## ----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)
#
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