# Example of processing tobacco and alcohol data from the Health Survey for England
library(hseclean)
# apply functions to create the variables for analysis and to retain only the required variables
# The variables to retain
keep_vars <- c("wt_int", "psu", "cluster", "year", "quarter",
"age", "age_cat", "censor_age", "sex", "imd_quintile",
"ethnicity_4cat", "ethnicity_2cat",
"degree", "relationship_status", "employ2cat", "social_grade", "kids", "income5cat",
"nssec3_lab", "man_nonman", "activity_lstweek", "eduend4cat",
"hse_cancer", "hse_endocrine", "hse_heart", "hse_mental", "hse_nervous", "hse_eye", "hse_ear", "hse_respir",
"hse_disgest", "hse_urinary", "hse_skin", "hse_muscskel", "hse_infect", "hse_blood",
"weight", "height", "bmi",
"cig_smoker_status", "smk_start_age", "years_since_quit",
"cigs_per_day", "smoker_cat", "giveup_smk",
"drinks_now",
"drink_freq_7d", "n_days_drink", "peakday", "binge_cat",
"beer_units", "wine_units", "spirit_units", "rtd_units",
"weekmean",
"perc_spirit_units", "perc_wine_units", "perc_rtd_units", "perc_beer_units",
"drinker_cat",
"spirits_pref_cat", "wine_pref_cat", "rtd_pref_cat", "beer_pref_cat",
"total_units7_ch"
)
# The variables that must have complete cases
complete_vars <-
c("age", "sex", "year", "quarter", "psu", "cluster")
# Vary from 2001 to 2017
year <- 2017
data <- read_hse(year, root = "/Volumes/Shared/")
data <- clean_age(data)
data <- clean_family(data)
data <- clean_demographic(data)
data <- clean_education(data)
data <- clean_economic_status(data)
data <- clean_income(data)
data <- clean_health_and_bio(data)
data <- smk_status(data)
data <- smk_former(data)
data <- smk_life_history(data)
data <- smk_amount(data)
data <- alc_drink_now_allages(data)
data <- alc_weekmean_adult(data)
data <- alc_sevenday_adult(data)
data <- alc_sevenday_child(data)
data <- select_data(data, ages = 11:89, years = year, keep_vars = keep_vars, complete_vars = complete_vars)
data <- clean_surveyweights(data)
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