R/read_2001.R

In STAPM/hseclean: Health Survey Data Wrangling

#' Read the Health Survey for England 2001
#'
#' Reads and does basic cleaning on the Health Survey for England 2001.
#'
#' @section Survey details:
#' A sample of the population living in private households. All persons living in the house, including those
#' under 2 years were eligible for inclusion. At addresses where there were more than two children under 16,
#' two children were selected at random. Information was obtained directly from persons aged 13 and
#' over. Information about children aged 0-12 was obtained from a parent, with the child present.
#'
#' @section Weighting:
#'
#' There is no weighted variable for household adult data.
#' For children under 16, the weighted variable Child_Wt should be used.
#'
#' @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 -2 Schedule not applicable: Used mainly for variables on the self-completions when the
#' respondent was not of the given age range, also used for children without legal guardians in the
#' home who could not participate in the nurse schedule.
#' \item -6 Schedule not obtained: Used to signify that a particular variable was not answered because the
#' respondent did not complete or agree to a particular schedule (i.e. nurse schedule or selfcompletions).
#' \item -7 Refused/ not obtained: Used only for variables on the nurse schedules, this code indicates that a
#' respondent refused a particular measurement or test or the measurement was attempted but not
#' obtained or not attempted.
#' \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. Note that:
#' \itemize{
#' \item Each data point is assigned a weight of 1 as there is no weight variable supplied.
#' \item A single sampling cluster is assigned.
#' \item The probabilistic sampling unit have the year appended to them.
#' }
#' @export
#'
#' @examples
#'
#' \dontrun{
#'
#' data_2001 <- read_2001("X:/", "ScHARR/PR_Consumption_TA/HSE/HSE 2001/UKDA-4628-tab/tab/hse01ai.tab")
#'
#' }
#'
read_2001 <- function(
    root = c("X:/", "/Volumes/Shared/")[1],
    file = "HAR_PR/PR/Consumption_TA/HSE/Health Survey for England (HSE)/HSE 2001/UKDA-4628-tab/tab/hse01ai.tab",
    select_cols = c("tobalc", "all")[1]
) {

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

  setnames(data, names(data), tolower(names(data)))

  if(select_cols == "tobalc") {

    alc_vars <- colnames(data[ , 1656:1783])
    smk_vars <- colnames(data[ , 927:984])
    health_vars <- paste0("compm", 1:15)

    other_vars <- Hmisc::Cs(

      mintb, addnum,

      area, child_wt, #cluster, #, #wt_int,

      #HHInc,

      # Identifiers
      hserial,pserial,

      eqv5,
      #eqvinc,

      # Education
      educend, topqual3,

      # Occupation
      econact, nssec3, nssec8,
      #econact2, #paidwk,
      activb,

      # Family
      marstatb, # marital status inc cohabitees
      children, # Number of children in HH (2 < age <= 15)
      infants, # Number of infants in HH (age <= 2)

      # demographic
      age,
      ethnici, # there are a number of cultural background variables that could be used
      nimd,
      sex,

      # how much they weigh
      htval, wtval #wtval2,

    )

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

    names <- tolower(names)

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

  }

  data.table::setnames(data,
                       c("area", "nimd", "d7unit", "marstatb", "ethnici",
                         "nberf", "sberf", "spirf", "sherf", "winef", "popsf",
                         "nberqhp", "nberqsm", "nberqlg", "nberqbt", "nberqpt",
                         "sberqhp", "sberqsm", "sberqlg", "sberqbt", "sberqpt",
                         "sherqgs", "spirqme", "pserial"),

                       c("psu", "qimd", "d7unitwg", "marstat", "ethnicity_raw",
                         "nbeer", "sbeer", "spirits", "sherry", "wine", "pops",
                         "nbeerq1", "nbeerq2", "nbeerq3", "nbeerq4", "nbeerq5",
                         "sbeerq1", "sbeerq2", "sbeerq3", "sbeerq4", "sbeerq5",
                         "sherryq", "spiritsq", "hse_id"))

  # Tidy survey weights
  data[ , wt_int := 1]
  data[age < 16, wt_int := child_wt]
  data[ , child_wt := NULL]

  # Set PSU and cluster
  data[ , psu := paste0("2001_", psu)]
  data[ , cluster := "2001_all"]

  data[ , year := 2001]
  data[ , country := "England"]

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

  return(data[])
}