data-raw/indicators.R
In validmeasures/bbw: Blocked Weighted Bootstrap
################################################################################
#
# Read data
#
################################################################################
surveyData <- read.csv("data-raw/surveyData.csv", header = TRUE, sep = ",")
################################################################################
#
# Maternal GAM
#
################################################################################
#
# create unique mother ID
#
temp1 <- formatC(surveyData$spid, width = 3, format = "d", flag = 0)
temp2 <- formatC(surveyData$mid, width = 2, format = "d", flag = 0)
mID <- paste(temp1, temp2, sep = "")
#
# Create spid vector
#
psu <- surveyData$spid
#
# Maternal MUAC < 230 mm
#
mMUAC <- ifelse(surveyData$mmuac < 230, 1, 0)
#
# Create mMUAC dataset
#
mMUAC <- data.frame(psu, mID, mMUAC)
names(mMUAC) <- c("psu", "mID", "mMUAC")
#
# Clean-up
#
rm(temp1, temp2)
################################################################################
#
# Antenatal care (ANC) indicators
#
################################################################################
#
# anc1 - at least 1 antenatal care visit with a trained health professional
#
anc1 <- ifelse(surveyData$a1 == 1 & surveyData$a2 %in% c(1:3) & surveyData$a4 >= 1, 1, 0)
#
# anc2 - at least 4 antenatal care visits with any service provider
#
anc2 <- ifelse(surveyData$a1 == 1 & surveyData$a4 >= 4, 1, 0)
#
# anc3 - FeFol coverage
#
anc3 <- ifelse(surveyData$a5 == 1, 1, 0)
#
# anc4 - Vitamin A coverage
#
anc4 <- ifelse(surveyData$a6 == 1, 1, 0)
#
# Create ANC indicators dataset
#
anc <- data.frame(psu, mID, anc1, anc2, anc3, anc4)
names(anc) <- c("psu", "mID", "anc1", "anc2", "anc3", "anc4")
#
# Clean-up
#
rm(anc1, anc2, anc3, anc4)
################################################################################
#
# Water, Sanitation and Hygiene (WASH) indicators
#
################################################################################
#
# Improved sources of drinking water
#
wash1 <- ifelse(surveyData$w1 %in% c(1:5, 7, 9:10), 1, 0)
#
# Improved sources of other water
#
wash2 <- ifelse(surveyData$w1a %in% c(1:5, 7, 9:10, 14), 1, 0)
#
# Probable safe drinking water
#
wash3 <- ifelse(wash1 == 1, 1, ifelse(surveyData$w2a == 1 |
surveyData$w2c == 1 |
surveyData$w2d == 1 |
surveyData$w2e == 1 |
surveyData$w2f == 1, 1, 0))
#
# Number of litres of water collected in a day
#
wash4 <- surveyData$w3
#
# Improved toilet facilities
#
wash5 <- ifelse(surveyData$w4 %in% c(1:3, 5:7, 9) & surveyData$w5 == 2, 1, 0)
#
# Human waste disposal practices / behaviour
#
wash6 <- ifelse(surveyData$w6 %in% c(1,2,5), 1, 0)
#
# Handwashing practices score (score of 0 to 5)
#
w7a <- ifelse(surveyData$w7a == 1, 1, 0)
w7b <- ifelse(surveyData$w7b == 1, 1, 0)
w7c <- ifelse(surveyData$w7c == 1, 1, 0)
w7d <- ifelse(surveyData$w7d == 1, 1, 0)
w7e <- ifelse(surveyData$w7e == 1, 1, 0)
wash7a <- w7a + w7b + w7c + w7d + w7e
wash7b <- ifelse(wash7a == 5, 1, 0)
#
# Create WASH indicators set
#
wash <- data.frame(psu, mID, wash1, wash2, wash3, wash4, wash5,
wash6, wash7a, wash7b)
names(wash) <- c("psu", "mID", "wash1", "wash2", "wash3", "wash4", "wash5",
"wash6", "wash7a", "wash7b")
#
# Clean-up
#
rm(wash1, wash2, wash3, wash4, wash5, wash6, wash7a, wash7b,
w7a, w7b, w7c, w7d, w7e)
################################################################################
#
# Household Hunger Scale (HHS) indicators
#
################################################################################
#
# Recode questions
#
h1 <- ifelse(surveyData$h1 > 10, 2, ifelse(surveyData$h1 == 0, 0, 1))
h2 <- ifelse(surveyData$h2 > 10, 2, ifelse(surveyData$h2 == 0, 0, 1))
h3 <- ifelse(surveyData$h3 > 10, 2, ifelse(surveyData$h3 == 0, 0, 1))
#
# Household hunger score
#
hhs1 <- h1 + h2 + h3
#
# Little or no hunger
#
hhs2 <- ifelse(hhs1 <= 1, 1, 0)
#
# Moderate hunger
#
hhs3 <- ifelse(hhs1 %in% 2:3, 1, 0)
#
# Severe hunger
#
hhs4 <- ifelse(hhs1 %in% 4:6, 1, 0)
#
# Create HHS indicator set
#
hhs <- data.frame(psu, mID, hhs1, hhs2, hhs3, hhs4)
names(hhs) <- c("psu", "mID", "hhs1", "hhs2", "hhs3", "hhs4")
#
# Clean-up
#
rm(h1, h2, h3, hhs1, hhs2, hhs3, hhs4)
################################################################################
#
# Mother's Dietary Diversity (MDD)
#
################################################################################
#
# Recode NAs to 0
#
surveyData$mfg1 <- recode(surveyData$mfg1, "1=1; 2=0; NA=0")
surveyData$mfg2 <- recode(surveyData$mfg2, "1=1; 2=0; NA=0")
surveyData$mfg3 <- recode(surveyData$mfg3, "1=1; 2=0; NA=0")
surveyData$mfg4 <- recode(surveyData$mfg4, "1=1; 2=0; NA=0")
surveyData$mfg5 <- recode(surveyData$mfg5, "1=1; 2=0; NA=0")
surveyData$mfg6 <- recode(surveyData$mfg6, "1=1; 2=0; NA=0")
surveyData$mfg7 <- recode(surveyData$mfg7, "1=1; 2=0; NA=0")
surveyData$mfg8 <- recode(surveyData$mfg8, "1=1; 2=0; NA=0")
surveyData$mfg9 <- recode(surveyData$mfg9, "1=1; 2=0; NA=0")
surveyData$mfg10 <- recode(surveyData$mfg10, "1=1; 2=0; NA=0")
surveyData$mfg11 <- recode(surveyData$mfg11, "1=1; 2=0; NA=0")
surveyData$mfg12 <- recode(surveyData$mfg12, "1=1; 2=0; NA=0")
surveyData$mfg13 <- recode(surveyData$mfg13, "1=1; 2=0; NA=0")
surveyData$mfg14 <- recode(surveyData$mfg14, "1=1; 2=0; NA=0")
surveyData$mfg15 <- recode(surveyData$mfg15, "1=1; 2=0; NA=0")
surveyData$mfg16 <- recode(surveyData$mfg16, "1=1; 2=0; NA=0")
surveyData$mfg17 <- recode(surveyData$mfg17, "1=1; 2=0; NA=0")
surveyData$mfg18 <- recode(surveyData$mfg18, "1=1; 2=0; NA=0")
#
# Recode for each of 9 food groups
#
mfg1 <- ifelse(surveyData$mfg4 == 1 | surveyData$mfg8 == 1, 1, 0)
mfg2 <- ifelse(surveyData$mfg7 == 1, 1, 0)
mfg3 <- ifelse(surveyData$mfg5 == 1 | surveyData$mfg6 == 1, 1, 0)
mfg4 <- ifelse(surveyData$mfg10 == 1, 1, 0)
mfg5 <- ifelse(surveyData$mfg11 == 1, 1, 0)
mfg6 <- ifelse(surveyData$mfg12 == 1 | surveyData$mfg13 == 1, 1, 0)
mfg7 <- ifelse(surveyData$mfg15 == 1, 1, 0)
mfg8 <- ifelse(surveyData$mfg9 == 1, 1, 0)
mfg9 <- ifelse(surveyData$mfg2 == 1 | surveyData$mfg14 == 1, 1, 0)
mfg <- mfg1 + mfg2 + mfg3 + mfg4 + mfg5 + mfg6 + mfg7 + mfg8 + mfg9
pVitA <- ifelse(mfg2 == 1 | mfg3 == 1, 1, 0)
aVitA <- ifelse(mfg5 == 1 | mfg7 == 1 | mfg9 == 1, 1, 0)
xVitA <- ifelse(pVitA == 1 | aVitA == 1, 1, 0)
iron <- ifelse(mfg5 == 1 | mfg6 == 1, 1, 0)
#
# Create MDD indicators dataset
#
mdd <- data.frame(psu, mID, mfg, pVitA, aVitA, xVitA, iron)
names(mdd) <- c("psu", "mID", "mfg", "pVitA", "aVitA", "xVitA", "iron")
#
# Clean-up
#
rm(mfg1, mfg2, mfg3, mfg4, mfg5, mfg6, mfg7, mfg8, mfg9, mfg,
pVitA, aVitA, xVitA, iron)
################################################################################
#
# Merge all HH datasets
#
################################################################################
#
#
#
temp <- merge(mMUAC, anc, by = c("psu", "mID"))
temp <- merge(temp, wash, by = c("psu", "mID"))
temp <- merge(temp, hhs, by = c("psu", "mID"))
indicatorsHH <- merge(temp, mdd, by = c("psu", "mID"))
#
#
#
write.csv(indicatorsHH, "data-raw/indicatorsHH.cscv", row.names = FALSE)
#
#
#
devtools::use_data(indicatorsHH, overwrite = TRUE)
################################################################################
#
# Child health and nutrition indicators
#
################################################################################
#
# Compile child data
#
child1 <- subset(surveyData, select = c1id:c1fg17)
child1 <- data.frame(psu, mID, child1)
names(child1) <- c("psu", "mID", "cid", "dob", "age", "sex", "cmuac", "coed",
"cc1", "cc2", "cc3", "cc4", "cc5", "cc5a", "cc6", "cc7",
"cbf1", "cbf1a", "cbf2", "cbf2a", "cbf3", "cbf3a", "cf1",
"cf2", "cfg1", "cfg2", "cfg3", "cfg4", "cfg5", "cfg6",
"cfg7", "cfg8", "cfg9", "cfg10", "cfg11", "cfg12", "cfg13",
"cfg14", "cfg15", "cfg16", "cfg17")
child1$cid <- 1
child2 <- subset(surveyData, select = c2id:c2fg17)
child2 <- data.frame(psu, mID, child2)
names(child2) <- c("psu", "mID", "cid", "dob", "age", "sex", "cmuac", "coed",
"cc1", "cc2", "cc3", "cc4", "cc5", "cc5a", "cc6", "cc7",
"cbf1", "cbf1a", "cbf2", "cbf2a", "cbf3", "cbf3a", "cf1",
"cf2", "cfg1", "cfg2", "cfg3", "cfg4", "cfg5", "cfg6",
"cfg7", "cfg8", "cfg9", "cfg10", "cfg11", "cfg12", "cfg13",
"cfg14", "cfg15", "cfg16", "cfg17")
child2$cid <- 2
child3 <- subset(surveyData, select = c3id:c3fg17)
child3 <- data.frame(psu, mID, child3)
names(child3) <- c("psu", "mID", "cid", "dob", "age", "sex", "cmuac", "coed",
"cc1", "cc2", "cc3", "cc4", "cc5", "cc5a", "cc6", "cc7",
"cbf1", "cbf1a", "cbf2", "cbf2a", "cbf3", "cbf3a", "cf1",
"cf2", "cfg1", "cfg2", "cfg3", "cfg4", "cfg5", "cfg6",
"cfg7", "cfg8", "cfg9", "cfg10", "cfg11", "cfg12", "cfg13",
"cfg14", "cfg15", "cfg16", "cfg17")
child3$cid <- 3
child <- rbind(child1, child2, child3)
child <- subset(child, !is.na(age) & !is.na(sex))
#
# Create unique child ID
#
cID <- paste(mID, child$cid, sep = "")
################################################################################
#
# Child illnesses
#
################################################################################
#
# Recode
#
ch1 <- ifelse(child$cc1 == 1, 1, 0)
ch2 <- ifelse(child$cc2 == 1, 1, 0)
ch3 <- ifelse(child$cc3 == 1, 1, 0)
#
# Create illnesses dataset
#
ill <- data.frame(child$psu, child$mID, child$cid, ch1, ch2, ch3)
names(ill) <- c("psu", "mID", "cID", "ch1", "ch2", "ch3")
#
# Clean-up
#
rm(child1, child2, child3, ch1, ch2, ch3)
################################################################################
#
# Child health services
#
################################################################################
#
# Recode
#
ch4 <- ifelse(child$cc4 == 1, 1, 0)
ch5 <- ifelse(child$cc5 == 1 | child$cc5a == 1, 1, 0)
ch6 <- ifelse(child$cc6 == 1, 1, 0)
ch7 <- ifelse(child$cc7 == 1, 1, 0)
#
# Create health services dataset
#
imm <- data.frame(child$psu, child$mID, child$cid, ch4, ch5, ch6, ch7)
names(imm) <- c("psu", "mID", "cID", "ch4", "ch5", "ch6", "ch7")
rm(ch4, ch5, ch6, ch7)
################################################################################
#
# Anthropometry
#
################################################################################
#
# Make year-centred age-groups, get sex, MUAC, oedema
#
ageGroup <- recode(child$age, "0:5=0; 6:17=1; 18:29=2; 30:41=3; 42:53=4; 54:hi=5")
sex <- child$sex
muac <- child$cmuac
oedema <- child$coed
#
# Create anthro data frame
#
anthro <- data.frame(child$psu, child$mID, child$cid, sex, muac, muac, oedema)
names(anthro) <- c("psu", "mID", "cID", "sex", "muac1", "muac2", "oedema")
#
# Clean up
#
rm(sex, muac, oedema)
############################# Make IYCF indicator set ##########################
################################################################################
#
# Indicator creation script for IYCF indicators
#
################################################################################
#
# Script assumes survey data is in data.frame 'x'.
#
# Creates data.frame 'iycf' with columns :
#
# psu PSU identifier
# mID HH identifier
# cID Child identifier
# EBF Exclusive breastfeeding
# CBF Continuing breastfeeding
# DDD Dietary diversity
# MFD Meal frequency
# ICFI ICFI score
# IYCF IYCF summary indicator
#
################################################################################
################################################################################
#
# Select data for IYCF age-group
#
x <- subset(child, age <= 24)
################################################################################
#
# Grouped age variable 'ageBand' (used when calculating age-specific scores)
#
ageBand <- recode(x$age, "0:5=1; 6:8=2; 9:11=3; 12:24=4")
################################################################################
#
# Exclusive breastfeeding
#
ebf <- ifelse(x$cbf2 == 1 & x$cbf3 == 0 & x$cf1 == 0, 1, 0)
ebf[ageBand != 1] <- NA
################################################################################
#
# Age-specific continuing breastfeeding
#
bfs <- ifelse(ageBand == 2, (x$cbf2 == 1) * 2,
ifelse(ageBand == 3, (x$cbf2 == 1) * 2,
ifelse(ageBand == 4, (x$cbf2 == 1), NA)))
bfd <- ifelse(bfs != 0, 1, 0)
################################################################################
#
# Age-specific dietary diversity
#
fg1 <- ifelse(x$cfg4 == 1 | x$cfg8 == 1, 1, 0)
fg2 <- ifelse(x$cfg9 == 1, 1, 0)
fg3 <- ifelse(x$cfg2 == 1 | x$cfg14 == 1, 1, 0)
fg4 <- ifelse(x$cfg11 == 1 | x$cfg12 == 1 | x$cfg13 == 1, 1, 0)
fg5 <- ifelse(x$cfg15 == 1, 1, 0)
fg6 <- ifelse(x$cfg5 == 1 | x$cfg6 == 1 | x$cfg7 == 1, 1, 0)
fg7 <- ifelse(x$cfg10 == 1, 1, 0)
fg <- fg1 + fg2 + fg3 + fg4 + fg5 + fg6 + fg7
dds <- ifelse(ageBand == 2, (fg == 1) + (fg > 1) * 2,
ifelse(ageBand == 3, (fg %in% 1:2) + (fg > 2) * 2,
ifelse(ageBand == 4, (fg %in% 2:3) + (fg > 3) * 2, NA)))
ddd <- ifelse(dds == 2, 1, 0)
rm(fg1, fg2, fg3, fg4, fg5, fg6, fg7, fg)
################################################################################
#
# Age-specific meal frequency score
#
mfs <- ifelse(ageBand == 2, (x$cf1 == 1) + (x$cf1 > 1) * 2,
ifelse(ageBand == 3, (x$cf1 %in% 1:2) + (x$cf1 > 2) * 2,
ifelse(ageBand == 4, (x$cf1 == 2) + (x$cf1 == 3) * 2 + (x$cf1 > 3) * 3, NA)))
mfd <- ifelse(mfs > 1, 1, 0)
################################################################################
#
# ICFI
#
icfi <- bfs + dds + mfs
icfiProp <- ifelse(icfi == 6, 1, 0)
################################################################################
#
# IYCF
#
iycf <- ifelse((ageBand == 1 & ebf == 1) | icfi == 6, 1, 0)
icfi[is.na(icfi)] <- 0
################################################################################
#
# Make a data.frame ('iycf') with indicators :
#
# ebf Exclusive breastfeeding
# bfd Continuing breastfeeding
# ddd ICFI dietary diversity score = 2
# mfd ICFI Meal frequency score > 1
# icfi ICFI score
# icfiProp ICFI score == 6
# iycf IYCF summary indicator
#
# and PSU, HH and Child identifiers ...
#
iycf <- data.frame(x$psu, x$mID, x$cid, ebf, bfd, ddd, mfd, icfi, iycf, icfiProp)
names(iycf) <- c("psu", "mID", "cID", "ebf", "cbf", "ddd", "mfd", "icfi", "iycf", "icfiProp")
#
# Clean up
#
rm(bfs, bfd, mfs, mfd, dds, ddd, icfi, icfiProp)
################################################################################
#
# Breastfeeding indicators
#
################################################################################
#
# Ever breastfed (bf1)
#
bf1 <- ifelse(child$cbf1 == 1, 1, 0)
#
# Early initiation of breastfeeding: within 1 hour of birth (bf2)
#
bf2 <- ifelse(child$cbf1a == 1, 1, 0)
#
# Continued breastfeeding up to 1 year
#
cbf <- data.frame(x$psu, x$mID, x$cid, x$age, x$cbf2, x$cbf2a)
names(cbf) <- c("psu", "mID", "cID", "age", "bf", "bfStop")
rm(bf1, bf2)
#
# Merge all CH datasets
#
temp <- merge(ill, imm, by = c("psu", "mID", "cID"))
temp <- merge(temp, anthro, by = c("psu", "mID", "cID"))
indicatorsCH1 <- temp
indicatorsCH2 <- merge(iycf, cbf, by = c("psu", "mID", "cID"))
rm(anc, anthro, cbf, child, hhs, ill, imm, iycf, mdd, mMUAC, surveyData, temp,
wash, x, ageBand, ageGroup, cID, ebf, mID, psu)
write.csv(indicatorsCH1, "data-raw/indicatorsCH1.csv", row.names = FALSE)
devtools::use_data(indicatorsCH1, overwrite = TRUE)
write.csv(indicatorsCH2, "data-raw/indicatorsCH2.csv", row.names = FALSE)
devtools::use_data(indicatorsCH2, overwrite = TRUE)
validmeasures/bbw documentation built on June 3, 2022, 2:52 p.m.
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################################################################################
#
# Read data
#
################################################################################
surveyData <- read.csv("data-raw/surveyData.csv", header = TRUE, sep = ",")
################################################################################
#
# Maternal GAM
#
################################################################################
#
# create unique mother ID
#
temp1 <- formatC(surveyData$spid, width = 3, format = "d", flag = 0)
temp2 <- formatC(surveyData$mid, width = 2, format = "d", flag = 0)
mID <- paste(temp1, temp2, sep = "")
#
# Create spid vector
#
psu <- surveyData$spid
#
# Maternal MUAC < 230 mm
#
mMUAC <- ifelse(surveyData$mmuac < 230, 1, 0)
#
# Create mMUAC dataset
#
mMUAC <- data.frame(psu, mID, mMUAC)
names(mMUAC) <- c("psu", "mID", "mMUAC")
#
# Clean-up
#
rm(temp1, temp2)
################################################################################
#
# Antenatal care (ANC) indicators
#
################################################################################
#
# anc1 - at least 1 antenatal care visit with a trained health professional
#
anc1 <- ifelse(surveyData$a1 == 1 & surveyData$a2 %in% c(1:3) & surveyData$a4 >= 1, 1, 0)
#
# anc2 - at least 4 antenatal care visits with any service provider
#
anc2 <- ifelse(surveyData$a1 == 1 & surveyData$a4 >= 4, 1, 0)
#
# anc3 - FeFol coverage
#
anc3 <- ifelse(surveyData$a5 == 1, 1, 0)
#
# anc4 - Vitamin A coverage
#
anc4 <- ifelse(surveyData$a6 == 1, 1, 0)
#
# Create ANC indicators dataset
#
anc <- data.frame(psu, mID, anc1, anc2, anc3, anc4)
names(anc) <- c("psu", "mID", "anc1", "anc2", "anc3", "anc4")
#
# Clean-up
#
rm(anc1, anc2, anc3, anc4)
################################################################################
#
# Water, Sanitation and Hygiene (WASH) indicators
#
################################################################################
#
# Improved sources of drinking water
#
wash1 <- ifelse(surveyData$w1 %in% c(1:5, 7, 9:10), 1, 0)
#
# Improved sources of other water
#
wash2 <- ifelse(surveyData$w1a %in% c(1:5, 7, 9:10, 14), 1, 0)
#
# Probable safe drinking water
#
wash3 <- ifelse(wash1 == 1, 1, ifelse(surveyData$w2a == 1 |
surveyData$w2c == 1 |
surveyData$w2d == 1 |
surveyData$w2e == 1 |
surveyData$w2f == 1, 1, 0))
#
# Number of litres of water collected in a day
#
wash4 <- surveyData$w3
#
# Improved toilet facilities
#
wash5 <- ifelse(surveyData$w4 %in% c(1:3, 5:7, 9) & surveyData$w5 == 2, 1, 0)
#
# Human waste disposal practices / behaviour
#
wash6 <- ifelse(surveyData$w6 %in% c(1,2,5), 1, 0)
#
# Handwashing practices score (score of 0 to 5)
#
w7a <- ifelse(surveyData$w7a == 1, 1, 0)
w7b <- ifelse(surveyData$w7b == 1, 1, 0)
w7c <- ifelse(surveyData$w7c == 1, 1, 0)
w7d <- ifelse(surveyData$w7d == 1, 1, 0)
w7e <- ifelse(surveyData$w7e == 1, 1, 0)
wash7a <- w7a + w7b + w7c + w7d + w7e
wash7b <- ifelse(wash7a == 5, 1, 0)
#
# Create WASH indicators set
#
wash <- data.frame(psu, mID, wash1, wash2, wash3, wash4, wash5,
wash6, wash7a, wash7b)
names(wash) <- c("psu", "mID", "wash1", "wash2", "wash3", "wash4", "wash5",
"wash6", "wash7a", "wash7b")
#
# Clean-up
#
rm(wash1, wash2, wash3, wash4, wash5, wash6, wash7a, wash7b,
w7a, w7b, w7c, w7d, w7e)
################################################################################
#
# Household Hunger Scale (HHS) indicators
#
################################################################################
#
# Recode questions
#
h1 <- ifelse(surveyData$h1 > 10, 2, ifelse(surveyData$h1 == 0, 0, 1))
h2 <- ifelse(surveyData$h2 > 10, 2, ifelse(surveyData$h2 == 0, 0, 1))
h3 <- ifelse(surveyData$h3 > 10, 2, ifelse(surveyData$h3 == 0, 0, 1))
#
# Household hunger score
#
hhs1 <- h1 + h2 + h3
#
# Little or no hunger
#
hhs2 <- ifelse(hhs1 <= 1, 1, 0)
#
# Moderate hunger
#
hhs3 <- ifelse(hhs1 %in% 2:3, 1, 0)
#
# Severe hunger
#
hhs4 <- ifelse(hhs1 %in% 4:6, 1, 0)
#
# Create HHS indicator set
#
hhs <- data.frame(psu, mID, hhs1, hhs2, hhs3, hhs4)
names(hhs) <- c("psu", "mID", "hhs1", "hhs2", "hhs3", "hhs4")
#
# Clean-up
#
rm(h1, h2, h3, hhs1, hhs2, hhs3, hhs4)
################################################################################
#
# Mother's Dietary Diversity (MDD)
#
################################################################################
#
# Recode NAs to 0
#
surveyData$mfg1 <- recode(surveyData$mfg1, "1=1; 2=0; NA=0")
surveyData$mfg2 <- recode(surveyData$mfg2, "1=1; 2=0; NA=0")
surveyData$mfg3 <- recode(surveyData$mfg3, "1=1; 2=0; NA=0")
surveyData$mfg4 <- recode(surveyData$mfg4, "1=1; 2=0; NA=0")
surveyData$mfg5 <- recode(surveyData$mfg5, "1=1; 2=0; NA=0")
surveyData$mfg6 <- recode(surveyData$mfg6, "1=1; 2=0; NA=0")
surveyData$mfg7 <- recode(surveyData$mfg7, "1=1; 2=0; NA=0")
surveyData$mfg8 <- recode(surveyData$mfg8, "1=1; 2=0; NA=0")
surveyData$mfg9 <- recode(surveyData$mfg9, "1=1; 2=0; NA=0")
surveyData$mfg10 <- recode(surveyData$mfg10, "1=1; 2=0; NA=0")
surveyData$mfg11 <- recode(surveyData$mfg11, "1=1; 2=0; NA=0")
surveyData$mfg12 <- recode(surveyData$mfg12, "1=1; 2=0; NA=0")
surveyData$mfg13 <- recode(surveyData$mfg13, "1=1; 2=0; NA=0")
surveyData$mfg14 <- recode(surveyData$mfg14, "1=1; 2=0; NA=0")
surveyData$mfg15 <- recode(surveyData$mfg15, "1=1; 2=0; NA=0")
surveyData$mfg16 <- recode(surveyData$mfg16, "1=1; 2=0; NA=0")
surveyData$mfg17 <- recode(surveyData$mfg17, "1=1; 2=0; NA=0")
surveyData$mfg18 <- recode(surveyData$mfg18, "1=1; 2=0; NA=0")
#
# Recode for each of 9 food groups
#
mfg1 <- ifelse(surveyData$mfg4 == 1 | surveyData$mfg8 == 1, 1, 0)
mfg2 <- ifelse(surveyData$mfg7 == 1, 1, 0)
mfg3 <- ifelse(surveyData$mfg5 == 1 | surveyData$mfg6 == 1, 1, 0)
mfg4 <- ifelse(surveyData$mfg10 == 1, 1, 0)
mfg5 <- ifelse(surveyData$mfg11 == 1, 1, 0)
mfg6 <- ifelse(surveyData$mfg12 == 1 | surveyData$mfg13 == 1, 1, 0)
mfg7 <- ifelse(surveyData$mfg15 == 1, 1, 0)
mfg8 <- ifelse(surveyData$mfg9 == 1, 1, 0)
mfg9 <- ifelse(surveyData$mfg2 == 1 | surveyData$mfg14 == 1, 1, 0)
mfg <- mfg1 + mfg2 + mfg3 + mfg4 + mfg5 + mfg6 + mfg7 + mfg8 + mfg9
pVitA <- ifelse(mfg2 == 1 | mfg3 == 1, 1, 0)
aVitA <- ifelse(mfg5 == 1 | mfg7 == 1 | mfg9 == 1, 1, 0)
xVitA <- ifelse(pVitA == 1 | aVitA == 1, 1, 0)
iron <- ifelse(mfg5 == 1 | mfg6 == 1, 1, 0)
#
# Create MDD indicators dataset
#
mdd <- data.frame(psu, mID, mfg, pVitA, aVitA, xVitA, iron)
names(mdd) <- c("psu", "mID", "mfg", "pVitA", "aVitA", "xVitA", "iron")
#
# Clean-up
#
rm(mfg1, mfg2, mfg3, mfg4, mfg5, mfg6, mfg7, mfg8, mfg9, mfg,
pVitA, aVitA, xVitA, iron)
################################################################################
#
# Merge all HH datasets
#
################################################################################
#
#
#
temp <- merge(mMUAC, anc, by = c("psu", "mID"))
temp <- merge(temp, wash, by = c("psu", "mID"))
temp <- merge(temp, hhs, by = c("psu", "mID"))
indicatorsHH <- merge(temp, mdd, by = c("psu", "mID"))
#
#
#
write.csv(indicatorsHH, "data-raw/indicatorsHH.cscv", row.names = FALSE)
#
#
#
devtools::use_data(indicatorsHH, overwrite = TRUE)
################################################################################
#
# Child health and nutrition indicators
#
################################################################################
#
# Compile child data
#
child1 <- subset(surveyData, select = c1id:c1fg17)
child1 <- data.frame(psu, mID, child1)
names(child1) <- c("psu", "mID", "cid", "dob", "age", "sex", "cmuac", "coed",
"cc1", "cc2", "cc3", "cc4", "cc5", "cc5a", "cc6", "cc7",
"cbf1", "cbf1a", "cbf2", "cbf2a", "cbf3", "cbf3a", "cf1",
"cf2", "cfg1", "cfg2", "cfg3", "cfg4", "cfg5", "cfg6",
"cfg7", "cfg8", "cfg9", "cfg10", "cfg11", "cfg12", "cfg13",
"cfg14", "cfg15", "cfg16", "cfg17")
child1$cid <- 1
child2 <- subset(surveyData, select = c2id:c2fg17)
child2 <- data.frame(psu, mID, child2)
names(child2) <- c("psu", "mID", "cid", "dob", "age", "sex", "cmuac", "coed",
"cc1", "cc2", "cc3", "cc4", "cc5", "cc5a", "cc6", "cc7",
"cbf1", "cbf1a", "cbf2", "cbf2a", "cbf3", "cbf3a", "cf1",
"cf2", "cfg1", "cfg2", "cfg3", "cfg4", "cfg5", "cfg6",
"cfg7", "cfg8", "cfg9", "cfg10", "cfg11", "cfg12", "cfg13",
"cfg14", "cfg15", "cfg16", "cfg17")
child2$cid <- 2
child3 <- subset(surveyData, select = c3id:c3fg17)
child3 <- data.frame(psu, mID, child3)
names(child3) <- c("psu", "mID", "cid", "dob", "age", "sex", "cmuac", "coed",
"cc1", "cc2", "cc3", "cc4", "cc5", "cc5a", "cc6", "cc7",
"cbf1", "cbf1a", "cbf2", "cbf2a", "cbf3", "cbf3a", "cf1",
"cf2", "cfg1", "cfg2", "cfg3", "cfg4", "cfg5", "cfg6",
"cfg7", "cfg8", "cfg9", "cfg10", "cfg11", "cfg12", "cfg13",
"cfg14", "cfg15", "cfg16", "cfg17")
child3$cid <- 3
child <- rbind(child1, child2, child3)
child <- subset(child, !is.na(age) & !is.na(sex))
#
# Create unique child ID
#
cID <- paste(mID, child$cid, sep = "")
################################################################################
#
# Child illnesses
#
################################################################################
#
# Recode
#
ch1 <- ifelse(child$cc1 == 1, 1, 0)
ch2 <- ifelse(child$cc2 == 1, 1, 0)
ch3 <- ifelse(child$cc3 == 1, 1, 0)
#
# Create illnesses dataset
#
ill <- data.frame(child$psu, child$mID, child$cid, ch1, ch2, ch3)
names(ill) <- c("psu", "mID", "cID", "ch1", "ch2", "ch3")
#
# Clean-up
#
rm(child1, child2, child3, ch1, ch2, ch3)
################################################################################
#
# Child health services
#
################################################################################
#
# Recode
#
ch4 <- ifelse(child$cc4 == 1, 1, 0)
ch5 <- ifelse(child$cc5 == 1 | child$cc5a == 1, 1, 0)
ch6 <- ifelse(child$cc6 == 1, 1, 0)
ch7 <- ifelse(child$cc7 == 1, 1, 0)
#
# Create health services dataset
#
imm <- data.frame(child$psu, child$mID, child$cid, ch4, ch5, ch6, ch7)
names(imm) <- c("psu", "mID", "cID", "ch4", "ch5", "ch6", "ch7")
rm(ch4, ch5, ch6, ch7)
################################################################################
#
# Anthropometry
#
################################################################################
#
# Make year-centred age-groups, get sex, MUAC, oedema
#
ageGroup <- recode(child$age, "0:5=0; 6:17=1; 18:29=2; 30:41=3; 42:53=4; 54:hi=5")
sex <- child$sex
muac <- child$cmuac
oedema <- child$coed
#
# Create anthro data frame
#
anthro <- data.frame(child$psu, child$mID, child$cid, sex, muac, muac, oedema)
names(anthro) <- c("psu", "mID", "cID", "sex", "muac1", "muac2", "oedema")
#
# Clean up
#
rm(sex, muac, oedema)
############################# Make IYCF indicator set ##########################
################################################################################
#
# Indicator creation script for IYCF indicators
#
################################################################################
#
# Script assumes survey data is in data.frame 'x'.
#
# Creates data.frame 'iycf' with columns :
#
# psu PSU identifier
# mID HH identifier
# cID Child identifier
# EBF Exclusive breastfeeding
# CBF Continuing breastfeeding
# DDD Dietary diversity
# MFD Meal frequency
# ICFI ICFI score
# IYCF IYCF summary indicator
#
################################################################################
################################################################################
#
# Select data for IYCF age-group
#
x <- subset(child, age <= 24)
################################################################################
#
# Grouped age variable 'ageBand' (used when calculating age-specific scores)
#
ageBand <- recode(x$age, "0:5=1; 6:8=2; 9:11=3; 12:24=4")
################################################################################
#
# Exclusive breastfeeding
#
ebf <- ifelse(x$cbf2 == 1 & x$cbf3 == 0 & x$cf1 == 0, 1, 0)
ebf[ageBand != 1] <- NA
################################################################################
#
# Age-specific continuing breastfeeding
#
bfs <- ifelse(ageBand == 2, (x$cbf2 == 1) * 2,
ifelse(ageBand == 3, (x$cbf2 == 1) * 2,
ifelse(ageBand == 4, (x$cbf2 == 1), NA)))
bfd <- ifelse(bfs != 0, 1, 0)
################################################################################
#
# Age-specific dietary diversity
#
fg1 <- ifelse(x$cfg4 == 1 | x$cfg8 == 1, 1, 0)
fg2 <- ifelse(x$cfg9 == 1, 1, 0)
fg3 <- ifelse(x$cfg2 == 1 | x$cfg14 == 1, 1, 0)
fg4 <- ifelse(x$cfg11 == 1 | x$cfg12 == 1 | x$cfg13 == 1, 1, 0)
fg5 <- ifelse(x$cfg15 == 1, 1, 0)
fg6 <- ifelse(x$cfg5 == 1 | x$cfg6 == 1 | x$cfg7 == 1, 1, 0)
fg7 <- ifelse(x$cfg10 == 1, 1, 0)
fg <- fg1 + fg2 + fg3 + fg4 + fg5 + fg6 + fg7
dds <- ifelse(ageBand == 2, (fg == 1) + (fg > 1) * 2,
ifelse(ageBand == 3, (fg %in% 1:2) + (fg > 2) * 2,
ifelse(ageBand == 4, (fg %in% 2:3) + (fg > 3) * 2, NA)))
ddd <- ifelse(dds == 2, 1, 0)
rm(fg1, fg2, fg3, fg4, fg5, fg6, fg7, fg)
################################################################################
#
# Age-specific meal frequency score
#
mfs <- ifelse(ageBand == 2, (x$cf1 == 1) + (x$cf1 > 1) * 2,
ifelse(ageBand == 3, (x$cf1 %in% 1:2) + (x$cf1 > 2) * 2,
ifelse(ageBand == 4, (x$cf1 == 2) + (x$cf1 == 3) * 2 + (x$cf1 > 3) * 3, NA)))
mfd <- ifelse(mfs > 1, 1, 0)
################################################################################
#
# ICFI
#
icfi <- bfs + dds + mfs
icfiProp <- ifelse(icfi == 6, 1, 0)
################################################################################
#
# IYCF
#
iycf <- ifelse((ageBand == 1 & ebf == 1) | icfi == 6, 1, 0)
icfi[is.na(icfi)] <- 0
################################################################################
#
# Make a data.frame ('iycf') with indicators :
#
# ebf Exclusive breastfeeding
# bfd Continuing breastfeeding
# ddd ICFI dietary diversity score = 2
# mfd ICFI Meal frequency score > 1
# icfi ICFI score
# icfiProp ICFI score == 6
# iycf IYCF summary indicator
#
# and PSU, HH and Child identifiers ...
#
iycf <- data.frame(x$psu, x$mID, x$cid, ebf, bfd, ddd, mfd, icfi, iycf, icfiProp)
names(iycf) <- c("psu", "mID", "cID", "ebf", "cbf", "ddd", "mfd", "icfi", "iycf", "icfiProp")
#
# Clean up
#
rm(bfs, bfd, mfs, mfd, dds, ddd, icfi, icfiProp)
################################################################################
#
# Breastfeeding indicators
#
################################################################################
#
# Ever breastfed (bf1)
#
bf1 <- ifelse(child$cbf1 == 1, 1, 0)
#
# Early initiation of breastfeeding: within 1 hour of birth (bf2)
#
bf2 <- ifelse(child$cbf1a == 1, 1, 0)
#
# Continued breastfeeding up to 1 year
#
cbf <- data.frame(x$psu, x$mID, x$cid, x$age, x$cbf2, x$cbf2a)
names(cbf) <- c("psu", "mID", "cID", "age", "bf", "bfStop")
rm(bf1, bf2)
#
# Merge all CH datasets
#
temp <- merge(ill, imm, by = c("psu", "mID", "cID"))
temp <- merge(temp, anthro, by = c("psu", "mID", "cID"))
indicatorsCH1 <- temp
indicatorsCH2 <- merge(iycf, cbf, by = c("psu", "mID", "cID"))
rm(anc, anthro, cbf, child, hhs, ill, imm, iycf, mdd, mMUAC, surveyData, temp,
wash, x, ageBand, ageGroup, cID, ebf, mID, psu)
write.csv(indicatorsCH1, "data-raw/indicatorsCH1.csv", row.names = FALSE)
devtools::use_data(indicatorsCH1, overwrite = TRUE)
write.csv(indicatorsCH2, "data-raw/indicatorsCH2.csv", row.names = FALSE)
devtools::use_data(indicatorsCH2, overwrite = TRUE)
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