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add health and benefits modules
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@djmorris1989
djmorris1989 committed Mar 11, 2024
1 parent 23095d0 commit 1974136
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Showing 35 changed files with 388 additions and 62 deletions.
2 changes: 1 addition & 1 deletion DESCRIPTION
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Expand Up @@ -15,7 +15,7 @@ Description: Reads in raw Labour Force Survey data obtained under the End User L
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
RoxygenNote: 7.3.0
Depends:
R (>= 3.6.1)
Imports:
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2 changes: 2 additions & 0 deletions NAMESPACE
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Expand Up @@ -2,11 +2,13 @@

export(RecodeSIC92to07)
export(combine_years)
export(lfs_clean_benefit)
export(lfs_clean_demographic)
export(lfs_clean_economic_activity)
export(lfs_clean_education)
export(lfs_clean_global)
export(lfs_clean_global_5q)
export(lfs_clean_health)
export(lfs_clean_hours_earn)
export(lfs_read_1993)
export(lfs_read_1994)
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35 changes: 35 additions & 0 deletions R/lfs_clean_benefit.R
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@@ -0,0 +1,35 @@
#' Clean benefits data
#'
#' Cleans the raw data for social security benefit related variables.
#'
#' @return Returns a new set of variables
#' @export
lfs_clean_benefit <- function(
data
) {

# Main type of benefit claimed by those out of work
if ("ooben" %in% colnames(data)) {
data[ooben %in% c(1) , oowben := "unemployed"]
data[ooben %in% c(2) , oowben := "sick_injured_disabled"]
data[ooben %in% c(3:5) , oowben := "lone_parent_carer_other_oow"]
data[ooben %in% c(6) , oowben := "other_benefits"]
data[ooben %in% c(7) , oowben := "no_benefits"]

} else {

data[, oowben := NA]
}

###############################
### RETAIN CLEANED VARIABLES

final_data <- data[, c("obs_id", "oowben")]

var_names <- c("oowben")

setnames(final_data, var_names, paste0("b_", var_names))

return(final_data)

}
10 changes: 8 additions & 2 deletions R/lfs_clean_demographic.R
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Expand Up @@ -145,23 +145,29 @@ lfs_clean_demographic <- function(

}

# dependent children in the family

data[is.na(fdpch16) | fdpch16 == 0, dep_children := "no"]
data[fdpch16 > 0, dep_children := "yes"]

### convert the variables to factors
data$country <- as.factor(data$country)
data$region <- as.factor(data$region)
data$ethnicity_4cat <- as.factor(data$ethnicity_4cat)
data$ethnicity_2cat <- as.factor(data$ethnicity_2cat)
data$marstat <- as.factor(data$marstat)
data$dep_children <- as.factor(data$dep_children)


###############################
### RETAIN CLEANED VARIABLES

final_data <- data[, c("obs_id",
"age", "sex", "ethnicity_2cat", "ethnicity_4cat",
"region", "country", "marstat")]
"region", "country", "marstat", "dep_children")]

var_names <- c("age", "sex", "ethnicity_2cat", "ethnicity_4cat",
"region", "country", "marstat")
"region", "country", "marstat", "dep_children")

setnames(final_data, var_names, paste0("d_", var_names))

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14 changes: 14 additions & 0 deletions R/lfs_clean_global.R
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Expand Up @@ -52,18 +52,32 @@ lfs_clean_global <- function(data,

earn <- lfsclean::lfs_clean_hours_earn(data = data, deflator = deflator)

### benefits

#cat(crayon::cyan("\n\t\tHours and earnings variables module\n"))

bene <- lfsclean::lfs_clean_benefit(data = data)

### education

#cat(crayon::cyan("\n\t\tEducation variables module\n\n"))

educ <- lfsclean::lfs_clean_education(data = data)

### health

#cat(crayon::cyan("\n\t\tHealth variables module\n\n"))

heal <- lfsclean::lfs_clean_health(data = data)

######################
### Merge datasets ###

merged_data <- merge(main_data, demographics, by = c("obs_id"))
merged_data <- merge(merged_data, lmkt, by = c("obs_id"))
merged_data <- merge(merged_data, bene, by = c("obs_id"))
merged_data <- merge(merged_data, educ, by = c("obs_id"))
merged_data <- merge(merged_data, heal, by = c("obs_id"))
merged_data <- merge(merged_data, earn, by = c("obs_id"))

final_data <- lfsclean::select_data(
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63 changes: 63 additions & 0 deletions R/lfs_clean_health.R
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@@ -0,0 +1,63 @@
#' Clean health data
#'
#' Cleans the raw data for health and health related variables.
#'
#' @return Returns a new set of variables
#' @export
lfs_clean_health <- function(
data
) {

# Disability (under the Equality Act 2010)
if ("disea" %in% colnames(data)) {
data[disea == 1 , disability := "disabled"]
data[disea == 2 , disability := "not_disabled"]
} else {

data[, disability := NULL]
}

# Main health problem/disability

if ("health" %in% colnames(data)){
data[ , health_condition := "none"]
data[health == 1 , health_condition := "msk_arms_hands"]
data[health == 2 , health_condition := "msk_legs_feet"]
data[health == 3 , health_condition := "msk_back_neck"]
data[health == 4 , health_condition := "difficulty_seeing"]
data[health == 5 , health_condition := "difficulty_hearing"]
data[health == 6 , health_condition := "speech_impediment"]
data[health == 7 , health_condition := "skin_conditions_disfigurement"]
data[health == 8 , health_condition := "respiratory_problems"]
data[health == 9 , health_condition := "heart_blood_circulatory_problems"]
data[health == 10 , health_condition := "digestive_problems"]
data[health == 11 , health_condition := "diabetes"]
data[health == 12 , health_condition := "depression_anxiety"]
data[health == 13 , health_condition := "epilepsy"]
data[health == 18 , health_condition := "autism"]
data[health == 14 , health_condition := "learning_difficulties"]
data[health == 15 , health_condition := "mental_illness"]
data[health == 16 , health_condition := "progressive_illness_nec"]
data[health == 17 , health_condition := "other_problems"]
} else {

data[, health_condition := NULL]
}

### convert the variables to factors
data$disability <- as.factor(data$disability)
data$health_condition <- as.factor(data$health_condition)

###############################
### RETAIN CLEANED VARIABLES

final_data <- data[, c("obs_id",
"disability", "health_condition")]

var_names <- c("disability", "health_condition")

setnames(final_data, var_names, paste0("h_", var_names))

return(final_data)

}
3 changes: 2 additions & 1 deletion R/lfs_read_2000.R
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Expand Up @@ -57,9 +57,10 @@ lfs_read_2000 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt07,piwt07)
demographic_vars <- Hmisc::Cs(age,sex,govtor,ethcen,marstt)
demographic_vars <- Hmisc::Cs(age,sex,govtor,ethcen,marstt,fdpch16)
education_vars <- Hmisc::Cs(edage,hiqual,hiquald,btec,sctvec,gnvq,nvqlev,rsa,candg,numal,numas,numol,qgcse,
quals01,quals02,quals03,quals04,quals05,quals06,quals07,quals08,quals09,quals10,quals11,qualch1)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecacr,grsswk,ftptwk,ttachr,ttushr,mpnor,publicr,indm92m,indd92m,inds92m,socmain,socminm)
other_vars <- Hmisc::Cs(refwkm,thiswv)

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8 changes: 5 additions & 3 deletions R/lfs_read_2001.R
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Expand Up @@ -57,9 +57,10 @@ lfs_read_2001 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt07)
demographic_vars <- Hmisc::Cs(age,sex,govtof)
demographic_vars <- Hmisc::Cs(age,sex,govtof,fdpch16)
education_vars <- Hmisc::Cs(edage,btec,sctvec,gnvq,nvqlev,rsa,candg,numal,numas,numol,gcse,qgcse,
quals01,quals02,quals03,quals04,quals05,quals06,quals07,quals08,quals09,quals10,quals11,qualch1)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecacr, ftptwk,ttachr,ttushr, publicr,indm92m,indd92m,inds92m)
other_vars <- Hmisc::Cs(refwkm,thiswv)

Expand Down Expand Up @@ -99,9 +100,10 @@ lfs_read_2001 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt07,piwt07)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt,fdpch16)
education_vars <- Hmisc::Cs(edage,btec,sctvec,gnvq,nvqlev,rsa,candg,numal,numas,numol,
quals01,quals02,quals03,quals04,quals05,quals06,quals07,quals08,quals09,quals10,quals11,qualch1)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecacr,grsswk,ftptwk,ttachr,ttushr,mpnr01,publicr,indm92m,indd92m,inds92m,soc2km,sc2kmmn)
other_vars <- Hmisc::Cs(refwkm)

Expand Down Expand Up @@ -142,7 +144,7 @@ lfs_read_2001 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt14,piwt14)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt,fdpch16)
education_vars <- Hmisc::Cs(hiqual,hiquald,btec,sctvec,gnvq,nvqlev,rsa,candg,numal,numas,numol,
quals01,quals02,quals03,quals04,quals05,quals06,quals07,quals08,quals09,quals10,quals11,qualch1)
work_vars <- Hmisc::Cs(inecacr,grsswk,ftptwk,ttachr,ttushr,mpnr01,publicr,indm92m,indd92m,inds92m,soc2km,sc2kmmn)
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6 changes: 4 additions & 2 deletions R/lfs_read_2002.R
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Expand Up @@ -57,9 +57,10 @@ lfs_read_2002 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt14,piwt14)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt,fdpch16)
education_vars <- Hmisc::Cs(edage,hiqual,hiquald,btec,sctvec,gnvq,nvqlev,rsa,candg,numal,numas,numol,gcse,qgcse,
quals01,quals02,quals03,quals04,quals05,quals06,quals07,quals08,quals09,quals10,quals11,qualch1)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecacr,grsswk,ftptwk,ttachr,ttushr,mpnr01,publicr,indm92m,indd92m,inds92m,soc2km,sc2kmmn)
other_vars <- Hmisc::Cs(refwkm,thiswv)

Expand Down Expand Up @@ -100,9 +101,10 @@ lfs_read_2002 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt14,piwt14)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt,fdpch16)
education_vars <- Hmisc::Cs(edage,btec,sctvec,gnvq,nvqlev,rsa,candg,numal,numas,numol,
quals01,quals02,quals03,quals04,quals05,quals06,quals07,quals08,quals09,quals10,quals11,qualch1)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecacr,grsswk,ftptwk,ttachr,ttushr,mpnr02,publicr,indm92m,indd92m,inds92m,soc2km,sc2kmmn,
undemp,undhrs,ovhrs,lespay2)
other_vars <- Hmisc::Cs(refwkm)
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3 changes: 2 additions & 1 deletion R/lfs_read_2003.R
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Expand Up @@ -57,9 +57,10 @@ lfs_read_2003 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt14,piwt14)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt,fdpch16)
education_vars <- Hmisc::Cs(edage,hiqual,hiquald,btec,sctvec,gnvq,nvqlev,rsa,candg,numal,numas,numol,gcse,qgcse,
quals01,quals02,quals03,quals04,quals05,quals06,quals07,quals08,quals09,quals10,quals11,qualch1)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecacr,grsswk,ftptwk,ttachr,ttushr,mpnr02,publicr,indm92m,indd92m,inds92m,soc2km,sc2kmmn,
undemp,undhrs,ovhrs,lespay2)
other_vars <- Hmisc::Cs(refwkm,thiswv)
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4 changes: 3 additions & 1 deletion R/lfs_read_2004.R
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Expand Up @@ -60,6 +60,7 @@ lfs_read_2004 <- function(
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt)
education_vars <- Hmisc::Cs(edage,btec,sctvec,qgnvq,nvqlev,rsa,candg,numal,numas,hst,advhst,
subcod1,subcod2,subcod3,subcod4,subcod5,subcod6,subcod7,subcod8)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecacr,grsswk,ftptwk,ttachr,ttushr,mpnr02,publicr,indm92m,indd92m,inds92m,soc2km,sc2kmmn)
other_vars <- Hmisc::Cs(refwkm,thiswv)

Expand Down Expand Up @@ -90,11 +91,12 @@ lfs_read_2004 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt14,piwt14)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt,fdpch16)
education_vars <- Hmisc::Cs(edage,hiqual4d,hiqual4,btec,sctvec,gnvq4,nvqlev,rsa,candg,numal,numas,numol4,hst,advhst,
gcseful1,gcseful2,gcseful3,gcseful4,qgcse41,qgcse42,qgcse43,qgcse44,
quals401,quals402,quals403,quals404,quals405,quals406,quals407,quals408,quals409,quals410,quals411,
subcod1,subcod2,subcod3,subcod4,subcod5,subcod6,subcod7,subcod8)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecacr,grsswk,ftptwk,ttachr,ttushr,mpnr02,publicr,indm92m,indd92m,inds92m,soc2km,sc2kmmn,
undemp,undhrs,ovhrs,lespay2)
other_vars <- Hmisc::Cs(refwkm)
Expand Down
6 changes: 4 additions & 2 deletions R/lfs_read_2005.R
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Expand Up @@ -57,9 +57,10 @@ lfs_read_2005 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt14,piwt14)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt,fdpch16)
education_vars <- Hmisc::Cs(edage,hiqual4d,hiqual4,btec,sctvec,gnvq4,nvqlev,rsa,candg,numal,numas,hst,advhst,
quals401,quals402,quals403,quals404,quals405,quals406,quals407,quals408,quals409,quals410,quals411)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecacr,grsswk,ftptwk,ttachr,ttushr,mpnr02,publicr,indm92m,indd92m,inds92m,soc2km,sc2kmmn,
undemp,undhrs,ovhrs,lespay2)
other_vars <- Hmisc::Cs(refwkm,thiswv)
Expand Down Expand Up @@ -101,10 +102,11 @@ lfs_read_2005 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt14,piwt14)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marstt,fdpch16)
education_vars <- Hmisc::Cs(hiqual5d,hiqual5,btec,sctvec,gnvq4,nvqlev,rsa,candg,numol5,numal,numas,hst,advhst,
gcseful1,gcseful2,gcseful3,gcseful4,qgcse41,qgcse42,qgcse43,qgcse44,
quals401,quals402,quals403,quals404,quals405,quals406,quals407,quals408,quals409,quals410,quals411,qualch51)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecac05,grsswk,ftptwk,ttachr,ttushr,mpnr02,publicr,indm92m,indd92m,inds92m,soc2km,sc2kmmn)
other_vars <- Hmisc::Cs(refwkm)

Expand Down
3 changes: 2 additions & 1 deletion R/lfs_read_2006.R
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Expand Up @@ -57,10 +57,11 @@ lfs_read_2006 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt14,piwt14)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marsta)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marsta,fdpch16)
education_vars <- Hmisc::Cs(edage,hiqual5d,hiqual5,btec,sctvec,gnvq4,nvqlev,rsa,candg,numol5,numal,numas,hst,advhst,typhst1,typhst2,typhst3,typhst4,typhst5,
gcseful1,gcseful2,gcseful3,gcseful4,qgcse41,qgcse42,qgcse43,qgcse44,
quals601,quals602,quals603,quals604,quals605,quals606,quals607,quals608,quals609,quals610,quals611,qualch51)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecac05,grsswk,ftptwk,ttachr,ttushr,mpnr02,publicr,indm92m,indd92m,inds92m,soc2km,sc2kmmn,
undemp,undhrs,ovhrs,lespay2)
other_vars <- Hmisc::Cs(refwkm,thiswv)
Expand Down
3 changes: 2 additions & 1 deletion R/lfs_read_2007.R
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Expand Up @@ -57,10 +57,11 @@ lfs_read_2007 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt14,piwt14)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marsta)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marsta,fdpch16)
education_vars <- Hmisc::Cs(edage,hiqual5d,hiqual5,btec7,sctvec,gnvq4,nvqlev,rsa,candg,numol5,numal,numas,hst,advhst,typhst1,typhst2,typhst3,typhst4,typhst5,
gcseful1,gcseful2,gcseful3,gcseful4,qgcse41,qgcse42,qgcse43,qgcse44,
quals601,quals602,quals603,quals604,quals605,quals606,quals607,quals608,quals609,quals610,quals611,qualch51)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecac05,grsswk,ftptwk,ttachr,ttushr,mpnr02,publicr,indm92m,indd92m,inds92m,soc2km,sc2kmmn,
undemp,undhrs,ovhrs,lespay2)
other_vars <- Hmisc::Cs(refwkm,thiswv)
Expand Down
3 changes: 2 additions & 1 deletion R/lfs_read_2008.R
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Expand Up @@ -57,10 +57,11 @@ lfs_read_2008 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt14,piwt14)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marsta)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marsta,fdpch16)
education_vars <- Hmisc::Cs(edage,hiqual8d,hiqual8,btec8,sctvec,gnvq4,nvqlev,rsa,candg,numol5,numal,numas,hst,advhst,typhst1,typhst2,typhst3,typhst4,typhst5,
gcseful1,gcseful2,gcseful3,gcseful4,qgcse41,qgcse42,qgcse43,qgcse44,
quals601,quals602,quals603,quals604,quals605,quals606,quals607,quals608,quals609,quals610,quals611,qualch51)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecac05,grsswk,ftptwk,ttachr,ttushr,mpnr02,publicr,indm92m,indd92m,inds92m,soc2km,sc2kmmn,
undemp,undhrs,ovhrs,lespay2)
other_vars <- Hmisc::Cs(refwkm,thiswv)
Expand Down
4 changes: 3 additions & 1 deletion R/lfs_read_2009.R
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Expand Up @@ -60,6 +60,7 @@ lfs_read_2009 <- function(
education_vars <- Hmisc::Cs(edage,hiqual8d,hiqual8,btec8,sctvec,gnvq4,nvqlev,rsa,candg,numol5,numal,numas,hst,advhst,typhst1,typhst2,typhst3,typhst4,typhst5,
gcseful1,gcseful2,gcseful3,gcseful4,qgcse41,qgcse42,qgcse43,qgcse44,
quals601,quals602,quals603,quals604,quals605,quals606,quals607,quals608,quals609,quals610,quals611,qualch51)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecac05,grsswk,ftptwk,ttachr,ttushr,mpnr02,publicr,indc07m,indd07m,inds07m,soc2km,sc2kmmn,
undemp,undhrs,ovhrs,lespay2)
other_vars <- Hmisc::Cs(refwkm,thiswv)
Expand Down Expand Up @@ -104,9 +105,10 @@ lfs_read_2009 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt14,piwt14)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marsta)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marsta,fdpch16)
education_vars <- Hmisc::Cs(hiqual8d,hiqual8,btec8,sctvec,gnvq4,nvqlev,rsa,candg,numol5,numal,numas,hst,advhst,
quals601,quals602,quals603,quals604,quals605,quals606,quals607,quals608,quals609,quals610,quals611,qualch91)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecac05,grsswk,ftptwk,ttachr,ttushr,mpnr02,publicr,indc07m,indd07m,inds07m,soc2km,sc2kmmn)
other_vars <- Hmisc::Cs(refwkm,thiswv)

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3 changes: 2 additions & 1 deletion R/lfs_read_2010.R
View file
Original file line number Diff line number Diff line change
Expand Up @@ -57,11 +57,12 @@ lfs_read_2010 <- function(
setnames(data, names(data), tolower(names(data)))

weights_vars <- Hmisc::Cs(pwt14,piwt14)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marsta)
demographic_vars <- Hmisc::Cs(age,sex,govtof,eth01,marsta,fdpch16)
education_vars <- Hmisc::Cs(edage,hiqual8d,hiqual8,btec8,sctvec,gnvq4,nvqlev,rsa,candg,numol5,numal,numas,hst,advhst,typhst1,typhst2,typhst3,typhst4,typhst5,
gcseful1,gcseful2,gcseful3,gcseful4,qgcse41,qgcse42,qgcse43,qgcse44,
quls1001,quls1002,quls1003,quls1004,quls1005,quls1006,quls1007,quls1008,quls1009,quls1010,
quls1011,quls1012,quls1013,quls1014,quls1015,quls1016,quls1017,quls1018,quls1019,quls1020,qualch91)
health_vars <- Hmisc::Cs(health,discurr)
work_vars <- Hmisc::Cs(inecac05,grsswk,ftptwk,ttachr,ttushr,mpnr02,publicr,indc07m,indd07m,inds07m,soc2km,sc2kmmn,
undemp,undhrs,ovhrs,lespay2)
other_vars <- Hmisc::Cs(refwkm,thiswv)
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