R/flag_nut_health_issues.R
In SaeedR1987/healthyr: REACH Nutrition & Health R Tools
Defines functions
flag_nut_health_issues
Documented in
flag_nut_health_issues
#' Flag Nutrition Health Issues
#'
#' @param df Inputs a dataframe which has already been processed by the format_nut_health_indicators, reformat_nut_health_indicators, and
#' calculate_nut_health_indicators functions. This function is not intended to be run directly, and instead is called automatically
#' within the format_nut_health_indicators function.
#' @param use_flags Optional input to specify if IYCF flags should be automatically blanked in the numerator.
#' @param lcs_variables Optional input passing livelihood coping strategy names to calculate indicator specific flags.
#'
#' @return Returns a dataframe with additional columns of flags/data quality checks for individual records.
#' @export
#'
#' @examples
#' \dontrun{flag_nut_health_issues(df)}
#'
#' @importFrom rlang .data
flag_nut_health_issues <- function(df, use_flags = NULL, lcs_variables = NULL) {
# Washington Group Flags ####
if(length(setdiff(c("wgss_sco_score", "age_years"), names(df)))==0) {
df <- df %>%
dplyr::mutate(flag_wgss_age = ifelse(is.na(.data$wgss_sco_score), NA, ifelse(.data$age_years <5, 1, 0)))
}
if(c("sex") %in% names(df)) {
df <- df %>%
dplyr::mutate(flag_missing_sex = ifelse(is.na(.data$sex), 1, 0))
}
if(c("age_years") %in% names(df)) {
df <- df %>%
dplyr::mutate(flag_missing_age_years = ifelse(is.na(.data$age_years), 1, 0))
}
if(c("wgss_sco_score") %in% names(df)) {
df <- df %>%
dplyr::mutate(flag_wgss_sco_score = ifelse(is.na(.data$wgss_sco_score), NA, ifelse(.data$wgss_sco_score >= 20, 1, 0)))
}
if(c("wgss_hd_score") %in% names(df)) {
df <- df %>%
dplyr::mutate(flag_wgss_extreme = ifelse(is.na(.data$wgss_hd_score), NA, ifelse(.data$wgss_hd_score >= 9, 1, 0)))
}
if(c("wgss_type_interview") %in% names(df)) {
df <- df %>%
dplyr::mutate(flag_wgss_proxy = ifelse(is.na(.data$wgss_hd_score), NA, ifelse(.data$wgss_hd_score == "proxy", 1, 0)))
}
# IYCF flags ####
liquids <- c("iycf_6a", "iycf_6b", "iycf_6c", "iycf_6d", "iycf_6e", "iycf_6f", "iycf_6g", "iycf_6h", "iycf_6i", "iycf_6j")
liquids_to_check <- intersect(liquids, colnames(df))
foods <- c("iycf_7a", "iycf_7b", "iycf_7c", "iycf_7d", "iycf_7e", "iycf_7f", "iycf_7g", "iycf_7h", "iycf_7i", "iycf_7j", "iycf_7k", "iycf_7l", "iycf_7m", "iycf_7n", "iycf_7o", "iycf_7p", "iycf_7q", "iycf_7r")
foods_to_check <- intersect(foods, colnames(df))
if(length(foods_to_check) > 0) {
df$foods_all_same <- apply(df[foods_to_check], 1, function(x) length(unique(x)) == 1)
df$foods_all_no <- apply(df[,foods_to_check]==2, 1, all)
df$foods_all_yes <- apply(df[,foods_to_check]==1, 1, all)
df <- df %>%
dplyr::mutate(flag_yes_foods = ifelse(.data$foods_all_yes == TRUE, 1, 0))
}
if(length(liquids_to_check) > 0) {
df$liquids_all_same <- apply(df[liquids_to_check], 1, function(x) length(unique(x)) == 1)
df$liquids_all_no <- apply(df[,liquids_to_check]==2, 1, all)
df$liquids_all_yes <- apply(df[,liquids_to_check]==1, 1, all)
df <- df %>%
dplyr::mutate(flag_yes_liquids = ifelse(.data$liquids_all_yes == TRUE, 1, 0))
}
if(length(setdiff(c("iycf_4", "age_months"), colnames(df)))==0 & length(foods_to_check) > 0 & length(liquids_to_check) > 0) {
df <- df %>%
dplyr::mutate(flag_no_anything = ifelse(is.na(.data$iycf_4), NA, ifelse(.data$iycf_4 != 1 & .data$foods_all_no == TRUE & .data$liquids_all_no == TRUE & .data$age_months > 5, 1, 0)))
}
if(length(setdiff(c("iycf_8", "age_months"), colnames(df)))==0 & length(foods_to_check) > 0) {
df <- df %>%
dplyr::mutate(flag_no_foods = ifelse(.data$foods_all_no == TRUE & .data$iycf_8 > 0 & .data$age_months > 5, 1, 0),
flag_all_foods_no_meal = ifelse(.data$foods_all_yes == TRUE & .data$iycf_8 == 0, 1, 0),
flag_some_foods_no_meal = ifelse(.data$foods_all_no == FALSE & .data$iycf_8 == 0 & .data$age_months > 5, 1, 0))
}
if(length(setdiff(c("iycf_mdd_score", "iycf_8"), colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_high_mdd_low_mmf = ifelse(is.na(.data$iycf_mdd_score), NA,
ifelse(is.na(.data$iycf_8), NA,
ifelse(.data$iycf_mdd_score >= 6 & .data$iycf_8 <= 1, 1, 0))))
}
if(length(setdiff(c("age_months", "iycf_4", "iycf_6b_num", "iycf_6c_num", "iycf_6d_num"), colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_under6_nobf_nomilk = ifelse(is.na(.data$age_months), NA,
ifelse(is.na(.data$iycf_4), NA,
ifelse(is.na(.data$iycf_6b_num), NA,
ifelse(is.na(.data$iycf_6c_num), NA,
ifelse(is.na(.data$iycf_6d_num), NA,
ifelse(.data$age_months < 6 & .data$iycf_4 != 1 & .data$iycf_6b_num == 0 & .data$iycf_6c_num == 0 & .data$iycf_6d_num == 0, 1, 0)))))))
}
if(length(setdiff(c("iycf_7b", "iycf_7d", "iycf_7i", "iycf_7j", "iycf_7k", "iycf_7l", "iycf_7m"), colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_meats_nostaples = ifelse(is.na(.data$iycf_7b), NA,
ifelse(is.na(.data$iycf_7d), NA,
ifelse(is.na(.data$iycf_7i), NA,
ifelse(is.na(.data$iycf_7j), NA,
ifelse(is.na(.data$iycf_7k), NA,
ifelse(is.na(.data$iycf_7l), NA,
ifelse(is.na(.data$iycf_7m), NA,
ifelse( .data$iycf_7b != 1 & .data$iycf_7d != 1 & (.data$iycf_7i == 1 | .data$iycf_7j == 1 | .data$iycf_7k == 1 | .data$iycf_7l == 1 | .data$iycf_7m == 1), 1 , 0 )))))))))
}
if(use_flags == "yes" & (c("iycf_ufc") %in% colnames(df))) {
df <- df %>% dplyr::mutate(iycf_ufc = ifelse(is.na(.data$flag_yes_foods), .data$iycf_ufc, ifelse(.data$flag_yes_foods == 1, 0, .data$iycf_ufc)))
}
if(use_flags == "yes" & (c("iycf_zvf") %in% colnames(df))) {
df <- df %>% dplyr::mutate(iycf_zvf = ifelse(is.na(.data$flag_no_foods), .data$iycf_zvf, ifelse(.data$flag_no_foods == 1, 0, .data$iycf_zvf)),
iycf_zvf = ifelse(is.na(.data$flag_yes_foods), .data$iycf_zvf, ifelse(.data$flag_yes_foods == 1, 0, .data$iycf_zvf)))
}
if(use_flags == "yes" & (c("iycf_eff") %in% colnames(df))) {
df <- df %>% dplyr::mutate(iycf_eff = ifelse(is.na(.data$flag_no_foods), .data$iycf_eff, ifelse(.data$flag_no_foods == 1, 0 , .data$iycf_eff)),
iycf_eff = ifelse(is.na(.data$flag_yes_foods), .data$iycf_eff, ifelse(.data$flag_yes_foods == 1, 0, .data$iycf_eff)))
}
if(use_flags == "yes" & (c("iycf_ebf") %in% colnames(df))) {
df <- df %>% dplyr::mutate(iycf_ebf = ifelse(is.na(.data$flag_yes_liquids), .data$iycf_ebf, ifelse(.data$flag_yes_liquids == 1, 0, .data$iycf_ebf)))
}
if(use_flags == "yes" & (c("iycf_swb") %in% colnames(df))) {
df <- df %>% dplyr::mutate(iycf_swb = ifelse(is.na(.data$flag_yes_liquids), .data$iycf_swb, ifelse(.data$flag_yes_liquids == 1, 0, .data$iycf_swb)))
}
if(use_flags == "yes" & (c("iycf_mad") %in% colnames(df))) {
df <- df %>%
dplyr::mutate(iycf_mad = ifelse(is.na(.data$flag_no_foods), .data$iycf_mad, ifelse(.data$flag_no_foods == 1, 0, .data$iycf_mad)),
iycf_mad = ifelse(is.na(.data$flag_yes_foods), .data$iycf_mad, ifelse(.data$flag_yes_foods == 1, 0, .data$iycf_mad)),
iycf_mad = ifelse(is.na(.data$flag_some_foods_no_meal), .data$iycf_mad, ifelse(.data$flag_some_foods_no_meal == 1, 0, .data$iycf_mad)))
}
if(use_flags == "yes" & (c("iycf_mdd_score") %in% colnames(df))) {
df <- df %>%
dplyr::mutate(iycf_mdd_score = ifelse(is.na(.data$flag_yes_foods), .data$iycf_mdd_score, ifelse(.data$flag_yes_foods == 1, NA, .data$iycf_mdd_score)),
iycf_mdd_score = ifelse(is.na(.data$flag_no_foods), .data$iycf_mdd_score, ifelse(.data$flag_no_foods == 1, NA, .data$iycf_mdd_score)))
}
if(use_flags == "yes" & (c("iycf_mdd_cat") %in% colnames(df))) {
df <- df %>%
dplyr::mutate(iycf_mdd_cat = ifelse(is.na(.data$flag_no_foods), .data$iycf_mdd_cat, ifelse(.data$flag_no_foods == 1, 0, .data$iycf_mdd_cat)),
iycf_mdd_cat = ifelse(is.na(.data$flag_yes_foods), .data$iycf_mdd_cat, ifelse(.data$flag_yes_foods == 1, 0, .data$iycf_mdd_cat)))
}
if(use_flags == "yes" & (c("iycf_mmf") %in% colnames(df))) {
df <- df %>%
dplyr::mutate(iycf_mmf = ifelse(is.na(.data$flag_no_foods), .data$iycf_mmf, ifelse(.data$flag_no_foods == 1, 0, .data$iycf_mmf)),
iycf_mmf = ifelse(is.na(.data$flag_yes_foods), .data$iycf_mmf, ifelse(.data$flag_yes_foods == 1, 0, .data$iycf_mmf)),
iycf_mmf = ifelse(is.na(.data$flag_some_foods_no_meal), .data$iycf_mmf, ifelse(.data$flag_some_foods_no_meal == 1, 0, .data$iycf_mmf)))
}
# FSL flags ####
fcs_vars <- c("fcs_cereal", "fcs_legumes", "fcs_dairy", "fcs_meat", "fcs_veg", "fcs_fruit", "fcs_oil", "fcs_sugar")
if(length(setdiff(fcs_vars, colnames(df)))==0) {
df[fcs_vars] <- lapply(df[fcs_vars], as.numeric)
# All 0s or 7s for FCS vars
# Much higher meats than cereals
df <- df %>%
dplyr::mutate(flag_above7_fcs = ifelse(is.na(.data$fcs_cereal), NA, ifelse(.data$fcs_cereal > 7 | .data$fcs_legumes > 7 | .data$fcs_dairy > 7 | .data$fcs_meat > 7 | .data$fcs_veg > 7 | .data$fcs_fruit > 7 | .data$fcs_oil > 7 | .data$fcs_sugar > 7, 1, 0)),
flag_meat_cereal_ratio = ifelse(is.na(.data$fcs_cereal), NA, ifelse(.data$fcs_cereal < .data$fcs_meat, 1, 0)),
flag_zero_fcs = ifelse(is.na(.data$fcs_cereal), NA, ifelse(.data$fcs_cereal == 0 & .data$fcs_legumes == 0 & .data$fcs_dairy == 0 & .data$fcs_meat == 0 & .data$fcs_veg == 0 & .data$fcs_fruit == 0 & .data$fcs_oil == 0 & .data$fcs_sugar == 0, 1, 0)),
flag_all_fcs = ifelse(is.na(.data$fcs_cereal), NA, ifelse(.data$fcs_cereal == 7 & .data$fcs_legumes == 7 & .data$fcs_dairy == 7 & .data$fcs_meat == 7 & .data$fcs_veg == 7 & .data$fcs_fruit == 7 & .data$fcs_oil == 7 & .data$fcs_sugar == 7, 1, 0)),
flag_low_cereal = ifelse(is.na(.data$fcs_cereal), NA, ifelse( .data$fcs_cereal < 5, 1, 0)),
flag_low_oil = ifelse(is.na(.data$fcs_cereal), NA, ifelse(.data$fcs_oil < 5, 1, 0))) %>%
dplyr::rowwise() %>%
dplyr::mutate(sd_foods = sd(c(.data$fcs_cereal, .data$fcs_legumes, .data$fcs_dairy, .data$fcs_meat, .data$fcs_veg, .data$fcs_fruit, .data$fcs_oil, .data$fcs_sugar), na.rm = TRUE),
flag_sd_foodgroup = dplyr::case_when(.data$sd_foods < 0.8 ~ 1, TRUE ~ 0)) %>%
dplyr::ungroup()
# Extreme FCS_score ( <3 or >60 )
if(c("fcs_score") %in% colnames(df)) {
df <- df %>%
dplyr::mutate(flag_low_fcs = ifelse(is.na(.data$fcs_score), NA, ifelse(.data$fcs_score < 7, 1, 0)),
flag_high_fcs = ifelse(is.na(.data$fcs_score), NA, ifelse(.data$fcs_score > 56, 1, 0)),
flag_protein_fcs = ifelse(is.na(.data$fcs_score), NA, ifelse(.data$fcs_score < 21 & (.data$fcs_meat >= 5 | .data$fcs_dairy >= 5), 1, 0)))
}
}
# HHS flags
hhs_vars <- c("hhs_nofoodhh_1", "hhs_nofoodhh_1a", "hhs_sleephungry_2", "hhs_sleephungry_2a", "hhs_alldaynight_3", "hhs_alldaynight_3a")
if(length(setdiff(hhs_vars, colnames(df)))==0) {
# All day night without food, but no food in the household issue.
if(length(setdiff(hhs_vars, colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_hhs_no1 = dplyr::case_when(.data$hhs_nofoodhh_1 != "1" & !is.na(.data$hhs_nofoodhh_1a) ~ 1, TRUE ~ 0),
flag_hhs_no2 = dplyr::case_when(.data$hhs_sleephungry_2 != "1" & !is.na(.data$hhs_sleephungry_2a) ~ 1, TRUE ~ 0),
flag_hhs_no3 = dplyr::case_when(.data$hhs_alldaynight_3 != "1" & !is.na(.data$hhs_alldaynight_3a) ~ 1, TRUE ~ 0))
}
# Very Severe HHS scores
if(c("hhs_score") %in% colnames(df)) {
df <- df %>%
dplyr::mutate(flag_severe_hhs = ifelse(is.na(.data$hhs_score), NA, ifelse(.data$hhs_score >= 5, 1, 0)))
}
}
# HDDS Flags
hdds_vars <- c("hdds_cereals", "hdds_tubers", "hdds_veg", "hdds_fruit", "hdds_meat", "hdds_eggs", "hdds_fish", "hdds_legumes",
"hdds_dairy", "hdds_oils", "hdds_sugars", "hdds_condiments", "hdds_score")
if(length(setdiff(hdds_vars, colnames(df)))==0) {
if(c("hdds_score") %in% colnames(df))
df <- df %>%
dplyr::mutate(flag_low_hdds = ifelse(is.na(.data$hdds_score), NA, ifelse(.data$hdds_score <2, 1, 0)),
flag_high_hdds = ifelse(is.na(.data$hdds_score), NA, ifelse(.data$hdds_score >9, 1, 0)),
flag_low_sugar_cond_hdds = ifelse(is.na(.data$hdds_score), NA, ifelse( (.data$hdds_score <= 2 & .data$hdds_sugars == 1 & .data$hdds_condiments == 1) | (.data$hdds_score <= 1 & .data$hdds_sugars == 1) | (.data$hdds_score <= 1 & .data$hdds_condiments == 1), 1, 0)))
}
# rCSI Flags
rcsi_vars <- c("rcsi_lesspreferred_1", "rcsi_borrowfood_2", "rcsi_limitportion_3", "rcsi_restrict_4", "rcsi_reducemeals5", "rcsi_score")
if(length(setdiff(rcsi_vars, colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_high_rcsi = ifelse(is.na(.data$rcsi_score), NA, ifelse(.data$rcsi_score >= 43, 1, 0))) %>%
dplyr::rowwise() %>%
dplyr::mutate(sd_rcsicoping = sd(c(.data$rcsi_lesspreferred_1, .data$rcsi_borrowfood_2, .data$rcsi_limitportion_3, .data$rcsi_restrict_4, .data$rcsi_reducemeals5), na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::mutate(flag_sd_rcsicoping = dplyr::case_when(.data$sd_rcsicoping < 0.8 & .data$rcsi_score < 4 ~ 1, TRUE ~ 0))
}
if(length(setdiff(c(fcs_vars, "rcsi_score"), colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_protein_rcsi = ifelse( is.na(.data$rcsi_score), NA,
ifelse(is.na(.data$fcs_cereal), NA,
ifelse(.data$rcsi_score >= 19 & ( as.numeric(.data$fcs_dairy) >= 5 | as.numeric(.data$fcs_meat) >= 5), 1, 0 ))),
flag_fcs_rcsi = ifelse(is.na(.data$rcsi_score), NA,
ifelse(is.na(.data$fcs_score), NA,
ifelse(.data$fcs_score < 35 & .data$rcsi_score <= 4, 1, 0 ))),
flag_fcsrcsi_box = dplyr::case_when(rcsi_score >= 18 & fcs_score >= 56 ~ 1, TRUE ~ 0))
}
# LCS flags
lcs_vars <- c("lcs_crisis", "lcs_emergency", "lcs_stress")
if(length(setdiff(lcs_vars, colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_lcsi_severity = dplyr::case_when(.data$lcs_emergency == 1 ~ 1,
TRUE ~ 0))
}
if(length(setdiff(lcs_vars, colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_lcsi_coherence = ifelse(is.na(.data$lcs_emergency), NA, ifelse( (.data$lcs_emergency == 1 & .data$lcs_stress == 0) | (.data$lcs_emergency == 1 & .data$lcs_crisis == 0) | (.data$lcs_crisis == 1 & .data$lcs_stress == 0), 1, 0)))
}
if(length(setdiff(c(lcs_variables), names(df)))==0) {
df$lcs.count.na <- apply(df[c(lcs_variables)], 1, function(x) sum(x == "4"))
df <- df %>%
dplyr::mutate(flag_lcsi_na = dplyr::case_when(lcs.count.na == 10 ~ 1, TRUE ~ 0))
}
# Checking Livelihood vs. LCS
if(length(setdiff(c("livelihood_livestock", "lcs_livestock_yn"), colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_lcsi_liv_livestock = dplyr::case_when(livelihood_livestock == "0" & lcs_livestock_yn == "1" ~ 1, TRUE ~ 0))
}
if(length(setdiff(c("livelihood_crops", "lcs_agriculture_yn"), colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_lcsi_liv_agriculture = dplyr::case_when(livelihood_crops == "0" & lcs_agriculture_yn == "1" ~ 1, TRUE ~ 0))
}
# FEWS NET flag
if(c("fc_cell") %in% names(df)) {
# Very few households (<0.1%) fall into cells 3, 4, 5, 8, 9, and 10.These represent cells which reflect very low levels of consumption coping and borderline or better food consumption, but Moderate/Severe household hunger – a
# FEWS NET Integrated analysis of survey-based indicators for classification of acute food insecurity May 2021
# Famine Early Warning Systems Network 12
# combination of indicators that seems implausible. If a significant number of households did report this combination of responses, it could raise questions about data quality and would, at a minimum, suggest the need for further enquiry.
df <- df %>%
dplyr::mutate(flag_fc_cell = ifelse(is.na(.data$fc_cell), NA, ifelse(.data$fc_cell == 3 | .data$fc_cell == 4 | .data$fc_cell == 5 | .data$fc_cell == 8 | .data$fc_cell == 9 | .data$fc_cell == 10, 1, 0)))
}
# Vaccination Flags
# to be added
# Health Expenditure Flags
# to be added
return(df)
}
SaeedR1987/healthyr documentation built on July 4, 2023, 11:17 p.m.
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Defines functions flag_nut_health_issues
Documented in flag_nut_health_issues
#' Flag Nutrition Health Issues
#'
#' @param df Inputs a dataframe which has already been processed by the format_nut_health_indicators, reformat_nut_health_indicators, and
#' calculate_nut_health_indicators functions. This function is not intended to be run directly, and instead is called automatically
#' within the format_nut_health_indicators function.
#' @param use_flags Optional input to specify if IYCF flags should be automatically blanked in the numerator.
#' @param lcs_variables Optional input passing livelihood coping strategy names to calculate indicator specific flags.
#'
#' @return Returns a dataframe with additional columns of flags/data quality checks for individual records.
#' @export
#'
#' @examples
#' \dontrun{flag_nut_health_issues(df)}
#'
#' @importFrom rlang .data
flag_nut_health_issues <- function(df, use_flags = NULL, lcs_variables = NULL) {
# Washington Group Flags ####
if(length(setdiff(c("wgss_sco_score", "age_years"), names(df)))==0) {
df <- df %>%
dplyr::mutate(flag_wgss_age = ifelse(is.na(.data$wgss_sco_score), NA, ifelse(.data$age_years <5, 1, 0)))
}
if(c("sex") %in% names(df)) {
df <- df %>%
dplyr::mutate(flag_missing_sex = ifelse(is.na(.data$sex), 1, 0))
}
if(c("age_years") %in% names(df)) {
df <- df %>%
dplyr::mutate(flag_missing_age_years = ifelse(is.na(.data$age_years), 1, 0))
}
if(c("wgss_sco_score") %in% names(df)) {
df <- df %>%
dplyr::mutate(flag_wgss_sco_score = ifelse(is.na(.data$wgss_sco_score), NA, ifelse(.data$wgss_sco_score >= 20, 1, 0)))
}
if(c("wgss_hd_score") %in% names(df)) {
df <- df %>%
dplyr::mutate(flag_wgss_extreme = ifelse(is.na(.data$wgss_hd_score), NA, ifelse(.data$wgss_hd_score >= 9, 1, 0)))
}
if(c("wgss_type_interview") %in% names(df)) {
df <- df %>%
dplyr::mutate(flag_wgss_proxy = ifelse(is.na(.data$wgss_hd_score), NA, ifelse(.data$wgss_hd_score == "proxy", 1, 0)))
}
# IYCF flags ####
liquids <- c("iycf_6a", "iycf_6b", "iycf_6c", "iycf_6d", "iycf_6e", "iycf_6f", "iycf_6g", "iycf_6h", "iycf_6i", "iycf_6j")
liquids_to_check <- intersect(liquids, colnames(df))
foods <- c("iycf_7a", "iycf_7b", "iycf_7c", "iycf_7d", "iycf_7e", "iycf_7f", "iycf_7g", "iycf_7h", "iycf_7i", "iycf_7j", "iycf_7k", "iycf_7l", "iycf_7m", "iycf_7n", "iycf_7o", "iycf_7p", "iycf_7q", "iycf_7r")
foods_to_check <- intersect(foods, colnames(df))
if(length(foods_to_check) > 0) {
df$foods_all_same <- apply(df[foods_to_check], 1, function(x) length(unique(x)) == 1)
df$foods_all_no <- apply(df[,foods_to_check]==2, 1, all)
df$foods_all_yes <- apply(df[,foods_to_check]==1, 1, all)
df <- df %>%
dplyr::mutate(flag_yes_foods = ifelse(.data$foods_all_yes == TRUE, 1, 0))
}
if(length(liquids_to_check) > 0) {
df$liquids_all_same <- apply(df[liquids_to_check], 1, function(x) length(unique(x)) == 1)
df$liquids_all_no <- apply(df[,liquids_to_check]==2, 1, all)
df$liquids_all_yes <- apply(df[,liquids_to_check]==1, 1, all)
df <- df %>%
dplyr::mutate(flag_yes_liquids = ifelse(.data$liquids_all_yes == TRUE, 1, 0))
}
if(length(setdiff(c("iycf_4", "age_months"), colnames(df)))==0 & length(foods_to_check) > 0 & length(liquids_to_check) > 0) {
df <- df %>%
dplyr::mutate(flag_no_anything = ifelse(is.na(.data$iycf_4), NA, ifelse(.data$iycf_4 != 1 & .data$foods_all_no == TRUE & .data$liquids_all_no == TRUE & .data$age_months > 5, 1, 0)))
}
if(length(setdiff(c("iycf_8", "age_months"), colnames(df)))==0 & length(foods_to_check) > 0) {
df <- df %>%
dplyr::mutate(flag_no_foods = ifelse(.data$foods_all_no == TRUE & .data$iycf_8 > 0 & .data$age_months > 5, 1, 0),
flag_all_foods_no_meal = ifelse(.data$foods_all_yes == TRUE & .data$iycf_8 == 0, 1, 0),
flag_some_foods_no_meal = ifelse(.data$foods_all_no == FALSE & .data$iycf_8 == 0 & .data$age_months > 5, 1, 0))
}
if(length(setdiff(c("iycf_mdd_score", "iycf_8"), colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_high_mdd_low_mmf = ifelse(is.na(.data$iycf_mdd_score), NA,
ifelse(is.na(.data$iycf_8), NA,
ifelse(.data$iycf_mdd_score >= 6 & .data$iycf_8 <= 1, 1, 0))))
}
if(length(setdiff(c("age_months", "iycf_4", "iycf_6b_num", "iycf_6c_num", "iycf_6d_num"), colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_under6_nobf_nomilk = ifelse(is.na(.data$age_months), NA,
ifelse(is.na(.data$iycf_4), NA,
ifelse(is.na(.data$iycf_6b_num), NA,
ifelse(is.na(.data$iycf_6c_num), NA,
ifelse(is.na(.data$iycf_6d_num), NA,
ifelse(.data$age_months < 6 & .data$iycf_4 != 1 & .data$iycf_6b_num == 0 & .data$iycf_6c_num == 0 & .data$iycf_6d_num == 0, 1, 0)))))))
}
if(length(setdiff(c("iycf_7b", "iycf_7d", "iycf_7i", "iycf_7j", "iycf_7k", "iycf_7l", "iycf_7m"), colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_meats_nostaples = ifelse(is.na(.data$iycf_7b), NA,
ifelse(is.na(.data$iycf_7d), NA,
ifelse(is.na(.data$iycf_7i), NA,
ifelse(is.na(.data$iycf_7j), NA,
ifelse(is.na(.data$iycf_7k), NA,
ifelse(is.na(.data$iycf_7l), NA,
ifelse(is.na(.data$iycf_7m), NA,
ifelse( .data$iycf_7b != 1 & .data$iycf_7d != 1 & (.data$iycf_7i == 1 | .data$iycf_7j == 1 | .data$iycf_7k == 1 | .data$iycf_7l == 1 | .data$iycf_7m == 1), 1 , 0 )))))))))
}
if(use_flags == "yes" & (c("iycf_ufc") %in% colnames(df))) {
df <- df %>% dplyr::mutate(iycf_ufc = ifelse(is.na(.data$flag_yes_foods), .data$iycf_ufc, ifelse(.data$flag_yes_foods == 1, 0, .data$iycf_ufc)))
}
if(use_flags == "yes" & (c("iycf_zvf") %in% colnames(df))) {
df <- df %>% dplyr::mutate(iycf_zvf = ifelse(is.na(.data$flag_no_foods), .data$iycf_zvf, ifelse(.data$flag_no_foods == 1, 0, .data$iycf_zvf)),
iycf_zvf = ifelse(is.na(.data$flag_yes_foods), .data$iycf_zvf, ifelse(.data$flag_yes_foods == 1, 0, .data$iycf_zvf)))
}
if(use_flags == "yes" & (c("iycf_eff") %in% colnames(df))) {
df <- df %>% dplyr::mutate(iycf_eff = ifelse(is.na(.data$flag_no_foods), .data$iycf_eff, ifelse(.data$flag_no_foods == 1, 0 , .data$iycf_eff)),
iycf_eff = ifelse(is.na(.data$flag_yes_foods), .data$iycf_eff, ifelse(.data$flag_yes_foods == 1, 0, .data$iycf_eff)))
}
if(use_flags == "yes" & (c("iycf_ebf") %in% colnames(df))) {
df <- df %>% dplyr::mutate(iycf_ebf = ifelse(is.na(.data$flag_yes_liquids), .data$iycf_ebf, ifelse(.data$flag_yes_liquids == 1, 0, .data$iycf_ebf)))
}
if(use_flags == "yes" & (c("iycf_swb") %in% colnames(df))) {
df <- df %>% dplyr::mutate(iycf_swb = ifelse(is.na(.data$flag_yes_liquids), .data$iycf_swb, ifelse(.data$flag_yes_liquids == 1, 0, .data$iycf_swb)))
}
if(use_flags == "yes" & (c("iycf_mad") %in% colnames(df))) {
df <- df %>%
dplyr::mutate(iycf_mad = ifelse(is.na(.data$flag_no_foods), .data$iycf_mad, ifelse(.data$flag_no_foods == 1, 0, .data$iycf_mad)),
iycf_mad = ifelse(is.na(.data$flag_yes_foods), .data$iycf_mad, ifelse(.data$flag_yes_foods == 1, 0, .data$iycf_mad)),
iycf_mad = ifelse(is.na(.data$flag_some_foods_no_meal), .data$iycf_mad, ifelse(.data$flag_some_foods_no_meal == 1, 0, .data$iycf_mad)))
}
if(use_flags == "yes" & (c("iycf_mdd_score") %in% colnames(df))) {
df <- df %>%
dplyr::mutate(iycf_mdd_score = ifelse(is.na(.data$flag_yes_foods), .data$iycf_mdd_score, ifelse(.data$flag_yes_foods == 1, NA, .data$iycf_mdd_score)),
iycf_mdd_score = ifelse(is.na(.data$flag_no_foods), .data$iycf_mdd_score, ifelse(.data$flag_no_foods == 1, NA, .data$iycf_mdd_score)))
}
if(use_flags == "yes" & (c("iycf_mdd_cat") %in% colnames(df))) {
df <- df %>%
dplyr::mutate(iycf_mdd_cat = ifelse(is.na(.data$flag_no_foods), .data$iycf_mdd_cat, ifelse(.data$flag_no_foods == 1, 0, .data$iycf_mdd_cat)),
iycf_mdd_cat = ifelse(is.na(.data$flag_yes_foods), .data$iycf_mdd_cat, ifelse(.data$flag_yes_foods == 1, 0, .data$iycf_mdd_cat)))
}
if(use_flags == "yes" & (c("iycf_mmf") %in% colnames(df))) {
df <- df %>%
dplyr::mutate(iycf_mmf = ifelse(is.na(.data$flag_no_foods), .data$iycf_mmf, ifelse(.data$flag_no_foods == 1, 0, .data$iycf_mmf)),
iycf_mmf = ifelse(is.na(.data$flag_yes_foods), .data$iycf_mmf, ifelse(.data$flag_yes_foods == 1, 0, .data$iycf_mmf)),
iycf_mmf = ifelse(is.na(.data$flag_some_foods_no_meal), .data$iycf_mmf, ifelse(.data$flag_some_foods_no_meal == 1, 0, .data$iycf_mmf)))
}
# FSL flags ####
fcs_vars <- c("fcs_cereal", "fcs_legumes", "fcs_dairy", "fcs_meat", "fcs_veg", "fcs_fruit", "fcs_oil", "fcs_sugar")
if(length(setdiff(fcs_vars, colnames(df)))==0) {
df[fcs_vars] <- lapply(df[fcs_vars], as.numeric)
# All 0s or 7s for FCS vars
# Much higher meats than cereals
df <- df %>%
dplyr::mutate(flag_above7_fcs = ifelse(is.na(.data$fcs_cereal), NA, ifelse(.data$fcs_cereal > 7 | .data$fcs_legumes > 7 | .data$fcs_dairy > 7 | .data$fcs_meat > 7 | .data$fcs_veg > 7 | .data$fcs_fruit > 7 | .data$fcs_oil > 7 | .data$fcs_sugar > 7, 1, 0)),
flag_meat_cereal_ratio = ifelse(is.na(.data$fcs_cereal), NA, ifelse(.data$fcs_cereal < .data$fcs_meat, 1, 0)),
flag_zero_fcs = ifelse(is.na(.data$fcs_cereal), NA, ifelse(.data$fcs_cereal == 0 & .data$fcs_legumes == 0 & .data$fcs_dairy == 0 & .data$fcs_meat == 0 & .data$fcs_veg == 0 & .data$fcs_fruit == 0 & .data$fcs_oil == 0 & .data$fcs_sugar == 0, 1, 0)),
flag_all_fcs = ifelse(is.na(.data$fcs_cereal), NA, ifelse(.data$fcs_cereal == 7 & .data$fcs_legumes == 7 & .data$fcs_dairy == 7 & .data$fcs_meat == 7 & .data$fcs_veg == 7 & .data$fcs_fruit == 7 & .data$fcs_oil == 7 & .data$fcs_sugar == 7, 1, 0)),
flag_low_cereal = ifelse(is.na(.data$fcs_cereal), NA, ifelse( .data$fcs_cereal < 5, 1, 0)),
flag_low_oil = ifelse(is.na(.data$fcs_cereal), NA, ifelse(.data$fcs_oil < 5, 1, 0))) %>%
dplyr::rowwise() %>%
dplyr::mutate(sd_foods = sd(c(.data$fcs_cereal, .data$fcs_legumes, .data$fcs_dairy, .data$fcs_meat, .data$fcs_veg, .data$fcs_fruit, .data$fcs_oil, .data$fcs_sugar), na.rm = TRUE),
flag_sd_foodgroup = dplyr::case_when(.data$sd_foods < 0.8 ~ 1, TRUE ~ 0)) %>%
dplyr::ungroup()
# Extreme FCS_score ( <3 or >60 )
if(c("fcs_score") %in% colnames(df)) {
df <- df %>%
dplyr::mutate(flag_low_fcs = ifelse(is.na(.data$fcs_score), NA, ifelse(.data$fcs_score < 7, 1, 0)),
flag_high_fcs = ifelse(is.na(.data$fcs_score), NA, ifelse(.data$fcs_score > 56, 1, 0)),
flag_protein_fcs = ifelse(is.na(.data$fcs_score), NA, ifelse(.data$fcs_score < 21 & (.data$fcs_meat >= 5 | .data$fcs_dairy >= 5), 1, 0)))
}
}
# HHS flags
hhs_vars <- c("hhs_nofoodhh_1", "hhs_nofoodhh_1a", "hhs_sleephungry_2", "hhs_sleephungry_2a", "hhs_alldaynight_3", "hhs_alldaynight_3a")
if(length(setdiff(hhs_vars, colnames(df)))==0) {
# All day night without food, but no food in the household issue.
if(length(setdiff(hhs_vars, colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_hhs_no1 = dplyr::case_when(.data$hhs_nofoodhh_1 != "1" & !is.na(.data$hhs_nofoodhh_1a) ~ 1, TRUE ~ 0),
flag_hhs_no2 = dplyr::case_when(.data$hhs_sleephungry_2 != "1" & !is.na(.data$hhs_sleephungry_2a) ~ 1, TRUE ~ 0),
flag_hhs_no3 = dplyr::case_when(.data$hhs_alldaynight_3 != "1" & !is.na(.data$hhs_alldaynight_3a) ~ 1, TRUE ~ 0))
}
# Very Severe HHS scores
if(c("hhs_score") %in% colnames(df)) {
df <- df %>%
dplyr::mutate(flag_severe_hhs = ifelse(is.na(.data$hhs_score), NA, ifelse(.data$hhs_score >= 5, 1, 0)))
}
}
# HDDS Flags
hdds_vars <- c("hdds_cereals", "hdds_tubers", "hdds_veg", "hdds_fruit", "hdds_meat", "hdds_eggs", "hdds_fish", "hdds_legumes",
"hdds_dairy", "hdds_oils", "hdds_sugars", "hdds_condiments", "hdds_score")
if(length(setdiff(hdds_vars, colnames(df)))==0) {
if(c("hdds_score") %in% colnames(df))
df <- df %>%
dplyr::mutate(flag_low_hdds = ifelse(is.na(.data$hdds_score), NA, ifelse(.data$hdds_score <2, 1, 0)),
flag_high_hdds = ifelse(is.na(.data$hdds_score), NA, ifelse(.data$hdds_score >9, 1, 0)),
flag_low_sugar_cond_hdds = ifelse(is.na(.data$hdds_score), NA, ifelse( (.data$hdds_score <= 2 & .data$hdds_sugars == 1 & .data$hdds_condiments == 1) | (.data$hdds_score <= 1 & .data$hdds_sugars == 1) | (.data$hdds_score <= 1 & .data$hdds_condiments == 1), 1, 0)))
}
# rCSI Flags
rcsi_vars <- c("rcsi_lesspreferred_1", "rcsi_borrowfood_2", "rcsi_limitportion_3", "rcsi_restrict_4", "rcsi_reducemeals5", "rcsi_score")
if(length(setdiff(rcsi_vars, colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_high_rcsi = ifelse(is.na(.data$rcsi_score), NA, ifelse(.data$rcsi_score >= 43, 1, 0))) %>%
dplyr::rowwise() %>%
dplyr::mutate(sd_rcsicoping = sd(c(.data$rcsi_lesspreferred_1, .data$rcsi_borrowfood_2, .data$rcsi_limitportion_3, .data$rcsi_restrict_4, .data$rcsi_reducemeals5), na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::mutate(flag_sd_rcsicoping = dplyr::case_when(.data$sd_rcsicoping < 0.8 & .data$rcsi_score < 4 ~ 1, TRUE ~ 0))
}
if(length(setdiff(c(fcs_vars, "rcsi_score"), colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_protein_rcsi = ifelse( is.na(.data$rcsi_score), NA,
ifelse(is.na(.data$fcs_cereal), NA,
ifelse(.data$rcsi_score >= 19 & ( as.numeric(.data$fcs_dairy) >= 5 | as.numeric(.data$fcs_meat) >= 5), 1, 0 ))),
flag_fcs_rcsi = ifelse(is.na(.data$rcsi_score), NA,
ifelse(is.na(.data$fcs_score), NA,
ifelse(.data$fcs_score < 35 & .data$rcsi_score <= 4, 1, 0 ))),
flag_fcsrcsi_box = dplyr::case_when(rcsi_score >= 18 & fcs_score >= 56 ~ 1, TRUE ~ 0))
}
# LCS flags
lcs_vars <- c("lcs_crisis", "lcs_emergency", "lcs_stress")
if(length(setdiff(lcs_vars, colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_lcsi_severity = dplyr::case_when(.data$lcs_emergency == 1 ~ 1,
TRUE ~ 0))
}
if(length(setdiff(lcs_vars, colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_lcsi_coherence = ifelse(is.na(.data$lcs_emergency), NA, ifelse( (.data$lcs_emergency == 1 & .data$lcs_stress == 0) | (.data$lcs_emergency == 1 & .data$lcs_crisis == 0) | (.data$lcs_crisis == 1 & .data$lcs_stress == 0), 1, 0)))
}
if(length(setdiff(c(lcs_variables), names(df)))==0) {
df$lcs.count.na <- apply(df[c(lcs_variables)], 1, function(x) sum(x == "4"))
df <- df %>%
dplyr::mutate(flag_lcsi_na = dplyr::case_when(lcs.count.na == 10 ~ 1, TRUE ~ 0))
}
# Checking Livelihood vs. LCS
if(length(setdiff(c("livelihood_livestock", "lcs_livestock_yn"), colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_lcsi_liv_livestock = dplyr::case_when(livelihood_livestock == "0" & lcs_livestock_yn == "1" ~ 1, TRUE ~ 0))
}
if(length(setdiff(c("livelihood_crops", "lcs_agriculture_yn"), colnames(df)))==0) {
df <- df %>%
dplyr::mutate(flag_lcsi_liv_agriculture = dplyr::case_when(livelihood_crops == "0" & lcs_agriculture_yn == "1" ~ 1, TRUE ~ 0))
}
# FEWS NET flag
if(c("fc_cell") %in% names(df)) {
# Very few households (<0.1%) fall into cells 3, 4, 5, 8, 9, and 10.These represent cells which reflect very low levels of consumption coping and borderline or better food consumption, but Moderate/Severe household hunger – a
# FEWS NET Integrated analysis of survey-based indicators for classification of acute food insecurity May 2021
# Famine Early Warning Systems Network 12
# combination of indicators that seems implausible. If a significant number of households did report this combination of responses, it could raise questions about data quality and would, at a minimum, suggest the need for further enquiry.
df <- df %>%
dplyr::mutate(flag_fc_cell = ifelse(is.na(.data$fc_cell), NA, ifelse(.data$fc_cell == 3 | .data$fc_cell == 4 | .data$fc_cell == 5 | .data$fc_cell == 8 | .data$fc_cell == 9 | .data$fc_cell == 10, 1, 0)))
}
# Vaccination Flags
# to be added
# Health Expenditure Flags
# to be added
return(df)
}
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