320 lines
18 KiB
R
320 lines
18 KiB
R
#################IMPORTANT!!!! CLEAN UP DATA SCRIPT FOR USE
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#############################Clean up scirpt folders, simulations of deaths should be seperated from this file. Death rate simulation is complicated and should be commented, and turned into a seperate script.
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##############WORKING ON REGRESSION OF BIRTHS FOR PREDICTIONS OF GROWTH, THINK ABOUT GOING FROM COUNTY TO CITY, SEPERATE REGRESION FROM DATA COLLOECTION
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library(rvest)
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library(tidyverse)
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library(readxl)
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#setwd("../")
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########County, Death, Birth and Migration Data
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#Data found on the page http://eadiv.state.wy.us/pop/
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PAGE <- read_html("http://eadiv.state.wy.us/pop/BirthDeathMig.htm")
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NODE <- html_element(PAGE ,"table")
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TBL <- html_table(NODE)
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ST <- which(toupper(TBL$X1)=="ALBANY")
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END <- which(toupper(TBL$X1)=="TOTAL")
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TYPES <- TBL[ST-2,1]
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ST_YEAR <- 1971
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ALL_DATA <- list()
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TBL <- TBL[,c(1,which(!is.na(as.numeric(TBL[ST[1],]))))]
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TBL <- TBL[,-ncol(TBL)]
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colnames(TBL) <- c("County",(ST_YEAR:(ST_YEAR+ncol(TBL)-1)))
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TBL$Type <- NA
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for(i in 1:length(ST)){
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TBL[ST[i]:END[i],"Type"]<- as.character(TYPES[i,1])
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}
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TBL[ST[2]:END[2],"Type"] <- as.character(TYPES[2,1])
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TBL$Type
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TBL <- TBL %>% filter(!is.na(Type)) %>% select(County,Type,everything())
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GROUP <- colnames(TBL)[-1:-2]
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Data <- pivot_longer(TBL,all_of(GROUP),names_to="Year",values_to="Pop_Change")
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Data$County <- ifelse(toupper(Data$County)=="TOTAL","Wyoming",Data$County)
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WY_COUNTY_DATA_SET <- pivot_wider(Data,names_from=Type,values_from=Pop_Change) %>% rename("Migration"=`Net Migration`) %>% mutate(Year=as.integer(Year),Births=parse_number(Births),Deaths=parse_number(Deaths),Migration=parse_number(Migration)) %>% mutate(Year=Year-1) #Data apears to be one off from populaiton
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WY_COUNTY_DATA_SET[,"County"] <- gsub(" ","_",WY_COUNTY_DATA_SET %>% pull(County))
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########################City and County Population Data 2020 to 2024
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PAGE <- read_html('http://eadiv.state.wy.us/pop/Place-24EST.htm')
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NODE <- html_element(PAGE ,"table")
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TBL <- html_table(NODE)
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ST <- which(toupper(TBL$X1)==toupper("Albany County"))
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END <- which(toupper(TBL$X1)==toupper("Balance of Weston County"))
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#More years than are pulled are listed to make more generic
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COLUMNS <- c(1,which(TBL[ST-2,] %in% 1970:2025))
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NAMES <- TBL[4,COLUMNS][-1]
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TBL <- TBL[ST:END,COLUMNS ]
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colnames(TBL) <- c("County",NAMES)
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TBL <- pivot_longer(TBL,all_of(colnames(TBL)[-1]),names_to="Year",values_to="Population") %>% mutate(Year=as.integer(Year),Population=parse_number(Population))
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TBL$County <- gsub(" "," ",gsub("\n","",gsub("\r","",TBL %>% pull(County))))
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COUNTY_POP<- TBL[grep("COUNTY",TBL %>% pull(County),ignore.case=TRUE),]
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COUNTY_POP<- COUNTY_POP[grep("Balance",COUNTY_POP%>% pull(County),invert=TRUE,ignore.case=TRUE),]
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COUNTY_POP$County <- gsub(" ","_",gsub(" County","",COUNTY_POP$County))
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CITY_POP <- TBL[sort(c(grep("County",TBL %>% pull(County),invert=TRUE,ignore.case=TRUE),grep("Balance",TBL %>% pull(County),ignore.case=TRUE))),]
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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))
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CITY_POP <- CITY_POP %>% rename("City"=County)
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########################City Population Data 2010 to 2020
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PAGE <- read_html('http://eadiv.state.wy.us/pop/sub-est11-19.htm')
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NODE <- html_element(PAGE ,"table")
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TBL <- html_table(NODE)
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ST <- which(toupper(TBL$X1)==toupper("Afton town, Wyoming"))
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END <- which(toupper(TBL$X1)==toupper("Yoder town, Wyoming"))
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#More years than are pulled are listed to make more generic
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COLUMNS <- c(1,which(TBL[ST-1,] %in% 1970:2025))
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NAMES <- TBL[3,COLUMNS][-1]
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TBL <- TBL[ST:END,COLUMNS ]
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colnames(TBL) <- c("City",NAMES)
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TBL <- pivot_longer(TBL,all_of(colnames(TBL)[-1]),names_to="Year",values_to="Population") %>% mutate(Year=as.integer(Year),Population=parse_number(Population))
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TBL$City <- gsub(" ","_",gsub(" $","",gsub("\r|\n| Wyoming|,| town| city","",TBL$City,ignore.case=TRUE)))
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TBL <- TBL %>% filter(Year!=2020)
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CITY_POP <- rbind(TBL,CITY_POP)
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########################County Population Data 2010 to 2020
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PAGE <- read_html('http://eadiv.state.wy.us/pop/ctyest11-19.htm')
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NODE <- html_element(PAGE ,"table")
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TBL <- html_table(NODE)
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ST <- grep("Albany",TBL$X1)
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END <- grep("Weston",TBL$X1)
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#More years than are pulled are listed to make more generic
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COLUMNS <- c(1,which(TBL[ST-2,] %in% 1970:2025))
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NAMES <- TBL[3,COLUMNS][-1]
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TBL <- TBL[ST:END,COLUMNS ]
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colnames(TBL) <- c("County",NAMES)
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TBL <- pivot_longer(TBL,all_of(colnames(TBL)[-1]),names_to="Year",values_to="Population") %>% mutate(Year=as.integer(Year),Population=parse_number(Population))
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TBL$County <- gsub(" ","_",gsub(" "," ",gsub(" $","",gsub("\r|\n| Wyoming|,| town| city| County|\\.","",TBL$County,ignore.case=TRUE))))
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TBL <- TBL %>% filter(Year!=2020)
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COUNTY_POP <- rbind(TBL,COUNTY_POP)
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########################County and City Population Data 2000 to 2010
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PAGE <- read_html('http://eadiv.state.wy.us/pop/sub-est01-09.htm')
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NODE <- html_element(PAGE ,"table")
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TBL <- html_table(NODE)
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ST <- which(toupper(TBL$X1)==toupper("Albany County"))
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END <- which(toupper(TBL$X1)==toupper("Balance of Weston County"))
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#More years than are pulled are listed to make more generic
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COLUMNS <- c(1,which(TBL[ST-4,] %in% 1970:2025))
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NAMES <- TBL[4,COLUMNS][-1]
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TBL <- TBL[ST:END,COLUMNS ]
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colnames(TBL) <- c("County",NAMES)
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TBL <- pivot_longer(TBL,all_of(colnames(TBL)[-1]),names_to="Year",values_to="Population") %>% mutate(Year=as.integer(Year),Population=parse_number(Population))
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TBL <- TBL %>% filter(Year!=2010)
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TBL$County <- gsub(" "," ",gsub("\n","",gsub("\r","",TBL %>% pull(County))))
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COUNTY_TBL <- TBL[grep("COUNTY",TBL %>% pull(County),ignore.case=TRUE),]
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COUNTY_TBL <-COUNTY_TBL[grep("Balance",COUNTY_TBL%>% pull(County),invert=TRUE,ignore.case=TRUE),]
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COUNTY_TBL$County <-gsub("_(pt.)","", gsub(" ","_",gsub(" County","",COUNTY_TBL$County)))
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CITY_TBL <- TBL[sort(c(grep("County",TBL %>% pull(County),invert=TRUE,ignore.case=TRUE),grep("Balance",TBL %>% pull(County),ignore.case=TRUE))),]
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CITY_TBL$County <- gsub(" ","_",gsub("Balance of","Unincorporated",gsub(" County","",gsub(" city","",gsub(" town","",CITY_TBL$County,ignore.case=TRUE),ignore.case=TRUE),ignore.case=TRUE),ignore.case=TRUE))
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CITY_TBL <- CITY_TBL %>% rename("City"=County)
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CITY_POP <- rbind(CITY_TBL,CITY_POP)
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#Cleanup names
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CITY_POP$City <- gsub("LaGrange","La_Grange",CITY_POP$City)
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COUNTY_POP <- rbind(COUNTY_TBL,COUNTY_POP)
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####################County and City Population Data for 1990-2000
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if(!file.exists("./Data/Pop_1990s.xls")){download.file('http://eadiv.state.wy.us/pop/c&sc90_00.xls',"./Data/Pop_1990s.xls")}
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TEMP <- read_xls("Data/Pop_1990s.xls",skip=2)[-1:-4,]
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colnames(TEMP)[1] <- "County"
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tail(TEMP)
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TEMP <- TEMP[1:which(TEMP[,1]=="Wind River Res."),]
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TEMP <- pivot_longer(TEMP,all_of(colnames(TEMP)[-1]),names_to="Year",values_to="Population") %>% mutate(Year=as.integer(Year),Population=as.numeric(Population))
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TEMP <- TEMP %>% filter(Year!=2000)
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TEMP_COUNTY <- TEMP[grepl("Cnty",TEMP %>% pull(County),ignore.case=TRUE),]
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TEMP_COUNTY$County <- gsub(" ","_",gsub(" "," ",gsub(" Cnty","",TEMP_COUNTY$County,ignore.case=TRUE)))
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TEMP_CITY <- TEMP[grep("Cnty",TEMP %>% pull(County),ignore.case=TRUE,invert=TRUE),]
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TEMP_CITY$County <- gsub("E_Therm","East_Therm",gsub(" ","_",gsub(" ","",TEMP_CITY %>% pull(County))))
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TEMP_CITY <- TEMP_CITY %>% rename(City=County)
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TEMP_CITY %>% pull(City) %>% unique %>% sort
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CITY_POP <- rbind(TEMP_CITY,CITY_POP)
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CITY_POP %>% pull(City) %>% unique %>% sort
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COUNTY_POP <- rbind(TEMP_COUNTY,COUNTY_POP)
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TEMP_CITY <- TEMP_CITY %>% filter(Year!=2000)
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try(rm(TEMP_CITY,TEMP_COUNTY,TEMP))
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####################County and City Population Data for 1980-1990
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if(!file.exists("./Data/Pop_1980s.xls")){download.file('http://eadiv.state.wy.us/pop/C&SC8090.xls',"./Data/Pop_1980s.xls")}
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TEMP <- read_xls("Data/Pop_1980s.xls",skip=2)[-1:-4,]
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colnames(TEMP)[1] <- "County"
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TEMP <- TEMP[2:which(TEMP[,1]=="Upton"),1:(min(which(is.na(TEMP[2,])))-1)]
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TEMP <- pivot_longer(TEMP,all_of(colnames(TEMP)[-1]),names_to="Year",values_to="Population") %>% mutate(Year=as.integer(Year),Population=as.numeric(Population))
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TEMP_COUNTY <- TEMP[grepl("Cty",TEMP %>% pull(County),ignore.case=TRUE),]
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TEMP_COUNTY$County <- gsub(" ","_",gsub(" "," ",gsub(" Cty","",TEMP_COUNTY$County,ignore.case=TRUE)))
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TEMP_CITY <- TEMP[grep("Cty",TEMP %>% pull(County),ignore.case=TRUE,invert=TRUE),]
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TEMP_CITY$County <-gsub("Frannie_","Frannie", gsub("Mtn._View","Mountain_View",gsub("E._Therm","East_Therm",gsub(" ","_",gsub(" ","",TEMP_CITY %>% pull(County))))))
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TEMP_CITY <- TEMP_CITY %>% rename(City=County)
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TEMP_CITY <- TEMP_CITY %>% filter(Year!=1990)
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TEMP_COUNTY <- TEMP_COUNTY %>% filter(Year!=1990)
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CITY_POP <- rbind(TEMP_CITY,CITY_POP)
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COUNTY_POP <- rbind(TEMP_COUNTY,COUNTY_POP)
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#ggplot(aes(x=Year,y=Population,group=County,color=County),data=COUNTY_POP)+geom_line()
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try(rm(TEMP_CITY,TEMP_COUNTY,TEMP))
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####################County Population Data for 1970-1980
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if(!file.exists("./Data/Pop_1970s.xls")){download.file('http://eadiv.state.wy.us/pop/Cnty7080.xls',"./Data/Pop_1970s.xls")}
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TEMP <- read_xls("Data/Pop_1970s.xls",skip=2)[-1:-4,]
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colnames(TEMP)[1] <- "County"
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TEMP <- TEMP[1:which(TEMP[,1]=="Weston"),]
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TEMP <- pivot_longer(TEMP,all_of(colnames(TEMP)[-1]),names_to="Year",values_to="Population") %>% mutate(Year=as.integer(Year),Population=as.numeric(Population))
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TEMP$County <- gsub(" ","_",TEMP$County)
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TEMP <- TEMP %>% filter(Year!=1980)
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COUNTY_POP <- rbind(TEMP,COUNTY_POP)
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#ggplot(aes(x=Year,y=Population,group=County,color=County),data=COUNTY_POP)+geom_line()
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try(rm(TEMP))
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###########Old data addtion:Period Ends in 1970
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#See in part http://eadiv.state.wy.us/demog_data/cntycity_hist.htm
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LN_OLD <- c(12487,10894,10286,9023,9018,8640) #Missing in 1910
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Year <- seq(1920,1970,by=10)
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TEMP <- cbind(Year,rep("Lincoln",6),LN_OLD)
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colnames(TEMP ) <- c("Year","County","Population")
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TEMP <- as_tibble(TEMP)
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COUNTY_POP <- rbind(TEMP,COUNTY_POP) %>% arrange(County,Year)
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KEM_OLD <- c(843,1517,1884,2026,1667,2028,2292) #1910 forward until 1970
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Year <- seq(1910,1970,by=10)
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TEMP <- cbind(Year,rep("kemmerer",7),KEM_OLD)
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colnames(TEMP ) <- c("Year","City","Population")
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TEMP <- as_tibble(TEMP)
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CITY_POP <- rbind(TEMP,CITY_POP)
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DIAMOND_OLD <- c(696,726,812,586,415,398,485)
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TEMP <- cbind(Year,rep("Diamondvile",7),DIAMOND_OLD)
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colnames(TEMP ) <- c("Year","City","Population")
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TEMP <- as_tibble(TEMP)
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CITY_POP <- rbind(TEMP,CITY_POP) %>% arrange(City,Year)
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#Add Other Data
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COUNTY_POP <- COUNTY_POP %>% mutate(Year=as.integer(Year)) %>% unique
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WY_COUNTY_DATA_SET <- COUNTY_POP %>% left_join(WY_COUNTY_DATA_SET ) %>% mutate(Population=as.numeric(Population)) %>% unique
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###Save Population Results
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write_csv(CITY_POP,file="./Data/Cleaned_Data/Wyoming_City_Population.csv")
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write_csv(WY_COUNTY_DATA_SET ,file="./Data/Cleaned_Data/Wyoming_County_Population.csv")
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saveRDS(CITY_POP,file="./Data/Cleaned_Data/Wyoming_City_Population.Rds")
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saveRDS(WY_COUNTY_DATA_SET ,file="./Data/Cleaned_Data/Wyoming_County_Population.Rds")
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###################Demographics
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if(!file.exists("./Data/Demo_Single_Year_2020s.xls")){download.file('http://eadiv.state.wy.us/Pop/CO_SYASEX24.xlsx',"./Data/Demo_Single_Year_2020s.xls")}
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TEMP <- read_xlsx("./Data/Demo_Single_Year_2020s.xls",skip=2)[,-1]
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TEMP <- TEMP[1:(min(which(is.na(TEMP[,1])))-1),]
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TEMP <- TEMP[!grepl("Base",TEMP$YEAR,ignore.case=TRUE),] #There are two population values provided. I believe one is the census baseline, and one is a estimate in July. Keep the later estimate, to line up with the same seasonal collection pattern of the rest of the data
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TEMP$YEAR <- year(as.Date(substr((TEMP$YEAR),1,8),format="%m/%d/%Y"))
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colnames(TEMP) <- c("County","Year","Age","Number","Num_Male","Num_Female")
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TEMP$County <- gsub(" County","",TEMP$County,ignore.case=TRUE)
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DEM_2020 <- TEMP %>% select(-Number)
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###Demogrpahics all
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DEM_DATA <- read_delim("Data/County_Demographics_Census/wy.1969_2023.singleages.through89.90plus.txt",delim=" ",col_names=c("ID","VALUES"),col_types=list('c','c'))
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DEM_DATA$Year <- as.integer(substr(DEM_DATA$ID,1,4))
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DEM_DATA$fips<- substr(DEM_DATA$ID,7,11)
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COUNTY_LIST <- read_csv("https://github.com/kjhealy/fips-codes/raw/refs/heads/master/county_fips_master.csv",col_types=list('c','c')) %>% filter(state_abbr=="WY") %>% select(fips,County=county_name) %>% mutate(County=gsub(" ","_",gsub(" County","",County,ignore.case=TRUE)))
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DEM_DATA <- DEM_DATA %>% left_join(COUNTY_LIST) %>% select(-fips)
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#16=3
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DEM_DATA$Sex <- ifelse(substr(DEM_DATA$VALUES,3,3)==1,"Male","Female")
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DEM_DATA$Age <- parse_number(substr(DEM_DATA$VALUES,4,5))
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DEM_DATA$Number <- parse_number(substr(DEM_DATA$VALUES,6,14))
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DEM_DATA <- DEM_DATA %>% select(-ID,-VALUES)
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DEM_DATA <- DEM_DATA %>% group_by(Year,County,Sex,Age) %>% summarize(Number=sum(Number)) %>% ungroup()#Aggregate to sex and age level
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#The Wyoming census data seems newer than this data set from SEER cancer data source. Drop any of these records that overlap with the Wyoming data before merging. Arrange so the column order is the same between the two data sets, so they can be easily bound together.
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DEM_DATA <- pivot_wider(DEM_DATA,names_from=Sex,values_from=Number) %>% rename(Num_Female=Female,Num_Male=Male) %>% select(colnames(DEM_2020)) %>% filter(Year<min(DEM_2020$Year)) %>% unique
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DEM_DATA <- rbind(DEM_2020,DEM_DATA) %>% ungroup %>% arrange(Year,Age) %>% unique
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###Save demographic data set
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LIN_DEM <- DEM_DATA %>% filter(County=='Lincoln')
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write_csv(LIN_DEM,file="./Data/Cleaned_Data/Lincoln_Demographic_Data.csv")
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write_csv(DEM_DATA,file="./Data/Cleaned_Data/Wyoming_County_Demographic_Data.csv")
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saveRDS(LIN_DEM,file="./Data/Cleaned_Data/Lincoln_Demographic_Data.Rds")
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saveRDS(DEM_DATA,file="./Data/Cleaned_Data/Wyoming_County_Demographic_Data.Rds")
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#########################################Mortality Rate
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GET_MORTALITY_DATA <- function(FILE,SEX,LOWER_AGE,UPPER_AGE){
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#Create clean mortality rate data
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#Data gathered from https://hdpulse.nimhd.nih.gov/data-portal/mortality/table?cod=247&cod_options=cod_15&ratetype=aa&ratetype_options=ratetype_2&race=00&race_options=race_6&sex=2&sex_options=sex_3&age=177&age_options=age_11&ruralurban=0&ruralurban_options=ruralurban_3&yeargroup=5&yeargroup_options=year5yearmort_1&statefips=56&statefips_options=area_states&county=56000&county_options=counties_wyoming&comparison=counties_to_us&comparison_options=comparison_counties&radio_comparison=areas&radio_comparison_options=cods_or_areas
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NAMES <- c("County","FIPS","Death_Rate","Lower_Rate","Upper_Rate","Deaths","Trend_Category","Trend","Lower_Trend","Upper_Trend")
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DF <- read_csv(FILE,skip=5,col_names=NAMES,col_types=list('c',"i",'d','d','d','d','c','d','d','d')) %>% filter(grepl("County|Wyoming",County)|County=="United States") %>% mutate(Rate_SD=(Upper_Rate-Lower_Rate)/(2*1.96),Trend_SD=(Upper_Trend-Lower_Trend)/(2*1.96)) %>% select(County,Death_Rate,Rate_SD,Trend,Trend_SD)
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DF$County <- gsub(" County","",DF$County,ignore.case=TRUE)
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DF[,-1] <- DF[,-1]/100000
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WYOMING_TREND <- pull(DF[DF$County=="Wyoming",],"Trend")
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US_TREND <- pull(DF[DF$County=="United States",],"Trend")
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WYOMING_RATE <- pull(DF[DF$County=="Wyoming",],"Death_Rate")
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US_RATE <- pull(DF[DF$County=="United States",],"Death_Rate")
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DF$Imparted_Trend <- FALSE
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if(is.na(WYOMING_TREND)){
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DF[1,4:5] <- DF[2,4:5]
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DF[1,6] <- TRUE
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}
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DF$Imparted_Rate <- FALSE
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if(is.na(WYOMING_RATE)){
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DF[1,4:5] <- DF[2,2:3]
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DF[1,6] <- TRUE
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}
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WYOMING_BASELINE_TREND <-cbind (DF[1,4:5] ,TRUE)
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WYOMING_BASELINE_RATE <-DF[1,2:3]
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for(i in 3:nrow(DF)){
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#Impart any missing trends based on higher levels
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if(is.na(pull(DF[i,],"Trend"))){ DF[i,4:6] <- WYOMING_BASELINE_TREND}
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#Impart any missing death rates based on higher levels
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if(is.na(pull(DF[i,],"Death_Rate"))){
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DF[i,2:3] <- WYOMING_BASELINE_RATE
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DF[i,"Imparted_Rate"] <- TRUE
|
|
}
|
|
}
|
|
DF$Sex <- SEX
|
|
DF$Min_Age <- LOWER_AGE
|
|
DF$Max_Age <- UPPER_AGE
|
|
DF <- DF %>% select(County,Sex,Min_Age,Max_Age,Death_Rate,Rate_SD,Imparted_Rate,everything())
|
|
return(DF)
|
|
}
|
|
MORTALITY_DATA_ALL <- rbind(
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Female/A_Under1.csv","Female",0,0),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Female/B_1_9.csv","Female",1,9),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Female/C_10_19.csv","Female",10,19),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Female/D_20_39.csv","Female",20,39),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Female/E_40_64.csv","Female",40,64),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Female/G_65_74.csv","Female",65,74),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Female/H_75_84.csv","Female",75,84),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Female/I_85+.csv","Female",85,Inf),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Male/A_Under1.csv","Male",0,0),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Male/B_1_9.csv","Male",1,9),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Male/C_10_19.csv","Male",10,19),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Male/D_20_39.csv","Male",20,39),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Male/E_40_64.csv","Male",40,64),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Male/G_65_74.csv","Male",65,74),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Male/H_75_84.csv","Male",75,84),
|
|
GET_MORTALITY_DATA("Data/Mortality_Rates/Male/I_85+.csv","Male",85,Inf)
|
|
|
|
)
|
|
LIN_MORTALITY <- MORTALITY_DATA_ALL %>% filter(County=="Lincoln")
|
|
###Save Mortality Rate data set
|
|
write_csv(LIN_MORTALITY,file="./Data/Cleaned_Data/Lincoln_Mortality_Rate.csv")
|
|
write_csv(MORTALITY_DATA_ALL,file="./Data/Cleaned_Data/Not_Used/Wyoming_County_Mortality_Rate.csv")
|
|
saveRDS(LIN_MORTALITY,file="./Data/Cleaned_Data/Lincoln_Mortality_Rate.Rds")
|
|
saveRDS(MORTALITY_DATA_ALL,file="./Data/Cleaned_Data/Not_Used/Wyoming_County_Mortality_Rate.Rds")
|
|
|
|
#Clean all data from memory. Will be loaded in any paticular script as needed
|
|
rm(list = ls())
|
|
gc()
|
|
|