R/read_2017.R

In STAPM/hseclean: Health Survey Data Wrangling

#' Read the Health Survey for England 2017
#'
#' Reads and does basic cleaning on the Health Survey for England 2017.
#'
#' @section Survey details:
#' The HSE 2017 sample comprised of a core general population sample. There was no boost sample in 2017.
#' The sample comprised 9,612 addresses selected at random in 534 postcode sectors,
#' issued over twelve months from January to December 2017. Field work was completed in March 2018.
#' Adults and children were interviewed at households identified at the selected addresses.
#' Up to four children in each household were selected to take part at random;
#' up to two aged 2 to 12 and up to two aged 13 to 15.
#' A total of 7,997 adults aged 16 and over and 1,985 children aged 0-15 were interviewed,
#' including 5,196 adults and 1,195 children who had a nurse visit.
#' From 2015 HSE data contains the 2015 English index of multiple deprivation, divided into quintiles.
#'
#' @template read-data-description
#'
#' @template read-data-args
#'
#' @importFrom data.table :=
#'
#' @return Returns a data table.
#'
#' @export
#'
#' @examples
#'
#' \dontrun{
#'
#' data_2017 <- read_2017("X:/",
#' "ScHARR/PR_Consumption_TA/HSE/HSE 2017/UKDA-8334-tab/tab/hse2016_eul.tab")
#'
#' }
#'
read_2017 <- function(
    root = c("X:/", "/Volumes/Shared/")[1],
    file = "HAR_PR/PR/Consumption_TA/HSE/Health Survey for England (HSE)/HSE 2017/UKDA-8488-tab/tab/hse17i_eul_v1.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[ , c(50, 61, 749:801, 925:969, 1180:1203, 1535:1578)])
    smk_vars <- colnames(data[ , c(19, 20, 44, 55, 62, 727:748, 905:924, 1019:1043, 1204:1332, 1579:1592)])
    health_vars <- paste0("complst", 1:15)

    other_vars <- Hmisc::Cs(
      qrtint, addnum,
      psu, cluster, wt_int, #wt_sc,
      seriala,
      age16g5, age35g, sex,
      origin2,
      qimd, nssec3, nssec8,
      stwork,
      activb2,
      #Ag015g4, #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("qrtint", "marstatd", "origin2", "activb2", "stwork","seriala", paste0("complst", 1:15)),
                       c("quarter", "marstat", "ethnicity_raw", "activb", "paidwk","hse_id", paste0("compm", 1:15)))

  data[ , psu := paste0("2017_", psu)]
  data[ , cluster := paste0("2017_", cluster)]

  data[ , year := 2017]
  data[ , country := "England"]

  return(data[])
}