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