R/read_2013.R

In STAPM/hseclean: Health Survey Data Wrangling

#' Read the Health Survey for England 2013
#'
#' Reads and does basic cleaning on the Health Survey for England 2013.
#'
#' @section Survey details:
#' The HSE 2013 included a general population sample of adults and children,
#' representative of the whole population at both national and regional level.
#' For the sample, 9,408 addresses were randomly selected in 588 postcode sectors,
#' issued over twelve months from January to December 2013. Where an address was found
#' to have multiple dwelling units, one dwelling unit was selected at random and where
#' there were multiple households at a dwelling unit, one household was selected at random.
#' In each selected household, all individuals were eligible for inclusion in the survey.
#' Where there were three or more children aged 0-15 in a household, two of the children were selected at random.
#' A nurse visit was arranged for all participants who consented.
#' A total of 8,795 adults aged 16 and over and 2,185 children aged 0-15 were interviewed.
#' A household response rate of 64% was achieved. Of those where a full
#' interview was achieved, 6,183 adults and 1,455 children also had a nurse visit.
#' Height was measured for those aged two and over and weight for all participants.
#' Nurses measured blood pressure (aged 5 and over) and waist and hip circumference (aged 11 and over).
#' Non-fasting blood samples (for the analysis of total and HDL cholesterol and glycated haemoglobin)
#' were collected from adults aged 16 and over. Saliva samples for cotinine analysis were collected from
#' all participants aged 4 and over. Nurses obtained written consent before taking samples from adults,
#' and parents gave written consent for their children’s samples. Consent was also obtained from adults to
#' send results to their GPs, and from parents to send their children’s results to their GPs.
#'
#' @section Weighting:
#'
#' For analyses at the individual level, the weighting variable to use is (wt_int). These weights are generated separately for adults and children:
#' \itemize{
#' \item for adults (aged 16 or more), the interview weights are a combination of the householdweight and a component which adjusts the sample to reduce bias from individual non-response within households;
#' \item for children (aged 0 to 15), the weights are generated from the household weights and the child selection weights – the selection weights correct for only including a maximum of two children in a household. The combined household and child selection weight were adjusted to ensure that the weighted age/sex distribution matched that of all children in co-operating households.
#' }
#' For analysis of children aged 0-15 in both the Core and the Boost sample, taking into account child selection only and not adjusting for non-response, the (wt_child) variable can be used. For analysis of children aged 2-15 in the only Boost sample the (wt_childb) variable can
#'
#' @section Missing values:
#'
#' \itemize{
#' \item -1 Not applicable: Used to signify that a particular variable did not apply to a given respondent
#' usually because of internal routing. For example, men in women only questions.
#' \item -8 Don't know, Can't say.
#' \item -9 No answer/ Refused
#' }
#'
#' @template read-data-description
#'
#' @template read-data-args
#'
#' @importFrom data.table :=
#'
#' @return Returns a data table.
#'
#' @export
#'
#' @examples
#'
#' \dontrun{
#'
#' data_2013 <- read_2013("X:/",
#' "ScHARR/PR_Consumption_TA/HSE/HSE 2013/UKDA-7649-tab/tab/hse2013ai.tab")
#'
#' }
#'
read_2013 <- function(
    root = c("X:/", "/Volumes/Shared/")[1],
    file = "HAR_PR/PR/Consumption_TA/HSE/Health Survey for England (HSE)/HSE 2013/UKDA-7649-tab/tab/hse2013ai.tab",
    select_cols = c("tobalc", "all")[1]
) {

  ##################################################################################
  # General population

  data <- data.table::fread(
    paste0(root, file),
    na.strings = c("NA", "", "-1", "-2", "-6", "-7", "-8", "-9", "-90", "-90.0", "N/A"))

  data.table::setnames(data, names(data), tolower(names(data)))

  if(select_cols == "tobalc") {

    alc_vars <- colnames(data[ , 677:836])
    smk_vars <- colnames(data[ , 1335:1578])
    health_vars <- paste0("complst", 1:15)

    other_vars <- Hmisc::Cs(
      mintb, addnum,
      PSU, Cluster, wt_int,
      hserial,pserial,
      Age, Sex,
      Origin,
      qimd, econact, nssec3, nssec8,
      #econact2,
      Paidwk,
      Activb, #HHInc,
      Children, Infants,
      EducEnd, topqual3,
      eqv5, #eqvinc,

      marstatd, # marital status inc cohabitees

      # how much they weigh
      Htval, Wtval)

    names <- c(other_vars, alc_vars, smk_vars, health_vars)

    names <- tolower(names)

    data <- data[ , names, with = F]

  }

  data.table::setnames(data,
                       c("longend2", "marstatd", "origin", "pserial", "hrollwk", "hrollwe", paste0("complst", 1:15)),
                       c("longend", "marstat", "ethnicity_raw", "hse_id", "rollwk", "rollwe", paste0("compm", 1:15)))

  data[ , psu := paste0("2013_", psu)]
  data[ , cluster := paste0("2013_", cluster)]

  data[ , year := 2013]
  data[ , country := "England"]

  data[ , quarter := c(1:4)[findInterval(mintb, c(1, 4, 7, 10))]]
  data[ , mintb := NULL]

  return(data[])
}