# Ring et al. 2017 Vignette 1: Generating subpopulations

#### Caroline Ring

#### 2019-04-25

This vignette provides the code used to generate the virtual populations for the ten subpopulations of interest, plus a non-obese adult subpopulation.

To use the code in this vignette, you’ll first need to load a few packages.

    library("httk")
    library("data.table")

Because we’ll be writing some files (and then reading them in other vignettes), be aware of where your current working directory is – the files will be written there. # Set up subpopulation specs Here, we set the number of individuals in each virtual population (1000). We also specify a list of names for the virtual populations. Then we specify corresponding lists of gender, age limits, and BMI categories. HTTK-Pop default values will be used for other population specifications (e.g. race/ethnicity, kidney function categories).

    nsamp<-1000
    #List subpop names
    ExpoCast.group<-list("Total",
                         "Age.6.11",
                         "Age.12.19",
                         "Age.20.65",
                         "Age.GT65",
                         "BMIgt30",
                         "BMIle30",
                         "Females",
                         "Males",
                         "ReproAgeFemale",
                         "Age.20.50.nonobese")
    #List subpop gender specifications
    gendernum <- c(rep(list(NULL),7),
                   list(list(Male=0, Female=1000)),
                   list(list(Male=1000, Female=0)),
                   list(list(Male=0, Female=1000)),
                   list(NULL))
    #List subpop age limits in years
    agelim<-c(list(c(0,79),
                   c(6,11),
                   c(12,19),
                   c(20,65),
                   c(66,79)),
              rep(list(c(0,79)),4),
              list(c(16,49)),
              list(c(20,50)))
    #List subpop weight categories
    bmi_category <- c(rep(list(c('Underweight',
                                 'Normal',
                                 'Overweight',
                                 'Obese')),
                          5),
                      list('Obese', c('Underweight','Normal', 'Overweight')),
                      rep(list(c('Underweight',
                                 'Normal',
                                 'Overweight',
                                 'Obese')),
                          3),
                      list(c('Underweight', 'Normal', 'Overweight')))

## Generate populations

First, define the loop body as a function; then use `parallel::clusterMap` to parallelize it. Warning: This might take a couple of minutes to run.

    tmpfun <- function(gendernum, agelim, bmi_category, ExpoCast_grp,
                       nsamp, method){
      result <- tryCatch({
                         pops <- httk::httkpop_generate(
                      method=method,
                      nsamp = nsamp,
                      gendernum = gendernum,
                      agelim_years = agelim,
                      weight_category = bmi_category)

                      filepart <- switch(method,
                                         'virtual individuals' = 'vi',
                                         'direct resampling' = 'dr')
    saveRDS(object=pops,
              file=paste0(paste('data/httkpop',
                          filepart, ExpoCast_grp,
                          sep='_'),
                          '.Rdata'))
    return(0)
    }, error = function(err){
      print(paste('Error occurred:', err))
      return(1)
    })
    }

    cluster <- parallel::makeCluster(2, # Reduced from 40 to 2 cores
                           outfile='subpopulations_parallel_out.txt')

    evalout <- parallel::clusterEvalQ(cl=cluster,
                 {library(data.table)
                  library(httk)})
    parallel::clusterExport(cl = cluster,
                  varlist = 'tmpfun')
    #Set seeds on all workers for reproducibility
    parallel::clusterSetRNGStream(cluster,
                                  TeachingDemos::char2seed("Caroline Ring"))
    out_vi <- parallel::clusterMap(cl=cluster,
                      fun = tmpfun,
                      gendernum=gendernum,
                      agelim=agelim,
                      bmi_category=bmi_category,
                      ExpoCast_grp = ExpoCast.group,
                      MoreArgs = list(nsamp = nsamp,
                                      method = 'virtual individuals'))
    out_dr <- parallel::clusterMap(cl=cluster,
                      fun = tmpfun,
                      gendernum=gendernum,
                      agelim=agelim,
                      bmi_category=bmi_category,
                      ExpoCast_grp = ExpoCast.group,
                      MoreArgs = list(nsamp = nsamp,
                                      method = 'direct resampling'))
    parallel::stopCluster(cluster)