FPEM: FPEM

## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(fpemdata)
library(fpemdata)
library(fpemmodeling)
library(ggplot2)
library(grid)
library(gridExtra)

## ---- eval = FALSE-------------------------------------------------------
#  wd <- getwd()
#  wholedf <- fpemdata::contraceptive_use
#  wholedf <- wholedf %>% dplyr::filter(division_numeric_code %in% fpemdata::fp2020$division_numeric_code)
#  if(!dir.exists("output")) dir.create("output")

## ---- eval = FALSE-------------------------------------------------------
#  for(div in wholedf$division_numeric_code) {
#    first_year = 1989,
#    last_year = 2030
#    run_y <- fpemmodeling::do_1country_run(
#      is_in_union = "Y",
#      surveydata_filepath = NULL,
#      service_stats = FALSE,
#      division_numeric_code = div,
#      first_year = first_year,
#      last_year = last_year
#    )
#    run_n <- fpemmodeling::do_1country_run(
#      is_in_union = "N",
#      surveydata_filepath = NULL,
#      service_stats = FALSE,
#      division_numeric_code = div,
#      first_year = first_year,
#      last_year = last_year
#    )
#    core_data <- run_y$core_data
#    core_data$observations <-
#      rbind(run_y$core_data$observations, run_n$core_data$observations)
#    samples_all <-
#      fpemmodeling::posterior_samples_all_women(
#        in_union_posterior_samples = run_y$posterior_samples,
#        not_in_union_posterior_samples = run_n$posterior_samples,
#        core_data = core_data
#      )
#    population_counts <- fpemdata::population_counts %>%
#      dplyr::filter(division_numeric_code == div)
#    results_y <- fpemreporting::fpem_calculate_results(
#      posterior_samples = run_y$posterior_samples,
#      country_population_counts = population_counts %>%
#        dplyr::filter(is_in_union == "Y"),
#      first_year = first_year)
#    )
#    results_n <- fpemreporting::fpem_calculate_results(
#      posterior_samples = run_n$posterior_samples,
#      country_population_counts = population_counts %>%
#        dplyr::filter(is_in_union == "N"),
#      first_year = first_year)
#    )
#    results_all <- fpemreporting::fpem_calculate_results(
#      posterior_samples = samples_all,
#      country_population_counts = population_counts,
#      first_year = first_year)
#    )
#    reslist <- list(results_y, results_n, results_all, core_data)
#    saveRDS(reslist, file.path("output", paste0("reslist", div, ".rds")))
#  }

## ---- eval = FALSE-------------------------------------------------------
#  #loop through all countries for one type of union, get the item from the list
#  #here we loop through in union results
#  indicators <- c(
#      "unmet_need_any",
#      "contraceptive_use_modern",
#      "contraceptive_use_traditional",
#      "contraceptive_use_any"
#      )
#  
#  for(div in wholedf$division_numeric_code){
#    reslist <- readRDS(file.path("output", paste0("reslist", div, ".rds")))
#    for(k in 1:length(indicators)) {
#      templs[[k]] <- fpem_plot_country_results(
#        country_results = reslist$results_y,
#        observations = core_data$observations,
#        first_year = first_year,
#        last_year = last_year,
#        is_in_union = "Y",
#        indicators = indicators
#    }
#    .pl[[i+j]] <- grid.arrange(grobs=templs[1:length(indicators)], top = textGrob("In-union women") ,ncol=2)
#  }
#