initial import

HT_importDHS.R

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+# Haiti DHS Activity Design -----------------------------------------
+# Script to pull data from the Demographic and Health Surveys on variables
+# related to water, sanitation, and hygiene (WASH)
+# 
+# Data are from the 2013 DHS, available at http://dhsprogram.com/what-we-do/survey/survey-display-436.cfm
+#
+# Laura Hughes, lhughes@usaid.gov, 15 August 2016
+# With Patrick Gault (pgault@usaid.gov) and Tim Essam (tessam@usaid.gov)
+#
+# Copyright 2016 by Laura Hughes via MIT License
+#
+# -------------------------------------------------------------------------
+
+
+# Import data -------------------------------------------------------------
+
+# Load necessary packages
+library(haven)
+library(dplyr)
+
+# Function to pull attribute (label, labels) data from Stata format
+pullAttributes <- function(data) {
+  
+  metadata = lapply(data, function(x) attr(x, 'label'))
+  metadata = data.frame(metadata)
+  
+  labels = lapply(data, function(x) attr(x, 'labels'))
+  
+  metadata = data.frame(var = colnames(metadata), varDescrip = t(metadata))
+  
+  df = mutate(metadata, varValues = labels)
+  return(df)
+}
+
+# Read in the base module and the children's
+
+#' DHS modules are as follows:
+#' (from http://dhsprogram.com/data/Dataset-Types.cfm)
+#' * HR: household data (household level description)
+#' * PR: household list (inidividual roster)
+#' * IR: women's module (one woman/hh)
+#' * MR: men's module (one man/hh)
+#' * CR: couple's module
+#' * KR: children's module (ea. child < 5 y old)
+#' * BR: birth data (birth history of all children) --> fertility / mortality rates
+#' * WI: wealth index data (for data before 1990 to calcualte wealth index)
+
+hh_raw = read_dta('~/Documents/USAID/Haiti/rawdata/Haiti_DHS2012/hthr61dt/HTHR61FL.DTA')
+child_raw = read_dta('~/Documents/USAID/Haiti/rawdata/Haiti_DHS2012/htkr61dt/HTKR61FL.DTA')
+
+hh_labels = pullAttributes(hh_raw)
+child_labels = pullAttributes(child_raw)
+
+hh = removeAttributes(hh_raw)
+child = removeAttributes(child_raw)
+
+
+# classify improved/ not improved -----------------------------------------
+
+# toilets
+toilet_types = attr(hh_raw$hv205, 'labels')
+write.csv(toilet_types, '~/GitHub/Haiti-WASH2016/dataout/DHS_toilet_types.csv')
+
+water_types = attr(hh_raw$hv201, 'labels')
+write.csv(water_types, '~/GitHub/Haiti-WASH2016/dataout/DHS_water_types.csv')
+
+# Pull diarrhea, water-sanitation access, geo data ------------------------
+hh = hh %>% 
+  select(hhid, 
+         interview_month = hv006, interview_year = hv007,
+         water_source = hv201, 
+         time2water = hv204, 
+         toilet_source = hv205,
+         share_toilet = hv225, water_treatment = hv237, hv237a, hv237b, hv237c, hv237d, hv237e, hv237f, hv237x, hv237z, 
+         owns_mobile = hv243a, 
+         wealth_idx = hv270,
+         province = shprov,
+         munic =  shmunic,
+         barang = shbarang,
+         ea = shea,
+         region = shregion,
+         island_grp = shislgrp)
+
+diarrhea =  hh_raw %>% 
+  select(hhid, sh204m_01, sh204m_02, sh204m_03, sh204m_04, sh204m_05, sh204m_06, sh204m_07, sh204m_08, sh204m_09, sh204m_10, sh204m_11, sh204m_12) %>% 
+  group_by(hhid) %>% 
+  summarise_each(funs(mean))
+
+
+# Figuring out Philippines geography --------------------------------------
+# Island Group and Region  are the only ones labeled within the dataset and seems to be their primary reporting unit.
+# island_grp: 3 [Should be Luzon, Visayas, and Mindanao]
+# region: 17 [Wiki says 18 as of 2015 https://en.wikipedia.org/wiki/Regions_of_the_Philippines]
+# munic: 48 [Wiki says 1490??!]
+# province: 84 [Wiki says 81 provinces https://en.wikipedia.org/wiki/Provinces_of_the_Philippines]
+# ea: 87
+# barang: 116 [over 42,000? https://en.wikipedia.org/wiki/Barangay]
+
+
+
+# df  %>% group_by(shmunic) %>% summarise(num=n()) %>% arrange(num)