Nothing
R/AH_TOBC_W_OTH.R
In surveyPrev: Mapping the Prevalence of Binary Indicators using Survey Data in Small Areas
Defines functions
AH_TOBC_W_OTH
Documented in
AH_TOBC_W_OTH
##'AH_TOBC_W_OTH
#' @param IRdata data.frame from surveyPrev::getDHSdata
#'
#' @return A partially processed data.frame that will be used in surveyPrev::getDHSindicator. The whole function can be used as a parameter in surveyPrev::getDHSindicator
#'
#' @author Miaolei Bao, Yunhan Wu, Qianyu Dong
#' @examples
#' \dontrun{
#' dhsData <- getDHSdata(country = "Zambia", indicator = "AH_TOBC_W_OTH", year = 2018)
#' }
#'
#' @export
AH_TOBC_W_OTH <- function(IRdata){
# ******************************************************************************
# Program: RC_CHAR_WM.R
# Purpose: Code to compute respondent characteristics of women
# Data inputs: IR dataset
# Data outputs: coded variables
# Author: Mahmoud Elkasabi
# Date last modified: April 01 2021 by Mahmoud Elkasabi
# ******************************************************************************
#The indicators are computed for age 15-49 in line 49. This can be commented out if the indicators are required for all women.
#Please check the note on health insurance. This can be country specific and also reported for specific populations.
#Please check the variables available for smoking and tobacco and see notes for these variables. Variable names have changed and these indicators are country specific.
# -----------------------------------------------------------------------------#
# # Variables created in this file:
# rc_edu "Highest level of schooling attended or completed"
# rc_edu_median "Median years of education"
# rc_litr_cats "Level of literacy"
# rc_litr "Literate - higher than secondary or can read part or whole sentence"
# rc_media_newsp "Reads a newspaper at least once a week"
# rc_media_tv "Watches television at least once a week"
# rc_media_radio "Listens to radio at least once a week"
# rc_media_allthree "Accesses to all three media at least once a week"
# rc_media_none "Accesses none of the three media at least once a week"
# rc_intr_ever "Ever used the internet"
# rc_intr_use12mo "Used the internet in the past 12 months"
# rc_intr_usefreq "Internet use frequency in the past month - among users in the past 12 months"
# rc_empl "Employment status"
# rc_occup "Occupation among those employed in the past 12 months"
# rc_empl_type "Type of employer among those employed in the past 12 months"
# rc_empl_earn "Type of earnings among those employed in the past 12 months"
# rc_empl_cont "Continuity of employment among those employed in the past 12 months"
# rc_hins_ss "Health insurance coverage - social security"
# rc_hins_empl "Health insurance coverage - other employer-based insurance"
# rc_hins_comm "Health insurance coverage - mutual health org. or community-based insurance"
# rc_hins_priv "Health insurance coverage - privately purchased commercial insurance"
# rc_hins_other "Health insurance coverage - other type of insurance"
# rc_hins_any "Have any health insurance"
# rc_tobc_cig "Smokes cigarettes"
# rc_tobc_other "Smokes other type of tobacco"
# rc_tobc_snuffm "Uses snuff smokeless tobacco by mouth"
# rc_tobc_snuffn "Uses snuff smokeless tobacco by nose"
# rc_tobc_chew "Chews tobacco"
# rc_tobv_betel "Uses betel quid with tobacco"
# rc_tobc_osmkless "Uses other type of smokeless tobacco"
# rc_tobc_anysmkless "Uses any type of smokeless tobacco"
# rc_tobc_any "Uses any type of tobacco - smoke or smokeless"
# -----------------------------------------------------------------------------#
IRdata <- IRdata %>%
filter(v012 <= 49) %>%
mutate(wt = v005/1000000) %>%
mutate(rc_edu = v149,
rc_edu = set_label(rc_edu, label = "Highest level of schooling attended or completed")) %>%
mutate(eduyr = case_when(
v133<=20 ~ v133,
v133>20 & v133<95 ~ 20,
v133>95 | v149>7 ~ 99)) %>%
replace_with_na(replace = list(eduyr = c(99)))
IRdata <- IRdata %>%
mutate(rc_litr_cats = case_when(
!v106 == 3 & v155 == 2 ~ 1,
!v106 == 3 & v155 == 1 ~ 2,
!v106 == 3 & v155 == 0 ~ 3,
!v106 == 3 & v155 == 3 ~ 4,
!v106 == 3 & v155 == 4 ~ 5,
v106 == 3 ~ 0),
rc_litr_cats = add_labels(rc_litr_cats, labels = c("Higher than secondary education"=0, "Can read a whole sentence"=1,
"Can read part of a sentence"=2, "Cannot read at all"=3,
"No card with required language"=4, "Blind/visually impaired"=5)),
rc_litr_cats = set_label(rc_litr_cats, label = "Level of literacy")) %>%
mutate(rc_litr = case_when(
v106==3 | v155==1 | v155==2 ~ 1, TRUE ~ 0),
rc_litr = add_labels(rc_litr, labels = c("No"=0, "Yes"=1)),
rc_litr = set_label(rc_litr, label = "Literate"))
# Media exposure
IRdata <- IRdata %>%
mutate(rc_media_newsp = case_when(
v157 == 2 | v157 == 3 ~ 1,
v157 == 0 | v157 == 1 ~ 0),
rc_media_newsp = add_labels(rc_media_newsp, labels = c("No"=0, "Yes"=1)),
rc_media_newsp = set_label(rc_media_newsp, label = "Reads a newspaper at least once a week")) %>%
mutate(rc_media_tv = case_when(
v159 == 2 | v159 == 3 ~ 1,
v159 == 0 | v159 == 1 ~ 0),
rc_media_tv = add_labels(rc_media_tv, labels = c("No"=0, "Yes"=1)),
rc_media_tv = set_label(rc_media_tv, label = "Watches television at least once a week")) %>%
mutate(rc_media_radio = case_when(
v158 == 2 | v158 == 3 ~ 1,
v158 == 0 | v158 == 1 ~ 0),
rc_media_radio = add_labels(rc_media_radio, labels = c("No"=0, "Yes"=1)),
rc_media_radio = set_label(rc_media_radio, label = "Listens to radio at least once a week")) %>%
mutate(rc_media_allthree = case_when(
rc_media_newsp == 1 & rc_media_tv == 1 & rc_media_radio == 1 ~ 1, TRUE ~ 0),
rc_media_allthree = add_labels(rc_media_allthree, labels = c("No"=0, "Yes"=1)),
rc_media_allthree = set_label(rc_media_allthree, label = "Accesses to all three media at least once a week")) %>%
mutate(rc_media_none = case_when(
rc_media_newsp == 0 & rc_media_tv == 0 & rc_media_radio == 0 ~ 1, TRUE ~ 0),
rc_media_none = add_labels(rc_media_none, labels = c("No"=0, "Yes"=1)),
rc_media_none = set_label(rc_media_none, label = "Accesses none of the three media at least once a week"))
if (!is.null(IRdata$v171a)){
IRdata <- IRdata %>%
mutate(rc_intr_ever = case_when(
v171a == 1 | v171a == 2 | v171a == 3 ~ 1,
v171a == 0 ~ 0),
rc_intr_ever = add_labels(rc_intr_ever, labels = c("No"=0, "Yes"=1)),
rc_intr_ever = set_label(rc_intr_ever, label = "Ever used the internet")) %>%
mutate(rc_intr_use12mo = case_when(
v171a == 1 ~ 1,
v171a == 0 | v171a == 2 | v171a == 3 ~ 0),
rc_intr_use12mo = add_labels(rc_intr_use12mo, labels = c("No"=0, "Yes"=1)),
rc_intr_use12mo = set_label(rc_intr_use12mo, label = "Used the internet in the past 12 months")) %>%
mutate(rc_intr_usefreq = case_when(
v171a == 1 ~ v171b, TRUE ~ 99)) %>%
replace_with_na(replace = list(rc_intr_usefreq = c(99))) %>%
mutate(rc_intr_usefreq = set_label(rc_intr_usefreq, label = "Internet use frequency in the past month - among users in the past 12 months"))
}
## Employment
IRdata <- IRdata %>%
mutate(rc_empl = case_when(
v731 == 0 ~ 0,
v731 == 1 ~ 1,
v731 == 2 | v731 == 3 ~ 2,
v731 == 8 ~ 9),
rc_empl = add_labels(rc_empl, labels = c("Not employed in last 12 months"=0,
"Not curcently working but was employed in last 12 months"=1,
"Curcently employed"=2,
"Don't know/missing"=9)),
rc_empl = set_label(rc_empl, label = "Employment status")) %>%
mutate(emp = case_when(
v731 == 1 | v731 == 2 | v731 == 3 ~ 1,
TRUE ~ 0)) %>%
mutate(rc_occup = case_when(
emp == 1 & v717 == 1 ~ 1,
emp == 1 & v717 == 2 ~ 2,
emp == 1 & (v717 == 3 | v717 == 7) ~ 3,
emp == 1 & v717 == 8 ~ 4,
emp == 1 & v717 == 9 ~ 5,
emp == 1 & v717 == 6 ~ 6,
emp == 1 & (v717 == 4 | v717 == 5) ~ 7,
emp == 1 & (v717 == 96 | v717 == 99 | is.na(v717)) ~ 9 ),
rc_occup = add_labels(rc_occup, labels = c("Professional"=1,
"Clerical"=2,
"Sales and services"=3,
"Skilled manual"=4,
"Unskilled manual"=5,
"Domestic service"=6,
"Agriculture"=7,
"Don't know/missing"=9)),
rc_occup = set_label(rc_occup, label = "Occupation among those employed in the past 12 months")) %>%
mutate(rc_empl_type = case_when(
emp == 1 ~ v719),
rc_empl_type = set_label(rc_empl_type, label = "Type of employer among those employed in the past 12 months")) %>%
mutate(rc_empl_earn = case_when(
emp == 1 ~ v741),
rc_empl_earn = set_label(rc_empl_earn, label = "Type of earnings among those employed in the past 12 months")) %>%
mutate(rc_empl_cont = case_when(
emp == 1 ~ v732),
rc_empl_cont = set_label(rc_empl_cont, label = "Continuity of employment among those employed in the past 12 months")) %>%
mutate(rc_agri = case_when(
rc_occup == 7 ~ 1,
!is.na(rc_occup) & !rc_occup==7 ~ 0),
rc_agri = add_labels(rc_agri, labels = c("Non-Agriculture"=0,
"Agriculture"=1)))
## Health insurance
# Note: The different types of health insurance can be country specific. Please check the v481* variables to see which ones you need.
# In addition, some surveys report this for all women/men and some report it among those that have heard of insurance. Please check what the population of interest is for reporting these indicators.
IRdata <- IRdata %>%
mutate(rc_hins_ss = v481c,
rc_hins_ss = set_label(rc_hins_ss, label = "Health insurance coverage - social security")) %>%
mutate(rc_hins_empl = v481b,
rc_hins_empl = set_label(rc_hins_empl, label = "Health insurance coverage - other employer-based insurance")) %>%
mutate(rc_hins_comm = v481a,
rc_hins_comm = set_label(rc_hins_comm, label = "Health insurance coverage - mutual health org. or community-based insurance")) %>%
mutate(rc_hins_priv = v481d,
rc_hins_priv = set_label(rc_hins_priv, label = "Health insurance coverage - privately purchased commercial insurance")) %>%
mutate(rc_hins_other = case_when(
v481e == 1 | v481f == 1 | v481g == 1 | v481h == 1 | v481x == 1 ~ 1,
TRUE ~ 0),
rc_hins_other = set_label(rc_hins_other, label = "Health insurance coverage - other type of insurance")) %>%
mutate(rc_hins_any = case_when(
v481a == 1 | v481b == 1 | v481c == 1 | v481d == 1 | v481e == 1 | v481f == 1 | v481g == 1 | v481h == 1 | v481x == 1 ~ 1,
TRUE ~ 0),
rc_hins_any = set_label(rc_hins_any, label = "Have any health insurance"))
## Tobacco use
# please check all v463* variables for types of smoking and tobacco use
#for some surveys v463a was used instead of v463aa
#however v463a is not a yes/no variable for cigarette smoking, v463aa is the frequency
if (!is.null(IRdata$v463a)){
IRdata <- IRdata %>%
mutate(v463aa = v463a)
}
IRdata <- IRdata %>%
mutate(rc_tobc_cig = case_when(
v463aa == 1 | v463aa == 2 | v463e == 1 ~ 1, TRUE ~ 0),
rc_tobc_cig = add_labels(rc_tobc_cig, labels = c("No"=0, "Yes"=1)),
rc_tobc_cig = set_label(rc_tobc_cig, label = "Smokes cigarettes"))%>%
mutate(rc_tobc_other = case_when(
v463b == 1 | v463f == 1 | v463g == 1 ~ 1, TRUE ~ 0),
rc_tobc_other = add_labels(rc_tobc_other, labels = c("No"=0, "Yes"=1)),
rc_tobc_other = set_label(rc_tobc_other, label = "Smokes other type of tobacco"))
colnames(IRdata)[colnames(IRdata) == 'rc_tobc_other'] <- 'value'
return(IRdata)
}
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surveyPrev documentation built on June 19, 2026, 5:06 p.m.
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Defines functions AH_TOBC_W_OTH
Documented in AH_TOBC_W_OTH
##'AH_TOBC_W_OTH
#' @param IRdata data.frame from surveyPrev::getDHSdata
#'
#' @return A partially processed data.frame that will be used in surveyPrev::getDHSindicator. The whole function can be used as a parameter in surveyPrev::getDHSindicator
#'
#' @author Miaolei Bao, Yunhan Wu, Qianyu Dong
#' @examples
#' \dontrun{
#' dhsData <- getDHSdata(country = "Zambia", indicator = "AH_TOBC_W_OTH", year = 2018)
#' }
#'
#' @export
AH_TOBC_W_OTH <- function(IRdata){
# ******************************************************************************
# Program: RC_CHAR_WM.R
# Purpose: Code to compute respondent characteristics of women
# Data inputs: IR dataset
# Data outputs: coded variables
# Author: Mahmoud Elkasabi
# Date last modified: April 01 2021 by Mahmoud Elkasabi
# ******************************************************************************
#The indicators are computed for age 15-49 in line 49. This can be commented out if the indicators are required for all women.
#Please check the note on health insurance. This can be country specific and also reported for specific populations.
#Please check the variables available for smoking and tobacco and see notes for these variables. Variable names have changed and these indicators are country specific.
# -----------------------------------------------------------------------------#
# # Variables created in this file:
# rc_edu "Highest level of schooling attended or completed"
# rc_edu_median "Median years of education"
# rc_litr_cats "Level of literacy"
# rc_litr "Literate - higher than secondary or can read part or whole sentence"
# rc_media_newsp "Reads a newspaper at least once a week"
# rc_media_tv "Watches television at least once a week"
# rc_media_radio "Listens to radio at least once a week"
# rc_media_allthree "Accesses to all three media at least once a week"
# rc_media_none "Accesses none of the three media at least once a week"
# rc_intr_ever "Ever used the internet"
# rc_intr_use12mo "Used the internet in the past 12 months"
# rc_intr_usefreq "Internet use frequency in the past month - among users in the past 12 months"
# rc_empl "Employment status"
# rc_occup "Occupation among those employed in the past 12 months"
# rc_empl_type "Type of employer among those employed in the past 12 months"
# rc_empl_earn "Type of earnings among those employed in the past 12 months"
# rc_empl_cont "Continuity of employment among those employed in the past 12 months"
# rc_hins_ss "Health insurance coverage - social security"
# rc_hins_empl "Health insurance coverage - other employer-based insurance"
# rc_hins_comm "Health insurance coverage - mutual health org. or community-based insurance"
# rc_hins_priv "Health insurance coverage - privately purchased commercial insurance"
# rc_hins_other "Health insurance coverage - other type of insurance"
# rc_hins_any "Have any health insurance"
# rc_tobc_cig "Smokes cigarettes"
# rc_tobc_other "Smokes other type of tobacco"
# rc_tobc_snuffm "Uses snuff smokeless tobacco by mouth"
# rc_tobc_snuffn "Uses snuff smokeless tobacco by nose"
# rc_tobc_chew "Chews tobacco"
# rc_tobv_betel "Uses betel quid with tobacco"
# rc_tobc_osmkless "Uses other type of smokeless tobacco"
# rc_tobc_anysmkless "Uses any type of smokeless tobacco"
# rc_tobc_any "Uses any type of tobacco - smoke or smokeless"
# -----------------------------------------------------------------------------#
IRdata <- IRdata %>%
filter(v012 <= 49) %>%
mutate(wt = v005/1000000) %>%
mutate(rc_edu = v149,
rc_edu = set_label(rc_edu, label = "Highest level of schooling attended or completed")) %>%
mutate(eduyr = case_when(
v133<=20 ~ v133,
v133>20 & v133<95 ~ 20,
v133>95 | v149>7 ~ 99)) %>%
replace_with_na(replace = list(eduyr = c(99)))
IRdata <- IRdata %>%
mutate(rc_litr_cats = case_when(
!v106 == 3 & v155 == 2 ~ 1,
!v106 == 3 & v155 == 1 ~ 2,
!v106 == 3 & v155 == 0 ~ 3,
!v106 == 3 & v155 == 3 ~ 4,
!v106 == 3 & v155 == 4 ~ 5,
v106 == 3 ~ 0),
rc_litr_cats = add_labels(rc_litr_cats, labels = c("Higher than secondary education"=0, "Can read a whole sentence"=1,
"Can read part of a sentence"=2, "Cannot read at all"=3,
"No card with required language"=4, "Blind/visually impaired"=5)),
rc_litr_cats = set_label(rc_litr_cats, label = "Level of literacy")) %>%
mutate(rc_litr = case_when(
v106==3 | v155==1 | v155==2 ~ 1, TRUE ~ 0),
rc_litr = add_labels(rc_litr, labels = c("No"=0, "Yes"=1)),
rc_litr = set_label(rc_litr, label = "Literate"))
# Media exposure
IRdata <- IRdata %>%
mutate(rc_media_newsp = case_when(
v157 == 2 | v157 == 3 ~ 1,
v157 == 0 | v157 == 1 ~ 0),
rc_media_newsp = add_labels(rc_media_newsp, labels = c("No"=0, "Yes"=1)),
rc_media_newsp = set_label(rc_media_newsp, label = "Reads a newspaper at least once a week")) %>%
mutate(rc_media_tv = case_when(
v159 == 2 | v159 == 3 ~ 1,
v159 == 0 | v159 == 1 ~ 0),
rc_media_tv = add_labels(rc_media_tv, labels = c("No"=0, "Yes"=1)),
rc_media_tv = set_label(rc_media_tv, label = "Watches television at least once a week")) %>%
mutate(rc_media_radio = case_when(
v158 == 2 | v158 == 3 ~ 1,
v158 == 0 | v158 == 1 ~ 0),
rc_media_radio = add_labels(rc_media_radio, labels = c("No"=0, "Yes"=1)),
rc_media_radio = set_label(rc_media_radio, label = "Listens to radio at least once a week")) %>%
mutate(rc_media_allthree = case_when(
rc_media_newsp == 1 & rc_media_tv == 1 & rc_media_radio == 1 ~ 1, TRUE ~ 0),
rc_media_allthree = add_labels(rc_media_allthree, labels = c("No"=0, "Yes"=1)),
rc_media_allthree = set_label(rc_media_allthree, label = "Accesses to all three media at least once a week")) %>%
mutate(rc_media_none = case_when(
rc_media_newsp == 0 & rc_media_tv == 0 & rc_media_radio == 0 ~ 1, TRUE ~ 0),
rc_media_none = add_labels(rc_media_none, labels = c("No"=0, "Yes"=1)),
rc_media_none = set_label(rc_media_none, label = "Accesses none of the three media at least once a week"))
if (!is.null(IRdata$v171a)){
IRdata <- IRdata %>%
mutate(rc_intr_ever = case_when(
v171a == 1 | v171a == 2 | v171a == 3 ~ 1,
v171a == 0 ~ 0),
rc_intr_ever = add_labels(rc_intr_ever, labels = c("No"=0, "Yes"=1)),
rc_intr_ever = set_label(rc_intr_ever, label = "Ever used the internet")) %>%
mutate(rc_intr_use12mo = case_when(
v171a == 1 ~ 1,
v171a == 0 | v171a == 2 | v171a == 3 ~ 0),
rc_intr_use12mo = add_labels(rc_intr_use12mo, labels = c("No"=0, "Yes"=1)),
rc_intr_use12mo = set_label(rc_intr_use12mo, label = "Used the internet in the past 12 months")) %>%
mutate(rc_intr_usefreq = case_when(
v171a == 1 ~ v171b, TRUE ~ 99)) %>%
replace_with_na(replace = list(rc_intr_usefreq = c(99))) %>%
mutate(rc_intr_usefreq = set_label(rc_intr_usefreq, label = "Internet use frequency in the past month - among users in the past 12 months"))
}
## Employment
IRdata <- IRdata %>%
mutate(rc_empl = case_when(
v731 == 0 ~ 0,
v731 == 1 ~ 1,
v731 == 2 | v731 == 3 ~ 2,
v731 == 8 ~ 9),
rc_empl = add_labels(rc_empl, labels = c("Not employed in last 12 months"=0,
"Not curcently working but was employed in last 12 months"=1,
"Curcently employed"=2,
"Don't know/missing"=9)),
rc_empl = set_label(rc_empl, label = "Employment status")) %>%
mutate(emp = case_when(
v731 == 1 | v731 == 2 | v731 == 3 ~ 1,
TRUE ~ 0)) %>%
mutate(rc_occup = case_when(
emp == 1 & v717 == 1 ~ 1,
emp == 1 & v717 == 2 ~ 2,
emp == 1 & (v717 == 3 | v717 == 7) ~ 3,
emp == 1 & v717 == 8 ~ 4,
emp == 1 & v717 == 9 ~ 5,
emp == 1 & v717 == 6 ~ 6,
emp == 1 & (v717 == 4 | v717 == 5) ~ 7,
emp == 1 & (v717 == 96 | v717 == 99 | is.na(v717)) ~ 9 ),
rc_occup = add_labels(rc_occup, labels = c("Professional"=1,
"Clerical"=2,
"Sales and services"=3,
"Skilled manual"=4,
"Unskilled manual"=5,
"Domestic service"=6,
"Agriculture"=7,
"Don't know/missing"=9)),
rc_occup = set_label(rc_occup, label = "Occupation among those employed in the past 12 months")) %>%
mutate(rc_empl_type = case_when(
emp == 1 ~ v719),
rc_empl_type = set_label(rc_empl_type, label = "Type of employer among those employed in the past 12 months")) %>%
mutate(rc_empl_earn = case_when(
emp == 1 ~ v741),
rc_empl_earn = set_label(rc_empl_earn, label = "Type of earnings among those employed in the past 12 months")) %>%
mutate(rc_empl_cont = case_when(
emp == 1 ~ v732),
rc_empl_cont = set_label(rc_empl_cont, label = "Continuity of employment among those employed in the past 12 months")) %>%
mutate(rc_agri = case_when(
rc_occup == 7 ~ 1,
!is.na(rc_occup) & !rc_occup==7 ~ 0),
rc_agri = add_labels(rc_agri, labels = c("Non-Agriculture"=0,
"Agriculture"=1)))
## Health insurance
# Note: The different types of health insurance can be country specific. Please check the v481* variables to see which ones you need.
# In addition, some surveys report this for all women/men and some report it among those that have heard of insurance. Please check what the population of interest is for reporting these indicators.
IRdata <- IRdata %>%
mutate(rc_hins_ss = v481c,
rc_hins_ss = set_label(rc_hins_ss, label = "Health insurance coverage - social security")) %>%
mutate(rc_hins_empl = v481b,
rc_hins_empl = set_label(rc_hins_empl, label = "Health insurance coverage - other employer-based insurance")) %>%
mutate(rc_hins_comm = v481a,
rc_hins_comm = set_label(rc_hins_comm, label = "Health insurance coverage - mutual health org. or community-based insurance")) %>%
mutate(rc_hins_priv = v481d,
rc_hins_priv = set_label(rc_hins_priv, label = "Health insurance coverage - privately purchased commercial insurance")) %>%
mutate(rc_hins_other = case_when(
v481e == 1 | v481f == 1 | v481g == 1 | v481h == 1 | v481x == 1 ~ 1,
TRUE ~ 0),
rc_hins_other = set_label(rc_hins_other, label = "Health insurance coverage - other type of insurance")) %>%
mutate(rc_hins_any = case_when(
v481a == 1 | v481b == 1 | v481c == 1 | v481d == 1 | v481e == 1 | v481f == 1 | v481g == 1 | v481h == 1 | v481x == 1 ~ 1,
TRUE ~ 0),
rc_hins_any = set_label(rc_hins_any, label = "Have any health insurance"))
## Tobacco use
# please check all v463* variables for types of smoking and tobacco use
#for some surveys v463a was used instead of v463aa
#however v463a is not a yes/no variable for cigarette smoking, v463aa is the frequency
if (!is.null(IRdata$v463a)){
IRdata <- IRdata %>%
mutate(v463aa = v463a)
}
IRdata <- IRdata %>%
mutate(rc_tobc_cig = case_when(
v463aa == 1 | v463aa == 2 | v463e == 1 ~ 1, TRUE ~ 0),
rc_tobc_cig = add_labels(rc_tobc_cig, labels = c("No"=0, "Yes"=1)),
rc_tobc_cig = set_label(rc_tobc_cig, label = "Smokes cigarettes"))%>%
mutate(rc_tobc_other = case_when(
v463b == 1 | v463f == 1 | v463g == 1 ~ 1, TRUE ~ 0),
rc_tobc_other = add_labels(rc_tobc_other, labels = c("No"=0, "Yes"=1)),
rc_tobc_other = set_label(rc_tobc_other, label = "Smokes other type of tobacco"))
colnames(IRdata)[colnames(IRdata) == 'rc_tobc_other'] <- 'value'
return(IRdata)
}
Try the surveyPrev package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
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