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title: "WHO MMR MD 4_4_20" author: "Li Shandross" date: "4/4/2020" output: html_document

knitr::opts_chunk$set(echo = TRUE)

#Read-in Data

library(tidyr)
library(tidyverse)
library(readxl)
library(devtools)
library(usethis)

Rfiles <- list.files(file.path(paste0(getwd(),"/R/")), ".R")
Rfiles <- Rfiles[grepl(".R", Rfiles)]
sapply(paste0(paste0(getwd(),"/R/"), Rfiles), source)

country_info <- read_excel("country list_ 26 March 2019.xlsx")
mmr_est_unrounded <- read.csv("mmr_unrounded.csv")
live_birth_projections <- read_excel("wpp2019_Births-TFR-GFR-Female1549.xlsx")
regional_groupings <- read.csv("regional_groupings_20190910_la_ac.csv")

#Data Cleaning

country_info <- country_info %>%
  select(`Code`, `ISO_Numeric_Code_CODE`, `Title`) %>%
  rename(ISOCode = `Code`, ISONum = `ISO_Numeric_Code_CODE`)
  
mmr_est_unrounded <- mmr_est_unrounded %>%
  filter(`bound` == "point", `year` >= 2010) %>%
  select(-c(`q`, `perc`, `bound`)) %>%
  mutate("MMR" = `value` * 100000)

mmr_est_unrounded_pwider <- mmr_est_unrounded %>%
  select(-c(`value`)) %>%
  pivot_wider(names_from = `year`, values_from = `MMR`)
  
regional_groupings <- regional_groupings %>%
  select(`ISOCode`, `Country.x`, `sdg_1`)

live_birth_projections2030 <- live_birth_projections %>% 
  filter(Year == 2030) %>%
  select(-c(`Year`)) %>%
  rename(name = 'Location') 

#Calc BAU ARR

calc_bau_arr_tibble <- calc_bau_arr(mmr_est_unrounded_pwider, 2010, 2017)
knitr::kable(calc_bau_arr_tibble)

#new function only has bau arr and iso code columns
cba_tibble <- cba(mmr_est_unrounded_pwider)
knitr::kable(cba_tibble)

#MMR Projections (for one country)

mmr_country_projections(mmr_est_unrounded_pwider, 2016, 2030, 2010, 2017, iso_code = "AFG")
    
#Recoded
mcp(mmr_est_unrounded_pwider, "AFG", 2016, 2030)

#MMR Projections (all countries)

mmr_allcountries_proj_tibble <- mmr_allcountries_projections(mmr_est_unrounded_pwider, 2010, 2017)
knitr::kable(mmr_allcountries_proj_tibble)

#new function does not have arr column
macp_tibble <- macp(mmr_est_unrounded_pwider, 2016, 2030)
knitr::kable(macp_tibble)

#SDG MMR Calculation, Categorization, and Adjustment #This function currently calculates the MMR of 2030 based on a global arr from a simple calculation

global_arr <- mean(cba(mmr_est_unrounded_pwider)$`arr`)

mmr2015 <- mmr_est_unrounded_pwider %>% 
  rename(MMR2015 = `2015`) %>%
  select(`MMR2015`)

mmr_sdg_proj <- get_mmr_sdg_projections(mmr2015, global_arr, 15)
knitr::kable(mmr_sdg_proj)

#Squared Diff

squared_diff(global_arr, mmr2015, live_birth_projections2030, 15)

#Get ARR SDG Target

get_arr_sdg_target(mmr2015, live_birth_projections2030, 15)
arr_sdg <- 0.05603232

#Calculate SDG ARR for each country Based on SDG MMR #From Pseudo Code

arr_sdg_target_country <- get_arr_sdg_target_country(mmr2015, arr_sdg, 15)
knitr::kable(arr_sdg_target_country)

#(BAU) MMR Regional Summaries

#some data cleaning
countries_and_regions <- country_info %>% 
  left_join(regional_groupings, by = c("ISOCode" = "ISOCode")) %>%
  select(-c(`Country.x`)) 

#test
mmr_regional_global_summaries <- mmr_regional_global_summarize(mmr_est_unrounded_pwider, countries_and_regions, live_birth_projections2030)
knitr::kable(mmr_regional_global_summaries)

#Recoded version

#data cleaning
#mmr, countries and regions, live births combined tibble
md <- left_join(mmr_allcountries_projections(mmr_est_unrounded_pwider, 2010, 2017) %>% select(-c(`name`, `arr`)), countries_and_regions, by = c("iso" = "ISOCode")) %>% left_join(live_birth_projections2030 %>% select(`LocID`, `Births`), by = c("ISONum" = "LocID")) 

#example
regional_proj_summaries <- bau_mmr_regional_projection_summaries(mmr_est_unrounded_pwider, countries_and_regions, live_birth_projections2030, 2010, 2017, 2016, 2030)
knitr::kable(regional_proj_summaries)

#Various tests

arr_sdg_predictions <- get_arr_sdg_target_country(mmr2015, arr_sdg, 15)

  #Global MMR using SDG ARR (avg of all countries)
  (sum(get_mmr_sdg_projections(mmr2015, sum(arr_sdg_predictions) / 185, 15) * md$Births)) / (sum(md$Births)) #68.754

  #Global MMR using BAU ARR (avg of all countries)
  bau_mmr_proj <- mmr_est_unrounded_pwider$`2015` * exp(-(rep(0.0273, 185)) * (2030-2015))
   (sum(bau_mmr_proj * md$Births)) / (sum(md$Births)) #166.529
  #(is 170.117 by summary table calculations (using all countries' invidual arrs) above)

  
  #Global MMR using ARR = 2.9%
  bau_mmr_proj2 <- mmr_est_unrounded_pwider$`2015` * exp(-(rep(0.029, 185)) * (2030-2015))
  (sum(bau_mmr_proj2 * md$Births)) / (sum(md$Births)) #162.336

#Read-in Data

library(tidyr) library(tidyverse) library(readxl) library(devtools) library(usethis)

Rfiles <- list.files(file.path(paste0(getwd(),"/R/")), ".R") Rfiles <- Rfiles[grepl(".R", Rfiles)] sapply(paste0(paste0(getwd(),"/R/"), Rfiles), source)

country_info <- read_excel("country list_ 26 March 2019.xlsx") mmr_est_unrounded <- read.csv("mmr_unrounded.csv") live_birth_projections <- read_excel("wpp2019_Births-TFR-GFR-Female1549.xlsx") regional_groupings <- read.csv("regional_groupings_20190910_la_ac.csv")

#Data Cleaning country_info <- country_info %>% select(Code, ISO_Numeric_Code_CODE, Title) %>% rename(ISOCode = Code, ISONum = ISO_Numeric_Code_CODE)

mmr_est_unrounded <- mmr_est_unrounded %>% filter(bound == "point", year >= 2010) %>% select(-c(q, perc, bound)) %>% mutate("MMR" = value * 100000)

mmr_est_unrounded_pwider <- mmr_est_unrounded %>% select(-c(value)) %>% pivot_wider(names_from = year, values_from = MMR)

regional_groupings <- regional_groupings %>% select(ISOCode, Country.x, sdg_1)

live_birth_projections2030 <- live_birth_projections %>% filter(Year == 2030) %>% select(-c(Year)) %>% rename(name = 'Location')

#Calc BAU ARR calc_bau_arr_tibble <- calc_bau_arr(mmr_est_unrounded_pwider, 2010, 2017) knitr::kable(calc_bau_arr_tibble)

#new function only has bau arr and iso code columns cba_tibble <- cba(mmr_est_unrounded_pwider) knitr::kable(cba_tibble)