99 lines
6.3 KiB
R
99 lines
6.3 KiB
R
#########################################Mortality Rate
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library(tidyverse)
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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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GET_MORTALITY_DATA <- function(FILE,SEX,LOWER_AGE,UPPER_AGE){
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#Create clean mortality rate data
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#Data gathered from https://hdpulse.nimhd.nih.gov/data-portal/mortality/table?cod=247&cod_options=cod_15&ratetype=aa&ratetype_options=ratetype_2&race=00&race_options=race_6&sex=2&sex_options=sex_3&age=177&age_options=age_11&ruralurban=0&ruralurban_options=ruralurban_3&yeargroup=5&yeargroup_options=year5yearmort_1&statefips=56&statefips_options=area_states&county=56000&county_options=counties_wyoming&comparison=counties_to_us&comparison_options=comparison_counties&radio_comparison=areas&radio_comparison_options=cods_or_areas
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NAMES <- c("County","FIPS","Death_Rate","Lower_Rate","Upper_Rate","Deaths","Trend_Category","Trend","Lower_Trend","Upper_Trend")
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DF <- read_csv(FILE,skip=5,col_names=NAMES,col_types=list('c',"i",'d','d','d','d','c','d','d','d')) %>% filter(grepl("County|Wyoming",County)|County=="United States") %>% mutate(Rate_SD=(Upper_Rate-Lower_Rate)/(2*1.96),Trend_SD=(Upper_Trend-Lower_Trend)/(2*1.96)) %>% select(County,Death_Rate,Rate_SD,Trend,Trend_SD)
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DF$County <- gsub(" County","",DF$County,ignore.case=TRUE)
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DF[,-1] <- DF[,-1]/100000
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WYOMING_TREND <- pull(DF[DF$County=="Wyoming",],"Trend")
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US_TREND <- pull(DF[DF$County=="United States",],"Trend")
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WYOMING_RATE <- pull(DF[DF$County=="Wyoming",],"Death_Rate")
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US_RATE <- pull(DF[DF$County=="United States",],"Death_Rate")
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DF$Imparted_Trend <- FALSE
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if(is.na(WYOMING_TREND)){
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DF[1,4:5] <- DF[2,4:5]
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DF[1,6] <- TRUE
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}
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DF$Imparted_Rate <- FALSE
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if(is.na(WYOMING_RATE)){
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DF[1,4:5] <- DF[2,2:3]
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DF[1,6] <- TRUE
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}
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WYOMING_BASELINE_TREND <-cbind (DF[1,4:5] ,TRUE)
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WYOMING_BASELINE_RATE <-DF[1,2:3]
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for(i in 3:nrow(DF)){
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#Impart any missing trends based on higher levels
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if(is.na(pull(DF[i,],"Trend"))){ DF[i,4:6] <- WYOMING_BASELINE_TREND}
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#Impart any missing death rates based on higher levels
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if(is.na(pull(DF[i,],"Death_Rate"))){
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DF[i,2:3] <- WYOMING_BASELINE_RATE
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DF[i,"Imparted_Rate"] <- TRUE
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}
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}
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DF$Sex <- SEX
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DF$Min_Age <- LOWER_AGE
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DF$Max_Age <- UPPER_AGE
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DF <- DF %>% select(County,Sex,Min_Age,Max_Age,Death_Rate,Rate_SD,Imparted_Rate,everything())
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return(DF)
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}
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SAVE_LOC_RAW_MORT <- paste0(SAVE_LOC_RAW,"Mortality_Rates/")
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RAW_MORTALITY_LOC_FEMALE <- paste0(SAVE_LOC_RAW_MORT,"Female/")
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RAW_MORTALITY_LOC_MALE <- paste0(SAVE_LOC_RAW_MORT,"Male/")
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#Gather all mortality Records
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MORTALITY_DATA_ALL <- rbind(
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_FEMALE,"A_Under1.csv"),"Female",0,0),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_FEMALE,"B_1_9.csv"),"Female",1,9),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_FEMALE,"C_10_19.csv"),"Female",10,19),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_FEMALE,"D_20_39.csv"),"Female",20,39),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_FEMALE,"E_40_64.csv"),"Female",40,64),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_FEMALE,"G_65_74.csv"),"Female",65,74),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_FEMALE,"H_75_84.csv"),"Female",75,84),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_FEMALE,"I_85+.csv"),"Female",85,Inf),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_MALE,"A_Under1.csv"),"Male",0,0),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_MALE,"B_1_9.csv"),"Male",1,9),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_MALE,"C_10_19.csv"),"Male",10,19),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_MALE,"D_20_39.csv"),"Male",20,39),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_MALE,"E_40_64.csv"),"Male",40,64),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_MALE,"G_65_74.csv"),"Male",65,74),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_MALE,"H_75_84.csv"),"Male",75,84),
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GET_MORTALITY_DATA(paste0(RAW_MORTALITY_LOC_MALE,"I_85+.csv"),"Male",85,Inf)
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)
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LIN_MORTALITY <- MORTALITY_DATA_ALL %>% filter(County=="Lincoln")
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##Save the mortality data
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if(!exists("SAVE_MORT_LOC")){SAVE_MORT_LOC <-"./Data/Cleaned_Data/Mortality_Rate_Data"}
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CSV_SAVE <- paste0(SAVE_MORT_LOC ,"/CSV")
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RDS_SAVE <- paste0(SAVE_MORT_LOC,"/RDS")
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#Save files for all county demographics
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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(MORTALITY_DATA_ALL,paste0(RDS_SAVE,"/All_Wyoming_Counties_Mortality_Rates.Rds" ))
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write_csv(MORTALITY_DATA_ALL,paste0(CSV_SAVE,"/All_Wyoming_Counties_Mortality_Rates.csv" ))
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saveRDS(LIN_MORTALITY,paste0(RDS_SAVE,"/Lincoln_County_Mortality_Rates.Rds" ))
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write_csv(LIN_MORTALITY,paste0(CSV_SAVE,"/Lincoln_County_Mortality_Rates.csv" ))
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#Create a short readme files to make the data sources more clear
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#Save a raw data readme
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sink(file=paste0(SAVE_LOC_RAW_MORT ,"/README_MORTALITY_DATA.txt"),append=FALSE)
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cat("Data files gathered manually from:
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National Institute of Health HDPules: An Ecosystem of Health Disparities and Minority Health Resources at
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https://hdpulse.nimhd.nih.gov/data-portal/mortality/table?cod=247&cod_options=cod_15&ratetype=aa&ratetype_options=ratetype_2&race=00&race_options=race_6&sex=2&sex_options=sex_3&age=177&age_options=age_11&ruralurban=0&ruralurban_options=ruralurban_3&yeargroup=5&yeargroup_options=year5yearmort_1&statefips=56&statefips_options=area_states&county=56000&county_options=counties_wyoming&comparison=counties_to_us&comparison_options=comparison_counties&radio_comparison=areas&radio_comparison_options=cods_or_areas
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Each file is single age group, so age weighting does not apply despite the variable names. Each age group file is named sequentially with a prefix “A_” for the first age group and a prefix “I_” for the oldest. There are separate directories for each sex. The cleaning script uses this directory structure to extract the right files and merge them into one data set.
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These files must be manually downloaded because there is a filter feature on the web page that is used to select the county and age. While there may be a way to scrape the data with code the trade off on my time was not worth it. Future runs will need to check these records, and can download the files to match this directory structure, in order to process a update in death rates.
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Valid data as of Nov 6 2025 Alex Gebben")
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sink()
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