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# @param agmeenpgi_dat a \code{list} equivalent to the output of \code{synth_data_inc}
# @param race_vec A vector containing counts of the total population
# as well as counts disagregating by gender and race.
# should be: \code{unlist(<synth_data>$estimates$pop_by_race[<row i>,])}
synth_data_race <- function(agmeenpgi_dat, race_vec) {
dat <- agmeenpgi_dat[[1]]
race_vec <- race_vec[!grepl("other", names(race_vec))] # remove other as category
# 2. create age buckets on which to condition
ag_list <- split(dat, dat$gender)
# 3. Apply race
race_levels <- c("black, afr amer", "native amer", "asian", "pacific islander", "two or more races",
"white alone", "hispanic, latino")
race_vec_m <- race_vec[grepl("m_", names(race_vec))]
race_vec_f <- race_vec[grepl("f_", names(race_vec))]
ag_list[[1]] <- race_lapply(ag_list[[1]], v= race_vec_m, levels= race_levels)
ag_list[[2]] <- race_lapply(ag_list[[2]], v= race_vec_f, levels= race_levels)
ag_list <- do.call("rbind", ag_list)
# final cleaning, etc, return
ag_list <- factor_return(ag_list, prob_name= "p")
if (!is.micro_synthetic(ag_list)) class(ag_list) <- c(class(ag_list), "micro_synthetic")
return(ag_list)
}
# helper function.
race_lapply <- function(l, v, levels) {
if (sum(v) > 0) comp <- v / sum(v)
st <- data.frame(pct= comp, levels= factor(levels, levels= levels))
st <- base::split(st, 1:nrow(st))
dat <- replicate(length(levels), l, simplify = FALSE)
dat <- do.call("rbind", mapply(add_synth_attr_level, dat= dat, prob_name= "p", attr= st,
attr_name= "race", SIMPLIFY = FALSE))
return(dat)
}
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