47 lines
2.9 KiB
R
47 lines
2.9 KiB
R
#library(tidyverse);setwd("../")
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library(tidycensus)
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library(zipcodeR)
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source("Scripts/Get_Sim_Intial_Demographic_Data.r")
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if(!exists("SAVE_DEMO_LOC")){SAVE_DEMO_LOC <-"./Data/Cleaned_Data/Demographic_Sex_Age_Data"}
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ACS_END_YEAR <- 2023 #most recent in package as of Nov 4 2025
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#Pull the relevant median age variables the value moe (margine of error) can be converted to standard error, following the link below
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#https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018_ch08.pdf
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#Packages to instal on computer if zipcodeR won't install
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#Sudo apt install libssl-dev libudunits2-dev libabsl-dev libcurl4-openssl-dev libgdal-dev cmake libfontconfig1-dev libharfbuzz-dev libfribidi-dev
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#install.packages("zipcodeR")
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#Add API key if missing
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#KEY <- '30e13ab22563318ff59286e433099f4174d4edd4'
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#census_api_key(KEY, install = TRUE)
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PROJ_TRACTS <- get_tracts(search_city('Kemmerer','WY')$zipcode) %>% full_join(get_tracts(search_city('Diamondville','WY')$zipcode))
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PROJ_TRACTS <- PROJ_TRACTS %>% select(GEOID) %>% mutate('IN_KEM'=1) %>% mutate(GEOID=as.character(GEOID))
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###Load data manually created which links vairable names to sex-age census data
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CODES <- read_csv("Data/API_CENSUS_CODES.csv",skip=1) %>% mutate(Med_Age=(Min_Age+Max_Age)/2) %>% rename(variable=Code)
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#Testing age Comparison between the two
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###Extract census data for all tracts in Lincoln county, clean up the data, and indicate if the tract is in Kemmerer/Diamondvile or not.
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DEMO_DATA_ALL <- do.call(rbind,lapply(2009:ACS_END_YEAR,MAKE_KEM_DEMO_DATA_YEAR))
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ORIG_DEMO_DATA_ALL <- DEMO_DATA_ALL
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#Merge Age of 85+ into one group, because NIH death spastics end in this age category so it is extra overhead to keep each year
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YOUNG <- DEMO_DATA_ALL %>% filter(Age<85)
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OLD <- DEMO_DATA_ALL %>% filter(Age>=85) %>% group_by(County,IN_KEM,Year,Sex) %>% summarize(Age=85,Population=sum(Population)) %>% ungroup
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DEMO_DATA_ALL <- rbind(YOUNG,OLD) %>% arrange(County,Year,IN_KEM,Sex)
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DEMO_DATA_ALL <- DEMO_DATA_ALL %>% mutate(Population=round(Population)) %>% pivot_wider(values_from=Population,names_from=Sex,names_prefix="Num_")
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OTHER_LIN_DEMO_DATA <- DEMO_DATA_ALL %>% filter(IN_KEM==0) %>% rename(Region=IN_KEM) %>% mutate(Region='Lincoln_Other')
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KEM_DEMO_DATA <- DEMO_DATA_ALL %>% filter(IN_KEM==1) %>% rename(Region=IN_KEM) %>% mutate(Region='Kemmerer')
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####Save results
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CSV_SAVE <- paste0(SAVE_DEMO_LOC,"/CSV")
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RDS_SAVE <- paste0(SAVE_DEMO_LOC,"/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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#Kemmerer Save
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saveRDS(KEM_DEMO_DATA,paste0(RDS_SAVE,"/Kemmerer_Diamondville_Demographics.Rds" ))
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write_csv(KEM_DEMO_DATA,paste0(CSV_SAVE,"/Kemmerer_Diamondville_Demographics.csv" ))
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#Other Lincoln area (not Kemmerer) save
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saveRDS(OTHER_LIN_DEMO_DATA,paste0(RDS_SAVE,"/Other_Lincoln_Demographics.Rds" ))
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write_csv(OTHER_LIN_DEMO_DATA,paste0(CSV_SAVE,"/Other_Lincoln_Demographics.csv" ))
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