educate_cohort <- function(filtered_data, current_year, education_covariance) {
for (x in unique(filtered_data$age_group)) {
for (y in unique(filtered_data$race)) {
for (z in unique(filtered_data$sex)) {
education_cohort <-
filtered_data %>%
filter(age_group == x) %>%
filter(race == y) %>%
filter(sex == z) %>%
mutate(weighted_theta = education_covariance * theta_mortality +
(1 - education_covariance) * theta_education) %>%
arrange(weighted_theta)
education_length <- nrow(education_cohort)
cohort_education <-
education_data %>%
filter(Year == ifelse(current_year < 1962, round(current_year * 0.1) * 10, current_year)) %>%
filter(Age_Group == x) %>%
filter(Race == y) %>%
filter(Sex == z) %>%
arrange(Education) %$% {
multiply_by(Proportion, education_length * 100) %>%
round() %>%
mapply(rep, Education, .)
}
if(length(cohort_education) > education_length) {
cohort_education <- cohort_education[1:education_length]
}
if(length(cohort_education) < education_length) {
cohort_education <-
length(cohort_education) %>%
subtract(education_length, .) %>%
rep(cohort_education[1], .) %>%
c(cohort_education)
}
education_cohort$education <- cohort_education
filtered_data %<>%
merge.data.frame(education_cohort, all.y = TRUE)
}
}
}
}
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