worldbank/pddcs
Prepare data for Data Collection System

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R/fetch_unicef.R

R/fetch_unicef.R
In worldbank/pddcs: Prepare data for Data Collection System

Defines functions apply_filter_unicef recode_unicef_survey_acronym recode_unicef_footnotes recode_unicef_codes create_url_unicef standardize_unicef fetch_unicef

Documented in fetch_unicef 

#' @importFrom stringi stri_unescape_unicode
NULL

#' Fetch data from UNICEF
#'
#' Fetch data from UNICEF API.
#'
#' @param indicator character: A CETS type indicator code.
#' @return data.frame
#' @keywords internal
fetch_unicef <- function(indicator) {

  # Create URL
  u <- create_url_unicef(indicator)

  # Fetch data
  resp <- httr::GET(u)
  df <- suppressMessages(httr::content(resp, encoding = "UTF-8", as = "parsed"))

  # Standardize data
  df <- standardize_unicef(df, indicator)

  return(df)
}

#' standardize_unicef
#' @param df data.frame: A data frame with data from the UNICEF API.
#' @inheritParams fetch_unicef
#' @return data.frame
#' @noRd
standardize_unicef <- function(df, indicator) {

  # Add ISO3 country codes
  if (any("Areas" %in% names(df))) {
    df$iso3c <- suppressWarnings(
      countrycode::countrycode(df$Areas, "country.name", "iso3c")
    )
  } else if (any("Country" %in% names(df))) {
    df$iso3c <- suppressWarnings(
      countrycode::countrycode(df$Country, "country.name", "iso3c")
    )
  } else if (any("Geographic area" %in% names(df))) {
    df$iso3c <- suppressWarnings(
      countrycode::countrycode(df$`Geographic area`, "country.name", "iso3c")
    )
  }

  # Select rows with country data
  df <- df[!is.na(df$iso3c), ]

  # Add WDI code
  df$indicator <- indicator

  # Create note column
  if (any("CDDATASOURCE" %in% names(df))) {
    df$note <- df$CDDATASOURCE
  } else if ("Data Source" %in% names(df)) {
    df$note <- df$`Data Source`
  } else if ("DATA_SOURCE" %in% names(df)) {
    df$note <- df$DATA_SOURCE
  } else {
    df$note <- ""
  }
  df$note <- ifelse(is.na(df$note), "", df$note)

  # Select and rename columns
  df <- df[c("iso3c", "TIME_PERIOD", "indicator", "OBS_VALUE", "note")]
  names(df) <- c("iso3c", "year", "indicator", "value", "note")

  # Remove anything after hyphen in year
  # Note: This can lead to duplicated country-years
  # that need to be removed with filter_unicef()
  df$year <- sub("-.*", "", df$year)

  # Select latest year of survey
  # based on information in footnote
  tmp <- regmatches(df$note, gregexpr("\\d{4}-\\d{2}", df$note))
  tmp2 <- regmatches(df$note, gregexpr("\\d{4}-\\d{4}", df$note))
  df$year_tmp <-
    ifelse(grepl("\\d{4}-\\d{2}", df$note),
      paste0(substr(tmp, 1, 2), substr(tmp, 6, 7)),
      df$year
    )
  df$year_tmp <-
    ifelse(grepl("\\d{4}-\\d{4}", df$note),
      substr(tmp2, 6, 9),
      df$year_tmp
    )
  df$year_tmp <-
    ifelse(df$year_tmp == "1900" & df$year == "1999",
      "2000",
      df$year_tmp
    )
  df$year <- df$year_tmp
  df$year_tmp <- NULL

  # Filter surveys
  df <- apply_filter_unicef(df)

  # Recode footnotes
  df$note <- recode_unicef_footnotes(df$note)

  # General standardization
  df <- standardize_all(df)

  return(df)
}

#' create_url_who
#' @inheritParams fetch_unicef
#' @return character
#' @noRd
create_url_unicef <- function(indicator) {

  # Base URL
  base_url <- "https://sdmx.data.unicef.org/ws/public/sdmxapi/rest/data/"

  # Indicator groups
  # Countdown - coverage indicators
  # cd2030 <- c(
  #   "SH.MLR.NETS.ZS", "SH.STA.ANV4.ZS", "SH.STA.ORTH",
  #   "SN.ITK.VITA.ZS", "SH.MLR.IPTP.ZS",
  #   "SH.STA.BRTC.ZS", "SH.STA.BFED.ZS"
  # )

  #delete: SH.STA.ANV4.ZS and SH.STA.ARIC.ZS
  cd2030 <- c(
    "SH.MLR.NETS.ZS",  "SH.STA.ORTH",
    "SN.ITK.VITA.ZS", "SH.MLR.IPTP.ZS",
    "SH.STA.BRTC.ZS", "SH.STA.BFED.ZS"
  )
  # Nutrition: delete "SH.STA.IYCF.ZS"
  nutrition <- c("SH.STA.BRTW.ZS", "SN.ITK.SALT.ZS")

  # Maternal, newborn and child health  - added SH.STA.ANV4.ZS and SH.STA.ARIC.ZS
  mnch <- c("SH.STA.ORCF.ZS", "SH.STA.ANVC.ZS", "SH.STA.ANV4.ZS", "SH.STA.ARIC.ZS")

  # PT - Child Protection #deleted: "SP.M18.2024.FE.ZS",  "SP.M15.2024.FE.ZS" - they need to be included in a new group- pt_cm - Child marriage
  pt_cp <- c(
    "SP.REG.BRTH.ZS", "SP.REG.BRTH.UR.ZS", "SP.REG.BRTH.RU.ZS",
    "SP.REG.BRTH.MA.ZS", "SP.REG.BRTH.FE.ZS")

  # New group - PT_CM - Child Marriage

  pt_cm<- c("SP.M18.2024.FE.ZS",  "SP.M15.2024.FE.ZS")

  # #HIV_AIDS - HIV/AIDS
  hiv_aids <- c(
   "SH.HIV.KNOW.FE.ZS", "SH.HIV.KNOW.MA.ZS",
   "SH.CON.AIDS.FE.ZS", "SH.CON.AIDS.MA.ZS", #deleted
   "SH.HIV.1524.KW.FE.ZS", "SH.HIV.1524.KW.MA.ZS",
   "SH.CON.1524.HR.FE.ZS", "SH.CON.1524.HR.MA.ZS" #discontinued
  )

  # Modify url based on indicator endpoints / data flows
  if (indicator %in% cd2030) {
    base_url <- paste0(base_url, "CD2030,CD2030,1.0/") #updated
  } else if (indicator %in% nutrition) {
    base_url <- paste0(base_url, "UNICEF,NUTRITION,1.0/")
  } else if (indicator %in% mnch) {
    base_url <- paste0(base_url, "UNICEF,MNCH,1.0/")
  } else if (indicator %in% pt_cp) {
    base_url <- paste0(base_url, "UNICEF,PT,1.0/")
  } else if (indicator %in% pt_cp) {
    base_url <- paste0(base_url, "UNICEF,PT_CM,1.0/") #added
  } else if (indicator %in% hiv_aids) {
    base_url <- paste0(base_url, "UNICEF,HIV_AIDS,1.0/")
  }

  # Check access
  if (httr::http_error("https://sdmx.data.unicef.org/")) {
    rlang::abort(c("Couldn't connect to UNICEF API.",
      i = "Check your Internet connection."
    ))
  }

  # Create main URL
  unicef_code <- recode_unicef_codes(indicator)
  the_url <- sprintf("%s%s...?format=csv", base_url, unicef_code)

  return(the_url)
}

#' recode_unicef_codes
#' @inheritParams fetch_unicef
#' @noRd
#' @return character
recode_unicef_codes <- function(indicator) {
  out <-
    dplyr::recode(
      indicator,
      # Pregnant women receiving prenatal care of at least four visits
      #"SH.STA.ANV4.ZS" = "A2.PCT..TOTAL",
      "SH.STA.ANV4.ZS" = ".MNCH_ANC4..Y15T49.._T._T._T.",
      # IPT of malaria in pregnancy (% of pregnant women)
      "SH.MLR.IPTP.ZS" = ".A9.......",
      # Births attended by skilled health staff (% of total)
      "SH.STA.BRTC.ZS" = ".A5....Y15T49...",
      # Use of insecticide-treated bed nets
      "SH.MLR.NETS.ZS" = ".A21.......",
      # ARI treatment (% of children under 5 taken to a health provider)
      "SH.STA.ARIC.ZS" = ".MNCH_PNEUCARE._T..._T._T._T.",
      # Diarrhea treatment (ORS packet)
      "SH.STA.ORTH" = ".A18....._T..",
      # Exclusive breastfeeding (% of children under 6 months)
      "SH.STA.BFED.ZS" = ".N13....._T..",
      # Vitamin A supplementation coverage rate
      "SN.ITK.VITA.ZS" = ".N6.......",
      # Low-birthweight babies (% of births)
      "SH.STA.BRTW.ZS" = ".NT_BW_LBW......",
      # Consumption of iodized salt (% of households),
      "SN.ITK.SALT.ZS" = ".NT_IOD_ANY_TH..._T._T..",
      # Diarrhea treatment (oral rehydration and continued feeding)
      "SH.STA.ORCF.ZS" = ".MNCH_ORTCF._T..._T._T._T.",
      # Pregnant women receiving prenatal care (%)
      "SH.STA.ANVC.ZS" = ".MNCH_ANC1..Y15T49.._T._T._T.",
      # Completeness of birth registration (%)
      "SP.REG.BRTH.ZS" = ".PT_CHLD_Y0T4_REG._T.Y0T4._T._T._T.",
      # Completeness of birth registration, urban (%)
      "SP.REG.BRTH.UR.ZS" = ".PT_CHLD_Y0T4_REG._T.Y0T4._T._T.U.",
      # Completeness of birth registration, rural (%)
      "SP.REG.BRTH.RU.ZS" = ".PT_CHLD_Y0T4_REG._T.Y0T4._T._T.R.",
      # Completeness of birth registration, male (%)
      "SP.REG.BRTH.MA.ZS" = ".PT_CHLD_Y0T4_REG.M.Y0T4._T._T._T.",
      # Completeness of birth registration, female (%)
      "SP.REG.BRTH.FE.ZS" = ".PT_CHLD_Y0T4_REG.F.Y0T4._T._T._T.",
      # Women who were first married by age 18 (% of women ages 20-24)
      "SP.M18.2024.FE.ZS" = ".PT_M_20-24_MRD_U18...._T._T.....",
      # Women who were first married by age 15 (% of women ages 20-24)
      "SP.M15.2024.FE.ZS" = ".PT_F_20-24_MRD_U15..._T._T._T.....",
      # Comprehensive correct knowledge of HIV/AIDS, ages 15-49, female
      "SH.HIV.KNOW.FE.ZS" = ".HVA_PREV_KNOW.Y15T49.F._T._T.",
      # Comprehensive correct knowledge of HIV/AIDS, ages 15-49, male
      "SH.HIV.KNOW.MA.ZS" = ".HVA_PREV_KNOW.Y15T49.M._T._T.",
      # Condom use at last high-risk sex, adult female (% ages 15-49)
      #"SH.CON.AIDS.FE.ZS" = ".HVA_PREV_CNDM_REG.Y15T49.F._T._T.",#not available in API
      # Condom use at last high-risk sex, adult male (% ages 15-49)
      #"SH.CON.AIDS.MA.ZS" = ".HVA_PREV_CNDM_REG.Y15T49.M._T._T." # ,#not available in API
      # Comprehensive correct knowledge of HIV/AIDS, ages 15-24, female
       "SH.HIV.1524.KW.FE.ZS" = ".HVA_PREV_KNOW.Y15T24.F._T._T.",
      # Comprehensive correct knowledge of HIV/AIDS, ages 15-24, male
       "SH.HIV.1524.KW.MA.ZS" = ".HVA_PREV_KNOW.Y15T24.M._T._T."
      # Condom use at last high-risk sex, youth female (% ages 15-24)
      # 'SH.CON.1524.HR.FE.ZS' = '.HVA_PREV_CNDM_REG.Y15T19+Y20T24.F._T._T.', #discontinued
      # Condom use at last high-risk sex, youth male (% ages 15-24)
      #'SH.CON.1524.HR.MA.ZS' = '.HVA_PREV_CNDM_REG.Y15T19+Y20T24.M._T._T.' #discontinued
    )

  return(out)
}

#' recode_unicef_footnotes
#' @param x character: A vector with UNICEF notes (CDDATASOURCE).
#' @return character
#' @noRd
recode_unicef_footnotes <- function(x) {
  acronyms <- unicef_survey_acronyms
  survey_names <- recode_unicef_survey_acronym(acronyms)
  for (i in seq_along(survey_names)) {
    x <- sub(paste0("\\b", acronyms[i], "\\b"), survey_names[i], x)
  }
  return(x)
}

#' recode_unicef_survey_acronym
#' @param x character: A vector with UNICEF survey acronyms.
#' @return character
#' @noRd
recode_unicef_survey_acronym <- function(x) {
  dplyr::recode(
    x,
    # National survey
    "NS" =  "National Survey",
    "NSS" =  "National Survey",
    "ONS" = "National Survey",
    "NUCS" = "National Health Survey Report",
    "NHC" = "National Health Commission",
    "NHS" = "National Health Survey",
    "NHMS" = "National Health and Morbidity Survey",
    "NNS" = "National Nutrition Survey",
    # DHS
    "CDHS" = "Continuous Demographic and Health Survey",
    "MIDHS" = "Multiple Indicator Demographic and Health Survey",
    "DHS KIR" = "Demographic and Health Survey Key Indicators Report",
    "DHS-MICS" = "Demographic and Health Survey, Multiple Indicator Cluster Survey",
    "DHS-Style" = "Demographic and Health Survey",
    "SDHS" = "Demographic and Health Survey",
    "DHS" =  "Demographic and Health Survey",
    # MICS
    "MICS" = "Multiple Indicator Cluster Survey",
    # Malaria
    "MIS" = "Malaria Indicator Survey",
    # HIV/AIDS
    "AIS" = "AIDS Indicator Survey",
    "AISMIS" = "HIV/AIDS and Malaria Indicator Survey",
    # Other surveys
    "ECOM" = "Enqu\\u00eate laise aupr\\u00e8s des M\\u00e9nages pour l'\\u00e9valuation de la pauvret\\u00e9",
    "ENISED"  = "Etude Nationale d'Evaluation d'Indicateurs SocioEconomiques et D\\u00e9mographiques",
    "IHLCA"  = "Integrated Household Living Conditions Survey",
    "NFHS" = "National Family Health Survey",
    "PNDS" = "Pesquisa Nacional de Demografia e Sa\\u00fade da Crian\\u00e7a e da Mulher",
    "SHHS" = "Sudan Household and Health Survey",
    "FHS" = "Family Health Survey",
    "EIP" = "R\\u00e9publique du Enqu\\u00eate sur les Indicateurs du Paludisme",
    "ENSOMD" = "L\\u2019Enque^te Nationale sur le Suivi des indicateurs des Objectifs du Mille\\u00b4naire pour le De\\u00b4veloppement",
    "ENPSF" = "National Survey on Population and Family Health",
    "ENDESA" = "Encuesta Nicarag\\u00fcense de Demograf\\u00eda y Salud", #' Nicaragua National Demographic and Health Survey'
    "EDSFPAPFAM" = "Deuxieme Enquete enne sur la Sante de la Famille",
    "ENHOGAR" = "Encuesta Nacional de Hogares de Prop\\u00f3sitos M\\u00faltiples",
    "ENSMI" = "Encuesta Nacional de Salud. Materno Infantil",
    "ENS" = "Encuesta National de Salud",
    "MCSDHM" = "Multisectoral Continuous Survey Demographic and Health Module",
    # 'EASF' = 'Family Health Survey',
    "PHS" = "Population and Health Survey",
    "RCOS" = "Rapid Survey on Children",
    "MoH" = "MoH PNDS",
    "WMS" = "Welfare Monitoring Survey",
    "SUSENAS" = "National Socioeconomic Survey",
    "DGEEC" = "Direcc\\u00f3n General de Estadística, Encuestas y Censos",
    "ICHNS" = "Integrated Child Health and Nutrition Survey",
    "EDSA" = "Encuesta de Demografía y Salud",
    "SMART" = "Standardized Monitoring and Assessment of Relief and Transitions Survey",
    "PAPFAM" = "Pan Arab Project for Family Health Survey"
  ) %>%
    stringi::stri_unescape_unicode()
}

#' apply_filter_unicef
#' @param df data.frame: A data frame with data from the UNICEF API.
#' @return data.frame
#' @noRd
apply_filter_unicef <- function(df) {
  mis_dhs_mics <-
    c("SH.MLR.IPTP.ZS", "SH.MLR.NETS.ZS")
  ais_dhs_mics <-
    c(
      "SH.HIV.KNOW.FE.ZS", "SH.HIV.KNOW.MA.ZS",
      "SH.HIV.1524.KW.FE.ZS", "SH.HIV.1524.KW.MA.ZS",
      "SH.CON.1524.HR.FE.ZS", "SH.CON.1524.HR.MA.ZS",
      "SH.CON.AIDS.FE.ZS", "SH.CON.AIDS.MA.ZS"
    )

  #delete "SH.STA.IYCF.ZS"
  dhs_mics <-
    c(
      "SH.STA.ARIC.ZS", "SH.STA.ORCF.ZS", "SH.STA.ORTH",
      "SH.STA.ANV4.ZS", "SH.STA.ANVC.ZS", "SH.STA.BRTC.ZS",
      "SH.STA.BFED.ZS", "SH.STA.BRTW.ZS", "SN.ITK.SALT.ZS",
      "SN.ITK.VITA.ZS", "SP.REG.BRTH.RU.ZS", "SP.REG.BRTH.UR.ZS",
      "SP.REG.BRTH.ZS", "SP.REG.BRTH.MA.ZS", "SP.REG.BRTH.FE.ZS",
      "SP.M18.2024.FE.ZS", "SP.M15.2024.FE.ZS"
    )

  if (any(df$indicator %in% mis_dhs_mics)) {
    df <- filter_unicef(df,
      priority =
        c(
          "MIS|Malaria Indicator Survey",
          "AISMIS|HIV/AIDS and Malaria Indicator Survey",
          "DHS|Demographic and Health Survey",
          "MICS|Multiple Indicator Cluster Survey"
        )
    )
  }
  if (any(df$indicator %in% ais_dhs_mics)) {
    df <- filter_unicef(df,
      priority =
        c(
          "AIS|AIDS Indicator Survey",
          "AISMIS|HIV/AIDS and Malaria Indicator Survey",
          "DHS|Demographic and Health Survey",
          "MICS|Multiple Indicator Cluster Survey"
        )
    )
  }
  if (any(df$indicator %in% dhs_mics)) {
    df <- filter_unicef(df,
      priority =
        c(
          "DHS|[Dd]emographic and [Hh]ealth [Ss]urvey", #|Demographic Survey'
          "MICS|[Mm]ultiple [Ii]ndicator [Cc]luster [Ss]urvey"
        )
    )
  }
  return(df)
}
worldbank/pddcs documentation built on Nov. 20, 2024, 5:41 a.m.

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