100 lines
7.3 KiB
R
100 lines
7.3 KiB
R
library(tidyverse)
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library(gt) #For nice color coded capacity limits table.
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if(!exists("SAVE_RES_LOC")){SAVE_RES_LOC <- "./Results/Primary_Simulation_Results/Lower_Bound_Results/"}
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dir.create(SAVE_RES_LOC, recursive = TRUE, showWarnings = FALSE)
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###Process the simulations and save the main percentile results by year
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RES <- read_csv("Results/Simulations/Kemmerer_2024_Simulation_Historic_Migration_Rate.csv")
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RES[,"Year"] <- RES[,"Year"]
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RES<- RES %>% filter(!is.na(Year))
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RES <- RES %>% filter(!is.na(Population))
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HIST <- readRDS("Data/Cleaned_Data/Population_Data/RDS/Kemmerer_Diamondville_Population_Data.Rds") %>% filter(County=='Lincoln') %>% mutate(Percentile="Actual Population") %>% filter(Year>=1940)
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######Population
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####Fan New
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GET_DATA <- function(RES,COL_NUM){
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YEARS <- min(RES$Year,na.rm=TRUE):max(RES$Year,na.rm=TRUE)
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FAN_DATA <- do.call(rbind,lapply(YEARS,function(x){quantile(as.numeric(t((RES %>% filter(Year==x))[,COL_NUM])),seq(0.01,0.99,by=0.01))})) %>% as_tibble %>% mutate(Year=YEARS)
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FAN_DATA <- rbind(FAN_DATA[1,],FAN_DATA)
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START_VALUE <- (HIST %>% filter(Year==2024))[,COL_NUM+1] %>% as.numeric
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FAN_DATA <- FAN_DATA %>% pivot_longer(colnames(FAN_DATA %>% select(-Year)),names_to="Percentile") %>% filter(Year>2024) %>% unique
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NUM_YEARS <- length(unique(FAN_DATA$Year) )
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FAN_DATA$Group <- rep(c(1:49,0,rev(1:49)),NUM_YEARS)
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FAN_DATA <- FAN_DATA %>% group_by(Year,Group) %>% summarize(MIN=min(value),MAX=max(value))
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TEMP <- FAN_DATA %>% filter(Year==2025) %>% mutate(Year=2024) %>% ungroup
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TEMP[,3:4] <- START_VALUE
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FAN_DATA <- rbind(TEMP,FAN_DATA %>% ungroup) %>% as_tibble
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return(FAN_DATA)
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}
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RES %>% pull(Sim_UUID) %>% unique %>% length()
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MAKE_GRAPH <- function(GRAPH_DATA,ALPHA=0.03,COLOR='firebrick2',LINE_WIDTH=0.75){
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PLOT <- ggplot(data=GRAPH_DATA)
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for(i in 1:49){
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C_DATA <- GRAPH_DATA%>% filter(Group==i)
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PLOT <- PLOT +geom_ribbon(data=C_DATA,aes(x=Year,ymin=MIN,ymax=MAX),alpha=ALPHA,fill=COLOR)
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}
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CI_90 <- rbind(GRAPH_DATA%>% filter(Group==20) %>% mutate(Interval='80%'),GRAPH_DATA%>% filter(Group==5) %>% mutate(Interval='95%'),GRAPH_DATA%>% filter(Group==0) %>% mutate(Interval='Median Prediction'))
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PLOT <- PLOT+geom_line(aes(x=Year,y=MIN,linetype=Interval,color=Interval),linewidth=LINE_WIDTH, data=CI_90)+geom_line(aes(x=Year,y=MAX,group=Interval,linetype=Interval,color=Interval),linewidth=LINE_WIDTH ,data=CI_90)+scale_color_manual(values=c("grey50","grey80","black"))+scale_linetype_manual(values = c("solid","solid","dotdash"))
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return(PLOT)
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}
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POP_DATA <- GET_DATA(RES,3)
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POP_PLOT <- MAKE_GRAPH(POP_DATA)
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POP_PLOT <- POP_PLOT+geom_line(data=HIST,aes(x=Year,y=Population),color='black',linewidth=0.75)+ scale_x_continuous(breaks = c(seq(1940, 2060, by = 10),2065))+ scale_y_continuous(breaks = seq(0, 35000, by = 500))+ggtitle("Kemmerer & Diamondville, Population Forecast")+ expand_limits( y = 0)+labs(color = "Prediction Interval",linetype="Prediction Interval",y="Population")+ theme_bw()+ theme(legend.position = "top",panel.grid.minor = element_blank())
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png(paste0(SAVE_RES_LOC,"Population_Fan_Chart.png"), width = 12, height = 8, units = "in", res = 600)
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POP_PLOT
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dev.off()
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BIRTH_DATA <- GET_DATA(RES,4)
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BIRTH_PLOT <- MAKE_GRAPH(BIRTH_DATA)
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BIRTH_PLOT <- BIRTH_PLOT+geom_line(data=HIST,aes(x=Year,y=Births),color='black',linewidth=0.75)+ scale_x_continuous(breaks = c(seq(2010, 2060, by = 5),2065),limits=c(2009,2065))+ scale_y_continuous(breaks = seq(0, 35000, by = 10))+ggtitle("Kemmerer & Diamondville, Birth Forecast")+ expand_limits( y = 0)+labs(color = "Prediction Interval",linetype="Prediction Interval",y="Births")+ theme_bw()+ theme(legend.position = "top",panel.grid.minor = element_blank())
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png(paste0(SAVE_RES_LOC,"Birth_Fan_Chart.png"), width = 12, height = 8, units = "in", res = 600)
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BIRTH_PLOT
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dev.off()
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DEATH_DATA <- GET_DATA(RES,5) %>% filter(!is.na(MIN))
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DEATH_PLOT <- MAKE_GRAPH(DEATH_DATA)
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DEATH_PLOT <- DEATH_PLOT+geom_line(data=HIST,aes(x=Year,y=Deaths),color='black',linewidth=0.75)+ scale_x_continuous(breaks = c(seq(2010, 2060, by = 5),2065),limits=c(2009,2065))+ scale_y_continuous(breaks = seq(0, 35000, by = 10))+ggtitle("Kemmerer & Diamondville, Mortality Forecast")+ expand_limits( y = 0)+labs(color = "Prediction Interval",linetype="Prediction Interval",y="Deaths")+ theme_bw()+ theme(legend.position = "top",panel.grid.minor = element_blank())
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png(paste0(SAVE_RES_LOC,"Mortality_Fan_Chart.png"), width = 12, height = 8, units = "in", res = 600)
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DEATH_PLOT
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dev.off()
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MIGRATION_DATA <- GET_DATA(RES,6) %>% filter(!is.na(MIN))
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MIGRATION_PLOT <- MAKE_GRAPH(MIGRATION_DATA)
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MIGRATION_PLOT <- MIGRATION_PLOT+geom_line(data=HIST,aes(x=Year,y=Migration),color='black',linewidth=0.75)+ scale_x_continuous(breaks = c(seq(2010, 2060, by = 5),2065),limits=c(2009,2065))+ scale_y_continuous(breaks = seq(-1000, 1000, by = 50))+ggtitle("Kemmerer & Diamondville, Net Migration Forecast")+ expand_limits( y = 0)+labs(color = "Prediction Interval",linetype="Prediction Interval",y="Migration")+ theme_bw()+ theme(legend.position = "top",panel.grid.minor = element_blank())
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png(paste0(SAVE_RES_LOC,"Migration_Fan_Chart.png"), width = 12, height = 8, units = "in", res = 600)
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MIGRATION_PLOT
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dev.off()
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#####Key year table
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KEY <- RES %>% filter(Year %in% c(2029,2030,2035,2045,2055,2065))
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AVG_VALUES <- KEY %>% group_by(Year) %>% summarize(MED=median(Population),MEAN=mean(Population))
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AVG_VALUES <- rbind(AVG_VALUES[,1:2]%>% rename(Value=MED) %>% mutate('Summary Stat.'="Median"),AVG_VALUES[,c(1,3)] %>% rename(Value=MEAN) %>% mutate('Summary Stat.'="Mean"))
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HISTOGRAM <- ggplot(KEY, aes(x = Population,group=-Year,Color=Year,fill=Year)) + geom_histogram(alpha=0.3,bins=800)+geom_vline(data = AVG_VALUES, aes(xintercept = Value,group=`Summary Stat.`,color = `Summary Stat.`), size = 0.75)+scale_fill_gradient(low = "grey", high = "darkred")+scale_color_manual(values=c("red","black","black"))+ facet_grid(rows=vars(Year))+ scale_x_continuous(breaks = c(seq(0, 10000, by = 500)))+ theme_bw()+ theme(legend.position = "top",panel.grid.minor = element_blank())+ylab("Number of Simulation")+guides(fill= guide_legend(nrow = 1))
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png(paste0(SAVE_RES_LOC,"Population_Histogram.png"), width = 8, height = 12, units = "in", res = 600)
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HISTOGRAM
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dev.off()
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#rm(KEY_YEARS)
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POP_LEVELS <- seq(2000,6000,100)
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YEARS <- c(2030,2035,2045,2055,2065)
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for(i in YEARS ){
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KEY <- RES %>% filter(Year==i ) %>% pull(Population)
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ECDF <- ecdf(KEY)
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ECDF_VALUES <- ECDF(POP_LEVELS)
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if(!exists("KEY_YEARS")){KEY_YEARS<- ECDF_VALUES} else{KEY_YEARS<- cbind(KEY_YEARS,ECDF_VALUES)}
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}
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colnames(KEY_YEARS) <- YEARS
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rownames(KEY_YEARS) <- POP_LEVELS
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PLOT_GREEN <- "forestgreen"
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PLOT_YELLOW <- "yellow"
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PLOT_RED <- "red"
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KEY_YEARS <- KEY_YEARS%>% as.data.frame
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Capacity_Risk <- KEY_YEARS%>% gt(rownames_to_stub = TRUE,caption="Year") %>% data_color( fn = scales::col_numeric( palette = c(PLOT_RED, PLOT_YELLOW, PLOT_GREEN), domain = c(0, 1) ) ) %>% fmt_percent( decimals = 1, drop_trailing_zeros = FALSE) %>% tab_stubhead(label =c("Capacity"))
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gtsave( data = Capacity_Risk , filename = "./Results/Primary_Simulation_Results/Lower_Bound_Results/Capacity_Table.html")
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system("wkhtmltopdf --disable-smart-shrinking --no-stop-slow-scripts --enable-local-file-access --page-width 85mm --page-height 328mm ./Results/Primary_Simulation_Results/Lower_Bound_Results/Capacity_Table.html ./Results/Primary_Simulation_Results/Lower_Bound_Results/Capacity_Table.pdf")
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