vignettes/archive/sim_data_config.R

In n8thangreen/STIecoPredict: Multilevel Regression With Post-Stratification Using Natsal Data

## ----readin, eval=FALSE--------------------------------------------------
#  
#   library(plyr, quietly = TRUE)
#   library(stringr)
#   library(reshape2)
#  
#  
#   setwd(".\\data")
#  
#   ethnicgroup_lookup <- read.csv("./ONSNatsal_ethgrp_mapping.csv", check.names=FALSE)
#  
#   ## ethnic group mapping to fewer, amalgamated groups
#   Natsalethgrp.mapping <- read.csv("C:/Users/nathan.green/Documents/chlamydia/classifier/data/Natsal_fewerethgrps_mapping.csv")
#  
#  
#   ## different area groupings look-up table
#   area.lookup <- read.csv(".\\lookup_data_area.csv")
#  
#  
#   ## LA population, by age & sex
#   ## ---------------------------
#   ## 2001-2012
#   popLAagesex.dat <- read.csv(".\\ONS_popn_age&sex\\pop_age_sex_LA.csv", check.names=FALSE)
#  
#  
#  
#  
#   ## smoking
#   ## -------
#   ### LA only prevalence
#   ###
#   smokingLA.dat <- read.csv(".\\risk_factors\\smoking\\smoking_LA.csv")
#   ### age-group only prevalence
#   ### 2010
#   smokingage.dat <- read.csv(".\\risk_factors\\smoking\\smoking_agegrp.csv", check.names=FALSE)
#   smokingLA.dat$Name <- LAnameClean(smokingLA.dat$Name)
#  
#  
#  
#  
#  
#   ## drinking
#   ## --------
#   ### LA only prevalence
#   ### 2008-2009, >16 yr olds
#   drinkingLA.dat <- read.csv(".\\risk_factors\\drinking\\increasingandhigherriskdrinking_LA.csv")
#   drinkingLA.dat$Name <- LAnameClean(drinkingLA.dat$Name)
#   ### age-group only prevalence
#   ### 2011
#   # drinkingage.dat <- read.csv(".\\risk_factors\\drinking\\drinking_agegrp_freq.csv", check.names=FALSE)    #deprecated in favour of below
#   drinkingage.dat <- read.csv(".\\risk_factors\\drinking\\drinking_agegrp_units.csv", check.names=FALSE)
#  
#  
#  
#  
#   ## income
#   ## ------
#   ## 2011-2012
#   #   incomeregion.dat <- read.csv(".\\risk_factors\\income\\income_by_regions.csv")  #deprecated
#   #   read.csv("./risk_factors/income/income_countymedian_2011.csv")
#   incomeage.dat <- read.csv(".\\risk_factors\\income\\income_by_age_sex.csv")
#   # ASHE (ONS) 2011
#   incomeLA.dat <- list()
#   incomeLA.dat[["Men"]]   <- read.csv("./risk_factors/income/incomeLA_male_2011.csv", colClasses=c("Median"="integer", "Mean"="integer"))
#   incomeLA.dat[["Women"]] <- read.csv("./risk_factors/income/incomeLA_female_2011.csv", colClasses=c("Median"="numeric", "Mean"="numeric"))
#  
#   incomeLA.dat[["Men"]]$LA_Name   <- LAnameClean(str_trim(incomeLA.dat[["Men"]]$LA_Name))
#   incomeLA.dat[["Women"]]$LA_Name <- LAnameClean(str_trim(incomeLA.dat[["Women"]]$LA_Name))
#  
#  
#  
#  
#   ## ethnicity
#   ## ---------
#   ## Census 2011
#   # ethnicityLA.dat <- read.csv("./risk_factors/ethnicity/la_ethgrp_pop.csv", check.names=FALSE)   #deprecated
#   # ethnicityLA.dat$Area <- LAnameClean(ethnicityLA.dat$Area)
#   ethnicityLA.dat <- list()
#   ethnicityLA.dat[["Men"]]   <- read.csv("./risk_factors/ethnicity/census2011_LA_ethgrp_agegrp_male.csv", check.names=FALSE)
#   ethnicityLA.dat[["Women"]] <- read.csv("./risk_factors/ethnicity/census2011_LA_ethgrp_agegrp_male.csv", check.names=FALSE)
#