Remove old data

old/BVAR_Pop.r

@@ -1,74 +0,0 @@
-library(BVAR)
-library(tidyverse)
-
-source("Scripts/Functions.r")
-#source("Scripts/Load_Wyoming_Web_Data.r")
-DATA_TO_GATHER <- list()
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c("WYPOP","WY_POP",TRUE)
-
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c("WYNQGSP","WY_GDP",TRUE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c("MEHOINUSWYA646N","WY_MED_INCOME",TRUE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c("BUSAPPWNSAWY","WY_BUISNESS_APPLICATIONS",FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c('ACTLISCOUWY','WY_HOUSES_FOR_SALE',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c('WYRVAC','WY_RENTAL_VACANCY_RATE',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c('WYBPPRIVSA','WY_PRIVATE_HOUSING',FALSE)
-#New Private Housing Units Authorized by Building Permits for Wyoming
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('B03002006E056023','LN_FIVE_YEAR_POP',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('GDPALL56023','LN_GDP',TRUE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYLINC3POP','LN_POP',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('LAUCN560230000000005','LN_EMPLOYMENT',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('BPPRIV056023','LN_PRIVE_HOUSING',FALSE)
-
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('ENU5602320510','LN_NUM_ESTABLISHMENTS',FALSE)
-
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('GDPALL56041','UINTA_GDP',TRUE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYUINT1POP','UINTA_POP',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYSUBL5POP','SUBLETTE_POP',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYSWEE7POP','SWEETWATER_POP',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYTETO9POP','TETON_POP',FALSE)
-##Idaho Counties
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('IDBEAR7POP','BEAR_LAKE_POP',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('IDCARI9POP','CARIBOU_POP',FALSE)
-
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('IDBONN0POP','BONNEVILLE_POP',FALSE)
-###US Population
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('POPTOTUSA647NWDB','US_POP',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('CE16OV','US_EMP',FALSE)
-
-
-
-
-for(x in 1:length(DATA_TO_GATHER)){
-  CURRENT <- DATA_TO_GATHER[[x]]
-  
-  if(CURRENT[3]){C_DATA <- CPI_ADJUST(FRED_GET(CURRENT[1],CURRENT[2]))}else{C_DATA <- FRED_GET(CURRENT[1],CURRENT[2])}
-  if(x==1){RES <- C_DATA}else{RES <- RES %>% full_join(C_DATA)}
-  rm(CURRENT,C_DATA)
-}
-
-DATA <- RES %>% mutate(US_POP=US_POP-WY_POP,WY_POP=WY_POP-LN_POP-UINTA_POP-SUBLETTE_POP-SWEETWATER_POP-TETON_POP)
-colnames(DATA)
-TS_DATA_ORIG <- DATA %>% select(YEAR,LN_POP,LN_EMPLOYMENT,US_EMP,US_POP,WY_POP,UINTA_POP,SUBLETTE_POP,SWEETWATER_POP,TETON_POP,BEAR_LAKE_POP,CARIBOU_POP,BONNEVILLE_POP) %>%
-  filter(!is.na(LN_POP),!is.na(LN_EMPLOYMENT)) %>%
-  arrange(YEAR) %>% select(-YEAR)
-TS_DATA <- log(ts(TS_DATA_ORIG,start=c(1970),end=c(2024),frequency=1))
-
-MOD <- bvar(TS_DATA,lags=2, n_draw=15000)
-opt_irf <- bv_irf(horizon = 25, identification = TRUE)
-
-plot(irf(MOD,opt_irf,conf_bands = c(0.05, 0.1,0.15)),area=TRUE,vars_impulse = c("LN_EMP"),vars_response = c("WY_POP","LN_POP","UINTA_POP","SUBLETTE_POP","SWEETWATER_POP"))
-plot(irf(MOD,opt_irf,conf_bands = c(0.05, 0.1,0.15)),area=TRUE,vars_impulse = c("LN_EMP"),vars_response = c("TETON_POP","BEAR_LAKE_POP","CARIBOU_POP","BONNEVILLE_POP"))
-
-plot(irf(MOD,opt_irf,conf_bands = c(0.05, 0.1,0.15)),area=TRUE,vars_impulse = c("LN_POP"),vars_response = c("WY_POP","LN_POP","UINTA_POP","SUBLETTE_POP","SWEETWATER_POP"))
-plot(irf(MOD,opt_irf,conf_bands = c(0.05, 0.1,0.15)),area=TRUE,vars_impulse = c("LN_POP"),vars_response = c("TETON_POP","BEAR_LAKE_POP","CARIBOU_POP","BONNEVILLE_POP"))
-
-DATA2 <- RES %>% mutate(US_POP=US_POP-WY_POP,WY_POP=WY_POP-LN_POP-UINTA_POP-SUBLETTE_POP-SWEETWATER_POP-TETON_POP)
-TS_DATA2 <- DATA2 %>% select(YEAR,LN_POP,US_POP,WY_POP,UINTA_POP,SUBLETTE_POP,SWEETWATER_POP,TETON_POP,BEAR_LAKE_POP,CARIBOU_POP,BONNEVILLE_POP) %>%
-  dplyr::filter(!is.na(LN_POP)) %>%
-  arrange(YEAR) %>% select(-YEAR) %>% ts %>% log
-MOD2 <- bvar(TS_DATA2,lags=5, n_draw=15000)
-plot(irf(MOD2,opt_irf,conf_bands = c(0.05, 0.1,0.15)),area=TRUE,vars_response = c("LN_POP"))
-?plot.bvar_irf
-plot(predict(MOD,horizon=25,conf_bands = c(0.05, 0.1,0.15)),area=TRUE,vars=c("LN_POP"))
-exp(3.5)-exp(3)
-acf(resid(MOD))

old/Bayes_Pop.r

@@ -1,35 +0,0 @@
-library(tidyverse)
-library(bayesPop)
-library(bayesMig)
-
-
-DAT <- read_tsv(us.mig.file)
-DAT[,2] %>% t
-sum(DAT$`2001`)
-
-# Toy simulation for US states
-dir.create("Output")
-sim.dir <- "./Output"
-us.mig.file <- file.path(find.package("bayesMig"), "extdata", "USmigrates.txt")
-m <- run.mig.mcmc(nr.chains = 2, iter = 100000, thin = 1, my.mig.file = us.mig.file, 
-                   output.dir = sim.dir, present.year = 2017, annual = TRUE)
-pred <- mig.predict(sim.dir = sim.dir, burnin = 5, end.year = 2050)
-mig.trajectories.plot(pred, "Wyoming", pi = 80, ylim = c(-0.03, 0.03)) 
-
-load("./Output/bayesMig.mcmc.meta.rda")
-#############
-#subnat example
-data.dir <- "./extdata"
-dir.create("Pop_Output")
-sim.dir <- "./Pop_Output"
-dir.create("Output")
-example(pop.predict.subnat)
-read_tsv(file.path(data.dir, "CANlocations.txt"))
-pred <- pop.predict.subnat(output.dir = sim.dir,  locations = file.path(data.dir, "CANlocations.txt"),
-                   inputs = list(popM = file.path(data.dir, "CANpopM.txt"),popF = file.path(data.dir, "CANpopF.txt")), 
-                   verbose = TRUE)
-pop.pyramid(pred, "Ontario")
-?pop.pyramid
-summary(pred)
-write.pop.trajectories(pred)
-load("Pop_Output/predictions/prediction.rda")

old/Popultaion.R

@@ -1,106 +0,0 @@
-library(tidyverse)
-library(fixest)
-source("Scripts/Functions.r")
-#source("Scripts/Load_Wyoming_Web_Data.r")
-DATA_TO_GATHER <- list()
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c("WYPOP","WY_POP",TRUE)
-
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c("WYNQGSP","WY_GDP",TRUE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c("MEHOINUSWYA646N","WY_MED_INCOME",TRUE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c("BUSAPPWNSAWY","WY_BUISNESS_APPLICATIONS",FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c('ACTLISCOUWY','WY_HOUSES_FOR_SALE',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c('WYRVAC','WY_RENTAL_VACANCY_RATE',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <- c('WYBPPRIVSA','WY_PRIVATE_HOUSING',FALSE)
-#New Private Housing Units Authorized by Building Permits for Wyoming
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('B03002006E056023','LN_FIVE_YEAR_POP',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('GDPALL56023','LN_GDP',TRUE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYLINC3POP','LN_POP',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('LAUCN560230000000005','LN_EMPLOYMENT',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('BPPRIV056023','LN_PRIVE_HOUSING',FALSE)
-
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('ENU5602320510','LN_NUM_ESTABLISHMENTS',FALSE)
-
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('GDPALL56041','UINTA_GDP',TRUE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYUINT1POP','UINTA_POP',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYSUBL5POP','SUBLETTE_POP',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYSWEE7POP','SWEETWATER_POP',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('WYTETO9POP','TETON_POP',FALSE)
-##Idaho Counties
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('IDBEAR7POP','BEAR_LAKE_POP',FALSE)
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('IDCARI9POP','CARIBOU_POP',FALSE)
-
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('IDBONN0POP','BONNEVILLE_POP',FALSE)
-###US Population
-DATA_TO_GATHER[[length(DATA_TO_GATHER)+1]] <-  c('POPTOTUSA647NWDB','US_POP',FALSE)
-
-
-
-
-for(x in 1:length(DATA_TO_GATHER)){
-	CURRENT <- DATA_TO_GATHER[[x]]
-
-	if(CURRENT[3]){C_DATA <- CPI_ADJUST(FRED_GET(CURRENT[1],CURRENT[2]))}else{C_DATA <- FRED_GET(CURRENT[1],CURRENT[2])}
-	if(x==1){RES <- C_DATA}else{RES <- RES %>% full_join(C_DATA)}
-	rm(CURRENT,C_DATA)
-}
-DATA <- RES %>% mutate(US_POP=US_POP-WY_POP,WY_POP=WY_POP-LN_POP-UINTA_POP-SUBLETTE_POP-SWEETWATER_POP-TETON_POP)
-colnames(DATA)
-feols(log(LN_POP) ~log(US_POP)+log(UINTA_POP)+log(WY_POP)+log(SUBLETTE_POP)+log(SWEETWATER_POP)+log(TETON_POP)+log(BEAR_LAKE_POP)+log(CARIBOU_POP)+log(BONNEVILLE_POP)+YEAR,data=DATA)
-TS_DATA_ORIG <- DATA %>% select(YEAR,LN_POP,US_POP,WY_POP,UINTA_POP,SUBLETTE_POP,SWEETWATER_POP,TETON_POP,BEAR_LAKE_POP,CARIBOU_POP,BONNEVILLE_POP) %>% filter(!is.na(LN_POP)) %>% arrange(YEAR) %>% select(-YEAR)
-
-TS_DATA <- diff(log(ts(TS_DATA_ORIG,start=c(1970),end=c(2024),frequency=1)))
-library("forecast")
-library("vars")
-VARselect(TS_DATA,lag.max=4,type="const")
-VAR1 <- VAR(TS_DATA,p=3,type="const",season=NULL,exog=NULL)
-plot(irf(VAR1,response="LN_POP"))
-plot(forecast(VAR1,))
-RES <- (predict(VAR1, n.ahead = 20, ci = 0.95))
-names(RES )
-names(RES$fcst)
-RES$fcst$LN_POP %>% as_tibble
-CURRENT_POP <- max(DATA$LN_POP,na.rm=TRUE)
-0.0157*CURRENT_POP*1000
-0.083*CURRENT_POP*1000
--0.0489*CURRENT_POP*1000
-
-
-
-
-# View the forecasted values and confidence intervals
-print(forecast_results)
-
-# You can also plot the forecasts
-plot(forecast_results)
-
-
-install.packages("sparsegl")
-plot(VAR1)
-
-
-
-#Check a VAR it looks like lags on changes to Private industry could affect other variables
-		#Idea check a SVAR placing limits on which shocks are first
-feols((LINC_POP)~(WY_POP)+log(LINC_PRIV_IND)+Year,data=DF)
-feols(log(1000*LINC_POP)~log(LINC_GDP)+log(LINC_PRIV_IND)+log(LINC_LABOR_FORCE)+log(LINC_PRIV_IND)+Year,DF)
-RES
-ggplot(data=RES) +geom_point(aes(x=YEAR,y=WY_POP),color="red")+geom_point(aes(x=YEAR,y=30*LN_POP),color="blue")+geom_point(aes(x=YEAR,y=LN_GDP/1700),color="black")+geom_point(aes(x=YEAR,y=LN_LABOR_FORCE/20),color="orange")
-itial information and data that will be required from Kemmerer-Diamondville Water &
-Wastewater Joint Powers Board includes names of key stakeholders that can be interviewed
-regarding future developments, new businesses, and business closures.
-version
-install.packages("pbkrtest")
-install.packages("bayesPop")
-update.packages(ask=FALSE )
-library("bayesMig")
-help("bayesPop")
-library(bayesMig)
-?mig.predict
-example(mig.predict)
-mig.predict(51)
-library("bayesPop")
-example(bayesMig)
-?bayesMig
-
-
-