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######################################DATA WORKING OFF OF DATA SCRIPT!!!!!!!!!!!!!!!!!!! Start there
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#Future things to consider
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###Naughton 1 & 2 natural gas conversion
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###Add some variance in employment at TerraPower (odds of more or less)
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@ -26,7 +28,7 @@ plot(ARMA_POP,main="Lincoln County Population Forecast",xlab="Year",ylab="Popula
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DIAMOND <- c(1000,1070,1114,1101,1082,1078,1063,991,916,876,864,863,847,835,835,827,808,774,748,732,705,695,690,689,695,700,710,723,738,745,731,704,677,667,652,629,613,586,559,540,523,526,527,521,517)
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AREA_POP <- KEM+DIAMOND
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LN <- c(12177,13254,14031,14110,14111,14319,14384,13658,12875,12552,12625,12975,13124,13329,13759,14073,14206,14099,14114,14338,14621,14697,14858,15117,15539,15917,16429,17013,17629,18082,18083,17946,17822,18148,18346,18473,18766,18899,19042,19379,19658,20174,20690,20909,21000)
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NO_CITY <- c(10095,10392,10747,10944,11043)
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# NO_CITY <- c(10095,10392,10747,10944,11043)
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YEAR <- 1980:2024
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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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@ -40,7 +42,7 @@ plot(ARMA_POP,main="Lincoln County Population Forecast",xlab="Year",ylab="Popula
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A <- cbind(YEAR2,LN2) %>% as_tibble %>% rename(Population=LN2) %>% mutate(Region='Lincoln County')
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B <- cbind(YEAR2,AREA2) %>% as_tibble %>% rename(Population=AREA2) %>% mutate(Region='Kemmerer & Diamondvile')
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DATA <- rbind(A,B) %>% rename(Year=YEAR2)
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ggplot(aes(x=Year,y=Population,group=Region,color=Region),data=DATA2) +geom_line(linewidth=1.5)
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ggplot(aes(x=Year,y=Population,group=Region,color=Region),data=DATA) +geom_line(linewidth=1.5)+scale_x_continuous()+geom_vline(xintercept= 2022, linetype = "dashed", color = "red",size = 1)
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###Kemmerer ARMA
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KEM_TS <- DATA %>% filter(Year>=1980,Region=='Kemmerer & Diamondvile') %>% pull(Population) %>% ts(start=c(1980),end=c(2024),frequency=1)
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BC <- BoxCox.lambda(KEM_TS)
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130
Scripts/Data_Load.r
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130
Scripts/Data_Load.r
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library(rvest)
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library(tidyverse)
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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$County)
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WY_COUNTY_DATA_SET <- pivot_wider(Data,names_from=Type,values_from=Pop_Change) %>% rename("Migration"=`Net Migration`) %>% mutate(Year=as.integer(Year),Births=parse_number(Births),Deaths=parse_number(Deaths),Migration=parse_number(Migration))
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########################City and County Population Data 2020 to 2024
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PAGE <- read_html('http://eadiv.state.wy.us/pop/Place-24EST.htm')
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NODE <- html_element(PAGE ,"table")
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TBL <- html_table(NODE)
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ST <- which(toupper(TBL$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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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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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$County <- gsub(" "," ",gsub("\n","",gsub("\r","",TBL %>% pull(County))))
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COUNTY_TBL <- TBL[grep("COUNTY",TBL %>% pull(County),ignore.case=TRUE),]
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COUNTY_TBL <-COUNTY_TBL[grep("Balance",COUNTY_TBL%>% pull(County),invert=TRUE,ignore.case=TRUE),]
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COUNTY_TBL$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$City)
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COUNTY_POP <- rbind(COUNTY_TBL,COUNTY_POP)
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####################County and City Population DAta for 1990-2000
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PAGE <- read_html('http://eadiv.state.wy.us/pop/c&sc90_00.htm')
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NODE <- html_element(PAGE ,"body")
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TEST <- read_table(html_text2(NODE),skip=21,skip_empty_rows=TRUE)
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TO_ADJ <- sum((!is.na(TEST[2,])))-11
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?read_table
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?html_text2
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?html_text
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TBL <- html_table(NODE)
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TBL
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ST <- which(toupper(TBL$X1)==toupper("Albany Cnty"))
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END <- which(toupper(TBL$X1)==toupper("Balance of Weston County"))
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