toupper(TBL$X1)=="TOTAL")
TYPES <- TBL[ST-2,1]

ST_YEAR <- 1971
ALL_DATA <- list()
TBL <- TBL[,c(1,which(!is.na(as.numeric(TBL[ST[1],]))))]
TBL <- TBL[,-ncol(TBL)]
colnames(TBL) <- c("County",(ST_YEAR:(ST_YEAR+ncol(TBL)-1)))
TBL$Type  <- NA
for(i in 1:length(ST)){
	TBL[ST[i]:END[i],"Type"]<- as.character(TYPES[i,1])
}
TBL[ST[2]:END[2],"Type"] <-  as.character(TYPES[2,1])
TBL$Type
TBL <- TBL %>% filter(!is.na(Type)) %>% select(County,Type,everything())
GROUP <- colnames(TBL)[-1:-2]
Data <- pivot_longer(TBL,all_of(GROUP),names_to="Year",values_to="Pop_Change")
Data$County <- ifelse(toupper(Data$County)=="TOTAL","Wyoming",Data$County)
WY_COUNTY_DATA_SET <- pivot_wider(Data,names_from=Type,values_from=Pop_Change) %>% rename("Migration"=`Net Migration`) %>% mutate(Year=as.integer(Year),Births=parse_number(Births),Deaths=parse_number(Deaths),Migration=parse_number(Migration))

########################City and County Population Data 2020 to 2024
PAGE <- read_html('http://eadiv.state.wy.us/pop/Place-24EST.htm')
NODE <- html_element(PAGE ,"table")
TBL <- html_table(NODE)

ST <- which(toupper(TBL$X1)==toupper("Albany County"))
END <- which(toupper(TBL$X1)==toupper("Balance of Weston County"))

#More years than are pulled are listed to make more generic
COLUMNS <- c(1,which(TBL[ST-2,] %in% 1970:2025))
NAMES <- TBL[4,COLUMNS][-1]
TBL <- TBL[ST:END,COLUMNS ]
colnames(TBL) <- c("County",NAMES)

TBL <- pivot_longer(TBL,all_of(colnames(TBL)[-1]),names_to="Year",values_to="Population") %>% mutate(Year=as.integer(Year),Population=parse_number(Population))
TBL$County <- gsub("  "," ",gsub("\n","",gsub("\r","",TBL %>% pull(County))))

COUNTY_POP<- TBL[grep("COUNTY",TBL %>% pull(County),ignore.case=TRUE),]
COUNTY_POP<-	COUNTY_POP[grep("Balance",COUNTY_POP%>% pull(County),invert=TRUE,ignore.case=TRUE),]
COUNTY_POP$County <- gsub(" ","_",gsub(" County","",COUNTY_POP$County))
CITY_POP <- TBL[sort(c(grep("County",TBL %>% pull(County),invert=TRUE,ignore.case=TRUE),grep("Balance",TBL %>% pull(County),ignore.case=TRUE))),]
CITY_POP$County <- gsub(" ","_",gsub("Balance of","Unincorporated",gsub(" County","",gsub(" city","",gsub(" town","",CITY_POP$County,ignore.case=TRUE),ignore.case=TRUE),ignore.case=TRUE),ignore.case=TRUE))
CITY_POP <- CITY_POP %>% rename("City"=County)
########################City Population Data 2010 to 2020
PAGE <- read_html('http://eadiv.state.wy.us/pop/sub-est11-19.htm')
NODE <- html_element(PAGE ,"table")
TBL <- html_table(NODE)
ST <- which(toupper(TBL$X1)==toupper("Afton town, Wyoming"))
END <- which(toupper(TBL$X1)==toupper("Yoder town, Wyoming"))
#More years than are pulled are listed to make more generic
COLUMNS <- c(1,which(TBL[ST-1,] %in% 1970:2025))
NAMES <- TBL[3,COLUMNS][-1]
TBL <- TBL[ST:END,COLUMNS ]
colnames(TBL) <- c("City",NAMES)
TBL <- pivot_longer(TBL,all_of(colnames(TBL)[-1]),names_to="Year",values_to="Population") %>% mutate(Year=as.integer(Year),Population=parse_number(Population))
TBL$City <- gsub(" ","_",gsub(" $","",gsub("\r|\n| Wyoming|,| town| city","",TBL$City,ignore.case=TRUE)))
CITY_POP <- rbind(TBL,CITY_POP)
########################County Population Data 2010 to 2020
PAGE <- read_html('http://eadiv.state.wy.us/pop/ctyest11-19.htm')
NODE <- html_element(PAGE ,"table")
TBL <- html_table(NODE)
ST <- grep("Albany",TBL$X1)
END <- grep("Weston",TBL$X1)
#More years than are pulled are listed to make more generic
COLUMNS <- c(1,which(TBL[ST-2,] %in% 1970:2025))
NAMES <- TBL[3,COLUMNS][-1]
TBL <- TBL[ST:END,COLUMNS ]
colnames(TBL) <- c("County",NAMES)
TBL <- pivot_longer(TBL,all_of(colnames(TBL)[-1]),names_to="Year",values_to="Population") %>% mutate(Year=as.integer(Year),Population=parse_number(Population))
TBL$County <-  gsub(" ","_",gsub("  "," ",gsub(" $","",gsub("\r|\n| Wyoming|,| town| city| County|\\.","",TBL$County,ignore.case=TRUE))))

COUNTY_POP <- rbind(TBL,COUNTY_POP)

########################County and City Population Data 2000 to 2010
PAGE <- read_html('http://eadiv.state.wy.us/pop/sub-est01-09.htm')
NODE <- html_element(PAGE ,"table")
TBL <- html_table(NODE)

ST <- which(toupper(TBL$X1)==toupper("Albany County"))
END <- which(toupper(TBL$X1)==toupper("Balance of Weston County"))

#More years than are pulled are listed to make more generic
COLUMNS <- c(1,which(TBL[ST-4,] %in% 1970:2025))
NAMES <- TBL[4,COLUMNS][-1]
TBL <- TBL[ST:END,COLUMNS ]
colnames(TBL) <- c("County",NAMES)
TBL <-       	pivot_longer(TBL,all_of(colnames(TBL)[-1]),names_to="Year",values_to="Population") %>% mutate(Year=as.integer(Year),Population=parse_number(Population))
TBL$County <- gsub("  "," ",gsub("\n","",gsub("\r","",TBL %>% pull(County))))

COUNTY_TBL <- TBL[grep("COUNTY",TBL %>% pull(County),ignore.case=TRUE),]
COUNTY_TBL <-COUNTY_TBL[grep("Balance",COUNTY_TBL%>% pull(County),invert=TRUE,ignore.case=TRUE),]
COUNTY_TBL$County <-gsub("_(pt.)","", gsub(" ","_",gsub(" County","",COUNTY_TBL$County)))

CITY_TBL <- TBL[sort(c(grep("County",TBL %>% pull(County),invert=TRUE,ignore.case=TRUE),grep("Balance",TBL %>% pull(County),ignore.case=TRUE))),]
CITY_TBL$County <- gsub(" ","_",gsub("Balance of","Unincorporated",gsub(" County","",gsub(" city","",gsub(" town","",CITY_TBL$County,ignore.case=TRUE),ignore.case=TRUE),ignore.case=TRUE),ignore.case=TRUE))
CITY_TBL <- CITY_TBL %>% rename("City"=County)
CITY_POP <- rbind(CITY_TBL,CITY_POP)
#Cleanup names
CITY_POP$City <- gsub("LaGrange","La_Grange",CITY_POP$City)
COUNTY_POP <- rbind(COUNTY_TBL,COUNTY_POP)
####################County and City Population DAta for 1990-2000
PAGE <- read_html('http://eadiv.state.wy.us/pop/c&sc90_00.htm')
NODE <- html_element(PAGE ,"body")
TEST <- read_table(html_text2(NODE),skip=21,skip_empty_rows=TRUE)
TO_ADJ <- sum((!is.na(TEST[2,])))-11

?read_table
?html_text2
?html_text
TBL <- html_table(NODE)
TBL 

ST <- which(toupper(TBL$X1)==toupper("Albany Cnty"))
END <- which(toupper(TBL$X1)==toupper("Balance of Weston County"))