R/08-calculate_ppi_civ.R
In ernestguevarra/ppicalc: Calculator for Determining Poverty Likelihood Based on the Poverty Probability Index (PPI)
Defines functions
get_data_civ
ppi_civ_ipa
Documented in
get_data_civ
ppi_civ_ipa
################################################################################
#
#' Get Ivory Coast PPI data from IPA's data analysis and collection tool
#'
#' @param path Directory path to IPA's Ivory Coast PPI data analysis and
#' collection XLSX file
#'
#' @return A dataframe of household data based on IPA's Ivory Coast PPI data
#' analysis and collection tool
#'
#' @examples
#' get_data_civ(path = paste(system.file("ppi", package = "ppicalc"), "ivoryCoast.xlsx", sep = "/"))
#'
#' @export
#'
#
################################################################################
get_data_civ <- function(path) {
df <- openxlsx::read.xlsx(xlsxFile = path,
sheet = "Data",
startRow = 8,
cols = 1:47)
## Return data.frame
return(df)
}
################################################################################
#
#' Function to calculate Poverty Probability Index (PPI) for Ivory Coast using
#' IPA's new PPI methodology
#'
#' @param path Directory path to IPA's Ivory Coast PPI data analysis and
#' collection XLSX file
#' @param category Poverty classifications that can be calculated for
#' Ivory Coast using IPA's new PPI methodology. By default, all 15 poverty
#' classifications for Ivory Coast are specified
#'
#' @return A dataframe of PPI for each household in \code{df}.
#'
#' @examples
#' \dontrun{
#' ppi_col_ipa(path = paste(system.file("ppi", package = "ppicalc"),
#' "ivoryCoast.xlsx", sep = "/"))
#' }
#'
#' @export
#'
#
################################################################################
ppi_civ_ipa <- function(path,
category = names(ppitables::ppiCIV2018)[2:ncol(ppitables::ppiCIV2018)]) {
df <- get_data_col(path = path)
scoreDF <- data.frame(matrix(data = NA, nrow = nrow(df), ncol = length(category) * 2))
names(scoreDF) <- c(paste(category, "score", sep = "_"),
paste(category, "likelihood", sep = "_"))
## Recode data
## q1: District?
q1a <- bbw::recode(var = df[["1A"]],
recodes = "NA=NA;else=1")
q1b <- bbw::recode(var = df[["1B"]],
recodes = "NA=NA;else=2")
q1c <- bbw::recode(var = df[["1C"]],
recodes = "NA=NA;else=3")
q1d <- bbw::recode(var = df[["1D"]],
recodes = "NA=NA;else=4")
q1e <- bbw::recode(var = df[["1E"]],
recodes = "NA=NA;else=5")
q1f <- bbw::recode(var = df[["1F"]],
recodes = "NA=NA;else=6")
q1g <- bbw::recode(var = df[["1G"]],
recodes = "NA=NA;else=7")
q1h <- bbw::recode(var = df[["1H"]],
recodes = "NA=NA;else=8")
q1i <- bbw::recode(var = df[["1I"]],
recodes = "NA=NA;else=9")
q1j <- bbw::recode(var = df[["1J"]],
recodes = "NA=NA;else=10")
q1k <- bbw::recode(var = df[["1K"]],
recodes = "NA=NA;else=11")
q1l <- bbw::recode(var = df[["1L"]],
recodes = "NA=NA;else=12")
q1m <- bbw::recode(var = df[["1M"]],
recodes = "NA=NA;else=13")
q1n <- bbw::recode(var = df[["1N"]],
recodes = "NA=NA;else=2")
q1 <- rowSums(cbind(q1a, q1b, q1c, q1d,
q1e, q1f, q1g, q1h,
q1i, q1j, q1k, q1l,
q1m, q1n), na.rm = TRUE)
## q2: Household members
q2a <- bbw::recode(var = df[["2A"]],
recodes = "NA=NA;else=1")
q2b <- bbw::recode(var = df[["2B"]],
recodes = "NA=NA;else=2")
q2 <- rowSums(cbind(q2a, q2b), na.rm = TRUE)
## q3: Highest educational level?
q3a <- bbw::recode(var = df[["3A"]],
recodes = "NA=NA;else=1")
q3b <- bbw::recode(var = df[["3B"]],
recodes = "NA=NA;else=2")
q3c <- bbw::recode(var = df[["3C"]],
recodes = "NA=NA;else=3")
q3d <- bbw::recode(var = df[["3D"]],
recodes = "NA=NA;else=4")
q3 <- rowSums(cbind(q3a, q3b, q3c, q3d), na.rm = TRUE)
## q4: children 6-16 attend school?
q4a <- bbw::recode(var = df[["4A"]],
recodes = "NA=NA;else=1")
q4b <- bbw::recode(var = df[["4B"]],
recodes = "NA=NA;else=2")
q4c <- bbw::recode(var = df[["4C"]],
recodes = "NA=NA;else=3")
q4 <- rowSums(cbind(q4a, q4b, q4c), na.rm = TRUE)
## q5: Mode of water supply?
q5a <- bbw::recode(var = df[["5A"]],
recodes = "NA=NA;else=1")
q5b <- bbw::recode(var = df[["5B"]],
recodes = "NA=NA;else=2")
q5c <- bbw::recode(var = df[["5C"]],
recodes = "NA=NA;else=3")
q5d <- bbw::recode(var = df[["5D"]],
recodes = "NA=NA;else=4")
q5e <- bbw::recode(var = df[["5E"]],
recodes = "NA=NA;else=5")
q5f <- bbw::recode(var = df[["5F"]],
recodes = "NA=NA;else=6")
q5g <- bbw::recode(var = df[["5G"]],
recodes = "NA=NA;else=7")
q5 <- rowSums(cbind(q5a, q5b, q5c, q5d, q5e, q5f, q5g), na.rm = TRUE)
## q6: type of toilet?
q6a <- bbw::recode(var = df[["6A"]],
recodes = "NA=NA;else=1")
q6b <- bbw::recode(var = df[["6B"]],
recodes = "NA=NA;else=2")
q6c <- bbw::recode(var = df[["6C"]],
recodes = "NA=NA;else=3")
q6d <- bbw::recode(var = df[["6D"]],
recodes = "NA=NA;else=4")
q6e <- bbw::recode(var = df[["6E"]],
recodes = "NA=NA;else=5")
q6 <- rowSums(cbind(q6a, q6b, q6c, q6d, q6e), na.rm = TRUE)
## q7: where do you take your shower?
q7a <- bbw::recode(var = df[["7A"]],
recodes = "NA=NA;else=1")
q7b <- bbw::recode(var = df[["7B"]],
recodes = "NA=NA;else=2")
q7c <- bbw::recode(var = df[["7C"]],
recodes = "NA=NA;else=3")
q7d <- bbw::recode(var = df[["7D"]],
recodes = "NA=NA;else=4")
q7 <- rowSums(cbind(q7a, q7b, q7c, q7d), na.rm = TRUE)
## q8: Onw moped, car or van?
q8a <- bbw::recode(var = df[["8A"]],
recodes = "NA=NA;else=1")
q8b <- bbw::recode(var = df[["8B"]],
recodes = "NA=NA;else=2")
q8c <- bbw::recode(var = df[["8C"]],
recodes = "NA=NA;else=3")
q8 <- rowSums(cbind(q8a, q8b, q8c), na.rm = TRUE)
## q9: own a fan?
q9 <- ifelse(!is.na(df[["9A"]]), 1, 0)
## q10: own a bed?
q10 <- ifelse(!is.na(df[["10A"]]), 1, 0)
## Create ppiDF object
ppiDF <- data.frame(hhid = df$HHID, q1, q2, q3, q4, q5, q6, q7, q8, q9, q10)
## National poverty line
if("nl100" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=7;2=5;3=9;4=4;5=0;6=3;7=3;8=2;9=5;10=0;11=2;12=2;13=4;14=4")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=17;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=5;4=12")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=11;2=7;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=10;2=4;3=4;4=1;5=2;6=2;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=7;2=6;3=5;4=5;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=9;4=1")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=15;2=9;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
## Add score to scoreDF
scoreDF[ , "nl100_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
## Add likelihood to scoreDF
scoreDF[ , "nl100_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "nl100_score"] + 1, "nl100"]
}
## National poverty line - 150%
if("nl150" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=7;2=6;3=6;4=3;5=0;6=3;7=2;8=3;9=5;10=0;11=0;12=1;13=2;14=3")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=19;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=5;4=11")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=13;2=7;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=10;2=4;3=2;4=2;5=2;6=2;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=8;2=7;3=5;4=6;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=9;4=2")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=14;2=7;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
## Add score to scoreDF
scoreDF[ , "nl150_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
## Add likelihood to scoreDF
scoreDF[ , "nl150_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "nl150_score"] + 1, "nl150"]
}
## National poverty line - 200%
if("nl200" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=6;2=5;3=7;4=3;5=0;6=4;7=2;8=4;9=7;10=1;11=0;12=1;13=2;14=3")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=18;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=5;4=10")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=13;2=6;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=10;2=5;3=2;4=3;5=2;6=3;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=8;2=7;3=6;4=6;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=6;4=1")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=17;2=8;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=5;2=0")
##
scoreDF[ , "nl200_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
## Add likelihood to scoreDF
scoreDF[ , "nl200_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "nl200_score"] + 1, "nl200"]
}
## 2011 Purchasing power parity $1.00/day
if("ppp100" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=5;2=6;3=6;4=6;5=0;6=0;7=11;8=0;9=3;10=3;11=3;12=4;13=3;14=8")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=13;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=5;3=7;4=14")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=9;2=7;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=9;2=8;3=10;4=3;5=3;6=2;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=8;2=6;3=4;4=6;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=7;3=13;4=5")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=3;2=10;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=7;2=0")
##
scoreDF[ , "ppp190_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
## Add likelihood to scoreDF
scoreDF[ , "ppp190_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "ppp190_score"] + 1, "ppp190"]
}
## 2011 Purchasing power parity $1.90/day
if("ppp190" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=7;2=6;3=10;4=3;5=0;6=2;7=6;8=0;9=5;10=0;11=2;12=5;13=5;14=5")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=18;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=5;4=11")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=11;2=8;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=9;2=4;3=6;4=1;5=1;6=1;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=9;2=7;3=5;4=6;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=5;3=10;4=2")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=9;2=11;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=7;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "ppp190_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
## Add likelihood to scoreDF
scoreDF[ , "ppp190_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "ppp190_score"] + 1, "ppp190"]
}
## 2011 Purchasing power parity $3.20
if("ppp320" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=7;2=5;3=9;4=5;5=0;6=3;7=3;8=3;9=5;10=0;11=1;12=3;13=3;14=3")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=17;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=5;3=5;4=11")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=11;2=7;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=10;2=4;3=4;4=2;5=3;6=3;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=7;2=6;3=5;4=5;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=8;4=2")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=16;2=8;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "ppp320_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
## Add likelihood to scoreDF
scoreDF[ , "ppp320_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "ppp320_score"] + 1, "ppp320"]
}
## 2011 Purchasing power parity $5.50
if("ppp550" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=7;2=6;3=7;4=3;5=0;6=3;7=2;8=4;9=6;10=1;11=0;12=1;13=2;14=3")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=19;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=5;4=10")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=13;2=6;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=9;2=4;3=1;4=2;5=2;6=3;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=9;2=8;3=6;4=6;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=7;4=1")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=16;2=7;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "ppp550_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
## Add likelihood to scoreDF
scoreDF[ , "ppp550_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "ppp550_score"] + 1, "ppp550"]
}
## 2005 Purchasing power parity $1.25
if("ppp125" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=9;2=6;3=10;4=4;5=0;6=2;7=5;8=1;9=4;10=0;11=1;12=4;13=5;14=4")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=18;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=4;4=12")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=10;2=8;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=9;2=3;3=5;4=1;5=2;6=1;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=7;2=6;3=5;4=5;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=4;3=10;4=3")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=11;2=10;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=8;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "ppp125_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
##
scoreDF[ , "ppp125_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "ppp125_score"] + 1, "ppp125"]
}
## 2005 Purchasing power parity $2.50
if("ppp250" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=7;2=6;3=7;4=3;5=0;6=4;7=3;8=3;9=5;10=0;11=0;12=2;13=2;14=3")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=19;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=5;4=11")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=13;2=7;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=9;2=4;3=3;4=2;5=2;6=2;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=8;2=7;3=6;4=7;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=9;4=2")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=14;2=8;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "ppp250_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
##
scoreDF[ , "ppp250_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "ppp250_score"] + 1, "ppp250"]
}
## 2005 Purchasing power parity $5.00
if("ppp500" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=6;2=3;3=6;4=3;5=0;6=3;7=2;8=5;9=7;10=1;11=0;12=2;13=0;14=2")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=20;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=3;3=5;4=10")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=12;2=3;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=8;2=4;3=0;4=3;5=2;6=1;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=8;2=5;3=4;4=5;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=5;4=1")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=21;2=5;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "ppp500_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
##
scoreDF[ , "ppp500_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "ppp500_score"] + 1, "ppp500"]
}
## Bottom 20th percentile
if("percentile20" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=9;2=6;3=10;4=4;5=0;6=2;7=5;8=1;9=4;10=0;11=1;12=4;13=5;14=4")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=18;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=4;4=12")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=10;2=7;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=9;2=3;3=4;4=1;5=1;6=1;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=8;2=6;3=5;4=5;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=4;3=10;4=3")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=11;2=10;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=7;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=3;2=0")
##
scoreDF[ , "percentile20_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
##
scoreDF[ , "percentile20_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "percentile20_score"] + 1, "percentile20"]
}
## Bottom 40th percentile
if("percentile40" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=8;2=5;3=9;4=5;5=0;6=4;7=3;8=3;9=5;10=0;11=2;12=2;13=3;14=4")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=18;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=5;3=5;4=12")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=11;2=6;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=10;2=4;3=4;4=2;5=3;6=3;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=7;2=6;3=5;4=5;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=2;3=8;4=1")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=15;2=8;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "percentile40_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
##
scoreDF[ , "percentile40_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "percentile40_score"] + 1, "percentile40"]
}
## Bottom 60th percentile
if("percentile60" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=7;2=6;3=7;4=3;5=0;6=3;7=2;8=3;9=5;10=0;11=0;12=2;13=2;14=3")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=19;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=5;4=11")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=13;2=7;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=10;2=4;3=3;4=2;5=2;6=3;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=8;2=7;3=5;4=6;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=9;4=2")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=15;2=7;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "percentile60_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
##
scoreDF[ , "percentile60_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "percentile60_score"] + 1, "percentile60"]
}
## Bottom 80th percentile
if("percentile80" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=6;2=5;3=7;4=3;5=0;6=4;7=3;8=5;9=7;10=1;11=0;12=2;13=1;14=4")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=19;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=6;4=11")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=12;2=4;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=8;2=4;3=1;4=3;5=2;6=3;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=9;2=6;3=5;4=6;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=5;4=1")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=19;2=6;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=5;2=0")
##
scoreDF[ , "percentile80_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
##
scoreDF[ , "percentile80_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "percentile80_score"] + 1, "percentile80"]
}
##
return(scoreDF)
}
ernestguevarra/ppicalc documentation built on Jan. 14, 2020, 2:49 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions get_data_civ ppi_civ_ipa
Documented in get_data_civ ppi_civ_ipa
################################################################################
#
#' Get Ivory Coast PPI data from IPA's data analysis and collection tool
#'
#' @param path Directory path to IPA's Ivory Coast PPI data analysis and
#' collection XLSX file
#'
#' @return A dataframe of household data based on IPA's Ivory Coast PPI data
#' analysis and collection tool
#'
#' @examples
#' get_data_civ(path = paste(system.file("ppi", package = "ppicalc"), "ivoryCoast.xlsx", sep = "/"))
#'
#' @export
#'
#
################################################################################
get_data_civ <- function(path) {
df <- openxlsx::read.xlsx(xlsxFile = path,
sheet = "Data",
startRow = 8,
cols = 1:47)
## Return data.frame
return(df)
}
################################################################################
#
#' Function to calculate Poverty Probability Index (PPI) for Ivory Coast using
#' IPA's new PPI methodology
#'
#' @param path Directory path to IPA's Ivory Coast PPI data analysis and
#' collection XLSX file
#' @param category Poverty classifications that can be calculated for
#' Ivory Coast using IPA's new PPI methodology. By default, all 15 poverty
#' classifications for Ivory Coast are specified
#'
#' @return A dataframe of PPI for each household in \code{df}.
#'
#' @examples
#' \dontrun{
#' ppi_col_ipa(path = paste(system.file("ppi", package = "ppicalc"),
#' "ivoryCoast.xlsx", sep = "/"))
#' }
#'
#' @export
#'
#
################################################################################
ppi_civ_ipa <- function(path,
category = names(ppitables::ppiCIV2018)[2:ncol(ppitables::ppiCIV2018)]) {
df <- get_data_col(path = path)
scoreDF <- data.frame(matrix(data = NA, nrow = nrow(df), ncol = length(category) * 2))
names(scoreDF) <- c(paste(category, "score", sep = "_"),
paste(category, "likelihood", sep = "_"))
## Recode data
## q1: District?
q1a <- bbw::recode(var = df[["1A"]],
recodes = "NA=NA;else=1")
q1b <- bbw::recode(var = df[["1B"]],
recodes = "NA=NA;else=2")
q1c <- bbw::recode(var = df[["1C"]],
recodes = "NA=NA;else=3")
q1d <- bbw::recode(var = df[["1D"]],
recodes = "NA=NA;else=4")
q1e <- bbw::recode(var = df[["1E"]],
recodes = "NA=NA;else=5")
q1f <- bbw::recode(var = df[["1F"]],
recodes = "NA=NA;else=6")
q1g <- bbw::recode(var = df[["1G"]],
recodes = "NA=NA;else=7")
q1h <- bbw::recode(var = df[["1H"]],
recodes = "NA=NA;else=8")
q1i <- bbw::recode(var = df[["1I"]],
recodes = "NA=NA;else=9")
q1j <- bbw::recode(var = df[["1J"]],
recodes = "NA=NA;else=10")
q1k <- bbw::recode(var = df[["1K"]],
recodes = "NA=NA;else=11")
q1l <- bbw::recode(var = df[["1L"]],
recodes = "NA=NA;else=12")
q1m <- bbw::recode(var = df[["1M"]],
recodes = "NA=NA;else=13")
q1n <- bbw::recode(var = df[["1N"]],
recodes = "NA=NA;else=2")
q1 <- rowSums(cbind(q1a, q1b, q1c, q1d,
q1e, q1f, q1g, q1h,
q1i, q1j, q1k, q1l,
q1m, q1n), na.rm = TRUE)
## q2: Household members
q2a <- bbw::recode(var = df[["2A"]],
recodes = "NA=NA;else=1")
q2b <- bbw::recode(var = df[["2B"]],
recodes = "NA=NA;else=2")
q2 <- rowSums(cbind(q2a, q2b), na.rm = TRUE)
## q3: Highest educational level?
q3a <- bbw::recode(var = df[["3A"]],
recodes = "NA=NA;else=1")
q3b <- bbw::recode(var = df[["3B"]],
recodes = "NA=NA;else=2")
q3c <- bbw::recode(var = df[["3C"]],
recodes = "NA=NA;else=3")
q3d <- bbw::recode(var = df[["3D"]],
recodes = "NA=NA;else=4")
q3 <- rowSums(cbind(q3a, q3b, q3c, q3d), na.rm = TRUE)
## q4: children 6-16 attend school?
q4a <- bbw::recode(var = df[["4A"]],
recodes = "NA=NA;else=1")
q4b <- bbw::recode(var = df[["4B"]],
recodes = "NA=NA;else=2")
q4c <- bbw::recode(var = df[["4C"]],
recodes = "NA=NA;else=3")
q4 <- rowSums(cbind(q4a, q4b, q4c), na.rm = TRUE)
## q5: Mode of water supply?
q5a <- bbw::recode(var = df[["5A"]],
recodes = "NA=NA;else=1")
q5b <- bbw::recode(var = df[["5B"]],
recodes = "NA=NA;else=2")
q5c <- bbw::recode(var = df[["5C"]],
recodes = "NA=NA;else=3")
q5d <- bbw::recode(var = df[["5D"]],
recodes = "NA=NA;else=4")
q5e <- bbw::recode(var = df[["5E"]],
recodes = "NA=NA;else=5")
q5f <- bbw::recode(var = df[["5F"]],
recodes = "NA=NA;else=6")
q5g <- bbw::recode(var = df[["5G"]],
recodes = "NA=NA;else=7")
q5 <- rowSums(cbind(q5a, q5b, q5c, q5d, q5e, q5f, q5g), na.rm = TRUE)
## q6: type of toilet?
q6a <- bbw::recode(var = df[["6A"]],
recodes = "NA=NA;else=1")
q6b <- bbw::recode(var = df[["6B"]],
recodes = "NA=NA;else=2")
q6c <- bbw::recode(var = df[["6C"]],
recodes = "NA=NA;else=3")
q6d <- bbw::recode(var = df[["6D"]],
recodes = "NA=NA;else=4")
q6e <- bbw::recode(var = df[["6E"]],
recodes = "NA=NA;else=5")
q6 <- rowSums(cbind(q6a, q6b, q6c, q6d, q6e), na.rm = TRUE)
## q7: where do you take your shower?
q7a <- bbw::recode(var = df[["7A"]],
recodes = "NA=NA;else=1")
q7b <- bbw::recode(var = df[["7B"]],
recodes = "NA=NA;else=2")
q7c <- bbw::recode(var = df[["7C"]],
recodes = "NA=NA;else=3")
q7d <- bbw::recode(var = df[["7D"]],
recodes = "NA=NA;else=4")
q7 <- rowSums(cbind(q7a, q7b, q7c, q7d), na.rm = TRUE)
## q8: Onw moped, car or van?
q8a <- bbw::recode(var = df[["8A"]],
recodes = "NA=NA;else=1")
q8b <- bbw::recode(var = df[["8B"]],
recodes = "NA=NA;else=2")
q8c <- bbw::recode(var = df[["8C"]],
recodes = "NA=NA;else=3")
q8 <- rowSums(cbind(q8a, q8b, q8c), na.rm = TRUE)
## q9: own a fan?
q9 <- ifelse(!is.na(df[["9A"]]), 1, 0)
## q10: own a bed?
q10 <- ifelse(!is.na(df[["10A"]]), 1, 0)
## Create ppiDF object
ppiDF <- data.frame(hhid = df$HHID, q1, q2, q3, q4, q5, q6, q7, q8, q9, q10)
## National poverty line
if("nl100" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=7;2=5;3=9;4=4;5=0;6=3;7=3;8=2;9=5;10=0;11=2;12=2;13=4;14=4")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=17;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=5;4=12")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=11;2=7;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=10;2=4;3=4;4=1;5=2;6=2;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=7;2=6;3=5;4=5;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=9;4=1")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=15;2=9;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
## Add score to scoreDF
scoreDF[ , "nl100_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
## Add likelihood to scoreDF
scoreDF[ , "nl100_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "nl100_score"] + 1, "nl100"]
}
## National poverty line - 150%
if("nl150" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=7;2=6;3=6;4=3;5=0;6=3;7=2;8=3;9=5;10=0;11=0;12=1;13=2;14=3")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=19;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=5;4=11")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=13;2=7;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=10;2=4;3=2;4=2;5=2;6=2;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=8;2=7;3=5;4=6;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=9;4=2")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=14;2=7;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
## Add score to scoreDF
scoreDF[ , "nl150_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
## Add likelihood to scoreDF
scoreDF[ , "nl150_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "nl150_score"] + 1, "nl150"]
}
## National poverty line - 200%
if("nl200" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=6;2=5;3=7;4=3;5=0;6=4;7=2;8=4;9=7;10=1;11=0;12=1;13=2;14=3")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=18;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=5;4=10")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=13;2=6;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=10;2=5;3=2;4=3;5=2;6=3;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=8;2=7;3=6;4=6;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=6;4=1")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=17;2=8;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=5;2=0")
##
scoreDF[ , "nl200_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
## Add likelihood to scoreDF
scoreDF[ , "nl200_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "nl200_score"] + 1, "nl200"]
}
## 2011 Purchasing power parity $1.00/day
if("ppp100" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=5;2=6;3=6;4=6;5=0;6=0;7=11;8=0;9=3;10=3;11=3;12=4;13=3;14=8")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=13;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=5;3=7;4=14")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=9;2=7;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=9;2=8;3=10;4=3;5=3;6=2;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=8;2=6;3=4;4=6;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=7;3=13;4=5")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=3;2=10;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=7;2=0")
##
scoreDF[ , "ppp190_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
## Add likelihood to scoreDF
scoreDF[ , "ppp190_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "ppp190_score"] + 1, "ppp190"]
}
## 2011 Purchasing power parity $1.90/day
if("ppp190" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=7;2=6;3=10;4=3;5=0;6=2;7=6;8=0;9=5;10=0;11=2;12=5;13=5;14=5")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=18;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=5;4=11")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=11;2=8;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=9;2=4;3=6;4=1;5=1;6=1;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=9;2=7;3=5;4=6;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=5;3=10;4=2")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=9;2=11;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=7;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "ppp190_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
## Add likelihood to scoreDF
scoreDF[ , "ppp190_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "ppp190_score"] + 1, "ppp190"]
}
## 2011 Purchasing power parity $3.20
if("ppp320" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=7;2=5;3=9;4=5;5=0;6=3;7=3;8=3;9=5;10=0;11=1;12=3;13=3;14=3")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=17;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=5;3=5;4=11")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=11;2=7;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=10;2=4;3=4;4=2;5=3;6=3;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=7;2=6;3=5;4=5;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=8;4=2")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=16;2=8;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "ppp320_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
## Add likelihood to scoreDF
scoreDF[ , "ppp320_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "ppp320_score"] + 1, "ppp320"]
}
## 2011 Purchasing power parity $5.50
if("ppp550" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=7;2=6;3=7;4=3;5=0;6=3;7=2;8=4;9=6;10=1;11=0;12=1;13=2;14=3")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=19;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=5;4=10")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=13;2=6;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=9;2=4;3=1;4=2;5=2;6=3;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=9;2=8;3=6;4=6;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=7;4=1")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=16;2=7;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "ppp550_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
## Add likelihood to scoreDF
scoreDF[ , "ppp550_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "ppp550_score"] + 1, "ppp550"]
}
## 2005 Purchasing power parity $1.25
if("ppp125" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=9;2=6;3=10;4=4;5=0;6=2;7=5;8=1;9=4;10=0;11=1;12=4;13=5;14=4")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=18;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=4;4=12")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=10;2=8;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=9;2=3;3=5;4=1;5=2;6=1;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=7;2=6;3=5;4=5;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=4;3=10;4=3")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=11;2=10;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=8;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "ppp125_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
##
scoreDF[ , "ppp125_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "ppp125_score"] + 1, "ppp125"]
}
## 2005 Purchasing power parity $2.50
if("ppp250" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=7;2=6;3=7;4=3;5=0;6=4;7=3;8=3;9=5;10=0;11=0;12=2;13=2;14=3")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=19;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=5;4=11")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=13;2=7;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=9;2=4;3=3;4=2;5=2;6=2;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=8;2=7;3=6;4=7;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=9;4=2")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=14;2=8;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "ppp250_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
##
scoreDF[ , "ppp250_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "ppp250_score"] + 1, "ppp250"]
}
## 2005 Purchasing power parity $5.00
if("ppp500" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=6;2=3;3=6;4=3;5=0;6=3;7=2;8=5;9=7;10=1;11=0;12=2;13=0;14=2")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=20;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=3;3=5;4=10")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=12;2=3;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=8;2=4;3=0;4=3;5=2;6=1;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=8;2=5;3=4;4=5;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=5;4=1")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=21;2=5;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "ppp500_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
##
scoreDF[ , "ppp500_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "ppp500_score"] + 1, "ppp500"]
}
## Bottom 20th percentile
if("percentile20" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=9;2=6;3=10;4=4;5=0;6=2;7=5;8=1;9=4;10=0;11=1;12=4;13=5;14=4")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=18;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=4;4=12")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=10;2=7;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=9;2=3;3=4;4=1;5=1;6=1;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=8;2=6;3=5;4=5;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=4;3=10;4=3")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=11;2=10;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=7;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=3;2=0")
##
scoreDF[ , "percentile20_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
##
scoreDF[ , "percentile20_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "percentile20_score"] + 1, "percentile20"]
}
## Bottom 40th percentile
if("percentile40" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=8;2=5;3=9;4=5;5=0;6=4;7=3;8=3;9=5;10=0;11=2;12=2;13=3;14=4")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=18;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=5;3=5;4=12")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=11;2=6;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=10;2=4;3=4;4=2;5=3;6=3;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=7;2=6;3=5;4=5;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=2;3=8;4=1")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=15;2=8;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "percentile40_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
##
scoreDF[ , "percentile40_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "percentile40_score"] + 1, "percentile40"]
}
## Bottom 60th percentile
if("percentile60" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=7;2=6;3=7;4=3;5=0;6=3;7=2;8=3;9=5;10=0;11=0;12=2;13=2;14=3")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=19;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=5;4=11")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=13;2=7;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=10;2=4;3=3;4=2;5=2;6=3;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=8;2=7;3=5;4=6;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=9;4=2")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=15;2=7;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=4;2=0")
##
scoreDF[ , "percentile60_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
##
scoreDF[ , "percentile60_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "percentile60_score"] + 1, "percentile60"]
}
## Bottom 80th percentile
if("percentile80" %in% category) {
## District where household resides?
s1 <- bbw::recode(var = ppiDF$q1,
recodes = "1=6;2=5;3=7;4=3;5=0;6=4;7=3;8=5;9=7;10=1;11=0;12=2;13=1;14=4")
## Number of household members?
s2 <- bbw::recode(var = ppiDF$q2,
recodes = "1=19;2=0")
## Highest educational leve?
s3 <- bbw::recode(var = ppiDF$q3,
recodes = "1=0;2=4;3=6;4=11")
## Children 6-16 attend school?
s4 <- bbw::recode(var = ppiDF$q4, recodes = "1=12;2=4;3=0")
## Mode of water supply?
s5 <- bbw::recode(var = ppiDF$q5, recodes = "1=8;2=4;3=1;4=3;5=2;6=3;7=0")
## Type of toilet?
s6 <- bbw::recode(var = ppiDF$q6, recodes = "1=9;2=6;3=5;4=6;5=0")
## Where shower is taken?
s7 <- bbw::recode(var = ppiDF$q7, recodes = "1=0;2=3;3=5;4=1")
## Moped or care or van?
s8 <- bbw::recode(var = ppiDF$q8, recodes = "1=19;2=6;3=0")
## fan?
s9 <- bbw::recode(var = ppiDF$q9, recodes = "1=6;0=0")
## bed
s10 <- bbw::recode(var = ppiDF$q10, recodes = "1=5;2=0")
##
scoreDF[ , "percentile80_score"] <- rowSums(cbind(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10))
##
scoreDF[ , "percentile80_likelihood"] <- ppitables::ppiCIV2018[scoreDF[ , "percentile80_score"] + 1, "percentile80"]
}
##
return(scoreDF)
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.