################################################################################
#
#' Function to recode household food expenditure share for the Myanmar MCCT
#' Programme Evaluation Survey
#'
#' @param df A data.frame collected for the Myanmar MCCT Programme Evaluation
#' Study containing household food expenditure data
#' @param core.columns A vector of variable names to include in resulting
#' data.frame
#'
#' @return A vector of recoded household food expenditure share indicators
#'
#' @examples
#' recode_hfes(df = hh)
#'
#' @export
#'
#
################################################################################
recode_hfes <- function(df,
core.columns = c("KEY",
"geo_state",
"geo_rural",
"geo_villward",
"sample_component")) {
## non-food expenditure
x <- df[ , c("exp_tution_cash", "exp_eduoth_cash",
"exp_healthadult_cash", "exp_healthchild_cash",
"exp_healthu5_cash", "exp_transpt_cash",
"exp_remitt_cash", "exp_construct_cash",
"exp_trade_cash", "exp_farm_cash", "exp_livestock_cash",
"exp_fish_cash", "exp_social_cash", "exp_othreg_cash",
"exp_debt_cash", "exp_donate_cash")]
x <- apply(X = x, MARGIN = 2, FUN = as.integer)
nonfood <- rowSums(x = x, na.rm = TRUE)
## food expenditure
y <- df[ , c("hhrice_cost", "hhpotatoes_cost", "hhpumpkin_cost",
"hhbeans_cost", "hhnuts_cost", "hhyogurt_cost", "hhorgan_cost",
"hhbeef_cost", "hhfish_cost", "hheggs_cost", "hhleafyveg_cost",
"hhvitveg_cost", "hhmango_cost", "hhveg_cost", "hhfruit_cost",
"hhfat_cost", "hhsweets_cost", "hhcondi_cost")]
y <- apply(X = y, MARGIN = 2, FUN = as.integer)
food <- rowSums(x = y, na.rm = TRUE)
## calculate HFES
hfes <- food / (food + nonfood)
## Classify households
vulnerable <- ifelse(hfes > 0.75, 1, 0)
high <- ifelse(hfes <= 0.75 & hfes > 0.65, 1, 0)
medium <- ifelse(hfes <= 0.65 & hfes >= 0.5, 1, 0)
low <- ifelse(hfes < 0.5, 1, 0)
## Concatenate HFES indicators data.frame
hfes <- data.frame(df[ , core.columns], nonfood, food, hfes, vulnerable, high, medium, low)
## Return data.frame
return(hfes)
}
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