344 lines
19 KiB
R
344 lines
19 KiB
R
#############################Clean up script folders, simulations of deaths should be separated from this file. Death rate simulation is complicated and should be commented, and turned into a separate script.
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library(rvest)
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library(tidyverse)
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library(readxl)
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library(curl) #To archive the html files
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#setwd("../")
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###Create Location to Save raw data sets
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if(!exists("SAVE_LOC_RAW")){SAVE_LOC_RAW <-"./Data/Raw_Data/"}
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dir.create(SAVE_LOC_RAW, recursive = TRUE, showWarnings = FALSE)
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SAVE_LOC_RAW_POP <- paste0(SAVE_LOC_RAW,"Population/")
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dir.create(SAVE_LOC_RAW_POP , recursive = TRUE, showWarnings = FALSE)
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RAW_HTML_LOC <- paste0(SAVE_LOC_RAW_POP,"HTML_Population_Files/")
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dir.create(RAW_HTML_LOC, recursive = TRUE, showWarnings = FALSE)
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RAW_EXCEL_LOC <- paste0(SAVE_LOC_RAW_POP,"Excel_Population_Files/")
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dir.create(RAW_EXCEL_LOC, recursive = TRUE, showWarnings = FALSE)
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##Start a log file about the data
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sink(file=paste0(SAVE_LOC_RAW_POP,"README_POPULATION_DATA.txt"),append=FALSE)
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cat("Most data is supplied by the Wyoming Department of Administration & Information Economic Analysis Division (WIEAD). Their data is either directly pulled from other sources like the census or is interpolated using this data (such as deaths, and migration).\n This folder saves the raw HTML of a few population data source, which were stored as a web table rather than a CSV or excel file so that in the future if the web pages are removed or changed the code will still run, and can be updated with new information.\nSome files are supplied as HTML tables from a webpage, while others are excel files. ")
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sink()
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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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#Website States: Wyoming Economic Analysis Division based on U.S. Census Bureau's population estimation and vital stats above
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BIRTH_DEATH_MIGRATION_HTML_LOC <- paste0(RAW_HTML_LOC,"County_Migration_Deaths_Births.html")
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try(curl_download("http://eadiv.state.wy.us/pop/BirthDeathMig.htm",destfile=BIRTH_DEATH_MIGRATION_HTML_LOC )) #Download the file rather than directly use the website, so a backup is available if it ever goes offline.
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#Add to the notes
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sink(file=paste0(SAVE_LOC_RAW_POP,"/README_POPULATION_DATA.txt"),append=TRUE)
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cat("\n\n 1) Wyoming births, deaths, and net migration by county of residence: 1971 - 2023:
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County_Migration_Deaths_Births.htm comes from http://eadiv.state.wy.us/pop/BirthDeathMig.htm
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Data Type: HTML table
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Data Source: WIEAD
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Original Source: Wyoming Department of Health and U.S. Census Bureau")
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sink()
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PAGE <- read_html(BIRTH_DEATH_MIGRATION_HTML_LOC )
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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 appears to be one off from population
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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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CITY_POPULATION_A <- paste0(RAW_HTML_LOC,"Wyoming_City_and_County_Population_2020_2024.html")
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try(curl_download("http://eadiv.state.wy.us/pop/Place-24EST.htm",destfile=CITY_POPULATION_A)) #Download the file rather than directly use the website, so a backup is available if it ever goes offline.
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#Add to the notes
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sink(file=paste0(SAVE_LOC_RAW_POP,"README_POPULATION_DATA.txt"),append=TRUE)
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cat("\n 2) Wyoming Incorporated Place Population Estimates: April 1, 2020 to July 1, 2024
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Wyoming_City_and_County_Population_2020_2024.html comes from http://eadiv.state.wy.us/pop/Place-24EST.htm
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Data Type: HTML table
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Data Source: WIEAD
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Original Source: U.S. Census Bureau, Population Division Wyoming Department of Health and U.S. Census Bureau")
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sink()
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PAGE <- read_html(CITY_POPULATION_A)
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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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CITY_POPULATION_B <- paste0(RAW_HTML_LOC,"Wyoming_City_and_County_Population_2010_2020.html")
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try(curl_download('http://eadiv.state.wy.us/pop/sub-est11-19.htm',destfile=CITY_POPULATION_B)) #Download the file rather than directly use the website, so a backup is available if it ever goes offline.
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#Add to the notes
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sink(file=paste0(SAVE_LOC_RAW_POP,"README_POPULATION_DATA.txt"),append=TRUE)
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cat("\n 3) Intercensal Estimates of the Resident Population for Incorporated Places in Wyoming: April 1, 2010 to April 1, 2020
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Data Type: HTML table
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Wyoming_City_and_County_Population_2010_2020.html comes from http://eadiv.state.wy.us/pop/sub-est11-19.htm
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Data Source: WIEAD
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Original Source: Source: U.S. Census Bureau, Population Division" )
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sink()
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PAGE <- read_html(CITY_POPULATION_B )
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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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COUNTY_POPULATION_B <- paste0(RAW_HTML_LOC,"Wyoming_County_Population_2010_2020.html")
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try(curl_download('http://eadiv.state.wy.us/pop/ctyest11-19.htm',destfile=COUNTY_POPULATION_B)) #Download the file rather than directly use the website, so a backup is available if it ever goes offline.
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#Add to the notes
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sink(file=paste0(SAVE_LOC_RAW_POP,"README_POPULATION_DATA.txt"),append=TRUE)
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cat("\n 4) Intercensal Estimates of the Resident Population for Counties in Wyoming: April 1, 2010 to April 1, 2020
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Wyoming_County_Population_2010_2020.html comes from http://eadiv.state.wy.us/pop/ctyest11-19.htm
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Data Type: HTML table
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Data Source: WIEAD
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Original Source: U.S. Census Bureau, Population Division" )
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sink()
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PAGE <- read_html(COUNTY_POPULATION_B )
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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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CITY_POPULATION_C <- paste0(RAW_HTML_LOC,"Wyoming_City_and_County_Population_2000_2009.html")
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try(curl_download('http://eadiv.state.wy.us/pop/sub-est01-09.htm',destfile=CITY_POPULATION_C)) #Download the file rather than directly use the website, so a backup is available if it ever goes offline.
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#Add to the notes
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sink(file=paste0(SAVE_LOC_RAW_POP,"README_POPULATION_DATA.txt"),append=TRUE)
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cat("\n 5) Intercensal Estimates of the Resident Population for Cities and Towns of Wyoming: April 1, 2000 to July 1, 2010
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Wyoming_City_and_County_Population_2000_2009.html comes from 'http://eadiv.state.wy.us/pop/sub-est01-09.htm'
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Data Type: HTML table
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Data Source: WIEAD
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Original Source: U.S. Census Bureau, Population Division" )
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sink()
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PAGE <- read_html(CITY_POPULATION_C)
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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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#Location to save any raw population files. Most files are not saved since they are pulled from a html and not a excel file, but older files are only available as excel files
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POP_FILE_1990 <- paste0(RAW_EXCEL_LOC,"Wyoming_County_Population_1990_2000.xls")
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try(if(!file.exists(POP_FILE_1990)){download.file('http://eadiv.state.wy.us/pop/c&sc90_00.xls',POP_FILE_1990)})
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#Add to the notes
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sink(file=paste0(SAVE_LOC_RAW_POP,"README_POPULATION_DATA.txt"),append=TRUE)
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cat("\n 6) Population for Wyoming, Counties, Cities, and Towns: 1990 to 2000
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Wyoming_County_Population_1980_1990.xls comes from 'http://eadiv.state.wy.us/pop/c&sc90_00.xls'
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Data Type: Excel file
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Data Source: WIEAD
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Original Source: U.S. Census Bureau, Population Division" )
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sink()
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TEMP <- read_xls(POP_FILE_1990,skip=2)[-1:-4,]
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colnames(TEMP)[1] <- "County"
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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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POP_FILE_1980 <- paste0(RAW_EXCEL_LOC,"/Wyoming_County_Population_1980_1990.xls")
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try(if(!file.exists(POP_FILE_1980)){download.file('http://eadiv.state.wy.us/pop/C&SC8090.xls',POP_FILE_1980)})
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#Add to the notes
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sink(file=paste0(SAVE_LOC_RAW_POP,"README_POPULATION_DATA.txt"),append=TRUE)
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cat("\n 7) Population for Wyoming, Counties and Municipalities: 1980 to 1990
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Wyoming_County_Population_1980_1990.xls comes from 'http://eadiv.state.wy.us/pop/C&SC8090.xls'
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Data Type: Excel file
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Data Source: WIEAD
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Original Source: U.S. Census Bureau, Population Division" )
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sink()
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TEMP <- read_xls(POP_FILE_1980,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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POP_FILE_1970 <- paste0(RAW_EXCEL_LOC,"/Wyoming_County_Population_1970_1980.xls")
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try(if(!file.exists(POP_FILE_1970)){download.file('http://eadiv.state.wy.us/pop/Cnty7080.xls',POP_FILE_1970)})
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#Add to the notes
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sink(file=paste0(SAVE_LOC_RAW_POP,"README_POPULATION_DATA.txt"),append=TRUE)
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cat("\n 8) Wyoming and County Population: 1970 to 1980
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Wyoming_County_Population_1970_1980.xls comes from 'http://eadiv.state.wy.us/pop/Cnty7080.xls'
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Data Type: Excel file
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Data Source: WIEAD
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Original Source: U.S. Census Bureau, Population Division" )
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sink()
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if(!file.exists(POP_FILE_1970)){download.file('http://eadiv.state.wy.us/pop/Cnty7080.xls',POP_FILE_1970)}
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TEMP <- read_xls(POP_FILE_1970,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 addition:Period Ends in 1970
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POP_FILE_OLD <- paste0(RAW_HTML_LOC,"Wyoming_City_and_County_Population_Prior_to_1970.htm")
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try(curl_download('http://eadiv.state.wy.us/demog_data/cntycity_hist.htm',destfile=POP_FILE_OLD))
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#Add to the notes
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sink(file=paste0(SAVE_LOC_RAW_POP,"README_POPULATION_DATA.txt"),append=TRUE)
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cat("\n 9) Historical decennial census population for Wyoming counties, cities, and towns
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Wyoming_City_and_County_Population_Prior_to_1970.htm comes from 'http://eadiv.state.wy.us/demog_data/cntycity_hist.htm'
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Data Type: HTML Tables
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Data Source: WIEAD
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Original Source: U.S. Census Bureau, Population Division
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Note: Two tables are included complicating extraction. The values are manually entered in R rather than scarped like the other data sets" )
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sink()
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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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#Remove empty values, ensure all numeric values are not saved as characters
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CITY_POP <- CITY_POP %>% filter(!is.na(Population) ) %>% mutate(Population=parse_number(Population),Year=parse_number(Year))
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#Add Other Data
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COUNTY_POP <- COUNTY_POP %>% mutate(Year=as.numeric(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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if(!exists("SAVE_LOC_POP")){SAVE_LOC_POP <-"./Data/Cleaned_Data/Population_Data"}
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CSV_SAVE <- paste0(SAVE_LOC_POP,"/CSV")
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RDS_SAVE <- paste0(SAVE_LOC_POP,"/RDS")
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dir.create(CSV_SAVE, recursive = TRUE, showWarnings = FALSE)
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dir.create(RDS_SAVE, recursive = TRUE, showWarnings = FALSE)
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saveRDS(CITY_POP,paste0(RDS_SAVE,"/All_Wyoming_City_Populations.Rds" ))
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write_csv(CITY_POP,paste0(CSV_SAVE,"/All_Wyoming_City_Populations.csv" ))
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saveRDS(WY_COUNTY_DATA_SET,paste0(RDS_SAVE,"/All_Wyoming_County_Populations.Rds" ))
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write_csv(WY_COUNTY_DATA_SET,paste0(CSV_SAVE,"/All_Wyoming_County_Populations.csv" ))
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run_datetime <- format(Sys.time(), "%Y-%m-%d %H:%M:%S")
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sink(file=paste0(SAVE_LOC_RAW_POP,"README_POPULATION_DATA.txt"),append=TRUE)
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cat(paste0("\n--- Run Date: ", run_datetime, " ---\n"))
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sink()
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