dcolombara
commented
4 days ago The following code shows this issue:
# Set up ----
rm(list=ls())
library(data.table)
library(rads)
# Create death data with no females >= 85 ----
deaths <- get_data_death(year = 2016:2020,
cols = c('date_of_birth', 'date_of_death', 'chi_sex'))
deaths[, age := calc_age(date_of_birth, date_of_death)]
deaths[, date_of_death := fifelse(chi_sex == 'Female' & age >= 85, date_of_death - 365*(age-80), date_of_death)]
deaths[, age := calc_age(date_of_birth, date_of_death)]
deaths[, age := NULL]
# Use life_table_prep ----
prepped = life_table_prep(ph.data = deaths,
group_by = 'chi_sex')
prepped <- prepped[!is.na(chi_sex) & !is.na(ages)]
setnames(prepped, "chi_sex", "gender")
# Get corresponding population ----
pop <- get_population(kingco = T,
years = 2016:2020,
group_by = c('genders', 'ages'))
pop <- pop[, .(gender, age, pop)]
my.cuts <- c(0, 1, 5, 10, 15, 18, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85)
pop[, ages := cut(age, my.cuts, right = F)] # create age intervals
pop[, ages := gsub("\\[|\\]|\\)", "", ages)] # tidy
pop[, ages := gsub("\\,", "-", ages)] #tidy
pop[age >= max(my.cuts), ages := paste0(max(my.cuts), "+")] # tidy
pop <- pop[, .(pop = sum(pop)), .(gender, ages)] #tidy
# Merge on corresponding population ----
prepped <- merge(prepped,
pop,
by = c('gender', 'ages'),
all = T)
# Run life table ----
lifetable <- life_table(ph.data = prepped,
group_by = 'gender')
# Review output ----
prepped[gender == 'Female' & ages %in% c('80-85', '85+')]
lifetable[gender == 'Female' & ages %in% c('80-85', '85+')]
because of small number for a few census tracts, 5-year combined data do not have the 85+ age group. As a result, life expectancy is not calculated. There are methods to adjust for this.