R/read_2005.R

In dosgillespie/hseclean: Health Survey Data Wrangling

#' Read HSE 2005
#'
#' Reads and does basic cleaning on the Health Survey for England 2005.
#'
#' The Health Survey for England 2005 was designed to provide data at both national and regional level about the population living in private households in England. The sample for the HSE 2005 comprised of three components: the core (general population) sample, a boost sample of people aged 65 and over (those living in institutions were not included) and a boost sample of children aged 2-15. The core sample was designed to be representative of the population living in private households in England and should be used for analyses at the national level.
#'
#' A random sample of 720 postcode sectors was then selected with probability proportional to the total number of addresses within them. Once selected, the PSUs were randomly allocated to the 12 months of the year (60 per month) so that each quarter provided a nationally representative sample.
#'
#' Within each selected postcode sector, a sample of 26 delivery points was selected, giving a total selected sample of 18,720 (720 x 26) addresses. From the 26 addresses within each postcode sector, ten were selected at random and allocated to the core sample, and the remaining 16 addresses were allocated to the boost sample.
#'
#' All private households in the general population sample are eligible for inclusion in the survey (up to a maximum of three households per address). For the core sample, up to two children aged 0-15 are interviewed in each household, as well as up to 10 adults aged 16 and over.
#'
#' At boost addresses interviewers screened for households containing at least one person of either of the age groups covered in the boost: persons aged 65 and over, or (for certain months) children aged 2-15 years. Because of funding restrictions, the boost included children only during January, February, October, November and December. At each household where people of the eligible ages were found, all persons aged 65 and over and up to two eligible children were selected by the interviewer for inclusion in the survey.
#'
#' An interview with each eligible person was followed by a nurse visit both using computer assisted interviewing. The 2005 survey for adults focused on the health of older people. All adults were asked modules of questions on general health, alcohol consumption, smoking, fruit and vegetable consumption and complementary and alternative medicine. Older informants were also asked about use of health, dental and social care services, cardiovascular disease (CVD), chronic diseases and quality of care, disabilities and falls. Older informants in the boost sample were asked a slightly shorter questionnaire, omitting questions about fruit and vegetable consumption and complementary and alternative medicines.
#'
#' Children aged 13-15 were interviewed themselves, and parents of children aged 0-12 were asked about their children, with the child interview including questions on physical activity and fruit and vegetable consumption.
#'
#' WEIGHTING
#'
#' Individual weight
#'
#' 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 household weight 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 the Core sample, taking into account child selection only and not adjusting for non-response, the child_wt variable can be used.
#'
#' 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 -8 Don't know, Can't say.
#' \item -9 No answer/ Refused
#' }
#'
#' @param root Character - the root directory.
#' @param file Character - the file path and name.
#' @importFrom data.table :=
#' @return Returns a data table. Note that:
#' \itemize{
#' \item Missing data ("NA", "", "-1", "-2", "-6", "-7", "-9", "-90", "-90.0", "N/A") is replace with NA,
#' except -8 ("don't know") as this is data.
#' \item All variable names are converted to lower case.
#' \item The cluster and probabilistic sampling unit have the year appended to them.
#' }
#' @export
#'
#' @examples
#'
#' \dontrun{
#'
#' data_2005 <- read_2005("X:/", "ScHARR/PR_Consumption_TA/HSE/HSE 2005/UKDA-5675-tab/tab/hse05ai.tab")
#'
#' }
#'
read_2005 <- function(
  root = c("X:/", "/Volumes/Shared/"),
  file = "ScHARR/PR_Consumption_TA/HSE/HSE 2005/UKDA-5675-tab/tab/hse05ai.tab"
) {

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

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

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

  alc_vars <- colnames(data[ , 818:886])
  smk_vars <- colnames(data[ , 1762:1819])
  health_vars <- paste0("compm", 1:15)

  other_vars <- Hmisc::Cs(
    mintb, addnum,
    area, cluster, wt_int, child_wt, wt_intCH, Wt_intEL,
    age, sex,
    ethinda,
    imd2004, econact, nssec3, nssec8,
    #econact2, #paidwk,
    activb, #HHInc,
    children, infants,
    educend, topqual3,
    eqv5, #eqvinc,

    marstatb, # 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, names(data), tolower(names(data)))

  data.table::setnames(data, c("area", "imd2004", "d7unit", "marstatb", "ethinda"),
           c("psu", "qimd", "d7unitwg", "marstat", "ethnicity_raw"))

  data[ , psu := paste0("2005_", psu)]
  data[ , cluster := paste0("2005_", cluster)]

  data[ , year := 2005]
  data[ , country := "England"]
  
  data[ , quarter := c(1:4)[findInterval(mintb, c(1, 4, 7, 10))]]
  data[ , mintb := NULL]

return(data[])
}