library(tidyr)
library(readr)
library(dplyr)
library(stringr)
library(magrittr)
library(glptools)
path <- "data-raw/health/overdose/"
overdose_county <- read_tsv(path %p% "overdose.txt")
overdose_county %<>%
clean_wonder(method = "weight") %>%
select(FIPS, year, od_rate = rate) %>%
mutate(race = "total", sex = "total") %>%
organize()
# Redo regular OD analysis? maybe
# YPLL calculation
#test <- bind_df(overdose, homicide) %>% filter(!(is.na(od_rate) & is.na(homicide_rate)))
#cor(test$od_rate, test$homicide_rate, na.rm = T)
# analysis...make health index a function that is exported to health index and this alternate analysis.
# Separate alternate OD work from this doc? yes.
# do magic
path <- "data-raw/health/homicide/"
process_mortality <- function(df, race_name){
#Extract data for 10 most populous counties
totals <- df %>%
filter(FIPS == "total") %>%
select(-FIPS) %>%
rename(
total_deaths = deaths,
total_population = population)
#append data to df and calculate homicide rate
df %<>%
filter(FIPS != "total") %>%
left_join(totals, by = c("year", "age_10")) %>%
mutate(
deaths = deaths - total_deaths,
population = population - total_population) %>%
select(-total_deaths, -total_population)
df
}
homicide_county <- wonder_time(path %p% "total")
homicide_county %<>%
clean_wonder %>%
process_mortality()
homicide_county %<>% age_adj_rate("deaths", age_var = "age_10")
homicide_county %<>%
rename(homicide_rate = deaths) %>%
mutate(race = "total", sex = "total") %>%
organize()
update_sysdata(overdose_county, homicide_county)
rm(process_mortality, path)
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