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flaneuse committed 2016-08-15 15:59:08 -04:00
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HT_importDHS.R
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@@ -2,7 +2,7 @@
# 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
# Data are from the 2012 DHS, available at http://dhsprogram.com/what-we-do/survey/survey-display-368.cfm
#
# Laura Hughes, lhughes@usaid.gov, 15 August 2016
# With Patrick Gault (pgault@usaid.gov) and Tim Essam (tessam@usaid.gov)
@@ -12,11 +12,7 @@
# -------------------------------------------------------------------------
# Import data -------------------------------------------------------------
# Load necessary packages
library(haven)
library(dplyr)
# Helper functions --------------------------------------------------------
# Function to pull attribute (label, labels) data from Stata format
pullAttributes <- function(data) {
@@ -32,12 +28,38 @@ pullAttributes <- function(data) {
return(df)
}
# Function to import shapefiles
importShp = function(workingDir = getwd(),
layerName) {
library(rgdal)
# Check that the layerName doesn't contain any extensions
# Check that layerName exists within the wd
# Log the current working directory, to change back at the end.
currentDir = getwd()
# Change directory to the file folder containing the shape file
setwd(workingDir)
# the dsn argument of '.' says to look for the layer in the current directory.
rawShp = rgdal::readOGR(dsn = ".", layer = layerName)
}
# Import data -------------------------------------------------------------
# Load necessary packages
library(haven)
library(dplyr)
# 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)
#' * PR: household list (individual roster)
#' * IR: women's module (one woman/hh)
#' * MR: men's module (one man/hh)
#' * CR: couple's module
@@ -45,9 +67,18 @@ pullAttributes <- function(data) {
#' * BR: birth data (birth history of all children) --> fertility / mortality rates
#' * WI: wealth index data (for data before 1990 to calcualte wealth index)
# -- Import household data --
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')
# -- Import children's data --
# NOTE: PR module should be used to reproduce DHS published stats for stunting, not KR (children's module).
child_raw = read_dta('~/Documents/USAID/Haiti/rawdata/Haiti_DHS2012/htpr61dt/HTPR61FL.DTA')
# -- Import coordinates of clusters --
geo_raw = importShp(workingDir = '~/Documents/USAID/Haiti/rawdata/Haiti_DHS2012/htge61fl/',
layerName = 'HTGE61FL')
# Remove labels
hh_labels = pullAttributes(hh_raw)
child_labels = pullAttributes(child_raw)
@@ -55,47 +86,73 @@ hh = removeAttributes(hh_raw)
child = removeAttributes(child_raw)
# classify improved/ not improved -----------------------------------------
# toilets
# Pull diarrhea, water-sanitation access, geo data ------------------------
hh = hh %>%
select(hhid,
cluster_id = hv001,
hh_num = hv002,
sample_wt = hv005,
urban = hv025,
region = hv024,
interview_month = hv006,
water_source = hv201,
time2water = hv204,
toilet_source = hv205,
share_toilet = hv225,
num_share_toilet = hv238,
# water_treatment = hv237, hv237a, hv237b, hv237c, hv237d, hv237e, hv237f, hv237x, hv237z,
wealth_idx = hv270) %>%
filter(region != 11) # Remove those households that were in camps, based on the assumption that they will be different than the rest of the population.
# classify improved/ not improved -----------------------------------------
# Toilets are defined as being 'improved' if they are one of the following types and aren't shared.
# toilet_type code improved
# flush toilet 10 Improved
# flush to piped sewer system 11 Improved
# flush to septic tank 12 Improved
# flush to pit latrine 13 Improved
# flush to somewhere else 14 Improved
# flush, don't know where 15 Improved
# pit toilet latrine 20 Improved
# ventilated improved pit latrine (vip) 21 Improved
# pit latrine with slab 22 Improved
# composting toilet 41 Improved
# no facility 30 open defecation
# no facility/bush/field 31 open defecation
# pit latrine without slab/open pit 23 Unimproved
# bucket toilet 42 Unimproved
# hanging toilet/latrine 43 Unimproved
# toilet hanging (on stilts) 44 Unimproved
# mobile chemical toilet 45 Unimproved
# other 96 Unimproved
# Water sources are defined as being 'improved' if they are one of the following types and can be reached in <= 30 minutes
# export types of toilets in survey
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))
# read in Liz Jordan's classification of whether or not a toilet or water source is improved.
toilet_types = read.csv('~/GitHub/Haiti-WASH2016/dataout/DHS_toilet_classification.csv')
# 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]
# clean and merge geodata -------------------------------------------------
geo = geo_raw@data %>%
select(cluster_id = DHSCLUST, departement = ADM1NAME, ADM1FIPSNA, urban = URBAN_RURA, lat = LATNUM, lon = LONGNUM) %>%
mutate(urban = ifelse(urban == 'U', 1,
ifelse(urban == 'R', 0, NA)))
library(leaflet)
# quick map of cluster locations
leaflet(geo) %>%
addCircles(~lon, ~lat, radius = 1000) %>%
addProviderTiles("Thunderforest.Landscape")
# df %>% group_by(shmunic) %>% summarise(num=n()) %>% arrange(num)