/
s1_data_clean.R
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/
s1_data_clean.R
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rm(list=ls())
library(data.table)
library(survey)
library(sp)
library(scales)
library(RColorBrewer)
library(gridExtra)
library(rgeos)
library(maptools)
library(latticeExtra)
library(xtable)
library(stringi)
library(DHScalendR)
library(gdata)
library(INLA)
library(spdep)
library(foreign)
library(rgdal)
library(raster)
library(Hmisc)
options(survey.lonely.psu = "adjust")
################################
# load and clean state map
################################
# Nigeria state map from GADM website
map_nga_state_shp <- readOGR(dsn = "path_to_data", layer = "layer_name", stringsAsFactors = F)
map_nga_state_shp$StateName <- as.character(map_nga_state_shp$NAME_1)
map_nga_state_shp$StateID <- as.integer(map_nga_state_shp$ID_1)
map_nga_state_shp <- map_nga_state_shp[c("StateName", "StateID")]
map_nga_state_shp$RegionName <-
ifelse(map_nga_state_shp$StateName %in% c("Benue", "Federal Capital Territory", "Kogi", "Kwara", "Nassarawa", "Niger", "Plateau"), "North Central",
ifelse(map_nga_state_shp$StateName %in% c("Adamawa", "Bauchi", "Borno", "Gombe", "Taraba", "Yobe"), "North East",
ifelse(map_nga_state_shp$StateName %in% c("Jigawa", "Kaduna", "Kano", "Katsina", "Kebbi", "Sokoto", "Zamfara"), "North West",
ifelse(map_nga_state_shp$StateName %in% c("Abia", "Anambra", "Ebonyi", "Enugu", "Imo"), "South East",
ifelse(map_nga_state_shp$StateName %in% c("Akwa Ibom", "Bayelsa", "Cross River", "Delta", "Edo", "Rivers"), "South South", "South West")))))
nb <- poly2nb(map_nga_state_shp, queen = F, row.names = map_nga_state_shp$StateID)
mat <- nb2mat(nb, style = "B", zero.policy = TRUE)
################################
# create SIA schedule lookup
################################
# Information from the WHO SIA calendar
state_sia_lookup_dt <- data.table(map_nga_state_shp@data)
state_sia_lookup_dt[, "sia_1_cmc" := ifelse(stri_sub(RegionName, 1, 1) == "N",
cmc_from_Date(as.Date("2005-12-10")),
cmc_from_Date(as.Date("2006-10-09")))]
state_sia_lookup_dt[, "sia_2_cmc" := ifelse(stri_sub(RegionName, 1, 1) == "N",
cmc_from_Date(as.Date("2008-12-15")),
cmc_from_Date(as.Date("2008-12-15")))]
state_sia_lookup_dt[, "sia_3_cmc" := ifelse(stri_sub(RegionName, 1, 1) == "N",
cmc_from_Date(as.Date("2011-01-30")),
cmc_from_Date(as.Date("2011-02-27")))]
state_sia_lookup_dt[, "sia_4_cmc" := ifelse(stri_sub(RegionName, 1, 1) == "N",
cmc_from_Date(as.Date("2013-10-09")),
cmc_from_Date(as.Date("2013-11-06")))]
state_sia_lookup_dt[, "sia_5_cmc" := ifelse(stri_sub(RegionName, 1, 1) == "N",
cmc_from_Date(as.Date("2015-11-25")),
cmc_from_Date(as.Date("2016-02-01")))]
state_sia_lookup_dt[, "sia_6_cmc" := ifelse(stri_sub(RegionName, 1, 1) == "N",
cmc_from_Date(as.Date("2017-11-15")),
cmc_from_Date(as.Date("2018-03-15")))]
################################################################
# compile child-level survey data - DHS 2003 as an example
################################################################
all_chil_dt <- NULL
# Raw survey data from the DHS website
dhs2003_chil_raw <- read.dta("path_to_data", convert.factors = F)
col_vt <- c("caseid", "v001", "v002", "v003", "v005", "v006", "v007", "v008", "v016",
"v021", "v022", "v023", "v024", "v025",
"h1", "h2", "h2d", "h2m", "h2y", "h3", "h3d", "h3m", "h3y",
"h4", "h4d", "h4m", "h4y", "h5", "h5d", "h5m", "h5y",
"h6", "h6d", "h6m", "h6y", "h7", "h7d", "h7m", "h7y",
"h8", "h8d", "h8m", "h8y", "h9", "h9d", "h9m", "h9y",
"h35",
"h0", "h0d", "h0m", "h0y", "h10",
"hw1", "hw16", "sstate")
col_name_vt <- c("caseid", "cluster", "hh", "respond", "hhsampwgt", "interv_m", "interv_y", "interv_date", "interv_d",
"psu", "strata", "domain", "region", "type",
"hascard", "bcg", "bcg_d", "bcg_m", "bcg_y", "dpt1", "dpt1_d", "dpt1_m", "dpt1_y",
"polio1", "polio1_d", "polio1_m", "polio1_y", "dpt2", "dpt2_d", "dpt2_m", "dpt2_y",
"polio2", "polio2_d", "polio2_m", "polio2_y", "dpt3", "dpt3_d", "dpt3_m", "dpt3_y",
"polio3", "polio3_d", "polio3_m", "polio3_y", "measles", "measles_d", "measles_m", "measles_y",
"polio0", "polio0_d", "polio0_m", "polio0_y", "evervacc",
"vacc_camp2y",
"age_m", "birth_d", "state")
dhs2003_chil_dt <- as.data.table(dhs2003_chil_raw[, col_vt])
setnames(dhs2003_chil_dt, col_name_vt)
state_lookup <- data.table(state = seq(1, 37, 1),
statename = c("Akwa Ibom", "Anambra", "Bauchi", "Edo", "Benue", "Borno", "Cross River",
"Adamawa", "Imo", "Kaduna", "Kano", "Katsina", "Kwara", "Lagos", "Niger",
"Ogun", "Ondo", "Oyo", "Plateau", "Rivers", "Sokoto", "Abia", "Delta", "Enugu",
"Jigawa", "Kebbi", "Kogi", "Osun", "Taraba", "Yobe", "Bayelsa", "Ebonyi", "Ekit",
"Gombe", "Nassarawa", "Zamfora", "FCT Abuja"))
state_lookup <- state_lookup[order(statename)]
state_lookup$StateName <- sort(map_nga_state_shp$StateName)
dhs2003_chil_dt <- merge(dhs2003_chil_dt, state_lookup[, .(state, StateName)], by = "state", all.x = T)
dhs2003_chil_dt[, "sampwgt" := hhsampwgt/1e6]
dhs2003_chil_dt[, "mcv" := ifelse(is.na(measles), 0,
ifelse(measles %in% c(1, 2, 3), 1, 0))]
dhs2003_chil_dt[, "interv_cmc" := interv_date ]
dhs2003_chil_dt[, "birth_cmc" := interv_cmc - age_m]
dhs2003_chil_dt <- merge(dhs2003_chil_dt, state_sia_lookup_dt,
by = "StateName", all.x = T)
dhs2003_chil_dt_clean <- dhs2003_chil_dt[!is.na(birth_cmc),
c("StateName", "StateID", "type", "strata", "cluster", "hh", "sampwgt", "mcv",
"age_m", "interv_cmc", "birth_cmc",
"sia_1_cmc", "sia_2_cmc", "sia_3_cmc", "sia_4_cmc", "sia_5_cmc", "sia_6_cmc")]
dhs2003_chil_dt_clean[, "svy_year" := 2003]
dhs2003_chil_dt_clean[, "svy_type" := "DHS"]
all_chil_dt <- rbind(all_chil_dt, dhs2003_chil_dt_clean)
# The survey data from other surveys can be cleaned and appended to "all_chil_dt" in a similar way.
###############################
# standardize child-level data
###############################
# determine ri indicator
all_chil_dt[, "N_ri" := ifelse((interv_cmc - birth_cmc >= 9), 1, 0)]
# determine sia indicator
all_chil_dt[, "sia_1_yes" := ifelse((sia_1_cmc < interv_cmc) & (sia_1_cmc - birth_cmc >= 9), 1, 0)]
all_chil_dt[, "sia_2_yes" := ifelse((sia_2_cmc < interv_cmc) & (sia_2_cmc - birth_cmc >= 9), 1, 0)]
all_chil_dt[, "sia_3_yes" := ifelse((sia_3_cmc < interv_cmc) & (sia_3_cmc - birth_cmc >= 9), 1, 0)]
all_chil_dt[, "sia_4_yes" := ifelse((sia_4_cmc < interv_cmc) & (sia_4_cmc - birth_cmc >= 9), 1, 0)]
all_chil_dt[, "sia_5_yes" := ifelse((sia_5_cmc < interv_cmc) & (sia_5_cmc - birth_cmc >= 9), 1, 0)]
all_chil_dt[, "sia_6_yes" := ifelse((sia_6_cmc < interv_cmc) & (sia_6_cmc - birth_cmc >= 9), 1, 0)]
# create 6-monthly birth cohort
calib <- cmc_from_Date("1999-12-31") # 2000-01 to 2000-06 -> b_6m == 1
all_chil_dt[, "b_6m" := ceiling((birth_cmc - calib)/6)]
# create 6_monthly observe cohort
all_chil_dt[, "t_6m" := ceiling((interv_cmc - calib)/6)]
svy_t_6m_dt <- all_chil_dt[, .(N_t_6m = .N), by = .(svy_year, svy_type, t_6m)]
svy_t_6m_lookup_dt <- svy_t_6m_dt[N_t_6m > 3000, c("svy_year", "svy_type", "t_6m")]
# count the no. of sia's experieced by each child
all_chil_dt[, "N_sia" := sia_1_yes + sia_2_yes + sia_3_yes + sia_4_yes + sia_5_yes + sia_6_yes]
# only use b_6m that (1) has ri oppotunities and (1) only 0 or 1 sia opportunities
state_svy_unique_N_sia_dt <- all_chil_dt[, .(max_N_sia = max(N_sia),
unique_N_sia = length(unique(N_sia)),
min_ri = min(N_ri),
unique_N_ri = length(unique(N_ri))),
by = .(StateName, StateID, svy_year, svy_type, b_6m)]
state_svy_b_6m_dt <- state_svy_unique_N_sia_dt[max_N_sia < 2 & unique_N_sia == 1 & min_ri == 1 & unique_N_ri == 1,
c("StateName", "StateID", "svy_year", "svy_type", "b_6m")]
all_chil_dt_1 <- merge(all_chil_dt[, -c("t_6m")], state_svy_b_6m_dt,
by = c("StateName", "StateID", "svy_year", "svy_type", "b_6m"), all.y = T)
# standardize t_6m for each survey
all_chil_dt_1 <- merge(all_chil_dt_1, svy_t_6m_lookup_dt, by = c("svy_year", "svy_type"), all.x = T)
# only use b_6m > 0
all_chil_dt_final <- all_chil_dt_1[b_6m > 0,
c("StateName", "StateID", "type", "strata", "cluster", "hh", "sampwgt", "mcv",
"b_6m", "t_6m", "svy_year", "svy_type", "age_m",
"birth_cmc", "interv_cmc", "N_sia", "N_ri")]
save(map_nga_state_shp, nb, mat,
state_sia_lookup_dt, svy_t_6m_lookup_dt,
all_chil_dt, all_chil_dt_final,
file = "all_chil_6m.RData")