# NHSO alcohol survey
options("width"=180)
options(width=180)
library(survey)
## Loading required package: grid
## Loading required package: Matrix
## Loading required package: survival
##
## Attaching package: 'survey'
## The following object is masked from 'package:graphics':
##
## dotchart
setwd("E:/Alcohol_Survey/")
library(epicalc)
## Loading required package: foreign
## Loading required package: MASS
## Loading required package: nnet
##
## Attaching package: 'epicalc'
## The following objects are masked from 'package:Matrix':
##
## expand, pack
zap()
load("Alcohol.Rdata")
use(Data)
# Primary sampling unit
psu <- cwt*10000+unclass(area)*1000+psu_csad
label.var(psu, "PSU")
length(unique(psu))
## [1] 2315
# Survey designs
Everyone <- svydesign(id=~psu, strata=~cwt+area, weights=~finalweight_pop, data=.data)
Current.drinkers <- subset(Everyone, subset=drinker=="3: Current drinker")
Regular.drinkers <- subset(Current.drinkers, subset=drinker4=="3: Current regular")
Younguns <- subset(Everyone, subset=age_15_17)
Adolescents <- subset(Everyone, subset=age_group2=="15-19")
Twenty <- subset(Everyone, subset=age_group2=="20+")
Drinkers <- subset(Everyone, subset=drinker!="1: Never drinker")
Current.drinkers <- subset(Everyone, subset=drinker=="3: Current drinker")
Former.drinkers <- subset(Everyone, subset=drinker=="2: Ex drinker")
Regular.drinkers <- subset(Everyone, subset=drink.regular)
Youths <- subset(Everyone, subset=age_group3=="15 - 24")
Youth.drinkers <- subset(Current.drinkers, subset=age_group3=="15 - 24")
Youth.drinkers.light <- subset(Youth.drinkers, subset=!drinking.heavy)
Youth.drinkers.heavy <- subset(Youth.drinkers, subset=drinking.heavy)
New.drinkers <- subset(Everyone, subset=a7-d4<=3)
source("alc.table.R")
#
# Table 2.1 ####
#
T <- coef(svytotal(~drinker, design=Everyone, na.rm=TRUE))
P <- coef(svymean(~drinker, design=Everyone, na.rm=TRUE))
T1 <- data.frame(Total=format(T, big.mark=","), Percent=sprintf("%.2f", 100*P))
T.female <- coef(svyby(~drinker, by=~a4, design=Everyone, FUN=svytotal,
na.rm=TRUE))[c(1,3,5)]
P.female <- coef(svyby(~drinker, by=~a4, design=Everyone, FUN=svymean,
na.rm=TRUE))[c(1,3,5)]
T2 <- data.frame(Females=format(T.female, big.mark=","), Percent.f=sprintf("%.2f",
100*P.female))
T.male <- coef(svyby(~drinker, by=~a4, design=Everyone, FUN=svytotal,
na.rm=TRUE))[c(2,4,6)]
P.male <- coef(svyby(~drinker, by=~a4, design=Everyone, FUN=svymean, na.rm=TRUE))[c(2,4,6)]
T3 <- data.frame(Males=format(T.male, big.mark=","), Percent.m=sprintf("%.2f", 100*P.male))
# Regular drinkers
Treg <- coef(svytotal(~drink.regular, design=Everyone, na.rm=TRUE))[2]
Preg <- coef(svymean(~drink.regular, design=Everyone, na.rm=TRUE))[2]
T1reg <- data.frame(Total=format(Treg, big.mark=","), Percent=sprintf("%.2f", 100*Preg))
Treg.female <- coef(svyby(~drink.regular, by=~a4, design=Everyone, FUN=svytotal,
na.rm=TRUE))[3]
Preg.female <- coef(svyby(~drink.regular, by=~a4, design=Everyone, FUN=svymean,
na.rm=TRUE))[3]
T2reg <- data.frame(Females=format(Treg.female, big.mark=","), Percent.f=sprintf("%.2f",
100*Preg.female))
Treg.male <- coef(svyby(~drink.regular, by=~a4, design=Everyone, FUN=svytotal,
na.rm=TRUE))[4]
Preg.male <- coef(svyby(~drink.regular, by=~a4, design=Everyone, FUN=svymean,
na.rm=TRUE))[4]
T3reg <- data.frame(Males=format(Treg.male, big.mark=","), Percent.m=sprintf("%.2f",
100*Preg.male))
# Social drinkers
Tsoc <- coef(svytotal(~drink.social, design=Everyone, na.rm=TRUE))[2]
Psoc <- coef(svymean(~drink.social, design=Everyone, na.rm=TRUE))[2]
T1soc <- data.frame(Total=format(Tsoc, big.mark=","), Percent=sprintf("%.2f", 100*Psoc))
Tsoc.female <- coef(svyby(~drink.social, by=~a4, design=Everyone, FUN=svytotal, na.rm=TRUE))[3]
Psoc.female <- coef(svyby(~drink.social, by=~a4, design=Everyone, FUN=svymean, na.rm=TRUE))[3]
T2soc <- data.frame(Females=format(Tsoc.female, big.mark=","), Percent.f=sprintf("%.2f", 100*Psoc.female))
Tsoc.male <- coef(svyby(~drink.social, by=~a4, design=Everyone, FUN=svytotal, na.rm=TRUE))[4]
Psoc.male <- coef(svyby(~drink.social, by=~a4, design=Everyone, FUN=svymean, na.rm=TRUE))[4]
T3soc <- data.frame(Males=format(Tsoc.male, big.mark=","), Percent.m=sprintf("%.2f", 100*Psoc.male))
Table2.1 <- rbind(data.frame(T1, T2, T3),
data.frame(T1reg, T2reg, T3reg),
data.frame(T1soc, T2soc, T3soc))
#
# Table 2.2 ####
# By Sex
#
P1 <- coef(svymean(~drinker, design=Everyone, na.rm=TRUE))
P2 <- coef(svyby(~drinker, by=~a4, design=Everyone, FUN=svymean, na.rm=TRUE))
Table2.2 <- rbind(data.frame(Total = sprintf("%.2f", 100*P1[1]),
Females = sprintf("%.2f", 100*P2[1]),
Males = sprintf("%.2f", 100*P2[2])),
data.frame(Total = sprintf("%.2f", 100*P1[2]),
Females = sprintf("%.2f", 100*P2[3]),
Males = sprintf("%.2f", 100*P2[4])))
rownames(Table2.2) <- c("Regular drinker", "Social drinker")
#
# Table 2.3 ####
# By Age (age_group)
#
Table2.3 <- alc.table(age_group, Everyone)
#
# Table 2.4 ####
# By governance area (area)
#
Table2.4 <- alc.table(area, Everyone)
#
# Table 2.5 ####
# By region (reg)
#
P0all <- round(100*coef(svymean(~drinker, design=Everyone, FUN=svymean, na.rm=TRUE)),2)
P0reg <- round(100*coef(svymean(~drink.regular, design=Everyone, FUN=svymean, na.rm=TRUE)),2)[2]
P0soc <- round(100*coef(svymean(~drink.social, design=Everyone, FUN=svymean, na.rm=TRUE)),2)[2]
P0 <- c(P0all, P0reg, P0soc)
P1all <- round(100*coef(svyby(~drinker, by=~reg, design=Everyone, FUN=svymean, na.rm=TRUE)),2)
P1reg <- round(100*coef(svyby(~drink.regular, by=~reg, design=Everyone, FUN=svymean, na.rm=TRUE)),2)[6:10]
P1soc <- round(100*coef(svyby(~drink.social, by=~reg, design=Everyone, FUN=svymean, na.rm=TRUE)),2)[6:10]
Table2.5 <- rbind(P0, rbind(cbind(P1all[1:5], P1all[6:10], P1all[11:15], P1reg, P1soc)))
rownames(Table2.5) <- c("Thailand", levels(reg))
colnames(Table2.5) <- c("Never","Former","Current", "Regular","Social")
#
# By sex and region
#
# Females
designF <- subset(Everyone, subset=a4=="female")
P0all <- round(100*coef(svymean(~drinker, design=designF, FUN=svymean, na.rm=TRUE)),2)
P0reg <- round(100*coef(svymean(~drink.regular, design=designF, FUN=svymean, na.rm=TRUE))[2],2)
P0soc <- round(100*coef(svymean(~drink.social, design=designF, FUN=svymean, na.rm=TRUE))[2],2)
P0 <- c(P0all, P0reg, P0soc)
P1all <- round(100*coef(svyby(~drinker, by=~reg, design=designF, FUN=svymean, na.rm=TRUE)),2)
P1reg <- round(100*coef(svyby(~drink.regular, by=~reg, design=designF, FUN=svymean, na.rm=TRUE))[6:10],2)
P1soc <- round(100*coef(svyby(~drink.social, by=~reg, design=designF, FUN=svymean, na.rm=TRUE))[6:10],2)
Table2.5F <- rbind(P0, rbind(cbind(P1all[1:5], P1all[6:10], P1all[11:15], P1reg, P1soc)))
rownames(Table2.5F) <- c("Thailand", levels(reg))
colnames(Table2.5F) <- c("Never","Former","Current", "Regular","Social")
# Males
designM <- subset(Everyone, subset=a4=="male")
P0all <- round(100*coef(svymean(~drinker, design=designM, FUN=svymean, na.rm=TRUE)),2)
P0reg <- round(100*coef(svymean(~drink.regular, design=designM, FUN=svymean, na.rm=TRUE))[2],2)
P0soc <- round(100*coef(svymean(~drink.social, design=designM, FUN=svymean, na.rm=TRUE))[2],2)
P0 <- c(P0all, P0reg, P0soc)
P1all <- round(100*coef(svyby(~drinker, by=~reg, design=designM, FUN=svymean, na.rm=TRUE)),2)
P1reg <- round(100*coef(svyby(~drink.regular, by=~reg, design=designM, FUN=svymean, na.rm=TRUE))[6:10],2)
P1soc <- round(100*coef(svyby(~drink.social, by=~reg, design=designM, FUN=svymean, na.rm=TRUE))[6:10],2)
Table2.5M <- rbind(P0, rbind(cbind(P1all[1:5], P1all[6:10], P1all[11:15], P1reg, P1soc)))
rownames(Table2.5M) <- c("Thailand", levels(reg))
colnames(Table2.5M) <- c("Never","Former","Current", "Regular","Social")
#
# Table 2.6 ####
# By education
#
Table2.6 <- alc.table(edu, Everyone)
#
# Table 2.7 ####
# By occupational group (occu)
#
Table2.7 <- alc.table(occu, Everyone)
# Section 2.2 ####
# Frequency of drinking
S2.2 <- round(100*coef(svymean(~drinker5, Everyone, na.rm=TRUE)),1)
names(S2.2) <- levels(drinker5)
library(sp)
THA_adm0 <- readRDS("gadm36_THA_0_sp.rds")
par(mar=c(0,0,0,0))
plot(THA_adm0, col="tan", border=NA)
text(coordinates(THA_adm0), "2560",
col="white", cex=3, font=2)
text(95,18,paste(names(S2.2[1]),S2.2[1],"%"), cex=1.5)
text(94.5,15,paste(names(S2.2[2]),S2.2[2],"%"), cex=1.5)
text(95,10,paste(names(S2.2[3]),S2.2[3],"%"), cex=1.5)
text(107,17,paste(names(S2.2[4]),S2.2[4],"%"), cex=1.5)
text(106,11,paste(names(S2.2[5]),S2.2[5],"%"), cex=1.5)
#
# Table 2.8 ####
# Drinking proportions by everything
# Columns are population, current drinkers (drink), past 30 day drinkers(d2), regular drinkers (drink.regular), social drinkers (drink.social)
# Rows are sex, area, edu, edu, reg.
# Population
T1 <- round(100*coef(svymean(~a4, Everyone, na.rm=TRUE)),2)
T2 <- round(100*coef(svyby(~a4, by=~drinker, Everyone, FUN=svymean, na.rm=TRUE))[c(3,6)],2)
T3 <- round(100*coef(svyby(~a4, by=~drink30, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4)],2)
T4 <- round(100*coef(svyby(~a4, by=~drink.regular, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4)],2)
T5 <- round(100*coef(svyby(~a4, by=~drink.social, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4)],2)
Tsex <- cbind(T1,T2,T3,T4,T5)
rownames(Tsex) <- levels(a4)
T1 <- round(100*coef(svymean(~area, Everyone, na.rm=TRUE)),2)
T2 <- round(100*coef(svyby(~area, by=~drinker, Everyone, FUN=svymean, na.rm=TRUE))[c(3,6)],2)
T3 <- round(100*coef(svyby(~area, by=~drink30, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4)],2)
T4 <- round(100*coef(svyby(~area, by=~drink.regular, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4)],2)
T5 <- round(100*coef(svyby(~area, by=~drink.social, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4)],2)
Tarea <- cbind(T1,T2,T3,T4,T5)
rownames(Tarea) <- levels(area)
T1 <- round(100*coef(svymean(~age_group, Everyone, na.rm=TRUE)),2)
T2 <- round(100*coef(svyby(~age_group, by=~drinker, Everyone, FUN=svymean, na.rm=TRUE))[c(3,6,9,12,15)],2)
T3 <- round(100*coef(svyby(~age_group, by=~drink30, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4,6,8,10)],2)
T4 <- round(100*coef(svyby(~age_group, by=~drink.regular, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4,6,8,10)],2)
T5 <- round(100*coef(svyby(~age_group, by=~drink.social, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4,6,8,10)],2)
Tage <- cbind(T1,T2,T3,T4,T5)
rownames(Tage) <- levels(factor(age_group))
T1 <- round(100*coef(svymean(~edu, Everyone, na.rm=TRUE)),2)
T2 <- round(100*coef(svyby(~edu, by=~drinker, Everyone, FUN=svymean, na.rm=TRUE))[c(3,6,9,12,15)],2)
T3 <- round(100*coef(svyby(~edu, by=~drink30, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4,6,8,10)],2)
T4 <- round(100*coef(svyby(~edu, by=~drink.regular, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4,6,8,10)],2)
T5 <- round(100*coef(svyby(~edu, by=~drink.social, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4,6,8,10)],2)
Tedu <- cbind(T1,T2,T3,T4,T5)
rownames(Tedu) <- levels(edu)
T1 <- round(100*coef(svymean(~reg, Everyone, na.rm=TRUE)),2)
T2 <- round(100*coef(svyby(~reg, by=~drinker, Everyone, FUN=svymean, na.rm=TRUE))[c(3,6,9,12,15)],2)
T3 <- round(100*coef(svyby(~reg, by=~drink30, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4,6,8,10)],2)
T4 <- round(100*coef(svyby(~reg, by=~drink.regular, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4,6,8,10)],2)
T5 <- round(100*coef(svyby(~reg, by=~drink.social, Everyone, FUN=svymean, na.rm=TRUE))[c(2,4,6,8,10)],2)
Treg <- cbind(T1,T2,T3,T4,T5)
rownames(Treg) <- levels(reg)
Table2.8 <- data.frame(rbind(Sex="", Tsex, Area="", Tarea,Age="", Tage, Education="", Tedu, Region="", Treg))
#
# Table 2.9 ####
# Columns are Daily drinkers, Almost daily, Every other day, weekly, etc., regular, social
#
Td1 <- round(100*coef(svymean(~d1, Current.drinkers, FUN=svymean, na.rm=TRUE))[3:10],2)
Treg <- round(100*coef(svymean(~drink.regular, Current.drinkers, FUN=svymean, na.rm=TRUE))[2],2)
Tsoc <- round(100*coef(svymean(~drink.social, Current.drinkers, FUN=svymean, na.rm=TRUE))[2],2)
Tall <- c(Td1, Treg, Tsoc)
T <- round(100*coef(svyby(~d1, by=~area, design=Current.drinkers, FUN=svymean, na.rm=TRUE)),2)
Tarea <- rbind(T[seq(5,20,2)], T[seq(6,20,2)])
Tregu <- round(100*coef(svyby(~drink.regular, by=~area, design=Current.drinkers, FUN=svymean, na.rm=TRUE))[3:4],2)
Tsoc <- round(100*coef(svyby(~drink.social, by=~area, design=Current.drinkers, FUN=svymean, na.rm=TRUE))[3:4],2)
Tarea <- cbind(Tarea, Tregu, Tsoc)
T <- round(100*coef(svyby(~d1, by=~age_group, design=Current.drinkers, FUN=svymean, na.rm=TRUE)),2)
Tage <- rbind(T[seq(11,50,5)], T[seq(12,50,5)], T[seq(13,50,5)], T[seq(14,50,5)], T[seq(15,50,5)])
Tregu <- round(100*coef(svyby(~drink.regular, by=~age_group, design=Current.drinkers, FUN=svymean, na.rm=TRUE))[6:10],2)
Tsoc <- round(100*coef(svyby(~drink.social, by=~age_group, design=Current.drinkers, FUN=svymean, na.rm=TRUE))[6:10],2)
Tage <- cbind(Tage, Tregu, Tsoc)
T <- round(100*coef(svyby(~d1, by=~reg, design=Current.drinkers, FUN=svymean, na.rm=TRUE)),2)
Treg <- rbind(T[seq(11,50,5)], T[seq(12,50,5)], T[seq(13,50,5)], T[seq(14,50,5)], T[seq(15,50,5)])
Tregu <- round(100*coef(svyby(~drink.regular, by=~reg, design=Current.drinkers, FUN=svymean, na.rm=TRUE))[6:10],2)
Tsoc <- round(100*coef(svyby(~drink.social, by=~reg, Current.drinkers, FUN=svymean, na.rm=TRUE))[6:10],2)
Treg <- cbind(Treg, Tregu, Tsoc)
T <- round(100*coef(svyby(~d1, by=~edu, design=Current.drinkers, FUN=svymean, na.rm=TRUE)),2)
Tedu <- rbind(T[seq(11,50,5)], T[seq(12,50,5)], T[seq(13,50,5)], T[seq(14,50,5)], T[seq(15,50,5)])
Tregu <- round(100*coef(svyby(~drink.regular, by=~edu, design=Current.drinkers, FUN=svymean, na.rm=TRUE))[6:10],2)
Tsoc <- round(100*coef(svyby(~drink.social, by=~edu, Current.drinkers, FUN=svymean, na.rm=TRUE))[6:10],2)
Tedu <- cbind(Tedu, Tregu, Tsoc)
T <- round(100*coef(svyby(~d1, by=~occu, design=Current.drinkers, FUN=svymean, na.rm=TRUE)),2)
Toccu <- rbind(T[seq(11,50,5)], T[seq(12,50,5)], T[seq(13,50,5)], T[seq(14,50,5)], T[seq(15,50,5)])
Tregu <- round(100*coef(svyby(~drink.regular, by=~occu, design=Current.drinkers, FUN=svymean, na.rm=TRUE))[6:10],2)
Tsoc <- round(100*coef(svyby(~drink.social, by=~occu, Current.drinkers, FUN=svymean, na.rm=TRUE))[6:10],2)
Toccu <- cbind(Toccu, Tregu, Tsoc)
T <- round(100*coef(svyby(~d1, by=~inc5, design=Current.drinkers, FUN=svymean, na.rm=TRUE)),2)
Tinc5 <- rbind(T[seq(11,50,5)], T[seq(12,50,5)], T[seq(13,50,5)], T[seq(14,50,5)], T[seq(15,50,5)])
Tregu <- round(100*coef(svyby(~drink.regular, by=~inc5, design=Current.drinkers, FUN=svymean, na.rm=TRUE))[6:10],2)
Tsoc <- round(100*coef(svyby(~drink.social, by=~inc5, Current.drinkers, FUN=svymean, na.rm=TRUE))[6:10],2)
Tinc5 <- cbind(Tinc5, Tregu, Tsoc)
rownames(Tarea) <- levels(area)
rownames(Tage) <- levels(factor(age_group))
rownames(Treg) <- levels(reg)
rownames(Tedu) <- levels(edu)
rownames(Toccu) <- levels(occu)
rownames(Tinc5) <- levels(inc5)
Table2.9 <- as.data.frame(rbind(Tall, Area="", Tarea, Age="", Tage, Region="", Treg, Education="", Tedu, Occupation="", Toccu, Income="", Tinc5))
colnames(Table2.9) <- 1:10
#
# Table 2.10 ####
# Who are regular drinkers?
Tsex <- sprintf("%.2f", 100*svymean(~a4, Regular.drinkers, na.rm=TRUE))
Tarea <- sprintf("%.2f", 100*svymean(~area, Regular.drinkers, na.rm=TRUE))
Tage <- sprintf("%.2f", 100*svymean(~age_group, Regular.drinkers, na.rm=TRUE))
Tedu <- sprintf("%.2f", 100*svymean(~edu, Regular.drinkers, na.rm=TRUE))
Treg <- sprintf("%.2f", 100*svymean(~reg, Regular.drinkers, na.rm=TRUE))
Table2.10 <- rbind(data.frame(Factor="Sex", Pct=""),
data.frame(Factor=levels(a4), Pct=Tsex),
data.frame(Factor="Area", Pct=""),
data.frame(Factor=levels(area), Pct=Tarea),
data.frame(Factor="Age group", Pct=""),
data.frame(Factor=levels(factor(age_group)), Pct=Tage),
data.frame(Factor="Education", Pct=""),
data.frame(Factor=levels(edu), Pct=Tedu),
data.frame(Factor="Region", Pct=""),
data.frame(Factor=levels(reg), Pct=Treg))
#
# Table 2.11 ####
# Proportion of regular drinkers to current drinkers
#
T1 <- round(coef(svyby(~drink.regular, by=~a4, FUN=svytotal, design=Everyone)))[3:4]
T1tot <- round(coef(svytotal(~drink.regular, design=Everyone)))[2]
T2 <- sprintf("%.2f", 100*coef(svyby(~drinker, by=~a4, FUN=svymean, design=Everyone, na.rm=TRUE))[1:2])
T2tot <- sprintf("%.2f", 100*coef(svymean(~drinker, design=Everyone, na.rm=TRUE))[1])
Table2.11 <- rbind(format(c(T1, T1tot), big.mark=","), c(T2, T2tot))
rownames(Table2.11) <- c("Regular drinkers","Percent of current drinkers")
colnames(Table2.11) <- c("Female","Male","Total")
# Section 2.3 ####
# Favourite drink (d36)
S2.3 <- round(100*coef(svymean(~d36, Everyone, na.rm=TRUE)),1)
names(S2.3)[1:4] <- c("Beer","Red spirits","White spirits","Wine")
par(mar=c(0,0,0,6))
plot(THA_adm0, col="tan", border=NA)
text(coordinates(THA_adm0), "2560",
col="white", cex=3, font=2)
text(107,18,paste(names(S2.3[1]),S2.3[1],"%"), cex=1.5)
text(107,15,paste(names(S2.3[2]),S2.3[2],"%"), cex=1.5)
text(107,12,paste(names(S2.3[3]),S2.3[3],"%"), cex=1.5)
text(107,9,paste(names(S2.3[4]),S2.3[4],"%"), cex=1.5)
#
# Table 2.12 ####
# Favourite drink among current drinkers (d36)
# By age and sex
Tsex <- round(100*t(svyby(~d36, by=~a4, FUN=svymean, Everyone, na.rm=TRUE)[,2:8]),2)
Tage <- round(100*t(svyby(~d36, by=~age_group, FUN=svymean, Everyone, na.rm=TRUE)[,2:8]),2)
Table2.12 <- data.frame(Tsex, Tage)
Table2.12
## female male X15...19 X20...24 X25...44 X45...59 X60.
## d36Beer 62.57 43.81 59.74 61.52 53.10 39.55 27.40
## d36Spirits (white) 12.33 30.07 15.81 13.67 20.26 34.14 49.09
## d36Spirits (red) 12.84 24.15 20.76 21.19 23.26 22.10 17.38
## d36Wine 2.97 0.38 0.70 0.78 0.83 1.05 0.84
## d36Wine cooler 7.13 0.25 2.56 2.49 1.97 1.05 0.21
## d36Other 0.41 0.29 0.29 0.18 0.20 0.45 0.53
## d36Spirits (other) 1.75 1.05 0.15 0.18 0.38 1.66 4.55
#
# Section 2.4 ####
# Quantity
#
#S2.4
par(mar=c(0,0,0,0))
plot(THA_adm0, col="tan", border=NA)
text(coordinates(THA_adm0), "2560",
col="white", cex=3, font=2)
text(106,17,"Alcohol consumption \nper person per year", cex=1.5)
text(106,11,"Ethanol consumption \nper person per year", cex=1.5)
#
# Table 2.13 ####
#
Beer <- svytotal(~beer, Current.drinkers, na.rm=TRUE)
Spirits.white <- svytotal(~spirits.white, Current.drinkers, na.rm=TRUE)
Spirits.red <- svytotal(~spirits.red, Current.drinkers, na.rm=TRUE)
spirits.other <- svytotal(~spirits.other, Current.drinkers, na.rm=TRUE)
Wine <- svytotal(~wine, Current.drinkers, na.rm=TRUE)
Wine.cooler <- svytotal(~wine.cooler, Current.drinkers, na.rm=TRUE)
Other <- svytotal(~other, Current.drinkers, na.rm=TRUE)
Alcohol <- as.data.frame(rbind(Beer,Spirits.white, Spirits.red, spirits.other, Wine, Wine.cooler, Other))
Alcohol <- transform(Alcohol, Pct=round(100*Alcohol/sum(Alcohol),2))
colnames(Alcohol)[1] <- "Quantity"
# per drinker
svymean(~drinks.beer, Current.drinkers, na.rm=TRUE)
## mean SE
## drinks.beerFALSE 0.41085 0.0069
## drinks.beerTRUE 0.58915 0.0069
svytotal(~drinks.beer, Current.drinkers, na.rm=TRUE)
## total SE
## drinks.beerFALSE 6531412 160377
## drinks.beerTRUE 9365852 224503
beer.drinkers <- round(coef(svytotal(~drinks.beer, Current.drinkers, na.rm=TRUE)))[[2]]
spirits.white.drinkers <- round(coef(svytotal(~drinks.spirits.white, Current.drinkers, na.rm=TRUE)))[[2]]
spirits.red.drinkers <- round(coef(svytotal(~drinks.spirits.red, Current.drinkers, na.rm=TRUE)))[[2]]
spirits.other.drinkers <- round(coef(svytotal(~drinks.spirits.other, Current.drinkers, na.rm=TRUE)))[[2]]
wine.drinkers <- round(coef(svytotal(~drinks.wine, Current.drinkers, na.rm=TRUE)))[[2]]
wine.cooler.drinkers <- round(coef(svytotal(~drinks.wine.cooler, Current.drinkers, na.rm=TRUE)))[[2]]
Other.drinkers <- round(coef(svytotal(~drinks.other, Current.drinkers, na.rm=TRUE)))[[2]]
all.drinkers <- c(beer.drinkers, spirits.white.drinkers,spirits.red.drinkers,
spirits.other.drinkers, wine.drinkers, wine.cooler.drinkers, Other.drinkers)
Table2.13 <- data.frame(Alcohol, all.drinkers, per.drinker=round(Alcohol$Quantity/all.drinkers,1))
# Ethanol
Table2.13 <- transform(Table2.13, Ethanol=Quantity*c(0.05, 0.4, 0.4, 0.4, 0.12, 0.03, 0.12))
Table2.13 <- transform(Table2.13, Pct.eth=round(100*Ethanol/sum(Ethanol),2))
# per drinker
Table2.13 <- transform(Table2.13, eth.per.drinker=round(Ethanol/all.drinkers, 2))
Table2.13 <- rbind(Table2.13, Total=colSums(Table2.13[]))
Table2.13$per.drinker[8] <- round(svytotal(~alcohol2, design=Everyone)/Table2.13$all.drinker[8],1)
Table2.13$eth.per.drinker[8] <- round(Table2.13$Ethanol[8]/Table2.13$all.drinkers[8],1)
Table2.13$Quantity=format(round(Table2.13$Quantity), big.mark=",")
Table2.13$all.drinkers=format(round(Table2.13$all.drinkers), big.mark=",")
Table2.13$Ethanol=format(round(Table2.13$Ethanol), big.mark=",")
colnames(Table2.13)[2] <- "Pct"
Table2.13
## Quantity Pct all.drinkers per.drinker Ethanol Pct.eth eth.per.drinker
## Beer 484,355,252 63.02 9,365,852 51.7 24,217,763 17.86 2.59
## Spirits.white 141,909,766 18.46 4,787,572 29.6 56,763,906 41.87 11.86
## Spirits.red 129,955,337 16.91 5,376,707 24.2 51,982,135 38.34 9.67
## spirits.other 4,776,351 0.62 246,844 19.3 1,910,540 1.41 7.74
## Wine 3,865,330 0.50 238,810 16.2 463,840 0.34 1.94
## Wine.cooler 2,399,729 0.31 373,311 6.4 71,992 0.05 0.19
## Other 1,289,219 0.17 88,429 14.6 154,706 0.11 1.75
## Total 768,550,983 99.99 20,477,525 37.5 135,564,882 99.98 6.60
#
# Table 2.14 ####
#
s1<-coef(svymean(~spirits.white, design=Everyone))
s2<-coef(svymean(~spirits.red, design=Everyone))
s3<-coef(svymean(~spirits.other, design=Everyone))
s4<-coef(svyby(~spirits.white, by=~a4, design=Everyone, FUN=svymean))
s5<-coef(svyby(~spirits.red, by=~a4, design=Everyone, FUN=svymean))
s6<-coef(svyby(~spirits.other, by=~a4, design=Everyone, FUN=svymean))
Table2.14 <- data.frame(Total=c(coef(svymean(~beer, design=Everyone)),
Spirits=sum(c(s1,s2,s3)),
s1,s2,s3,
coef(svymean(~wine, design=Everyone)),
coef(svymean(~wine.cooler, design=Everyone)),
coef(svymean(~other, design=Everyone))),
rbind(coef(svyby(~beer, by=~a4, design=Everyone, FUN=svymean)),
Spirits=colSums(rbind(s4,s5,s6)),
s4,s5,s6,
coef(svyby(~wine, by=~a4, design=Everyone, FUN=svymean)),
coef(svyby(~wine.cooler, by=~a4, design=Everyone, FUN=svymean)),
coef(svyby(~other, by=~a4, design=Everyone, FUN=svymean))))
Table2.14 <- transform(Table2.14, Total.Eth=Total*c(0.05, 0.4, 0.4, 0.4, 0.12, 0.03, 0.12, 0.12),
male.Eth=male*c(0.05, 0.4, 0.4, 0.4, 0.12, 0.03, 0.12, 0.12),
female.Eth=female*c(0.05, 0.4, 0.4, 0.4, 0.12, 0.03, 0.12, 0.12))
Table2.14 <- round(rbind(Table2.14, Total=colSums(Table2.14)),2)
Table2.14
## Total female male Total.Eth male.Eth female.Eth
## beer 8.66 1.26 16.57 0.43 0.83 0.06
## Spirits 4.94 0.63 9.56 1.98 3.82 0.25
## spirits.white 2.54 0.37 4.86 1.01 1.94 0.15
## spirits.red 2.32 0.25 4.54 0.93 1.82 0.10
## spirits.other 0.09 0.02 0.16 0.01 0.02 0.00
## wine 0.07 0.02 0.12 0.00 0.00 0.00
## wine.cooler 0.04 0.03 0.06 0.01 0.01 0.00
## other 0.02 0.00 0.05 0.00 0.01 0.00
## Total 18.68 2.58 35.92 4.37 8.45 0.57
#
# Section 2.5 ####
# Place of buying (d8) and drinking (d10) alcohol
#
round(100*svymean(~d8, Everyone, na.rm=TRUE),2)
## mean SE
## d8Store 71.66 0.0076
## d87/11 6.15 0.0045
## d8Supermarket 0.62 0.0010
## d8Restaurant 5.22 0.0044
## d8Bar 0.72 0.0012
## d8Other 15.64 0.0050
round(100*svymean(~d10, Everyone, na.rm=TRUE),2)
## mean SE
## d10Own house 40.09 0.0078
## d10Other house 22.52 0.0068
## d10Restaurant 9.54 0.0050
## d10Bar 1.32 0.0016
## d10Party 20.02 0.0064
## d10Cultural place 4.58 0.0032
## d10Public event 0.36 0.0006
## d10Other 1.57 0.0017
# Table 2.15 ####
Tall <- round(100*coef(svymean(~d8, Everyone, na.rm=TRUE)),2)
Tarea <- round(100*svyby(~d8, by=~area, design=Current.drinkers, FUN=svymean, na.rm=TRUE)[,2:7],2)
Treg <- round(100*svyby(~d8, by=~reg, design=Current.drinkers, FUN=svymean, na.rm=TRUE)[,2:7],2)
Tage <- round(100*svyby(~d8, by=~age_group, design=Current.drinkers, FUN=svymean, na.rm=TRUE)[,2:7],2)
Tinc5 <- round(100*svyby(~d8, by=~inc5, design=Current.drinkers, FUN=svymean, na.rm=TRUE)[,2:7],2)
Table2.15 <- rbind(Total=Tall, Area="", Tarea, Region="", Treg, Age="", Tage, Income="",Tinc5)
Table2.15
## d8Store d87/11 d8Supermarket d8Restaurant d8Bar d8Other
## Total 71.66 6.15 0.62 5.22 0.72 15.64
## Area
## Urban 64.19 11.09 0.94 8.23 1.11 14.44
## Rural 77.51 2.28 0.36 2.85 0.41 16.58
## Region
## Bangkok 54 17.02 1.13 13.48 1.29 13.09
## Central 68.53 10.09 0.84 6.06 0.96 13.53
## North 73.85 2.47 0.54 4.36 0.66 18.12
## North-east 79.6 1.06 0.33 2.24 0.33 16.44
## South 73.16 5.03 0.29 3.36 0.65 17.51
## Age
## 15 - 19 74.08 3.6 0.4 2.56 0.7 18.66
## 20 - 24 70.11 6.5 0.35 8.42 2.15 12.47
## 25 - 44 69.53 8.55 0.51 6.31 0.98 14.12
## 45 - 59 73.44 4.42 0.76 4.15 0.07 17.16
## 60+ 76.29 1.49 0.97 1.48 0.02 19.75
## Income
## Q 1 73.38 3.15 0.51 1.69 0.38 20.89
## Q 2 76.62 1.52 0.21 1.05 0.39 20.2
## Q 3 78.8 2.47 0.18 1.71 0.36 16.49
## Q 4 75.83 5.39 0.21 4.65 0.53 13.4
## Q 5 59.67 12.71 1.55 11.39 1.42 13.26
# Table 2.16 ####
Tall <- round(100*coef(svymean(~d10, Everyone, na.rm=TRUE)),2)
Tarea <- round(100*svyby(~d10, by=~area, design=Current.drinkers, FUN=svymean, na.rm=TRUE)[,2:7],2)
Treg <- round(100*svyby(~d10, by=~reg, design=Current.drinkers, FUN=svymean, na.rm=TRUE)[,2:7],2)
Tage <- round(100*svyby(~d10, by=~age_group, design=Current.drinkers, FUN=svymean, na.rm=TRUE)[,2:7],2)
Tinc5 <- round(100*svyby(~d10, by=~inc5, design=Current.drinkers, FUN=svymean, na.rm=TRUE)[,2:7],2)
Table2.16 <- rbind(Total=Tall, Area="", Tarea, Region="", Treg, Age="", Tage, Income="",Tinc5)
Table2.16
## d10Own house d10Other house d10Restaurant d10Bar d10Party d10Cultural place
## Total 40.09 22.52 9.54 1.32 20.02 4.58
## Area
## Urban 40.16 20.31 14.26 2.18 18.03 3.02
## Rural 40.04 24.24 5.84 0.65 21.58 5.8
## Region
## Bangkok 46.7 16.31 17.95 2.42 13.43 0.97
## Central 46.6 17.95 12.02 1.48 19.63 1.28
## North 37.15 24.35 8.99 1.27 21.61 3.99
## North-east 34.27 28.24 4.93 0.85 19.71 9.96
## South 36.91 21.13 7.07 1.04 29.02 2.4
## Age
## 15 - 19 16.28 52.36 6.78 1.86 12.6 7.66
## 20 - 24 22.89 34.76 14.81 4.45 16.1 5.72
## 25 - 44 39.58 22.45 11.71 1.58 19.32 3.69
## 45 - 59 45.34 17.38 7.18 0.24 22.93 5
## 60+ 53.04 14.24 2.88 0.02 21.45 4.89
## Income
## Q 1 40.07 25.49 4.84 0.95 18.46 7.45
## Q 2 38.21 27.1 3.71 0.58 20.9 7.2
## Q 3 39.18 25.8 3.98 0.9 21.16 6.81
## Q 4 41.14 23.91 9.64 1 18.6 3.55
## Q 5 40.66 15.63 17.68 2.43 20.68 1.82
#
# Section 2.6 ####
# Cost of buying alcohol for drinking at home (d31)
# and at a shop (d34) - average per month
#
svymean(~cost, Current.drinkers)
## mean SE
## cost 715.22 17.698
svymean(~cost.home, Current.drinkers, na.rm=TRUE)
## mean SE
## cost.home 628.52 15.164
svymean(~cost.shop, Current.drinkers, na.rm=TRUE)
## mean SE
## cost.shop 522.96 16.697
svymean(I(~cost==0), Current.drinkers, na.rm=TRUE)
## mean SE
## cost == 0FALSE 0.81767 0.005
## cost == 0TRUE 0.18233 0.005
T31 <- coef(svymean(~d31, Current.drinkers, na.rm=TRUE))
T34 <- coef(svymean(~d34, Current.drinkers, na.rm=TRUE))
#
# Table 2.17 ####
#
T31sex <- coef(svyby(~d31, by=~a4, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
T31area <- coef(svyby(~d31, by=~area, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
T31reg <- coef(svyby(~d31, by=~reg, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
T31age <- coef(svyby(~d31, by=~age_group, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
T31inc <- coef(svyby(~d31, by=~inc5, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
T34sex <- coef(svyby(~d34, by=~a4, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
T34area <- coef(svyby(~d34, by=~area, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
T34reg <- coef(svyby(~d34, by=~reg, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
T34age <- coef(svyby(~d34, by=~age_group, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
T34inc <- coef(svyby(~d34, by=~inc5, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
Tsex <- coef(svyby(~cost, by=~a4, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
Tarea <- coef(svyby(~cost, by=~area, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
Treg <- coef(svyby(~cost, by=~reg, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
Tage <- coef(svyby(~cost, by=~age_group, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
Tinc <- coef(svyby(~cost, by=~inc5, design=Current.drinkers, FUN=svymean, na.rm=TRUE))
Table2.17 <- rbind(round(data.frame(Home=T31, Shop=T34, Total=T31+T34),2),
Sex="",
round(data.frame(Home=T31sex, Shop=T34sex, Total=T31sex+T34sex),2),
Area="",
round(data.frame(Home=T31area, Shop=T34area, Total=T31area+T34area),2),
Region="",
round(data.frame(Home=T31reg, Shop=T34reg, Total=T31reg+T34reg),2),
Age="",
round(data.frame(Home=T31age, Shop=T34age, Total=T31age+T34age),2),
Income="",
round(data.frame(Home=T31inc, Shop=T34inc, Total=T31inc+T34inc),2))
rownames(Table2.17)[1] <- "Total"
Table2.17
## Home Shop Total
## Total 628.52 522.96 1151.48
## Sex
## female 356.46 425.93 782.39
## male 671.62 537.95 1209.57
## Area
## Urban 744.97 643.14 1388.11
## Rural 541.5 420.15 961.65
## Region
## Bangkok 861.83 852.29 1714.12
## Central 843.98 632.59 1476.57
## North 445.69 412.1 857.79
## North-east 468.5 383.72 852.22
## South 601.47 478.12 1079.59
## Age
## 15 - 19 303.09 346.63 649.72
## 20 - 24 474.88 413.52 888.41
## 25 - 44 689.84 580.3 1270.15
## 45 - 59 656.61 530.45 1187.06
## 60+ 531.9 401.41 933.31
## Income
## Q 1 447.22 358.8 806.02
## Q 2 420.1 333.72 753.82
## Q 3 481.99 339.14 821.13
## Q 4 628.16 471.08 1099.24
## Q 5 891.05 776.67 1667.72
#
# Table 2.18 ####
#
Table2.18 <- rbind(coef(svyby(~d31, by=~drinker4, design=Current.drinkers, FUN=svymean, na.rm=TRUE)),
coef(svyby(~d34, by=~drinker4, design=Current.drinkers, FUN=svymean, na.rm=TRUE)))
Table2.18 <- round(rbind(Table2.18, colSums(Table2.18)),2)
rownames(Table2.18) <- c("Drink at home","Drink at shop","Total")
Table2.18
## 3: Current regular 4: Current social
## Drink at home 904.38 294.62
## Drink at shop 668.57 384.17
## Total 1572.95 678.79
#
# Table 2.19 ####
#
Table2.19 <- as.data.frame(rbind(Drinks_at_home="",
round(100*t(svyby(~cost.home.gp, by=~drinker4, design=Current.drinkers, FUN=svymean, na.rm=TRUE)[,2:7]),2),
Drinks_at_shop="",
round(100*t(svyby(~cost.shop.gp, by=~drinker4, design=Current.drinkers, FUN=svymean, na.rm=TRUE)[,2:7]),2)))
Table2.19
## 3: Current regular 4: Current social
## Drinks_at_home
## cost.home.gpFree 0 0
## cost.home.gp1-199 11.35 40.85
## cost.home.gp200-499 27.46 38.51
## cost.home.gp500-999 26.35 14.11
## cost.home.gp1000-1999 22.54 5.59
## cost.home.gp2000+ 12.3 0.93
## Drinks_at_shop
## cost.shop.gpFree 0 0
## cost.shop.gp1-199 19.2 30.64
## cost.shop.gp200-499 31.64 39.61
## cost.shop.gp500-999 24.36 18.84
## cost.shop.gp1000-1999 15.93 8.47
## cost.shop.gp2000+ 8.87 2.45
#
# Section 2.7 ####
# Heavy drinking (d40)
#
round(100*svymean(~d40, design=Current.drinkers, na.rm=TRUE),2)
## mean SE
## d401 58.13 0.0083
## d402 1.66 0.0013
## d403 1.03 0.0012
## d404 2.43 0.0018
## d405 5.64 0.0029
## d406 9.10 0.0039
## d407 5.20 0.0031
## d408 3.91 0.0021
## d409 12.90 0.0049
round(100*svymean(~drinking.heavy.gp, design=Current.drinkers, na.rm=TRUE),2)
## mean SE
## drinking.heavy.gpNo 58.13 0.0083
## drinking.heavy.gpRegular 10.76 0.0042
## drinking.heavy.gpSocial 31.11 0.0074
#
# Table 2.20 ####
#
Tall <- round(100*coef(svymean(~drinking.heavy.gp, design=Current.drinkers, na.rm=TRUE)),2)
Tsex <- round(100*svyby(~drinking.heavy.gp, by=~a4, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[,2:4],2)
Tage <- round(100*svyby(~drinking.heavy.gp, by=~age_group, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[,2:4],2)
Tarea <- round(100*svyby(~drinking.heavy.gp, by=~area, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[,2:4],2)
Treg <- round(100*svyby(~drinking.heavy.gp, by=~reg, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[,2:4],2)
Tinc <- round(100*svyby(~drinking.heavy.gp, by=~inc5, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[,2:4],2)
Table2.20 <- rbind(Tall, Tsex, Tage, Tarea, Treg, Tinc)
Table2.20 <- rbind(Tall, Sex="", Tsex, Age="", Tage, Area="", Tarea, Region="", Treg, Income="", Tinc)
rownames(Table2.20)[1] <- "Total"
colnames(Table2.20) <- c("Never","Regular","Social")
Table2.20
## Never Regular Social
## Total 58.13 10.76 31.11
## Sex
## female 77.47 3.66 18.86
## male 53.5 12.46 34.04
## Age
## 15 - 19 60.29 4.55 35.16
## 20 - 24 54.32 8.18 37.5
## 25 - 44 55.28 11.46 33.26
## 45 - 59 59.13 11.91 28.96
## 60+ 70.22 9.41 20.37
## Area
## Urban 58.16 11.65 30.19
## Rural 58.11 10.06 31.83
## Region
## Bangkok 58.99 12.39 28.62
## Central 57.7 13.77 28.54
## North 59.45 10.34 30.21
## North-east 57.25 7.71 35.03
## South 58.34 10.37 31.28
## Income
## Q 1 66.45 8.18 25.37
## Q 2 61.07 9.62 29.31
## Q 3 57.56 10.01 32.43
## Q 4 55.79 11.43 32.79
## Q 5 56.69 12.01 31.31
#
# Table 2.21 ####
# Cost of heavy drinking
#
T31a <- coef(svyby(~d31, by=~drinking.heavy, FUN=svymean, design=Current.drinkers, na.rm=TRUE)); T31a
## FALSE TRUE
## 472.5410 790.5852
T34a <- coef(svyby(~d34, by=~drinking.heavy, FUN=svymean, design=Current.drinkers, na.rm=TRUE)); T34a
## FALSE TRUE
## 415.9419 620.9065
T31b <- coef(svyby(~d31, by=~drinking.heavy.gp, FUN=svymean, design=Current.drinkers, na.rm=TRUE))[2:3]; T31b
## Regular Social
## 1216.9907 626.0993
T34b <- coef(svyby(~d34, by=~drinking.heavy.gp, FUN=svymean, design=Current.drinkers, na.rm=TRUE))[2:3]; T34b
## Regular Social
## 861.5361 540.7914
Table2.21 <- rbind(c(T31a, T31b), c(T34a, T34b))
Table2.21 <- rbind(Table2.21, colSums(Table2.21))
rownames(Table2.21) <- c("Drinks at home","Drinks at shop","Total")
colnames(Table2.21)[1:2] <- c("Light drinkers","Heavy drinkers")
Table2.21
## Light drinkers Heavy drinkers Regular Social
## Drinks at home 472.5410 790.5852 1216.9907 626.0993
## Drinks at shop 415.9419 620.9065 861.5361 540.7914
## Total 888.4829 1411.4918 2078.5267 1166.8907
#
# Table 2.22 ####
#
T1 <- round(t(100*svyby(~cost.home.gp, by=~drinking.heavy, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[,2:7]),2); T1
## FALSE TRUE
## cost.home.gpFree 0.00 0.00
## cost.home.gp1-199 33.05 16.02
## cost.home.gp200-499 33.55 31.32
## cost.home.gp500-999 18.15 23.58
## cost.home.gp1000-1999 11.55 18.32
## cost.home.gp2000+ 3.69 10.76
T2 <- round(t(100*svyby(~cost.home.gp, by=~drinking.heavy.gp, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[,2:7])[,-1],2); T2
## Regular Social
## cost.home.gpFree 0.00 0.00
## cost.home.gp1-199 7.52 19.30
## cost.home.gp200-499 21.68 35.04
## cost.home.gp500-999 24.43 23.25
## cost.home.gp1000-1999 25.25 15.65
## cost.home.gp2000+ 21.11 6.77
T3 <- round(t(100*svyby(~cost.shop.gp, by=~drinking.heavy, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[,2:7]),2); T3
## FALSE TRUE
## cost.shop.gpFree 0.00 0.00
## cost.shop.gp1-199 31.66 19.01
## cost.shop.gp200-499 38.09 33.54
## cost.shop.gp500-999 18.74 24.08
## cost.shop.gp1000-1999 7.96 15.91
## cost.shop.gp2000+ 3.54 7.45
T4 <- round(t(100*svyby(~cost.shop.gp, by=~drinking.heavy.gp, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[,2:7]),2)[,-1]; T4
## Regular Social
## cost.shop.gpFree 0.00 0.00
## cost.shop.gp1-199 12.03 21.34
## cost.shop.gp200-499 27.02 35.71
## cost.shop.gp500-999 26.36 23.33
## cost.shop.gp1000-1999 20.09 14.52
## cost.shop.gp2000+ 14.50 5.11
Table2.22 <- rbind(Home="", data.frame(T1, T2), Shop="", data.frame(T3, T4))
colnames(Table2.22)[1:2] <- c("Current drinkers","Heavy drinkers")
Table2.22
## Current drinkers Heavy drinkers Regular Social
## Home
## cost.home.gpFree 0 0 0 0
## cost.home.gp1-199 33.05 16.02 7.52 19.3
## cost.home.gp200-499 33.55 31.32 21.68 35.04
## cost.home.gp500-999 18.15 23.58 24.43 23.25
## cost.home.gp1000-1999 11.55 18.32 25.25 15.65
## cost.home.gp2000+ 3.69 10.76 21.11 6.77
## Shop
## cost.shop.gpFree 0 0 0 0
## cost.shop.gp1-199 31.66 19.01 12.03 21.34
## cost.shop.gp200-499 38.09 33.54 27.02 35.71
## cost.shop.gp500-999 18.74 24.08 26.36 23.33
## cost.shop.gp1000-1999 7.96 15.91 20.09 14.52
## cost.shop.gp2000+ 3.54 7.45 14.5 5.11
# Section 3 ####
# Table 3.1 ####
T1a <- round(svytotal(~drinker, design=Youths)[1:3],2)
T1b <- round(svytotal(~drinker4, design=Youths)[1:4],2)[3:4]
T1c <- round(t(svyby(~drinker, by=~a4, FUN=svytotal, design=Youths)[,c(2:4)]),2)
T1d <- round(t(svyby(~drinker4, by=~a4, FUN=svytotal, design=Youths)[,c(2:5)]),2)[3:4,]
Table3.1 <- cbind(Total=c(T1a, T1b), rbind(T1c, T1d))
Table3.1
## Total female male
## drinker1: Never drinker 6813282.5 4087294.12 2725988.4
## drinker2: Ex drinker 449681.5 199487.78 250193.8
## drinker3: Current drinker 2282523.0 434563.10 1847959.9
## drinker43: Current regular 684598.0 40839.19 643758.8
## drinker44: Current social 1597925.0 393723.91 1204201.1
T2a <- round(100*svymean(~drinker, design=Youths)[1:3],2)
T2b <- round(100*svymean(~drinker4, design=Youths)[1:4],2)[3:4]
T2c <- round(100*t(svyby(~drinker, by=~a4, FUN=svymean, design=Youths)[,c(2:4)]),2)
T2d <- round(100*t(svyby(~drinker4, by=~a4, FUN=svymean, design=Youths)[,c(2:5)]),2)[3:4,]
Table3.1 <- cbind(Total=c(T2a, T2b), rbind(T2c, T2d))
Table3.1
## Total female male
## drinker1: Never drinker 71.38 86.57 56.51
## drinker2: Ex drinker 4.71 4.23 5.19
## drinker3: Current drinker 23.91 9.20 38.31
## drinker43: Current regular 7.17 0.86 13.34
## drinker44: Current social 16.74 8.34 24.96
# Table 3.2 ####
# (Heavy drinking by sex)
T1a <- round(100*svyby(~a4, by=~drinker, FUN=svymean, design=Youths)[,c(2:3)],2)
T1b <- round(100*svyby(~a4, by=~drinker4, FUN=svymean, design=Youths)[,c(2:3)],2)[3:4,]
T1c <- round(100*svyby(~a4, by=~drinking.heavy.gp, FUN=svymean, design=Youth.drinkers)[,c(2:3)],2)
Table3.2 <- rbind(T1a, T1b, T1c)
Table3.2
## a4female a4male
## 1: Never drinker 59.99 40.01
## 2: Ex drinker 44.36 55.64
## 3: Current drinker 19.04 80.96
## 3: Current regular 5.97 94.03
## 4: Current social 24.64 75.36
## No 25.26 74.74
## Regular 9.88 90.12
## Social 11.38 88.62
# Column percentages
T1a <- t(round(100*svyby(~drinker, by=~a4, FUN=svymean, design=Youths)[,c(2:4)],2))
T1b <- t(round(100*svyby(~drinking.heavy.gp, by=~a4, FUN=svymean, design=Youth.drinkers)[,c(2:4)],2))
Table3.2.column <- rbind(T1a, T1b)
Table3.2.column
## female male
## drinker1: Never drinker 86.57 56.51
## drinker2: Ex drinker 4.23 5.19
## drinker3: Current drinker 9.20 38.31
## drinking.heavy.gpNo 74.25 51.65
## drinking.heavy.gpRegular 3.73 8.00
## drinking.heavy.gpSocial 22.03 40.35
# Table 3.3 ####
# Prevalence of drinking by sex
T1 <- round(100*svymean(~drinker4, design=Youth.drinkers.light)[1:2],2)
T2 <- round(100*svyby(~drinker4, by=~a4, FUN=svymean, design=Youth.drinkers.light)[,c(2:3)],2)
T3 <- round(100*svymean(~drinking.heavy.gp2, design=Youth.drinkers.heavy, na.rm=TRUE)[1:2],2)
T4 <- round(100*svyby(~drinking.heavy.gp2, by=~a4, FUN=svymean, design=Youth.drinkers.heavy, na.rm=TRUE)[,c(2:3)],2)
Table3.3 <- cbind(rbind(Total=T1, T2), rbind(Total=T3, T4))
colnames(Table3.3) <- c("1: Regular","2: Social","3: Regular heavy","4: Social heavy")
Table3.3
## 1: Regular 2: Social 3: Regular heavy 4: Social heavy
## Total 21.23 78.77 16.32 83.68
## female 4.54 95.46 14.48 85.52
## male 26.87 73.13 16.55 83.45
# Table 3.4 ####
T0 <- t(round(100*t(svymean(~d40, design=Youth.drinkers, na.rm=TRUE)[1:9]),2))
colnames(T0) <- "Total"
T1 <- round(100*t(svyby(~d40, by=~a4, FUN=svymean, design=Youth.drinkers, na.rm=TRUE)[,2:10]),2)
Table3.4 <- cbind(T0, T1)
Table3.4
## Total female male
## d401 55.95 74.25 51.65
## d402 0.21 0.23 0.21
## d403 0.70 0.00 0.87
## d404 0.97 0.52 1.07
## d405 5.31 2.97 5.86
## d406 10.10 3.50 11.66
## d407 6.75 6.41 6.83
## d408 3.95 1.81 4.45
## d409 16.06 10.31 17.41
# Table 3.5 ####
# Cost of drinking stratified by place
# Total
T.home <- round(coef(svymean(~cost.home, design=Youth.drinkers, na.rm=TRUE)),2)
T.home
## cost.home
## 428.85
T.shop <- round(coef(svymean(~cost.shop, design=Youth.drinkers, na.rm=TRUE)),2)
T.shop
## cost.shop
## 398.68
T.total <- c(T.home, T.shop, T.home+T.shop)
# By sex
Tsex.home <- round(coef(svyby(~cost.home, by=~a4, FUN=svymean, design=Youth.drinkers, na.rm=TRUE)),2)
Tsex.home
## female male
## 239.51 455.46
Tsex.shop <- round(coef(svyby(~cost.shop, by=~a4, FUN=svymean, design=Youth.drinkers, na.rm=TRUE)),2)
Tsex.shop
## female male
## 389.47 400.37
Tsex <- cbind(Tsex.home, Tsex.shop)
Tsex <- cbind(Tsex, Total=rowSums(Tsex))
Tsex
## Tsex.home Tsex.shop Total
## female 239.51 389.47 628.98
## male 455.46 400.37 855.83
# By area
Tarea.home <- round(coef(svyby(~cost.home, by=~area, FUN=svymean, design=Youth.drinkers, na.rm=TRUE)),2)
Tarea.home
## Urban Rural
## 463.11 404.54
Tarea.shop <- round(coef(svyby(~cost.shop, by=~area, FUN=svymean, design=Youth.drinkers, na.rm=TRUE)),2)
Tarea.shop
## Urban Rural
## 436.05 366.99
Tarea <- cbind(Tarea.home, Tarea.shop)
Tarea <- cbind(Tarea, Total=rowSums(Tarea))
Tarea
## Tarea.home Tarea.shop Total
## Urban 463.11 436.05 899.16
## Rural 404.54 366.99 771.53
# By reg
Treg.home <- round(coef(svyby(~cost.home, by=~reg, FUN=svymean, design=Youth.drinkers, na.rm=TRUE)),2)
Treg.home
## Bangkok Central North North-east South
## 636.96 535.65 304.74 407.27 278.28
Treg.shop <- round(coef(svyby(~cost.shop, by=~reg, FUN=svymean, design=Youth.drinkers, na.rm=TRUE)),2)
Treg.shop
## Bangkok Central North North-east South
## 490.81 509.25 410.75 330.82 246.24
Treg <- cbind(Treg.home, Treg.shop)
Treg <- cbind(Treg, Total=rowSums(Treg))
Treg
## Treg.home Treg.shop Total
## Bangkok 636.96 490.81 1127.77
## Central 535.65 509.25 1044.90
## North 304.74 410.75 715.49
## North-east 407.27 330.82 738.09
## South 278.28 246.24 524.52
Table3.5 <- rbind(T.total, Tsex, Tarea, Treg)
colnames(Table3.5)[3] <- "cost.total"
Table3.5
## cost.home cost.shop cost.total
## T.total 428.85 398.68 827.53
## female 239.51 389.47 628.98
## male 455.46 400.37 855.83
## Urban 463.11 436.05 899.16
## Rural 404.54 366.99 771.53
## Bangkok 636.96 490.81 1127.77
## Central 535.65 509.25 1044.90
## North 304.74 410.75 715.49
## North-east 407.27 330.82 738.09
## South 278.28 246.24 524.52
# Table 3.6 ####
# Quantity by sex
Beer <- round(coef(svyby(~beer, by=~a4, FUN=svymean, Youth.drinkers, na.rm=TRUE)),2)
Beer.T <- round(coef(svymean(~beer, Youth.drinkers, na.rm=TRUE)),2)
Spirits.white <- round(coef(svyby(~spirits.white, by=~a4, FUN=svymean, Youth.drinkers, na.rm=TRUE)),2)
Spirits.wh.T <- round(coef(svymean(~spirits.white, Youth.drinkers, na.rm=TRUE)),2)
Spirits.red <- round(coef(svyby(~spirits.red, by=~a4, FUN=svymean, Youth.drinkers, na.rm=TRUE)),2)
Spirits.red.T <- round(coef(svymean(~spirits.red, Youth.drinkers, na.rm=TRUE)),2)
spirits.other <- round(coef(svyby(~spirits.other, by=~a4, FUN=svymean, Youth.drinkers, na.rm=TRUE)),2)
spirits.other.T <- round(coef(svymean(~spirits.other, Youth.drinkers, na.rm=TRUE)),2)
Wine <- round(coef(svyby(~wine, by=~a4, FUN=svymean, Youth.drinkers, na.rm=TRUE)),2)
Wine.T <- round(coef(svymean(~wine, Youth.drinkers, na.rm=TRUE)),2)
Wine.cooler <- round(coef(svyby(~wine.cooler, by=~a4, FUN=svymean, Youth.drinkers, na.rm=TRUE)),2)
Wine.cooler.T <- round(coef(svymean(~wine.cooler, Youth.drinkers, na.rm=TRUE)),2)
Other <- round(coef(svyby(~other, by=~a4, FUN=svymean, Youth.drinkers, na.rm=TRUE)),2)
Other.T <- round(coef(svymean(~other, Youth.drinkers, na.rm=TRUE)),2)
Alcohol <- as.data.frame(cbind(Total=c(Beer.T,Spirits.wh.T,Spirits.red.T,spirits.other.T,Wine.T, Wine.cooler.T, Other.T), rbind(Beer,Spirits.white, Spirits.red, spirits.other, Wine, Wine.cooler, Other)))
colnames(Alcohol)[1] <- "Alcohol"
# Ethanol
Ethanol <- round(Alcohol*c(0.05, 0.4, 0.4, 0.12, 0.03, 0.12, 0.4), 2)
colnames(Ethanol)[1] <- "Ethanol"
Table3.6 <- cbind(Alcohol, Ethanol)
Table3.6
## Alcohol female male Ethanol female male
## beer 26.98 8.19 31.40 1.35 0.41 1.57
## spirits.white 4.96 0.28 6.06 1.98 0.11 2.42
## spirits.red 6.68 2.26 7.72 2.67 0.90 3.09
## spirits.other 0.01 0.00 0.01 0.00 0.00 0.00
## wine 0.09 0.21 0.07 0.00 0.01 0.00
## wine.cooler 0.23 0.37 0.19 0.03 0.04 0.02
## other 0.01 0.01 0.01 0.00 0.00 0.00
# Section 3.2 ####
# Female drinkers ####
# Table 3.7 ####
Female.drinkers <- subset(Current.drinkers, subset=a4=="female")
Table3.7Females <- rbind(
Total=round(100*coef(svymean(~d1, design=Female.drinkers)),2)[3:10],
Area="",
round(100*svyby(~d1, by=~area, FUN=svymean, design=Female.drinkers)[,4:11],2),
Age_group="",
round(100*svyby(~d1, by=~age_group, FUN=svymean, design=Female.drinkers)[,4:11],2),
Region="",
round(100*svyby(~d1, by=~reg, FUN=svymean, design=Female.drinkers)[,4:11],2),
Education="",
round(100*svyby(~d1, by=~edu, FUN=svymean, design=Female.drinkers)[,4:11],2),
Occupation="",
round(100*svyby(~d1, by=~occu, FUN=svymean, design=Female.drinkers)[,4:11],2),
Income="",
round(100*svyby(~d1, by=~inc5, FUN=svymean, design=Female.drinkers)[,4:11],2))
Male.drinkers <- subset(Current.drinkers, subset=a4=="male")
Table3.7Males <- rbind(
Total=round(100*coef(svymean(~d1, design=Male.drinkers)),2)[3:10],
Area="",
round(100*svyby(~d1, by=~area, FUN=svymean, design=Male.drinkers)[,4:11],2),
Age_group="",
round(100*svyby(~d1, by=~age_group, FUN=svymean, design=Male.drinkers)[,4:11],2),
Region="",
round(100*svyby(~d1, by=~reg, FUN=svymean, design=Male.drinkers)[,4:11],2),
Education="",
round(100*svyby(~d1, by=~edu, FUN=svymean, design=Male.drinkers)[,4:11],2),
Occupation="",
round(100*svyby(~d1, by=~occu, FUN=svymean, design=Male.drinkers)[,4:11],2),
Income="",
round(100*svyby(~d1, by=~inc5, FUN=svymean, design=Male.drinkers)[,4:11],2))
# Table 3.8 ####
# Drinking by sex
Table3.8 <- t(round(100*svyby(~drinker4, by=~a4, FUN=svymean, design=Current.drinkers)[,2:3],2))
# Table 3.9 ####
# Heavy drinking by sex
Table3.9 <- t(round(100*svyby(~d40, by=~a4, FUN=svymean, design=Current.drinkers)[,2:10],2))
# Table 3.10 ####
# Cost by place and ... for males and females
# Females
Tcost <- c(round(coef(svymean(~cost.home, design=Female.drinkers, na.rm=TRUE)),2),
round(coef(svymean(~cost.shop, design=Female.drinkers, na.rm=TRUE)),2))
Table3.10Females <- c(Tcost, Total=sum(Tcost))
Tage <- cbind(Home=round(coef(svyby(~cost.home, by=~age_group, FUN=svymean, design=Female.drinkers, na.rm=TRUE)),2),
Shop=round(coef(svyby(~cost.shop, by=~age_group, FUN=svymean, design=Female.drinkers, na.rm=TRUE)),2))
Tage <- cbind(Tage, Total=rowSums(Tage))
Tage
## Home Shop Total
## 15 - 19 158.52 213.93 372.45
## 20 - 24 264.16 422.35 686.51
## 25 - 44 355.84 502.13 857.97
## 45 - 59 403.36 342.74 746.10
## 60+ 350.88 250.60 601.48
Tarea <- cbind(Home=round(coef(svyby(~cost.home, by=~area, FUN=svymean, design=Female.drinkers, na.rm=TRUE)),2),
Shop=round(coef(svyby(~cost.shop, by=~area, FUN=svymean, design=Female.drinkers, na.rm=TRUE)),2))
Tarea <- cbind(Tarea, Total=rowSums(Tarea))
Tarea
## Home Shop Total
## Urban 433.27 586.62 1019.89
## Rural 303.56 274.15 577.71
Treg <- cbind(Home=round(coef(svyby(~cost.home, by=~reg, FUN=svymean, design=Female.drinkers, na.rm=TRUE)),2),
Shop=round(coef(svyby(~cost.shop, by=~reg, FUN=svymean, design=Female.drinkers, na.rm=TRUE)),2))
Treg <- cbind(Treg, Total=rowSums(Treg))
Treg
## Home Shop Total
## Bangkok 476.88 674.83 1151.71
## Central 510.29 630.50 1140.79
## North 293.50 347.42 640.92
## North-east 277.17 295.03 572.20
## South 296.84 423.60 720.44
Tinc <- cbind(Home=round(coef(svyby(~cost.home, by=~inc5, FUN=svymean, design=Female.drinkers, na.rm=TRUE)),2),
Shop=round(coef(svyby(~cost.shop, by=~inc5, FUN=svymean, design=Female.drinkers, na.rm=TRUE)),2))
Tinc <- cbind(Tinc, Total=rowSums(Tinc))
Tinc
## Home Shop Total
## Q 1 362.15 254.96 617.11
## Q 2 266.97 201.10 468.07
## Q 3 336.17 265.44 601.61
## Q 4 342.97 397.48 740.45
## Q 5 471.70 708.78 1180.48
Table3.10Females <- rbind(Table3.10Females, Age="", Tage, Area="", Tarea, Region="", Treg, Income="",Tinc)
print.noquote(Table3.10Females)
## cost.home cost.shop Total
## Table3.10Females 356.46 425.93 782.39
## Age
## 15 - 19 158.52 213.93 372.45
## 20 - 24 264.16 422.35 686.51
## 25 - 44 355.84 502.13 857.97
## 45 - 59 403.36 342.74 746.1
## 60+ 350.88 250.6 601.48
## Area
## Urban 433.27 586.62 1019.89
## Rural 303.56 274.15 577.71
## Region
## Bangkok 476.88 674.83 1151.71
## Central 510.29 630.5 1140.79
## North 293.5 347.42 640.92
## North-east 277.17 295.03 572.2
## South 296.84 423.6 720.44
## Income
## Q 1 362.15 254.96 617.11
## Q 2 266.97 201.1 468.07
## Q 3 336.17 265.44 601.61
## Q 4 342.97 397.48 740.45
## Q 5 471.7 708.78 1180.48
# Males
Tcost <- c(round(coef(svymean(~cost.home, design=Male.drinkers, na.rm=TRUE)),2),
round(coef(svymean(~cost.shop, design=Male.drinkers, na.rm=TRUE)),2))
Table3.10Males <- c(Tcost, Total=sum(Tcost))
Tage <- cbind(Home=round(coef(svyby(~cost.home, by=~age_group, FUN=svymean, design=Male.drinkers, na.rm=TRUE)),2),
Shop=round(coef(svyby(~cost.shop, by=~age_group, FUN=svymean, design=Male.drinkers, na.rm=TRUE)),2))
Tage <- cbind(Tage, Total=rowSums(Tage))
Tage
## Home Shop Total
## 15 - 19 320.46 362.99 683.45
## 20 - 24 506.11 411.75 917.86
## 25 - 44 743.07 592.82 1335.89
## 45 - 59 698.79 557.23 1256.02
## 60+ 559.74 417.32 977.06
Tarea <- cbind(Home=round(coef(svyby(~cost.home, by=~area, FUN=svymean, design=Male.drinkers, na.rm=TRUE)),2),
Shop=round(coef(svyby(~cost.shop, by=~area, FUN=svymean, design=Male.drinkers, na.rm=TRUE)),2))
Tarea <- cbind(Tarea, Total=rowSums(Tarea))
Tarea
## Home Shop Total
## Urban 791.71 652.42 1444.13
## Rural 580.72 441.52 1022.24
Treg <- cbind(Home=round(coef(svyby(~cost.home, by=~reg, FUN=svymean, design=Male.drinkers, na.rm=TRUE)),2),
Shop=round(coef(svyby(~cost.shop, by=~reg, FUN=svymean, design=Male.drinkers, na.rm=TRUE)),2))
Treg <- cbind(Treg, Total=rowSums(Treg))
Treg
## Home Shop Total
## Bangkok 915.96 879.45 1795.41
## Central 884.07 632.81 1516.88
## North 479.80 427.76 907.56
## North-east 504.63 399.94 904.57
## South 617.41 481.36 1098.77
Tinc <- cbind(Home=round(coef(svyby(~cost.home, by=~inc5, FUN=svymean, design=Male.drinkers, na.rm=TRUE)),2),
Shop=round(coef(svyby(~cost.shop, by=~inc5, FUN=svymean, design=Male.drinkers, na.rm=TRUE)),2))
Tinc <- cbind(Tinc, Total=rowSums(Tinc))
Tinc
## Home Shop Total
## Q 1 472.79 382.99 855.78
## Q 2 452.04 357.45 809.49
## Q 3 507.90 351.33 859.23
## Q 4 667.13 480.94 1148.07
## Q 5 936.91 786.34 1723.25
Table3.10Males <- rbind(Table3.10Males, Age="", Tage, Area="", Tarea, Region="", Treg, Income="",Tinc)
print.noquote(Table3.10Males)
## cost.home cost.shop Total
## Table3.10Males 671.62 537.95 1209.57
## Age
## 15 - 19 320.46 362.99 683.45
## 20 - 24 506.11 411.75 917.86
## 25 - 44 743.07 592.82 1335.89
## 45 - 59 698.79 557.23 1256.02
## 60+ 559.74 417.32 977.06
## Area
## Urban 791.71 652.42 1444.13
## Rural 580.72 441.52 1022.24
## Region
## Bangkok 915.96 879.45 1795.41
## Central 884.07 632.81 1516.88
## North 479.8 427.76 907.56
## North-east 504.63 399.94 904.57
## South 617.41 481.36 1098.77
## Income
## Q 1 472.79 382.99 855.78
## Q 2 452.04 357.45 809.49
## Q 3 507.9 351.33 859.23
## Q 4 667.13 480.94 1148.07
## Q 5 936.91 786.34 1723.25
# Table 3.11 ####
# Quantity by sex
Beer <- round(coef(svyby(~beer, by=~a4, FUN=svymean, Current.drinkers, na.rm=TRUE)),2)
Spirits.white <- round(coef(svyby(~spirits.white, by=~a4, FUN=svymean, Current.drinkers, na.rm=TRUE)),2)
Spirits.red <- round(coef(svyby(~spirits.red, by=~a4, FUN=svymean, Current.drinkers, na.rm=TRUE)),2)
spirits.other <- round(coef(svyby(~spirits.other, by=~a4, FUN=svymean, Current.drinkers, na.rm=TRUE)),2)
Wine <- round(coef(svyby(~wine, by=~a4, FUN=svymean, Current.drinkers, na.rm=TRUE)),2)
Wine.cooler <- round(coef(svyby(~wine.cooler, by=~a4, FUN=svymean, Current.drinkers, na.rm=TRUE)),2)
Other <- round(coef(svyby(~other, by=~a4, FUN=svymean, Current.drinkers, na.rm=TRUE)),2)
Alcohol <- as.data.frame(rbind(Beer,Spirits.white, Spirits.red, spirits.other, Wine, Wine.cooler, Other))
Alcohol <- rbind(Alcohol[1,], Spirits=colSums(Alcohol[2:4,]), Alcohol[2:7,])
colnames(Alcohol) <- c("Alcohol.Female","Alcohol.Male")
# Ethanol
Ethanol <- cbind(round(Alcohol[,1]*c(0.05, 0.4, 0.4, 0.4, 0.4, 0.12, 0.03, 0.12),2),
round(Alcohol[,2]*c(0.05, 0.4, 0.4, 0.4, 0.4, 0.12, 0.03, 0.12),2))
colnames(Ethanol) <- c("Ethanol.Female","Ethanol.Male")
Table3.11 <- cbind(Alcohol, Ethanol)
Table3.11
## Alcohol.Female Alcohol.Male Ethanol.Female Ethanol.Male
## Beer 11.86 34.92 0.59 1.75
## Spirits 5.98 20.14 2.39 8.06
## Spirits.white 3.45 10.24 1.38 4.10
## Spirits.red 2.35 9.57 0.94 3.83
## spirits.other 0.18 0.33 0.07 0.13
## Wine 0.18 0.26 0.02 0.03
## Wine.cooler 0.28 0.12 0.01 0.00
## Other 0.01 0.10 0.00 0.01
# Table 3.12 ####
# Proportion of new drinkers by age
Table3.12 <- cbind(Percent=round(100*coef(svymean(~age_group, design=New.drinkers)),2),
Total=round(coef(svytotal(~age_group, design=New.drinkers)),2))
Table3.12 <- as.data.frame(rbind(Table3.12, colSums(Table3.12)))
Table3.12[,2] <- format(Table3.12[,2], big.mark=",")
Table3.12
## Percent Total
## age_group15 - 19 38.40 639,421.95
## age_group20 - 24 48.07 800,555.51
## age_group25 - 44 10.84 180,571.74
## age_group45 - 59 2.04 33,943.83
## age_group60+ 0.65 10,883.58
## 100.00 1,665,376.61
# Table 3.13 ####
Table3.13 <- rbind(round(100*t(svyby(~drinker, by=~a4, FUN=svymean, design=New.drinkers)[3:4]),2),
round(100*t(svyby(~drinker4, by=~a4, FUN=svymean, design=New.drinkers)[3:4]),2))
Table3.13
## female male
## drinker2: Ex drinker 25.02 10.70
## drinker3: Current drinker 74.98 89.30
## drinker43: Current regular 7.60 25.09
## drinker44: Current social 67.38 64.21
# Table 3.14 ####
# Heavy drinking among new drinkers by sex
Table3.14 <- round(100*t(svyby(~d40, by=~a4, FUN=svymean, design=New.drinkers, na.rm=TRUE)[2:10]),2)
Table3.14
## female male
## d401 77.48 57.85
## d402 0.31 0.05
## d403 0.00 1.08
## d404 0.48 0.88
## d405 0.49 4.47
## d406 4.50 10.59
## d407 6.52 6.99
## d408 0.86 3.11
## d409 9.37 14.98
# Table 3.15 ####
# Cost by place and sex
Table3.15 <-
cbind(Total=c(coef(svymean(~cost.home, design=New.drinkers, na.rm=TRUE)),
coef(svymean(~cost.shop, design=New.drinkers, na.rm=TRUE))),
rbind(Home=coef(svyby(~cost.home, by=~a4, FUN=svymean, design=New.drinkers, na.rm=TRUE)),
Shop=coef(svyby(~cost.shop, by=~a4, FUN=svymean, design=New.drinkers, na.rm=TRUE))))
Table3.15 <- round(rbind(Table3.15, Total=colSums(Table3.15)),2)
rownames(Table3.15) <- c("Home","Shop", "Total")
Table3.15
## Total female male
## Home 359.23 201.74 395.11
## Shop 357.06 324.16 365.69
## Total 716.29 525.90 760.80
# Table 3.16 ####
# Quantity for new drinkers by sex
Beer <- round(coef(svyby(~beer, by=~a4, FUN=svymean, New.drinkers, na.rm=TRUE)),2)
Beer.T <- round(coef(svymean(~beer, New.drinkers, na.rm=TRUE)),2)
Spirits.white <- round(coef(svyby(~spirits.white, by=~a4, FUN=svymean, New.drinkers, na.rm=TRUE)),2)
Spirits.wh.T <- round(coef(svymean(~spirits.white, New.drinkers, na.rm=TRUE)),2)
Spirits.red <- round(coef(svyby(~spirits.red, by=~a4, FUN=svymean, New.drinkers, na.rm=TRUE)),2)
Spirits.red.T <- round(coef(svymean(~spirits.red, New.drinkers, na.rm=TRUE)),2)
spirits.other <- round(coef(svyby(~spirits.other, by=~a4, FUN=svymean, New.drinkers, na.rm=TRUE)),2)
spirits.other.T <- round(coef(svymean(~spirits.other, New.drinkers, na.rm=TRUE)),2)
Wine <- round(coef(svyby(~wine, by=~a4, FUN=svymean, New.drinkers, na.rm=TRUE)),2)
Wine.T <- round(coef(svymean(~wine, New.drinkers, na.rm=TRUE)),2)
Wine.cooler <- round(coef(svyby(~wine.cooler, by=~a4, FUN=svymean, New.drinkers, na.rm=TRUE)),2)
Wine.cooler.T <- round(coef(svymean(~wine.cooler, New.drinkers, na.rm=TRUE)),2)
Other <- round(coef(svyby(~other, by=~a4, FUN=svymean, New.drinkers, na.rm=TRUE)),2)
Other.T <- round(coef(svymean(~other, New.drinkers, na.rm=TRUE)),2)
Alcohol <- as.data.frame(cbind(Total=c(Beer.T,Spirits.wh.T,Spirits.red.T,spirits.other.T,Wine.T, Wine.cooler.T, Other.T), rbind(Beer,Spirits.white, Spirits.red, spirits.other, Wine, Wine.cooler, Other)))
Alcohol
## Total female male
## beer 19.54 4.25 26.32
## spirits.white 4.75 0.19 6.76
## spirits.red 3.84 1.38 4.93
## spirits.other 0.04 0.11 0.01
## wine 0.15 0.24 0.12
## wine.cooler 0.25 0.28 0.24
## other 0.01 0.00 0.02
# Ethanol
Ethanol <- round(Alcohol*c(0.05, 0.4, 0.4, 0.4, 0.12, 0.03, 0.12), 2)
Ethanol
## Total female male
## beer 0.98 0.21 1.32
## spirits.white 1.90 0.08 2.70
## spirits.red 1.54 0.55 1.97
## spirits.other 0.02 0.04 0.00
## wine 0.02 0.03 0.01
## wine.cooler 0.01 0.01 0.01
## other 0.00 0.00 0.00
Table3.16 <- cbind(Alcohol, Ethanol)
Table3.16
## Total female male Total female male
## beer 19.54 4.25 26.32 0.98 0.21 1.32
## spirits.white 4.75 0.19 6.76 1.90 0.08 2.70
## spirits.red 3.84 1.38 4.93 1.54 0.55 1.97
## spirits.other 0.04 0.11 0.01 0.02 0.04 0.00
## wine 0.15 0.24 0.12 0.02 0.03 0.01
## wine.cooler 0.25 0.28 0.24 0.01 0.01 0.01
## other 0.01 0.00 0.02 0.00 0.00 0.00
# Section 4 ####
# Table 4.1 ####
T41m <- c(round(coef(svyby(~d4, by=~drinker, FUN=svymean, design=Drinkers)),2),
Total=round(coef(svymean(~d4, design=Drinkers, na.rm=TRUE)),2))
T41q <- rbind(svyby(~d4, by=~drinker, design=Drinkers, FUN=svyquantile, quantiles=c(0,1), ci=TRUE)[2:3],
Total=coef(svyquantile(~d4, design=Drinkers, quantiles=c(0,1), ci=TRUE)))
## Warning in vcov.svyquantile(X[[i]], ...): Only diagonal of vcov() available
## Warning in vcov.svyquantile(X[[i]], ...): Only diagonal of vcov() available
Table4.1 <- cbind(T41m, T41q)
Table4.1
## T41m 0 1
## 2: Ex drinker 20.63 9 73
## 3: Current drinker 20.14 8 75
## Total.d4 20.31 8 75
# Table 4.2 ####
# by ...
# Region
T42m <- cbind(Current=round(coef(svyby(~d4, by=~reg, FUN=svymean, design=Current.drinkers)),2),
Former=round(coef(svyby(~d4, by=~reg, FUN=svymean, design=Former.drinkers)),2),
Total=round(coef(svyby(~d4, by=~reg, FUN=svymean, design=Drinkers, na.rm=TRUE)),2))
T42q <- cbind(svyby(~d4, by=~reg, FUN=svyquantile, design=Current.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~reg, FUN=svyquantile, design=Former.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~reg, FUN=svyquantile, design=Drinkers, na.rm=TRUE, quantiles=c(0,1), ci=TRUE)[2:3])
## Warning in vcov.svyquantile(X[[i]], ...): Only diagonal of vcov() available
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T42q
## 0 1 0 1 0 1
## Bangkok 10 62 13 60 10 62
## Central 8 75 9 73 8 75
## North 10 70 10 65 10 70
## North-east 9 75 10 65 9 75
## South 11 68 11 62 11 68
T42reg <- cbind(Current=T42m[,1], T42q[,1:2],
Former=T42m[,2], T42q[,3:4],
Total=T42m[,3], T42q[,5:6])
T42reg
## Current 0 1 Former 0 1 Total 0 1
## Bangkok 20.14 10 62 20.06 13 60 20.11 10 62
## Central 19.96 8 75 20.78 9 73 20.22 8 75
## North 20.14 10 70 20.70 10 65 20.33 10 70
## North-east 20.31 9 75 20.61 10 65 20.40 9 75
## South 20.20 11 68 21.00 11 62 20.50 11 68
Table4.2 <- T42reg
# Area
T42m <- cbind(Current=round(coef(svyby(~d4, by=~area, FUN=svymean, design=Current.drinkers)),2),
Former=round(coef(svyby(~d4, by=~area, FUN=svymean, design=Former.drinkers)),2),
Total=round(coef(svyby(~d4, by=~area, FUN=svymean, design=Drinkers, na.rm=TRUE)),2))
T42q <- cbind(
svyby(~d4, by=~area, FUN=svyquantile, design=Current.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~area, FUN=svyquantile, design=Former.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~area, FUN=svyquantile, design=Drinkers, na.rm=TRUE, quantiles=c(0,1), ci=TRUE)[2:3])
## Warning in vcov.svyquantile(X[[i]], ...): Only diagonal of vcov() available
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T42area <- cbind(Current=T42m[,1], T42q[,1:2],
Former=T42m[,2], T42q[,3:4],
Total=T42m[,3], T42q[,5:6])
# Sex
T42m <- cbind(Current=round(coef(svyby(~d4, by=~a4, FUN=svymean, design=Current.drinkers)),2),
Former=round(coef(svyby(~d4, by=~a4, FUN=svymean, design=Former.drinkers)),2),
Total=round(coef(svyby(~d4, by=~a4, FUN=svymean, design=Drinkers, na.rm=TRUE)),2))
T42q <- cbind(svyby(~d4, by=~a4, FUN=svyquantile, design=Current.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~a4, FUN=svyquantile, design=Former.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~a4, FUN=svyquantile, design=Drinkers, na.rm=TRUE, quantiles=c(0,1), ci=TRUE)[2:3])
## Warning in vcov.svyquantile(X[[i]], ...): Only diagonal of vcov() available
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T42q
## 0 1 0 1 0 1
## female 11 75 9 73 9 75
## male 8 68 9 70 8 70
T42sex <- cbind(Current=T42m[,1], T42q[,1:2],
Former=T42m[,2], T42q[,3:4],
Total=T42m[,3], T42q[,5:6])
# Age group
T42m <- cbind(Current=round(coef(svyby(~d4, by=~age_group, FUN=svymean, design=Current.drinkers)),2),
Former=round(coef(svyby(~d4, by=~age_group, FUN=svymean, design=Former.drinkers)),2),
Total=round(coef(svyby(~d4, by=~age_group, FUN=svymean, design=Drinkers, na.rm=TRUE)),2))
T42q <- cbind(svyby(~d4, by=~age_group, FUN=svyquantile, design=Current.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~age_group, FUN=svyquantile, design=Former.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~age_group, FUN=svyquantile, design=Drinkers, na.rm=TRUE, quantiles=c(0,1), ci=TRUE)[2:3])
## Warning in vcov.svyquantile(X[[i]], ...): Only diagonal of vcov() available
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T42q
## 0 1 0 1 0 1
## 15 - 19 9 19 11 18 9 19
## 20 - 24 10 24 11 23 10 24
## 25 - 44 8 43 10 41 8 43
## 45 - 59 10 58 10 55 10 58
## 60+ 8 75 9 73 8 75
T42age <- cbind(Current=T42m[,1], T42q[,1:2],
Former=T42m[,2], T42q[,3:4],
Total=T42m[,3], T42q[,5:6])
T42age
## Current 0 1 Former 0 1 Total 0 1
## 15 - 19 15.68 9 19 15.51 11 18 15.65 9 19
## 20 - 24 17.81 10 24 17.85 11 23 17.82 10 24
## 25 - 44 19.53 8 43 19.79 10 41 19.59 8 43
## 45 - 59 21.46 10 58 20.94 10 55 21.28 10 58
## 60+ 22.96 8 75 21.53 9 73 22.06 8 75
# Marital status
T42m <- cbind(Current=round(coef(svyby(~d4, by=~marital, FUN=svymean, design=Current.drinkers)),2),
Former=round(coef(svyby(~d4, by=~marital, FUN=svymean, design=Former.drinkers)),2),
Total=round(coef(svyby(~d4, by=~marital, FUN=svymean, design=Drinkers, na.rm=TRUE)),2))
T42q <- cbind(svyby(~d4, by=~marital, FUN=svyquantile, design=Current.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~marital, FUN=svyquantile, design=Former.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~marital, FUN=svyquantile, design=Drinkers, na.rm=TRUE, quantiles=c(0,1), ci=TRUE)[2:3])
## Warning in vcov.svyquantile(X[[i]], ...): Only diagonal of vcov() available
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T42q
## 0 1 0 1 0 1
## Single 9 70 10 55 9 70
## Married 8 75 9 73 8 75
## Previously married 10 75 10 70 10 75
T42marital <- cbind(Current=T42m[,1], T42q[,1:2],
Former=T42m[,2], T42q[,3:4],
Total=T42m[,3], T42q[,5:6])
T42marital
## Current 0 1 Former 0 1 Total 0 1
## Single 18.51 9 70 19.21 10 55 18.66 9 70
## Married 20.46 8 75 20.47 9 73 20.47 8 75
## Previously married 22.42 10 75 22.67 10 70 22.53 10 75
# Education
T42m <- cbind(Current=round(coef(svyby(~d4, by=~edu, FUN=svymean, design=Current.drinkers)),2),
Former=round(coef(svyby(~d4, by=~edu, FUN=svymean, design=Former.drinkers)),2),
Total=round(coef(svyby(~d4, by=~edu, FUN=svymean, design=Drinkers, na.rm=TRUE)),2))
T42q <- cbind(svyby(~d4, by=~edu, FUN=svyquantile, design=Current.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~edu, FUN=svyquantile, design=Former.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~edu, FUN=svyquantile, design=Drinkers, na.rm=TRUE, quantiles=c(0,1), ci=TRUE)[2:3])
## Warning in vcov.svyquantile(X[[i]], ...): Only diagonal of vcov() available
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T42q
## 0 1 0 1 0 1
## Primary 8 75 9 73 8 75
## Secondary 8 62 10 60 8 62
## Vocational 10 47 13 54 10 54
## Tertiary 11 58 10 60 10 60
## Other 12 25 15 21 12 25
T42edu <- cbind(Current=T42m[,1], T42q[,1:2],
Former=T42m[,2], T42q[,3:4],
Total=T42m[,3], T42q[,5:6])
T42edu
## Current 0 1 Former 0 1 Total 0 1
## Primary 21.05 8 75 21.07 9 73 21.06 8 75
## Secondary 19.08 8 62 19.71 10 60 19.24 8 62
## Vocational 19.28 10 47 19.67 13 54 19.38 10 54
## Tertiary 20.53 11 58 20.67 10 60 20.58 10 60
## Other 19.53 12 25 18.21 15 21 19.28 12 25
# Occupation
T42m <- cbind(Current=round(coef(svyby(~d4, by=~a14, FUN=svymean, design=Current.drinkers)),2),
Former=round(coef(svyby(~d4, by=~a14, FUN=svymean, design=Former.drinkers)),2),
Total=round(coef(svyby(~d4, by=~a14, FUN=svymean, design=Drinkers, na.rm=TRUE)),2))
T42q <- cbind(svyby(~d4, by=~a14, FUN=svyquantile, design=Current.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~a14, FUN=svyquantile, design=Former.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~a14, FUN=svyquantile, design=Drinkers, na.rm=TRUE, quantiles=c(0,1), ci=TRUE)[2:3])
## Warning in vcov.svyquantile(X[[i]], ...): Only diagonal of vcov() available
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T42q
## 0 1 0 1 0 1
## 1: Employer 10 71 9 55 9 71
## 2: Business assistant 10 75 11 55 10 75
## 3: Govt/state employee 12 55 12 57 12 57
## 4: Private employee 8 66 10 60 8 66
## 5: Student 10 24 11 21 10 24
## 6: House manager 12 70 12 53 12 70
## 7: Infant/elderly 10 75 10 73 10 75
## 8: Unemployed 8 70 9 50 8 70
## 9: Unknown 15 50 12 50 12 50
T42occu <- cbind(Current=T42m[,1], T42q[,1:2],
Former=T42m[,2], T42q[,3:4],
Total=T42m[,3], T42q[,5:6])
T42occu
## Current 0 1 Former 0 1 Total 0 1
## 1: Employer 20.49 10 71 20.36 9 55 20.45 9 71
## 2: Business assistant 20.35 10 75 21.20 11 55 20.61 10 75
## 3: Govt/state employee 20.55 12 55 20.79 12 57 20.62 12 57
## 4: Private employee 19.54 8 66 20.09 10 60 19.67 8 66
## 5: Student 16.98 10 24 16.99 11 21 16.98 10 24
## 6: House manager 23.46 12 70 22.42 12 53 22.93 12 70
## 7: Infant/elderly 23.59 10 75 21.42 10 73 21.98 10 75
## 8: Unemployed 19.07 8 70 20.13 9 50 19.60 8 70
## 9: Unknown 19.58 15 50 20.30 12 50 19.85 12 50
# Occupation group
T42m <- cbind(Current=round(coef(svyby(~d4, by=~occu, FUN=svymean, design=Current.drinkers)),2),
Former=round(coef(svyby(~d4, by=~occu, FUN=svymean, design=Former.drinkers)),2),
Total=round(coef(svyby(~d4, by=~occu, FUN=svymean, design=Drinkers, na.rm=TRUE)),2))
T42q <- cbind(svyby(~d4, by=~occu, FUN=svyquantile, design=Current.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~occu, FUN=svyquantile, design=Former.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~occu, FUN=svyquantile, design=Drinkers, na.rm=TRUE, quantiles=c(0,1), ci=TRUE)[2:3])
## Warning in vcov.svyquantile(X[[i]], ...): Only diagonal of vcov() available
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T42q
## 0 1 0 1 0 1
## Agricultural 10 75 10 58 10 75
## Technical 10 50 12 55 10 55
## Commercial 8 66 9 60 8 66
## Professional 10 62 12 43 10 62
## Not working 8 75 9 73 8 75
T42occ <- cbind(Current=T42m[,1], T42q[,1:2],
Former=T42m[,2], T42q[,3:4],
Total=T42m[,3], T42q[,5:6])
T42occ
## Current 0 1 Former 0 1 Total 0 1
## Agricultural 20.46 10 75 20.61 10 58 20.51 10 75
## Technical 19.71 10 50 19.64 12 55 19.69 10 55
## Commercial 19.81 8 66 20.44 9 60 19.98 8 66
## Professional 20.93 10 62 20.58 12 43 20.83 10 62
## Not working 20.54 8 75 21.12 9 73 20.86 8 75
# Income quintile
T42m <- cbind(Current=round(coef(svyby(~d4, by=~inc5, FUN=svymean, design=Current.drinkers)),2),
Former=round(coef(svyby(~d4, by=~inc5, FUN=svymean, design=Former.drinkers)),2),
Total=round(coef(svyby(~d4, by=~inc5, FUN=svymean, design=Drinkers, na.rm=TRUE)),2))
T42q <- cbind(svyby(~d4, by=~inc5, FUN=svyquantile, design=Current.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~inc5, FUN=svyquantile, design=Former.drinkers, quantiles=c(0,1), ci=TRUE)[2:3],
svyby(~d4, by=~inc5, FUN=svyquantile, design=Drinkers, na.rm=TRUE, quantiles=c(0,1), ci=TRUE)[2:3])
## Warning in vcov.svyquantile(X[[i]], ...): Only diagonal of vcov() available
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T42q
## 0 1 0 1 0 1
## Q 1 9 75 9 70 9 75
## Q 2 8 75 10 70 8 75
## Q 3 10 71 10 73 10 73
## Q 4 8 60 9 60 8 60
## Q 5 10 63 10 58 10 63
T42income <- cbind(Current=T42m[,1], T42q[,1:2],
Former=T42m[,2], T42q[,3:4],
Total=T42m[,3], T42q[,5:6])
Table4.2 <- rbind(Region="", T42reg, Area="",T42area, Sex="", T42sex, Age=T42age,
Marital="", T42marital, Education="", T42edu, Occupation="", T42occu,
Occupational_Gp="", T42occ, Income=T42income)
# Figure 4.1 ####
X <- round(100*coef(svymean(~d5_reason, design=Drinkers)),1)
pie(X, labels=paste(levels(d5_reason), "\n", X, "%"), main="Reason for initiating drinking")
#tab2(d5_reason)
# Table 4.3 ####
# by ...
# Region
T43reg <- round(100*svyby(~d5_reason, by=~reg, FUN=svymean, design=Current.drinkers)[2:5],2)
colnames(T43reg) <- levels(d5_reason)
# Area
T43area <- round(100*svyby(~d5_reason, by=~area, FUN=svymean, design=Current.drinkers)[2:5],2)
colnames(T43area) <- levels(d5_reason)
# Sex
T43sex <- round(100*svyby(~d5_reason, by=~a4, FUN=svymean, design=Current.drinkers)[2:5],2)
colnames(T43sex) <- levels(d5_reason)
# Age group
T43age <- round(100*svyby(~d5_reason, by=~age_group, FUN=svymean, design=Current.drinkers)[2:5],2)
colnames(T43age) <- levels(d5_reason)
# Marital status
T43mar <- round(100*svyby(~d5_reason, by=~marital, FUN=svymean, design=Current.drinkers)[2:5],2)
colnames(T43mar) <- levels(d5_reason)
# Education
T43edu <- round(100*svyby(~d5_reason, by=~edu, FUN=svymean, design=Current.drinkers)[2:5],2)
colnames(T43edu) <- levels(d5_reason)
# Occupation
T43occ <- round(100*svyby(~d5_reason, by=~a14, FUN=svymean, design=Current.drinkers)[2:5],2)
colnames(T43occ) <- levels(d5_reason)
# Occupation group
T43occu <- round(100*svyby(~d5_reason, by=~occu, FUN=svymean, design=Current.drinkers)[2:5],2)
colnames(T43occu) <- levels(d5_reason)
# Income
T43inc <- round(100*svyby(~d5_reason, by=~inc5, FUN=svymean, design=Current.drinkers)[2:5],2)
colnames(T43inc) <- levels(d5_reason)
Table4.3 <- rbind(Region="", T43reg, Area="",T43area, Sex="", T43sex, Age=T43age,
Marital="", T43mar, Education="", T43edu, Occupation="", T43occu,
Occupational_Gp="", T43occ, Income=T43inc)
# Figure 4.2 ####
#tab2(d6)
X <- round(100*coef(svymean(~d6_reason, design=Drinkers, na.rm=TRUE)),1)
pie(X, labels=paste(levels(d6_reason), X, "%"), main="Characteristic of drinking debut")
# Table 4.4 ####
# by ...
# Region
T44reg <- round(100*svyby(~d6_reason, by=~reg, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:6],2)
colnames(T44reg) <- levels(d6_reason)
# Area
T44area <- round(100*svyby(~d6_reason, by=~area, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:6],2)
colnames(T44area) <- levels(d6_reason)
# Sex
T44sex <- round(100*svyby(~d6_reason, by=~a4, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:6],2)
colnames(T44sex) <- levels(d6_reason)
# Age group
T44age <- round(100*svyby(~d6_reason, by=~age_group, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:6],2)
colnames(T44age) <- levels(d6_reason)
# Marital status
T44mar <- round(100*svyby(~d6_reason, by=~marital, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:6],2)
colnames(T44mar) <- levels(d6_reason)
# Education
T44edu <- round(100*svyby(~d6_reason, by=~edu, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:6],2)
colnames(T44edu) <- levels(d6_reason)
# Occupation
T44occ <- round(100*svyby(~d6_reason, by=~a14, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:6],2)
colnames(T44occ) <- levels(d6_reason)
# Occupation group
T44occu <- round(100*svyby(~d6_reason, by=~occu, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:6],2)
colnames(T44occu) <- levels(d6_reason)
# Income
T44inc <- round(100*svyby(~d6_reason, by=~inc5, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:6],2)
colnames(T44inc) <- levels(d6_reason)
Table4.4 <- rbind(Region="", T44reg, Area="",T44area, Sex="", T44sex, Age=T44age,
Marital="", T44mar, Education="", T44edu, Occupation="", T44occu,
Occupational_Gp="", T44occ, Income=T44inc)
# Figure 4.3 ####
#tab2(d7)
X <- round(100*coef(svymean(~d7_type, design=Current.drinkers, na.rm=TRUE)),1)
pie(X, labels=paste(levels(d7_type), X, "%"), main="Drink types on drinking debut")
# Table 4.5 ####
# Initial beverage type by ...
# Region
T45reg <- round(100*svyby(~d7_type, by=~reg, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:7],2)
colnames(T45reg) <- levels(d7_type)
# Area
T45area <- round(100*svyby(~d7_type, by=~area, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:7],2)
colnames(T45area) <- levels(d7_type)
# Sex
T45sex <- round(100*svyby(~d7_type, by=~a4, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:7],2)
colnames(T45sex) <- levels(d7_type)
# Age group
T45age <- round(100*svyby(~d7_type, by=~age_group, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:7],2)
colnames(T45age) <- levels(d7_type)
# Marital status
T45mar <- round(100*svyby(~d7_type, by=~marital, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:7],2)
colnames(T45mar) <- levels(d7_type)
# Education
T45edu <- round(100*svyby(~d7_type, by=~edu, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:7],2)
colnames(T45edu) <- levels(d7_type)
# Occupation
T45occ <- round(100*svyby(~d7_type, by=~a14, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:7],2)
colnames(T45occ) <- levels(d7_type)
# Occupation group
T45occu <- round(100*svyby(~d7_type, by=~occu, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:7],2)
colnames(T45occu) <- levels(d7_type)
# Income
T45inc <- round(100*svyby(~d7_type, by=~inc5, FUN=svymean, design=Current.drinkers, na.rm=TRUE)[2:7],2)
colnames(T45inc) <- levels(d7_type)
Table4.5 <- rbind(Region="", T45reg, Area="",T45area, Sex="", T45sex, Age=T45age,
Marital="", T45mar, Education="", T45edu, Occupation="", T45occu,
Occupational_Gp="", T45occ, Income=T45inc)
# Section 5 ####
# Table 5.1 ####
# Place where alcohol illegally bought
T5.1 <- round(100*c(svymean(~d11, design=Current.drinkers)[1],
svymean(~d12, design=Current.drinkers)[1],
svymean(~d13, design=Current.drinkers)[1],
svymean(~d14, design=Current.drinkers)[1],
svymean(~d15, design=Current.drinkers)[1],
svymean(~d16, design=Current.drinkers)[1],
svymean(~d17, design=Current.drinkers)[1],
svymean(~d18, design=Current.drinkers)[1],
svymean(~illegal.place1, design=Current.drinkers)[2]),2)
names(T5.1) <- c("Temple","School","Clinic","Govt.","Dorm","Pump","Park","Factory", "Any illegal place")
Table5.1 <- data.frame(Percent=T5.1)
# Table 5.2 ####
# by ...
# Sex
T52sex <- round(100*cbind(
coef(svyby(~d11, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d12, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d13, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d14, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d15, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d16, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d17, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d18, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~illegal.place1, by=~a4, FUN=svymean, design=Current.drinkers))[3:4]),2)
colnames(T52sex) <- c("Temple","School","Clinic","Gov.","Dorm","Pump","Park","Factory", "Any")
# Age group
T52age <- round(100*cbind(
coef(svyby(~d11, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d12, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d13, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d14, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d15, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d16, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d17, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d18, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~illegal.place1, by=~age_group, FUN=svymean, design=Current.drinkers))[6:10]),2)
# Drinker
T52drink <- round(100*cbind(
coef(svyby(~d11, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d12, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d13, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d14, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d15, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d16, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d17, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d18, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~illegal.place1, by=~drinker4, FUN=svymean, design=Current.drinkers))[3:4]),2)
# Income
T52inc <- round(100*cbind(
coef(svyby(~d11, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d12, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d13, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d14, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d15, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d16, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d17, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d18, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~illegal.place1, by=~inc5, FUN=svymean, design=Current.drinkers))[6:10]),2)
Table5.2 <- rbind(Sex="----", T52sex, Age_group="----", T52age, Drinker="----", T52drink, Income="----", T52inc)
# Table 5.3 ####
T5.3 <- round(100*c(svymean(~d21, design=Current.drinkers)[1],
svymean(~d22, design=Current.drinkers)[1],
svymean(~d23, design=Current.drinkers)[1],
svymean(~d24, design=Current.drinkers)[1],
svymean(~d25, design=Current.drinkers)[1],
svymean(~d26, design=Current.drinkers)[1],
svymean(~d27, design=Current.drinkers)[1],
svymean(~d28, design=Current.drinkers)[1],
svymean(~d29, design=Current.drinkers)[1],
svymean(~illegal.place2, design=Current.drinkers)[2]),2)
names(T5.3) <- c("Temple","School","Clinic","Govt.","Dorm","Pump","Park","Plant","Path","Any")
Table5.3 <- data.frame(Percent=T5.3)
# Table 5.4 ####
# by ...
# Sex
T54sex <- round(100*cbind(
coef(svyby(~d21, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d22, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d23, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d24, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d25, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d26, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d27, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d28, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d29, by=~a4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~illegal.place2, by=~a4, FUN=svymean, design=Current.drinkers))[3:4]),2)
colnames(T54sex) <- c("Temple","School","Clinic","Govt.","Dorm","Pump","Park","Plant","Path", "Any")
# Age group
T54age <- round(100*cbind(
coef(svyby(~d21, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d22, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d23, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d24, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d25, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d26, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d27, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d28, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d29, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~illegal.place2, by=~age_group, FUN=svymean, design=Current.drinkers))[6:10]),2)
# Drinker
T54drink <- round(100*cbind(
coef(svyby(~d21, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d22, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d23, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d24, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d25, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d26, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d27, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d28, by=~drinker4, FUN=svymean, design=Current.drinkers))[1:2],
coef(svyby(~d29, by=~age_group, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~illegal.place2, by=~drinker4, FUN=svymean, design=Current.drinkers))[3:4]),2)
## Warning in cbind(coef(svyby(~d21, by = ~drinker4, FUN = svymean, design = Current.drinkers))[1:2], : number of rows of result is not a multiple of vector length (arg 1)
# Income
T54inc <- round(100*cbind(
coef(svyby(~d21, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d22, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d23, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d24, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d25, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d26, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d27, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d28, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~d29, by=~inc5, FUN=svymean, design=Current.drinkers))[1:5],
coef(svyby(~illegal.place2, by=~inc5, FUN=svymean, design=Current.drinkers))[6:10]),2)
Table5.4 <- rbind(Sex="----", T54sex, Age_group="----", T54age, Drinker="----", T54drink, Income="----", T54inc)
# Table 5.5 ####
# Illegal alcohol (Moonshine)
Table5.5 <- rbind(round(100*svymean(~(d42>1 & d42<10), design=Current.drinkers)[2],2),
round(100*coef(svymean(~(d43>1 & d43<10), design=Current.drinkers))[2],2),
round(100*coef(svymean(~illegal, design=Current.drinkers))[2],2))
rownames(Table5.5) <- c("White whiskey","Red whiskey","Any type")
# Table 5.6 ####
# by ...
# Sex
#round(100*svyby(~d42 %in% 2:9, by=~a4, FUN=svymean, design=Current.drinkers)[,2:3],2)
#round(100*svyby(~d43 %in% 2:9, by=~a4, FUN=svymean, design=Current.drinkers)[,2:3],2)
T56sex <- round(100*svyby(~illegal, by=~a4, FUN=svymean, design=Current.drinkers)[,2:3],2)
# Age group
T56age <- round(100*svyby(~illegal, by=~age_group, FUN=svymean, design=Current.drinkers)[,2:3],2)
# Drinker
T56drink <- round(100*svyby(~illegal, by=~drinker4, FUN=svymean, design=Current.drinkers)[,2:3],2)
# Income
T56inc <- round(100*svyby(~illegal, by=~inc5, FUN=svymean, design=Current.drinkers)[,2:3],2)
Table5.6 <- rbind(Sex="",T56sex, Age_group="", T56age, Drinker="", T56drink, Income="", T56inc)
colnames(Table5.6) <- c("Legal","Illegal")
# Table 5.7 ####
# Proportion of drinkers by age and sex
Table5.7 <- cbind(
rbind(t(round(100*svyby(~age_15_17, by=~a4, FUN=svymean, design=Everyone)[3],2)),
t(round(100*svyby(~age_group2, by=~a4, FUN=svymean, design=Everyone)[,2:3],2))),
rbind(t(round(100*svyby(~age_15_17, by=~a4, FUN=svymean, design=Current.drinkers)[3],2)),
t(round(100*svyby(~age_group2, by=~a4, FUN=svymean, design=Current.drinkers)[,2:3],2))))
T57Total <- format(c(coef(svytotal(~a4, design=Everyone)),
coef(svytotal(~a4, design=Current.drinkers))), big.mark=",")
# Table 5.8 ####
Table5.8 <- cbind(
Total=round(c(100*coef(svyby(~drinker, by=~age_15_17, FUN=svymean, design=Everyone))[6],
100*coef(svyby(~drinker, by=~age_group2, FUN=svymean, design=Everyone))[5:6]),2),
100*rbind(svyby(~a4, by=~age_15_17, FUN=svymean, design=Current.drinkers)[2,2:3],
svyby(~a4, by=~age_group2, FUN=svymean, design=Current.drinkers)[,2:3]))
rownames(Table5.8) <- c("15-17","15-19","20+")
Table5.8
## Total a4female a4male
## 15-17 9.71 12.99699 87.00301
## 15-19 13.60 14.52176 85.47824
## 20+ 29.74 19.51919 80.48081
100*svyby(~a4, by=~age_group, FUN=svymean, design=Current.drinkers)[,2:3]
## a4female a4male
## 15 - 19 14.52176 85.47824
## 20 - 24 20.73732 79.26268
## 25 - 44 19.87399 80.12601
## 45 - 59 19.75688 80.24312
## 60+ 16.20597 83.79403
# Alternative giving prevalence within each age group and sex
100*coef(svymean(~drinker, design=Younguns))[3]
## drinker3: Current drinker
## 9.714392
100*coef(svymean(~drinker, design=Adolescents))[3]
## drinker3: Current drinker
## 13.59868
100*coef(svymean(~drinker, design=Twenty))[3]
## drinker3: Current drinker
## 29.7374
rbind(round(100*coef(svyby(~drinker, by=~a4, FUN=svymean, design=Younguns))[5:6],2),
round(100*coef(svyby(~drinker, by=~a4, FUN=svymean, design=Adolescents))[5:6],2),
round(100*coef(svyby(~drinker, by=~a4, FUN=svymean, design=Twenty))[5:6],2))
## female:drinker3: Current drinker male:drinker3: Current drinker
## [1,] 2.54 16.77
## [2,] 4.01 22.93
## [3,] 11.18 49.77
# Table 5.9 ####
Table5.9 <- cbind(
t(round(100*svyby(~drinker4, by=~a4, FUN=svymean, design=Younguns)[,2:5],2)),
t(round(100*svyby(~drinker4, by=~a4, FUN=svymean, design=Adolescents)[,2:5],2)))
# Table 5.10 ####
Tarea <- cbind(Age15_17=round(100*coef(svyby(~age_15_17, by=~area, FUN=svymean, design=Everyone))[3:4],2),
round(100*svyby(~age_group2, by=~area, FUN=svymean, design=Everyone)[,2:3],2))
rownames(Tarea) <- levels(area)
Treg <- cbind(Age15_17=round(100*coef(svyby(~age_15_17, by=~reg, FUN=svymean, design=Everyone))[6:10],2),
round(100*svyby(~age_group2, by=~reg, FUN=svymean, design=Everyone)[,2:3],2))
rownames(Treg) <- levels(reg)
Table5.10 <- rbind(Tarea, Treg)
# Table 5.11 ####
# Quantity of alcohol per drinker (litres/person/year)
drinkers1 <- coef(svytotal(~age_15_17, design=Current.drinkers))[2]
beer1 <- coef(svyby(~beer, by=~age_15_17, FUN=svytotal, design=Current.drinkers))[2]
pc.beer1 <- round(beer1/drinkers1,2)
spirits.w1 <- coef(svyby(~spirits.white, by=~age_15_17, FUN=svytotal, design=Current.drinkers))[2]
pc.spirits.w1 <- round(spirits.w1/drinkers1,2)
spirits.r1 <- coef(svyby(~spirits.red, by=~age_15_17, FUN=svytotal, design=Current.drinkers))[2]
pc.spirits.r1 <- round(spirits.r1/drinkers1,2)
spirits.o1 <- coef(svyby(~spirits.other, by=~age_15_17, FUN=svytotal, design=Current.drinkers))[2]
pc.spirits.o1 <- round(spirits.o1/drinkers1,2)
wine1 <- coef(svyby(~wine, by=~age_15_17, FUN=svytotal, design=Current.drinkers))[2]
pc.wine1 <- round(wine1/drinkers1,2)
wine1 <- coef(svyby(~wine, by=~age_15_17, FUN=svytotal, design=Current.drinkers))[2]
pc.wine1 <- round(wine1/drinkers1,2)
wine.cooler1 <- coef(svyby(~wine.cooler, by=~age_15_17, FUN=svytotal, design=Current.drinkers))[2]
pc.wine.cooler1 <- round(wine.cooler1/drinkers1,2)
other1 <- coef(svyby(~other, by=~age_15_17, FUN=svytotal, design=Current.drinkers))[2]
pc.other1 <- round(other1/drinkers1,2)
#
drinkers2 <- coef(svytotal(~age_group2, design=Current.drinkers))
beer2 <- coef(svyby(~beer, by=~age_group2, FUN=svytotal, design=Current.drinkers))
pc.beer2 <- round(beer2/drinkers2,2)
spirits.w2 <- coef(svyby(~spirits.white, by=~age_group2, FUN=svytotal, design=Current.drinkers))
pc.spirits.w2 <- round(spirits.w2/drinkers2,2)
spirits.r2 <- coef(svyby(~spirits.red, by=~age_group2, FUN=svytotal, design=Current.drinkers))
pc.spirits.r2 <- round(spirits.r2/drinkers2,2)
spirits.o2 <- coef(svyby(~spirits.other, by=~age_group2, FUN=svytotal, design=Current.drinkers))
pc.spirits.o2 <- round(spirits.o2/drinkers2,2)
wine2 <- coef(svyby(~wine, by=~age_group2, FUN=svytotal, design=Current.drinkers))
pc.wine2 <- round(wine2/drinkers2,2)
wine.cooler2 <- coef(svyby(~wine.cooler, by=~age_group2, FUN=svytotal, design=Current.drinkers))
pc.wine.cooler2 <- round(wine.cooler2/drinkers2,2)
other2 <- coef(svyby(~other, by=~age_group2, FUN=svytotal, design=Current.drinkers))
pc.other2 <- round(other2/drinkers2,2)
# by sex
Young.drinkers <- subset(Current.drinkers, subset=age_15_17)
drinkers3 <- coef(svyby(~age_15_17, by=~a4, FUN=svytotal, design=Current.drinkers))[3:4]
beer3 <- coef(svyby(~beer, by=~a4, FUN=svytotal, design=Young.drinkers))
pc.beer3 <- round(beer3/drinkers3,2)
spirits.w3 <- coef(svyby(~spirits.white, by=~a4, FUN=svytotal, design=Young.drinkers))
pc.spirits.w3 <- round(spirits.w3/drinkers3,2)
spirits.r3 <- coef(svyby(~spirits.red, by=~a4, FUN=svytotal, design=Young.drinkers))
pc.spirits.r3 <- round(spirits.r3/drinkers3,2)
spirits.o3 <- coef(svyby(~spirits.other, by=~a4, FUN=svytotal, design=Young.drinkers))
pc.spirits.o3 <- round(spirits.o3/drinkers3,2)
wine3 <- coef(svyby(~wine, by=~a4, FUN=svytotal, design=Young.drinkers))
pc.wine3 <- round(wine3/drinkers3,2)
wine.cooler3 <- coef(svyby(~wine.cooler, by=~a4, FUN=svytotal, design=Young.drinkers))
pc.wine.cooler3 <- round(wine.cooler3/drinkers3,2)
other3 <- coef(svyby(~other, by=~a4, FUN=svytotal, design=Young.drinkers))
pc.other3 <- round(other3/drinkers3,2)
Youngish.drinkers <- subset(Current.drinkers, subset=age_group2=="15-19")
drinkers4 <- coef(svytotal(~a4, design=Youngish.drinkers))
beer4 <- coef(svyby(~beer, by=~a4, FUN=svytotal, design=Youngish.drinkers))
pc.beer4 <- round(beer4/drinkers4,2)
spirits.w4 <- coef(svyby(~spirits.white, by=~a4, FUN=svytotal, design=Youngish.drinkers))
pc.spirits.w4 <- round(spirits.w4/drinkers4,2)
spirits.r4 <- coef(svyby(~spirits.red, by=~a4, FUN=svytotal, design=Youngish.drinkers))
pc.spirits.r4 <- round(spirits.r4/drinkers4,2)
spirits.o4 <- coef(svyby(~spirits.other, by=~a4, FUN=svytotal, design=Youngish.drinkers))
pc.spirits.o4 <- round(spirits.o4/drinkers4,2)
wine4 <- coef(svyby(~wine, by=~a4, FUN=svytotal, design=Youngish.drinkers))
pc.wine4 <- round(wine4/drinkers4,2)
wine.cooler4 <- coef(svyby(~wine.cooler, by=~a4, FUN=svytotal, design=Youngish.drinkers))
pc.wine.cooler4 <- round(wine.cooler4/drinkers4,2)
other4 <- coef(svyby(~other, by=~a4, FUN=svytotal, design=Youngish.drinkers))
pc.other4 <- round(other4/drinkers4,2)
Twenty.drinkers <- subset(Current.drinkers, subset=age_group2=="20+")
drinkers5 <- coef(svytotal(~a4, design=Twenty.drinkers))
beer5 <- coef(svyby(~beer, by=~a4, FUN=svytotal, design=Twenty.drinkers))
pc.beer5 <- round(beer5/drinkers5,2)
spirits.w5 <- coef(svyby(~spirits.white, by=~a4, FUN=svytotal, design=Twenty.drinkers))
pc.spirits.w5 <- round(spirits.w5/drinkers5,2)
spirits.r5 <- coef(svyby(~spirits.red, by=~a4, FUN=svytotal, design=Twenty.drinkers))
pc.spirits.r5 <- round(spirits.r5/drinkers5,2)
spirits.o5 <- coef(svyby(~spirits.other, by=~a4, FUN=svytotal, design=Twenty.drinkers))
pc.spirits.o5 <- round(spirits.o5/drinkers5,2)
wine5 <- coef(svyby(~wine, by=~a4, FUN=svytotal, design=Twenty.drinkers))
pc.wine5 <- round(wine5/drinkers5,2)
wine.cooler5 <- coef(svyby(~wine.cooler, by=~a4, FUN=svytotal, design=Twenty.drinkers))
pc.wine.cooler5 <- round(wine.cooler5/drinkers5,2)
other5 <- coef(svyby(~other, by=~a4, FUN=svytotal, design=Twenty.drinkers))
pc.other5 <- round(other5/drinkers5,2)
Table5.11 <- cbind("15-17"=c(pc.beer1, pc.spirits.w1, pc.spirits.r1, pc.spirits.o1, pc.wine1, pc.wine.cooler1, pc.other1),
rbind(pc.beer3, pc.spirits.w3, pc.spirits.r3, pc.spirits.o3, pc.wine3, pc.wine.cooler3, pc.other3),
cbind("15-19"=rbind(pc.beer2, pc.spirits.w2, pc.spirits.r2, pc.spirits.o2, pc.wine2, pc.wine.cooler2, pc.other2)[,1],
rbind(pc.beer4, pc.spirits.w4, pc.spirits.r4, pc.spirits.o4, pc.wine4, pc.wine.cooler4, pc.other4),
"20+"=rbind(pc.beer2, pc.spirits.w2, pc.spirits.r2, pc.spirits.o2, pc.wine2, pc.wine.cooler2, pc.other2)[,2]),
rbind(pc.beer5, pc.spirits.w5, pc.spirits.r5, pc.spirits.o5, pc.wine5, pc.wine.cooler5, pc.other5))
rownames(Table5.11) <- c("Beer","Spirits (white)","Spirits (red)","Spirits (other)","Wine","Wine cooler","Other")
Table5.11
## 15-17 female male 15-19 female male 20+ female male
## Beer 13.68 4.32 15.08 17.92 3.93 20.30 30.98 12.10 35.56
## Spirits (white) 3.53 0.74 3.95 3.28 0.30 3.79 9.16 3.54 10.52
## Spirits (red) 2.95 1.07 3.23 4.41 4.46 4.40 8.33 2.29 9.79
## Spirits (other) 0.00 0.01 0.00 0.00 0.00 0.00 0.31 0.18 0.34
## Wine 0.00 0.02 0.00 0.08 0.54 0.00 0.25 0.17 0.27
## Wine cooler 0.11 0.76 0.01 0.28 0.39 0.26 0.15 0.28 0.11
## Other 0.04 0.00 0.04 0.04 0.01 0.04 0.08 0.01 0.10
# Section 6 ####
# Table 6.1 ####
Table6.1 <- round(100*rbind(svymean(~prob.annoy, design=Everyone)[2],
svymean(~prob.abuse, design=Everyone)[2],
svymean(~prob.viol, design=Everyone)[2],
svymean(~prob.money, design=Everyone)[2],
svymean(~prob.work, design=Everyone)[2],
svymean(~prob.any, design=Everyone)[2]),2)
rownames(Table6.1) <- c("Annoy","Abuse","Violence","Money","Work","Any problem")
# Table 6.2 ####
# By ...
Table6.2 <- round(100*
rbind(cbind(svyby(~prob.annoy, by=~a4, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.abuse, by=~a4, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.viol, by=~a4, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.money, by=~a4, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.work, by=~a4, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.any, by=~a4, FUN=svymean, design=Everyone)[,2:3]),
cbind(svyby(~prob.annoy, by=~age_group, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.abuse, by=~age_group, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.viol, by=~age_group, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.money, by=~age_group, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.work, by=~age_group, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.any, by=~age_group, FUN=svymean, design=Everyone)[,2:3]),
cbind(svyby(~prob.annoy, by=~drinker4, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.abuse, by=~drinker4, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.viol, by=~drinker4, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.money, by=~drinker4, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.work, by=~drinker4, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.any, by=~drinker4, FUN=svymean, design=Everyone)[,2:3]),
cbind(svyby(~prob.annoy, by=~inc5, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.abuse, by=~inc5, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.viol, by=~inc5, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.money, by=~inc5, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.work, by=~inc5, FUN=svymean, design=Everyone)[,2:3],
svyby(~prob.any, by=~inc5, FUN=svymean, design=Everyone)[,2:3]))[,c(2,4,6,8,10,12)],2)
colnames(Table6.2) <- rownames(Table6.1)
Table6.2
## Annoy Abuse Violence Money Work Any problem
## female 4.31 0.90 0.07 1.19 0.58 5.68
## male 4.51 1.66 0.15 2.92 3.17 8.49
## 15 - 19 4.05 0.71 0.20 1.64 0.99 5.69
## 20 - 24 4.18 1.26 0.04 1.75 1.89 6.96
## 25 - 44 4.75 1.27 0.15 2.38 2.24 7.88
## 45 - 59 4.69 1.66 0.07 2.33 2.17 7.63
## 60+ 3.67 0.94 0.08 1.26 0.97 5.36
## 1: Never drinker 3.81 0.57 0.05 0.90 0.52 4.84
## 2: Ex drinker 3.12 0.94 0.09 1.21 1.19 4.92
## 3: Current regular 9.18 4.73 0.50 8.24 8.45 19.73
## 4: Current social 3.98 1.33 0.04 1.91 1.94 6.92
## Q 1 3.89 1.07 0.10 1.62 0.88 5.68
## Q 2 5.33 1.42 0.13 2.60 1.99 8.33
## Q 3 4.71 1.55 0.11 2.25 2.14 7.68
## Q 4 4.57 1.55 0.09 2.31 2.00 7.73
## Q 5 3.71 0.81 0.11 1.44 2.09 5.96
#
# Percent that can legally drive?
#
# Drive under influence of alcohol
DUI <- round(data.frame(100*svymean(~dui, Everyone, na.rm=TRUE),
100*svymean(~dui, Current.drinkers, na.rm=TRUE))[c(1,3)],2)
colnames(DUI) <- c("Among everyone","Among current drinkers")
DUI
## Among everyone Among current drinkers
## duiRegular 1.63 5.72
## duiIrregular 9.92 34.90
## duiNo 9.23 32.49
## duiNot drive 7.64 26.88
## duiNot drink 71.59 0.00
# Table 6.3 ####
# Drinking + driving and Safety
100*svymean(~dui, design=Current.drinkers, na.rm=TRUE) # includes non-drivers
## mean SE
## duiRegular 5.7227 0.0032
## duiIrregular 34.9036 0.0088
## duiNo 32.4894 0.0093
## duiNot drive 26.8844 0.0088
## duiNot drink 0.0000 0.0000
100*svymean(~dui2, design=Current.drinkers, na.rm=TRUE) # excludes non-drivers
## mean SE
## dui2Regular 7.8269 0.0044
## dui2Irregular 47.7375 0.0105
## dui2No 44.4356 0.0112
## dui2Not drink 0.0000 0.0000
100*svymean(~dui3, design=Current.drinkers, na.rm=TRUE) # yes or no
## mean SE
## dui3Yes 55.564 0.0112
## dui3No 44.436 0.0112
100*svymean(~d49<=2, design=Current.drinkers, na.rm=TRUE) # includes on-drivers
## mean SE
## d49 <= 2FALSE 59.374 0.0094
## d49 <= 2TRUE 40.626 0.0094
T63a <- 100 - round(100*svymean(~dui, design=Current.drinkers),2)[4]
T63b <- sum(round(100*svymean(~dui2, design=Everyone, na.rm=TRUE),2)[1:2])
T63b1 <- sum(round(100*svymean(~dui2, design=Current.drinkers, na.rm=TRUE),2)[1:2])
T63b2 <- round(100*svymean(~dui2, design=Current.drinkers, na.rm=TRUE),2)[-3]
T63b3 <- round(100*svymean(~dui2, design=Current.drinkers, na.rm=TRUE),2)[-3]
T63c <- round(100*svymean(~d50==1, design=Current.drinkers, na.rm=TRUE),2)[2]
T63d <- round(100*svymean(~d51>1, design=Current.drinkers, na.rm=TRUE),2)[2]
T63e <- round(100*svymean(~d52>1, design=Everyone, na.rm=TRUE),2)[2]
T63f <- round(100*svymean(~I(d52>1 & d52<5), design=Everyone, na.rm=TRUE),2)[2]
T63g <- round(100*svymean(~d52==6, design=Everyone, na.rm=TRUE),2)[2]
Table6.3 <- rbind(T63a, T63b, T63b1, T63b2[1], T63b2[2], T63c, T63d, T63e, T63f, T63g)
rownames(Table6.3) <- c("Drives (Among current drinkers)","...while drinking (Among everyone - for comparison)","...while drinking (Among current drinkers)","......regularly","......occasionally","Breathalysed (among current drink drivers)","Injured while drink driving","Injured by others drink driving (Among everyone)","...while in the car","...while walking")
colnames(Table6.3) <- c("Percent")
Table6.3
## Percent
## Drives (Among current drinkers) 73.12
## ...while drinking (Among everyone - for comparison) 12.50
## ...while drinking (Among current drinkers) 55.57
## ......regularly 7.83
## ......occasionally 47.74
## Breathalysed (among current drink drivers) 6.93
## Injured while drink driving 5.10
## Injured by others drink driving (Among everyone) 1.10
## ...while in the car 1.01
## ...while walking 0.02
# Table 6.4 ####
Drivers <- subset(Current.drinkers, subset=d49<4)
#svymean(~d49<=2, Current.drinkers)
T1 <- rbind(round(100*cbind(svyby(~dui3, by=~a4, FUN=svymean, design=Drivers, na.rm=TRUE)[2],
svyby(~d51>1, by=~a4, FUN=svymean, design=Drivers, na.rm=TRUE)[3]),2),
round(100*cbind(svyby(~dui3, by=~age_group, FUN=svymean, design=Drivers, na.rm=TRUE)[2],
svyby(~d51>1, by=~age_group, FUN=svymean, design=Drivers, na.rm=TRUE)[3]),2),
round(100*cbind(svyby(~dui3, by=~drinker4, FUN=svymean, design=Drivers, na.rm=TRUE)[2],
svyby(~d51>1, by=~drinker4, FUN=svymean, design=Drivers, na.rm=TRUE)[3]),2),
round(100*cbind(svyby(~dui3, by=~inc5, FUN=svymean, design=Drivers, na.rm=TRUE)[2],
svyby(~d51>1, by=~inc5, FUN=svymean, design=Drivers, na.rm=TRUE)[3]),2))
T2 <- rbind(round(100*cbind(svyby(~d52>1, by=~a4, FUN=svymean, design=Everyone, na.rm=TRUE)[3],
svyby(~d52%%2==0, by=~a4, FUN=svymean, design=Everyone, na.rm=TRUE)[3]),2),
round(100*cbind(svyby(~d52>1, by=~age_group, FUN=svymean, design=Everyone, na.rm=TRUE)[3],
svyby(~d52%%2==0, by=~age_group, FUN=svymean, design=Everyone, na.rm=TRUE)[3]),2),
round(100*cbind(svyby(~d52>1, by=~drinker4, FUN=svymean, design=Everyone, na.rm=TRUE)[3],
svyby(~d52%%2==0, by=~drinker4, FUN=svymean, design=Everyone, na.rm=TRUE)[3]),2),
round(100*cbind(svyby(~d52>1, by=~inc5, FUN=svymean, design=Everyone, na.rm=TRUE)[3],
svyby(~d52%%2==0, by=~inc5, FUN=svymean, design=Everyone, na.rm=TRUE)[3]),2))
T1a <- data.frame(by=rownames(T1), T1)
T2$by=rownames(T2)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following object is masked _by_ '.GlobalEnv':
##
## .data
## The following objects are masked from 'package:epicalc':
##
## recode, rename
## The following object is masked from 'package:MASS':
##
## select
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
Table6.4 <- right_join(T1a,T2)
## Joining, by = "by"
## Warning: Column `by` joining factor and character vector, coercing into character vector
colnames(Table6.4) <- c("Factor","DUI","Accident","Other","Other DUI")
rownames(Table6.4) <- Table6.4$Factor
Table6.4$Factor <- NULL
Table6.4
## DUI Accident Other Other DUI
## female 39.03 1.51 0.81 0.62
## male 58.58 5.53 1.42 1.16
## 15 - 19 65.17 6.74 1.13 0.87
## 20 - 24 61.27 5.30 1.53 1.35
## 25 - 44 54.84 4.83 1.08 0.90
## 45 - 59 54.65 5.00 1.08 0.84
## 60+ 50.22 5.70 0.96 0.72
## 1: Never drinker NA NA 0.75 0.57
## 2: Ex drinker NA NA 1.34 1.04
## 3: Current regular 65.37 6.83 1.85 1.61
## 4: Current social 47.45 3.12 1.58 1.31
## Q 1 50.32 7.14 1.00 0.79
## Q 2 60.31 5.69 1.29 1.01
## Q 3 62.35 5.82 1.11 0.98
## Q 4 56.60 5.37 1.10 0.92
## Q 5 49.12 3.31 1.03 0.75
# Summary ####
# Current drinkers
format(svytotal(~drinker, design=Everyone), big.mark=",")
## drinker1: Never drinker drinker2: Ex drinker drinker3: Current drinker
## "31,989,074" " 8,061,891" "15,897,264"
100*svymean(~drinker, design=Everyone)
## mean SE
## drinker1: Never drinker 57.176 0.0047
## drinker2: Ex drinker 14.410 0.0032
## drinker3: Current drinker 28.414 0.0039
# By Region
100*svyby(~drinker, by=~reg, FUN=svymean, design=Everyone)[,c(4,7)]
## drinker3: Current drinker se.drinker3: Current drinker
## Bangkok 25.29512 1.1333708
## Central 27.32573 0.7285870
## North 35.39732 0.9068566
## North-east 32.82264 0.7117332
## South 16.05834 0.7496967
# By sex
100*svyby(~drinker, by=~a4, FUN=svymean, design=Everyone)[,c(4,7)]
## drinker3: Current drinker se.drinker3: Current drinker
## female 10.62047 0.3183212
## male 47.45863 0.5790698
# Regular drinkers
# By age
100*svyby(~drinker, by=~age_group, FUN=svymean, design=Current.drinkers)[,2:3]
## drinker1: Never drinker drinker2: Ex drinker
## 15 - 19 0 0
## 20 - 24 0 0
## 25 - 44 0 0
## 45 - 59 0 0
## 60+ 0 0
t(100*svyby(~age_group, by=~drinker, FUN=svymean, design=Current.drinkers)[,2:4])
## 3: Current drinker
## age_group15 - 19 3.923778
## age_group20 - 24 10.434183
## age_group25 - 44 45.107742
# By sex
t(100*svyby(~a4, by=~drinker, FUN=svymean, design=Current.drinkers)[,2:4])
## 3: Current drinker
## a4female 19.3231050
## a4male 80.6768950
## se.a4female 0.4524445
# End ####
# Tables ####
Table2.1
## Total Percent Females Percent.f Males Percent.m
## drinker1: Never drinker 31,989,074 57.18 23,442,850 81.05 8,546,223 31.62
## drinker2: Ex drinker 8,061,891 14.41 2,409,114 8.33 5,652,777 20.92
## drinker3: Current drinker 15,897,264 28.41 3,071,845 10.62 12,825,419 47.46
## drink.regularTRUE 6,984,785 12.48 591,819.2 2.05 6,392,966 23.66
## drink.socialTRUE 8,912,479 15.93 2,480,026 8.57 6,432,453 23.80
Table2.2
## Total Females Males
## Regular drinker 57.18 81.05 31.62
## Social drinker 14.41 8.33 20.92
Table2.3
## 15 - 19 20 - 24 25 - 44 45 - 59 60+
## Non-drinkers 83.60 60.07 53.11 51.99 59.34
## Ex-drinkers 2.80 6.48 10.90 16.93 25.43
## Drinkers 13.60 33.45 36.00 31.08 15.23
## Regular drinkers 3.32 10.74 16.20 14.68 7.46
## Social drinkers 10.28 22.72 19.80 16.40 7.77
Table2.4
## Urban Rural
## Non-drinkers 57.69 56.74
## Ex-drinkers 14.87 14.03
## Drinkers 27.44 29.23
## Regular drinkers 11.88 12.99
## Social drinkers 15.56 16.24
Table2.5
## Never Former Current Regular Social
## Thailand 57.18 14.41 28.41 12.48 15.93
## Bangkok 59.67 15.03 25.30 10.51 14.79
## Central 59.95 12.73 27.33 13.69 13.64
## North 46.10 18.51 35.40 15.98 19.42
## North-east 51.37 15.81 32.82 12.38 20.44
## South 74.53 9.41 16.06 7.48 8.57
Table2.5F
## Never Former Current Regular Social
## Thailand 81.05 8.33 10.62 2.05 8.57
## Bangkok 81.51 8.87 9.62 1.72 7.90
## Central 84.65 7.21 8.14 2.05 6.09
## North 67.91 14.77 17.31 3.48 13.83
## North-east 78.25 8.22 13.53 2.06 11.47
## South 95.37 2.10 2.54 0.48 2.06
Table2.5M
## Never Former Current Regular Social
## Thailand 31.62 20.92 47.46 23.66 23.80
## Bangkok 36.03 21.71 42.26 20.02 22.24
## Central 33.75 18.58 47.67 26.03 21.64
## North 22.64 22.52 54.84 29.40 25.44
## North-east 22.31 24.01 53.68 23.54 30.14
## South 52.60 17.11 30.29 14.86 15.43
Table2.6
## Primary Secondary Vocational Tertiary Other
## Non-drinkers 56.37 57.83 50.06 62.87 71.89
## Ex-drinkers 17.65 10.54 12.45 12.57 5.19
## Drinkers 25.97 31.62 37.49 24.56 22.92
## Regular drinkers 12.98 13.35 14.58 7.24 14.25
## Social drinkers 12.99 18.27 22.91 17.32 8.68
Table2.7
## Agricultural Technical Commercial Professional Not working
## Non-drinkers 48.86 45.50 51.33 55.43 73.91
## Ex-drinkers 16.52 13.35 13.34 13.10 14.47
## Drinkers 34.63 41.15 35.32 31.47 11.62
## Regular drinkers 14.38 19.69 17.33 11.06 3.91
## Social drinkers 20.25 21.47 17.99 20.41 7.71
Table2.8
## T1 T2 T3 T4 T5
## Sex
## female 51.7 19.32 12.59 8.47 27.83
## male 48.3 80.68 87.41 91.53 72.17
## Area
## Urban 45.47 43.91 43.46 43.27 44.41
## Rural 54.53 56.09 56.54 56.73 55.59
## Age
## 15 - 19 8.2 3.92 3.26 2.18 5.29
## 20 - 24 8.86 10.43 9.74 7.62 12.64
## 25 - 44 35.61 45.11 45.75 46.2 44.26
## 45 - 59 27.18 29.73 30.41 31.96 27.99
## 60+ 20.15 10.8 10.84 12.04 9.83
## Education
## Primary 48.73 44.54 46.55 50.69 39.72
## Secondary 29.97 33.35 32.87 32.06 34.36
## Vocational 8.17 10.78 10.65 9.55 11.75
## Tertiary 12.96 11.2 9.78 7.51 14.08
## Other 0.17 0.14 0.15 0.19 0.09
## Region
## Bangkok 13.52 12.04 11.02 11.38 12.55
## Central 29.71 28.57 30.35 32.57 25.44
## North 16.97 21.14 21.88 21.71 20.69
## North-east 26.71 30.85 29.63 26.49 34.28
## South 13.09 7.4 7.12 7.85 7.04
Table2.9
## 1 2 3 4 5 6 7 8 9 10
## Tall 11.22 4.98 9.74 18 19.65 13.27 8.35 14.79 43.94 56.06
## Area
## Urban 10.27 5.15 9.13 18.75 19.86 12.84 8.34 15.67 43.3 56.7
## Rural 11.96 4.85 10.22 17.41 19.5 13.6 8.37 14.1 44.44 55.56
## Age
## 15 - 19 1.08 2.36 5.25 15.73 26.27 15.14 11.28 22.9 24.42 75.58
## 20 - 24 4.13 2.16 8.28 17.52 24.94 18.21 9.79 14.97 32.09 67.91
## 25 - 44 9.94 4.99 10.21 19.86 20.66 12.57 7.82 13.95 45 55
## 45 - 59 14.27 5.77 9.96 17.23 17.38 12.95 8.17 14.27 47.23 52.77
## 60+ 18.67 6.45 10.24 13.64 14.2 11.57 8.65 16.58 48.99 51.01
## Region
## Bangkok 11.79 3.67 7.47 18.62 17.98 11.77 8.44 20.26 41.55 58.45
## Central 13 5.98 10.27 20.84 19.99 11.35 7.55 11.02 50.09 49.91
## North 10.53 5.71 11.99 16.9 19.61 14.23 8.05 12.98 45.13 54.87
## North-east 9.19 3.7 8.42 16.39 20.77 15.34 9.74 16.43 37.72 62.28
## South 13.78 6.49 10.46 15.88 16.53 11.7 6.4 18.77 46.6 53.4
## Education
## Primary 15.92 6.13 10.45 17.49 16.82 11.39 7.72 14.08 49.98 50.02
## Secondary 8.55 4.39 9.84 19.44 21.03 13.99 8.35 14.4 42.22 57.78
## Vocational 7.33 4.04 8.27 19.24 22.7 14.33 9.8 14.28 38.89 61.11
## Tertiary 4.21 2.8 7.95 14.52 24.01 17.65 9.52 19.35 29.48 70.52
## Other 3.48 30.39 20.43 7.85 13.14 4.33 9.47 10.91 62.15 37.85
## Occupation
## Agricultural 11.39 4.5 9.63 16.01 19.78 14.46 8.63 15.6 41.52 58.48
## Technical 9.89 3.63 10.2 24.13 21.28 11.46 7.6 11.81 47.84 52.16
## Commercial 12.29 6.55 10.74 19.48 18.97 11.56 7.69 12.73 49.06 50.94
## Professional 7.05 3.36 10.07 14.66 23.38 16.45 8.73 16.3 35.14 64.86
## Not working 11.14 3.48 6.09 12.96 17.68 16.2 10.47 21.96 33.68 66.32
## Income
## Q 1 13.45 4.06 7.36 12.8 16.44 12.96 10.16 22.77 37.67 62.33
## Q 2 12.02 4.98 9.42 16.25 18.61 14.34 8.44 15.93 42.68 57.32
## Q 3 12.24 5.28 11.14 17.01 19.4 13.1 8.3 13.54 45.67 54.33
## Q 4 11.01 5.72 9.71 19.95 20.6 12.81 8.01 12.2 46.39 53.61
## Q 5 9.6 4.34 9.74 19.31 20.45 13.38 8.12 15.07 42.99 57.01
Table2.10
## Factor Pct
## 1 Sex
## 2 female 8.47
## 3 male 91.53
## 4 Area
## 5 Urban 43.27
## 6 Rural 56.73
## 7 Age group
## 8 15 - 19 2.18
## 9 20 - 24 7.62
## 10 25 - 44 46.20
## 11 45 - 59 31.96
## 12 60+ 12.04
## 13 Education
## 14 Primary 50.69
## 15 Secondary 32.06
## 16 Vocational 9.55
## 17 Tertiary 7.51
## 18 Other 0.19
## 19 Region
## 20 Bangkok 11.38
## 21 Central 32.57
## 22 North 21.71
## 23 North-east 26.49
## 24 South 7.85
Table2.11
## Female Male Total
## Regular drinkers " 591,819" "6,392,966" "6,984,785"
## Percent of current drinkers "81.05" "31.62" "57.18"
Table2.12
## female male X15...19 X20...24 X25...44 X45...59 X60.
## d36Beer 62.57 43.81 59.74 61.52 53.10 39.55 27.40
## d36Spirits (white) 12.33 30.07 15.81 13.67 20.26 34.14 49.09
## d36Spirits (red) 12.84 24.15 20.76 21.19 23.26 22.10 17.38
## d36Wine 2.97 0.38 0.70 0.78 0.83 1.05 0.84
## d36Wine cooler 7.13 0.25 2.56 2.49 1.97 1.05 0.21
## d36Other 0.41 0.29 0.29 0.18 0.20 0.45 0.53
## d36Spirits (other) 1.75 1.05 0.15 0.18 0.38 1.66 4.55
Table2.13
## Quantity Pct all.drinkers per.drinker Ethanol Pct.eth eth.per.drinker
## Beer 484,355,252 63.02 9,365,852 51.7 24,217,763 17.86 2.59
## Spirits.white 141,909,766 18.46 4,787,572 29.6 56,763,906 41.87 11.86
## Spirits.red 129,955,337 16.91 5,376,707 24.2 51,982,135 38.34 9.67
## spirits.other 4,776,351 0.62 246,844 19.3 1,910,540 1.41 7.74
## Wine 3,865,330 0.50 238,810 16.2 463,840 0.34 1.94
## Wine.cooler 2,399,729 0.31 373,311 6.4 71,992 0.05 0.19
## Other 1,289,219 0.17 88,429 14.6 154,706 0.11 1.75
## Total 768,550,983 99.99 20,477,525 37.5 135,564,882 99.98 6.60
Table2.14
## Total female male Total.Eth male.Eth female.Eth
## beer 8.66 1.26 16.57 0.43 0.83 0.06
## Spirits 4.94 0.63 9.56 1.98 3.82 0.25
## spirits.white 2.54 0.37 4.86 1.01 1.94 0.15
## spirits.red 2.32 0.25 4.54 0.93 1.82 0.10
## spirits.other 0.09 0.02 0.16 0.01 0.02 0.00
## wine 0.07 0.02 0.12 0.00 0.00 0.00
## wine.cooler 0.04 0.03 0.06 0.01 0.01 0.00
## other 0.02 0.00 0.05 0.00 0.01 0.00
## Total 18.68 2.58 35.92 4.37 8.45 0.57
Table2.15
## d8Store d87/11 d8Supermarket d8Restaurant d8Bar d8Other
## Total 71.66 6.15 0.62 5.22 0.72 15.64
## Area
## Urban 64.19 11.09 0.94 8.23 1.11 14.44
## Rural 77.51 2.28 0.36 2.85 0.41 16.58
## Region
## Bangkok 54 17.02 1.13 13.48 1.29 13.09
## Central 68.53 10.09 0.84 6.06 0.96 13.53
## North 73.85 2.47 0.54 4.36 0.66 18.12
## North-east 79.6 1.06 0.33 2.24 0.33 16.44
## South 73.16 5.03 0.29 3.36 0.65 17.51
## Age
## 15 - 19 74.08 3.6 0.4 2.56 0.7 18.66
## 20 - 24 70.11 6.5 0.35 8.42 2.15 12.47
## 25 - 44 69.53 8.55 0.51 6.31 0.98 14.12
## 45 - 59 73.44 4.42 0.76 4.15 0.07 17.16
## 60+ 76.29 1.49 0.97 1.48 0.02 19.75
## Income
## Q 1 73.38 3.15 0.51 1.69 0.38 20.89
## Q 2 76.62 1.52 0.21 1.05 0.39 20.2
## Q 3 78.8 2.47 0.18 1.71 0.36 16.49
## Q 4 75.83 5.39 0.21 4.65 0.53 13.4
## Q 5 59.67 12.71 1.55 11.39 1.42 13.26
Table2.16
## d10Own house d10Other house d10Restaurant d10Bar d10Party d10Cultural place
## Total 40.09 22.52 9.54 1.32 20.02 4.58
## Area
## Urban 40.16 20.31 14.26 2.18 18.03 3.02
## Rural 40.04 24.24 5.84 0.65 21.58 5.8
## Region
## Bangkok 46.7 16.31 17.95 2.42 13.43 0.97
## Central 46.6 17.95 12.02 1.48 19.63 1.28
## North 37.15 24.35 8.99 1.27 21.61 3.99
## North-east 34.27 28.24 4.93 0.85 19.71 9.96
## South 36.91 21.13 7.07 1.04 29.02 2.4
## Age
## 15 - 19 16.28 52.36 6.78 1.86 12.6 7.66
## 20 - 24 22.89 34.76 14.81 4.45 16.1 5.72
## 25 - 44 39.58 22.45 11.71 1.58 19.32 3.69
## 45 - 59 45.34 17.38 7.18 0.24 22.93 5
## 60+ 53.04 14.24 2.88 0.02 21.45 4.89
## Income
## Q 1 40.07 25.49 4.84 0.95 18.46 7.45
## Q 2 38.21 27.1 3.71 0.58 20.9 7.2
## Q 3 39.18 25.8 3.98 0.9 21.16 6.81
## Q 4 41.14 23.91 9.64 1 18.6 3.55
## Q 5 40.66 15.63 17.68 2.43 20.68 1.82
Table2.17
## Home Shop Total
## Total 628.52 522.96 1151.48
## Sex
## female 356.46 425.93 782.39
## male 671.62 537.95 1209.57
## Area
## Urban 744.97 643.14 1388.11
## Rural 541.5 420.15 961.65
## Region
## Bangkok 861.83 852.29 1714.12
## Central 843.98 632.59 1476.57
## North 445.69 412.1 857.79
## North-east 468.5 383.72 852.22
## South 601.47 478.12 1079.59
## Age
## 15 - 19 303.09 346.63 649.72
## 20 - 24 474.88 413.52 888.41
## 25 - 44 689.84 580.3 1270.15
## 45 - 59 656.61 530.45 1187.06
## 60+ 531.9 401.41 933.31
## Income
## Q 1 447.22 358.8 806.02
## Q 2 420.1 333.72 753.82
## Q 3 481.99 339.14 821.13
## Q 4 628.16 471.08 1099.24
## Q 5 891.05 776.67 1667.72
Table2.18
## 3: Current regular 4: Current social
## Drink at home 904.38 294.62
## Drink at shop 668.57 384.17
## Total 1572.95 678.79
Table2.19
## 3: Current regular 4: Current social
## Drinks_at_home
## cost.home.gpFree 0 0
## cost.home.gp1-199 11.35 40.85
## cost.home.gp200-499 27.46 38.51
## cost.home.gp500-999 26.35 14.11
## cost.home.gp1000-1999 22.54 5.59
## cost.home.gp2000+ 12.3 0.93
## Drinks_at_shop
## cost.shop.gpFree 0 0
## cost.shop.gp1-199 19.2 30.64
## cost.shop.gp200-499 31.64 39.61
## cost.shop.gp500-999 24.36 18.84
## cost.shop.gp1000-1999 15.93 8.47
## cost.shop.gp2000+ 8.87 2.45
Table2.20
## Never Regular Social
## Total 58.13 10.76 31.11
## Sex
## female 77.47 3.66 18.86
## male 53.5 12.46 34.04
## Age
## 15 - 19 60.29 4.55 35.16
## 20 - 24 54.32 8.18 37.5
## 25 - 44 55.28 11.46 33.26
## 45 - 59 59.13 11.91 28.96
## 60+ 70.22 9.41 20.37
## Area
## Urban 58.16 11.65 30.19
## Rural 58.11 10.06 31.83
## Region
## Bangkok 58.99 12.39 28.62
## Central 57.7 13.77 28.54
## North 59.45 10.34 30.21
## North-east 57.25 7.71 35.03
## South 58.34 10.37 31.28
## Income
## Q 1 66.45 8.18 25.37
## Q 2 61.07 9.62 29.31
## Q 3 57.56 10.01 32.43
## Q 4 55.79 11.43 32.79
## Q 5 56.69 12.01 31.31
Table2.21
## Light drinkers Heavy drinkers Regular Social
## Drinks at home 472.5410 790.5852 1216.9907 626.0993
## Drinks at shop 415.9419 620.9065 861.5361 540.7914
## Total 888.4829 1411.4918 2078.5267 1166.8907
Table2.22
## Current drinkers Heavy drinkers Regular Social
## Home
## cost.home.gpFree 0 0 0 0
## cost.home.gp1-199 33.05 16.02 7.52 19.3
## cost.home.gp200-499 33.55 31.32 21.68 35.04
## cost.home.gp500-999 18.15 23.58 24.43 23.25
## cost.home.gp1000-1999 11.55 18.32 25.25 15.65
## cost.home.gp2000+ 3.69 10.76 21.11 6.77
## Shop
## cost.shop.gpFree 0 0 0 0
## cost.shop.gp1-199 31.66 19.01 12.03 21.34
## cost.shop.gp200-499 38.09 33.54 27.02 35.71
## cost.shop.gp500-999 18.74 24.08 26.36 23.33
## cost.shop.gp1000-1999 7.96 15.91 20.09 14.52
## cost.shop.gp2000+ 3.54 7.45 14.5 5.11
Table3.1
## Total female male
## drinker1: Never drinker 71.38 86.57 56.51
## drinker2: Ex drinker 4.71 4.23 5.19
## drinker3: Current drinker 23.91 9.20 38.31
## drinker43: Current regular 7.17 0.86 13.34
## drinker44: Current social 16.74 8.34 24.96
Table3.2
## a4female a4male
## 1: Never drinker 59.99 40.01
## 2: Ex drinker 44.36 55.64
## 3: Current drinker 19.04 80.96
## 3: Current regular 5.97 94.03
## 4: Current social 24.64 75.36
## No 25.26 74.74
## Regular 9.88 90.12
## Social 11.38 88.62
Table3.3
## 1: Regular 2: Social 3: Regular heavy 4: Social heavy
## Total 21.23 78.77 16.32 83.68
## female 4.54 95.46 14.48 85.52
## male 26.87 73.13 16.55 83.45
Table3.4
## Total female male
## d401 55.95 74.25 51.65
## d402 0.21 0.23 0.21
## d403 0.70 0.00 0.87
## d404 0.97 0.52 1.07
## d405 5.31 2.97 5.86
## d406 10.10 3.50 11.66
## d407 6.75 6.41 6.83
## d408 3.95 1.81 4.45
## d409 16.06 10.31 17.41
Table3.5
## cost.home cost.shop cost.total
## T.total 428.85 398.68 827.53
## female 239.51 389.47 628.98
## male 455.46 400.37 855.83
## Urban 463.11 436.05 899.16
## Rural 404.54 366.99 771.53
## Bangkok 636.96 490.81 1127.77
## Central 535.65 509.25 1044.90
## North 304.74 410.75 715.49
## North-east 407.27 330.82 738.09
## South 278.28 246.24 524.52
Table3.6
## Alcohol female male Ethanol female male
## beer 26.98 8.19 31.40 1.35 0.41 1.57
## spirits.white 4.96 0.28 6.06 1.98 0.11 2.42
## spirits.red 6.68 2.26 7.72 2.67 0.90 3.09
## spirits.other 0.01 0.00 0.01 0.00 0.00 0.00
## wine 0.09 0.21 0.07 0.00 0.01 0.00
## wine.cooler 0.23 0.37 0.19 0.03 0.04 0.02
## other 0.01 0.01 0.01 0.00 0.00 0.00
Table3.7Females
## d13 d14 d15 d16 d17 d18 d19 d110
## Total 5.67 2.23 3.64 7.72 15.21 19.05 15.53 30.94
## Area
## Urban 4.51 2.38 3.24 8.8 16.2 17.78 15.9 31.2
## Rural 6.6 2.11 3.96 6.87 14.43 20.05 15.24 30.74
## Age_group
## 15 - 19 0 1.06 3.72 5.63 15.41 9.44 17.63 47.1
## 20 - 24 0.72 0.74 3.79 3.88 15.55 30.5 15.04 29.78
## 25 - 44 3.75 1.84 2.43 7.75 16.62 19.53 16.56 31.52
## 45 - 59 8.28 2.74 4.64 8.09 13.77 17.38 15.27 29.84
## 60+ 14.77 4.73 6.3 11.74 12.32 11.18 11.08 27.87
## Region
## Bangkok 4.96 2.17 1.14 9.63 14.45 17.56 12.96 37.13
## Central 6.8 2.99 3.38 11.97 15.13 13.72 17.03 28.98
## North 5.62 2.26 5.66 6.6 18.46 21.31 14.79 25.32
## North-east 5.3 1.58 3.18 5.14 13.08 21.42 16.54 33.75
## South 4.96 3.78 2.52 7.54 13.29 17.72 10.23 39.97
## Education
## Primary 10.23 3.35 4.97 9.23 14.11 14.65 14.19 29.26
## Secondary 2.69 1.35 2.49 7.2 16.14 22.76 15.35 32.02
## Vocational 0.88 1.17 2.12 7.04 13.91 20.72 22.82 31.35
## Tertiary 0.34 1.22 2.81 4.43 17.23 23.74 16.57 33.67
## Occupation
## Agricultural 5.93 1.42 3.16 5.92 12.48 20.84 14.67 35.59
## Technical 2.24 0.08 0.16 7.2 18.94 27.85 16.44 27.09
## Commercial 5.35 3.28 4.19 8.46 17.26 16.24 16.85 28.37
## Professional 1.52 2.1 6.71 4.6 10.65 21.84 19.24 33.35
## Not working 8.56 2.12 3.71 9.59 14.4 17.88 12.66 31.09
## Income
## Q 1 10.07 3.31 4.98 7.89 9.56 11.52 12.76 39.91
## Q 2 7.11 2.08 3.9 8.28 13.58 19.71 13.66 31.68
## Q 3 7.35 1.91 4.78 7.94 15.41 19.79 15.42 27.4
## Q 4 4.32 1.98 2.28 7.78 19.05 18.05 18.95 27.6
## Q 5 1.83 2.27 3.06 6.9 15.47 23.45 15.02 32
Table3.7Males
## d13 d14 d15 d16 d17 d18 d19 d110
## Total 12.54 5.64 11.2 20.46 20.72 11.88 6.64 10.92
## Area
## Urban 11.65 5.82 10.54 21.15 20.74 11.65 6.52 11.94
## Rural 13.24 5.5 11.72 19.93 20.7 12.06 6.73 10.12
## Age_group
## 15 - 19 1.26 2.58 5.51 17.44 28.11 16.1 10.2 18.79
## 20 - 24 5.02 2.53 9.45 21.09 27.39 14.99 8.42 11.1
## 25 - 44 11.48 5.77 12.13 22.86 21.66 10.85 5.65 9.59
## 45 - 59 15.74 6.51 11.27 19.48 18.27 11.86 6.42 10.44
## 60+ 19.42 6.78 11 14.01 14.57 11.65 8.17 14.4
## Region
## Bangkok 13.47 4.03 9.03 20.84 18.85 10.34 7.33 16.1
## Central 14.13 6.52 11.51 22.45 20.87 10.93 5.84 7.76
## North 12.19 6.89 14.14 20.4 20.01 11.83 5.76 8.79
## North-east 10.26 4.28 9.85 19.46 22.87 13.68 7.89 11.72
## South 14.56 6.73 11.16 16.62 16.81 11.17 6.06 16.9
## Education
## Primary 17.32 6.81 11.8 19.53 17.49 10.59 6.12 10.33
## Secondary 9.94 5.11 11.59 22.35 22.19 11.91 6.7 10.22
## Vocational 8.28 4.47 9.18 21.05 24.01 13.38 7.88 11.75
## Tertiary 5.46 3.31 9.61 17.78 26.2 15.69 7.24 14.72
## Other 3.48 30.39 20.43 7.85 13.14 4.33 9.47 10.91
## Occupation
## Agricultural 12.51 5.13 10.96 18.07 21.28 13.16 7.39 11.51
## Technical 10.94 4.11 11.57 26.44 21.6 9.22 6.39 9.72
## Commercial 13.98 7.35 12.34 22.16 19.39 10.41 5.46 8.92
## Professional 8.2 3.62 10.77 16.76 26.04 15.33 6.54 12.75
## Not working 12.42 4.16 7.28 14.63 19.31 15.37 9.39 17.43
## Income
## Q 1 14.82 4.36 8.32 14.77 19.21 13.54 9.12 15.86
## Q 2 13.53 5.87 11.12 18.69 20.15 12.69 6.83 11.1
## Q 3 13.55 6.18 12.84 19.44 20.47 11.31 6.39 9.82
## Q 4 12.38 6.49 11.24 22.45 20.92 11.73 5.76 9.03
## Q 5 11 4.71 10.93 21.54 21.34 11.57 6.88 12.03
Table3.8
## female male
## drinker43: Current regular 19.27 49.85
## drinker44: Current social 80.73 50.15
Table3.9
## female male
## d401 77.47 53.50
## d402 0.82 1.86
## d403 0.31 1.20
## d404 0.55 2.88
## d405 1.99 6.52
## d406 4.00 10.33
## d407 3.06 5.71
## d408 2.29 4.29
## d409 9.51 13.71
Table3.10Females
## cost.home cost.shop Total
## Table3.10Females "356.46" "425.93" "782.39"
## Age "" "" ""
## 15 - 19 "158.52" "213.93" "372.45"
## 20 - 24 "264.16" "422.35" "686.51"
## 25 - 44 "355.84" "502.13" "857.97"
## 45 - 59 "403.36" "342.74" "746.1"
## 60+ "350.88" "250.6" "601.48"
## Area "" "" ""
## Urban "433.27" "586.62" "1019.89"
## Rural "303.56" "274.15" "577.71"
## Region "" "" ""
## Bangkok "476.88" "674.83" "1151.71"
## Central "510.29" "630.5" "1140.79"
## North "293.5" "347.42" "640.92"
## North-east "277.17" "295.03" "572.2"
## South "296.84" "423.6" "720.44"
## Income "" "" ""
## Q 1 "362.15" "254.96" "617.11"
## Q 2 "266.97" "201.1" "468.07"
## Q 3 "336.17" "265.44" "601.61"
## Q 4 "342.97" "397.48" "740.45"
## Q 5 "471.7" "708.78" "1180.48"
Table3.10Males
## cost.home cost.shop Total
## Table3.10Males "671.62" "537.95" "1209.57"
## Age "" "" ""
## 15 - 19 "320.46" "362.99" "683.45"
## 20 - 24 "506.11" "411.75" "917.86"
## 25 - 44 "743.07" "592.82" "1335.89"
## 45 - 59 "698.79" "557.23" "1256.02"
## 60+ "559.74" "417.32" "977.06"
## Area "" "" ""
## Urban "791.71" "652.42" "1444.13"
## Rural "580.72" "441.52" "1022.24"
## Region "" "" ""
## Bangkok "915.96" "879.45" "1795.41"
## Central "884.07" "632.81" "1516.88"
## North "479.8" "427.76" "907.56"
## North-east "504.63" "399.94" "904.57"
## South "617.41" "481.36" "1098.77"
## Income "" "" ""
## Q 1 "472.79" "382.99" "855.78"
## Q 2 "452.04" "357.45" "809.49"
## Q 3 "507.9" "351.33" "859.23"
## Q 4 "667.13" "480.94" "1148.07"
## Q 5 "936.91" "786.34" "1723.25"
Table3.11
## Alcohol.Female Alcohol.Male Ethanol.Female Ethanol.Male
## Beer 11.86 34.92 0.59 1.75
## Spirits 5.98 20.14 2.39 8.06
## Spirits.white 3.45 10.24 1.38 4.10
## Spirits.red 2.35 9.57 0.94 3.83
## spirits.other 0.18 0.33 0.07 0.13
## Wine 0.18 0.26 0.02 0.03
## Wine.cooler 0.28 0.12 0.01 0.00
## Other 0.01 0.10 0.00 0.01
Table3.12
## Percent Total
## age_group15 - 19 38.40 639,421.95
## age_group20 - 24 48.07 800,555.51
## age_group25 - 44 10.84 180,571.74
## age_group45 - 59 2.04 33,943.83
## age_group60+ 0.65 10,883.58
## 100.00 1,665,376.61
Table3.13
## female male
## drinker2: Ex drinker 25.02 10.70
## drinker3: Current drinker 74.98 89.30
## drinker43: Current regular 7.60 25.09
## drinker44: Current social 67.38 64.21
Table3.14
## female male
## d401 77.48 57.85
## d402 0.31 0.05
## d403 0.00 1.08
## d404 0.48 0.88
## d405 0.49 4.47
## d406 4.50 10.59
## d407 6.52 6.99
## d408 0.86 3.11
## d409 9.37 14.98
Table3.15
## Total female male
## Home 359.23 201.74 395.11
## Shop 357.06 324.16 365.69
## Total 716.29 525.90 760.80
Table3.16
## Total female male Total female male
## beer 19.54 4.25 26.32 0.98 0.21 1.32
## spirits.white 4.75 0.19 6.76 1.90 0.08 2.70
## spirits.red 3.84 1.38 4.93 1.54 0.55 1.97
## spirits.other 0.04 0.11 0.01 0.02 0.04 0.00
## wine 0.15 0.24 0.12 0.02 0.03 0.01
## wine.cooler 0.25 0.28 0.24 0.01 0.01 0.01
## other 0.01 0.00 0.02 0.00 0.00 0.00
Table4.1
## T41m 0 1
## 2: Ex drinker 20.63 9 73
## 3: Current drinker 20.14 8 75
## Total.d4 20.31 8 75
Table4.2
## Current 0 1 Former 0 1 Total 0 1
## Region
## Bangkok 20.14 10 62 20.06 13 60 20.11 10 62
## Central 19.96 8 75 20.78 9 73 20.22 8 75
## North 20.14 10 70 20.7 10 65 20.33 10 70
## North-east 20.31 9 75 20.61 10 65 20.4 9 75
## South 20.2 11 68 21 11 62 20.5 11 68
## Area
## Urban 19.96 8 75 20.36 10 73 20.1 8 75
## Rural 20.29 9 75 20.87 9 70 20.48 9 75
## Sex
## female 24.07 11 75 23.24 9 73 23.71 9 75
## male 19.2 8 68 19.51 9 70 19.3 8 70
## Age.15 - 19 15.68 9 19 15.51 11 18 15.65 9 19
## Age.20 - 24 17.81 10 24 17.85 11 23 17.82 10 24
## Age.25 - 44 19.53 8 43 19.79 10 41 19.59 8 42.9999999999775
## Age.45 - 59 21.46 10 58 20.94 10 55 21.28 10 58
## Age.60+ 22.96 8 75 21.53 9 73 22.06 8 75
## Marital
## Single 18.51 9 70 19.21 10 55 18.66 9 70
## Married 20.46 8 75 20.47 9 73 20.47 8 75
## Previously married 22.42 10 75 22.67 10 70 22.53 10 75
## Education
## Primary 21.05 8 75 21.07 9 73 21.06 8 75
## Secondary 19.08 8 62 19.71 10 60 19.24 8 62
## Vocational 19.28 10 47 19.67 13 54 19.38 10 54
## Tertiary 20.53 11 58 20.67 10 60 20.58 10 60
## Other 19.53 12 25 18.21 15 21 19.28 12 25
## Occupation
## 1: Employer 20.49 10 71 20.36 9 55 20.45 9 71
## 2: Business assistant 20.35 10 75 21.2 11 55 20.61 10 75
## 3: Govt/state employee 20.55 12 55 20.79 12 57 20.62 12 57
## 4: Private employee 19.54 8 66 20.09 10 60 19.67 8 66
## 5: Student 16.98 10 24 16.99 11 21 16.98 10 24
## 6: House manager 23.46 12 70 22.42 12 53 22.93 12 70
## 7: Infant/elderly 23.59 10 75 21.42 10 73 21.98 10 75
## 8: Unemployed 19.07 8 70 20.13 9 50 19.6 8 70
## 9: Unknown 19.58 15 50 20.3 12 50 19.85 12 50
## Occupational_Gp
## Agricultural 20.46 10 75 20.61 10 58 20.51 10 75
## Technical 19.71 10 50 19.64 12 55 19.69 10 55
## Commercial 19.81 8 66 20.44 9 60 19.98 8 66
## Professional 20.93 10 62 20.58 12 43 20.83 10 62
## Not working 20.54 8 75 21.12 9 73 20.86 8 75
## Income.Q 1 20.92 9 75 21.16 9 70 21.04 9 75
## Income.Q 2 20.66 8 75 21 10 70 20.79 8 74.9999999999913
## Income.Q 3 20.07 10 71 20.56 10 73 20.23 10 73
## Income.Q 4 19.66 8 60 20.27 9 60 19.83 8 60
## Income.Q 5 20.16 10 63 20.3 10 58 20.2 10 63
Table4.3
## 1 - Experiment 2 - Socialise 3 - Peer pressure 4 - Other
## Region
## Bangkok 25.7 18.7 44.39 11.22
## Central 30.25 19.01 37.82 12.92
## North 24.21 24.97 41.32 9.5
## North-east 26.48 21.6 39.95 11.98
## South 25.52 30.25 33.04 11.2
## Area
## Urban 27.99 19.83 40.21 11.97
## Rural 26.06 23.45 39.23 11.26
## Sex
## female 18.91 36.82 30.04 14.23
## male 28.83 18.28 41.96 10.94
## Age.15 - 19 32.13 10.55 50.87 6.45
## Age.20 - 24 29.18 14.4 49.57 6.85
## Age.25 - 44 27.92 21.49 41.11 9.48
## Age.45 - 59 24.35 26.03 35.67 13.95
## Age.60+ 25.65 23.27 30.92 20.16
## Marital
## Single 29.24 16.53 46.71 7.53
## Married 26.24 23.87 37.35 12.54
## Previously married 25.35 21.87 36.92 15.85
## Education
## Primary 26.65 22.46 35.89 15
## Secondary 28.72 18.77 43.08 9.42
## Vocational 28.31 20.34 43.78 7.57
## Tertiary 21.24 30.45 40.25 8.07
## Other 29.25 0 59.73 11.01
## Occupation
## Agricultural 26.7 24.71 37.15 11.43
## Technical 27.6 20.05 40.91 11.45
## Commercial 28.14 19.08 41.46 11.32
## Professional 20.01 35.66 35.56 8.76
## Not working 26.47 18.55 40.62 14.37
## Occupational_Gp
## 1: Employer 26.07 24.07 37.65 12.21
## 2: Business assistant 25.71 24.26 39.88 10.16
## 3: Govt/state employee 23.87 30.6 36.97 8.56
## 4: Private employee 28.91 18.35 41.57 11.17
## 5: Student 26.53 11.59 54.3 7.58
## 6: House manager 24.55 25.1 34.92 15.43
## 7: Infant/elderly 22.92 23.84 28.16 25.07
## 8: Unemployed 30.85 15.96 42.68 10.52
## 9: Unknown 25.47 9.78 52.57 12.17
## Income.Q 1 26.49 19.21 40.21 14.09
## Income.Q 2 26.62 21.77 38.84 12.77
## Income.Q 3 27.34 23.06 38.22 11.38
## Income.Q 4 28.51 19.53 41.19 10.77
## Income.Q 5 25.34 24.22 39.37 11.07
Table4.4
## 1: Buy/drink by self 2: Buy with friend 3: Stole 4: Given 5: Other
## Region
## Bangkok 5.06 41.21 0.12 53.53 0.08
## Central 5.17 41.24 0.41 53.03 0.14
## North 4.93 38.52 0.29 56.05 0.2
## North-east 5.04 41.55 0.2 52.9 0.32
## South 6.9 37.39 0.4 55.25 0.05
## Area
## Urban 4.56 41.29 0.37 53.71 0.07
## Rural 5.69 39.82 0.22 53.97 0.29
## Sex
## female 5.74 29.33 0.37 64.22 0.35
## male 5.06 43.16 0.27 51.35 0.15
## Age.15 - 19 1.96 48.78 0.33 48.93 0
## Age.20 - 24 2.64 47.45 0.19 49.71 0
## Age.25 - 44 4.37 42.17 0.18 53.13 0.15
## Age.45 - 59 6.1 36.81 0.38 56.51 0.2
## Age.60+ 9.91 33.41 0.55 55.51 0.62
## Marital
## Single 2.93 46.5 0.21 50.29 0.07
## Married 5.67 38.61 0.26 55.22 0.23
## Previously married 8.15 37.1 0.75 53.77 0.22
## Education
## Primary 7.44 37.15 0.35 54.74 0.32
## Secondary 3.75 42.58 0.32 53.23 0.12
## Vocational 2.94 44.93 0.09 52.02 0.02
## Tertiary 2.86 42.74 0.13 54.21 0.06
## Other 0 81.95 0 18.05 0
## Occupation
## Agricultural 5.74 38.6 0.28 55.1 0.28
## Technical 4.04 43.57 0.16 52.15 0.07
## Commercial 5.43 41.8 0.25 52.35 0.17
## Professional 3.23 36.64 0.25 59.78 0.1
## Not working 5.54 39.26 0.56 54.37 0.26
## Occupational_Gp
## 1: Employer 6.09 38.92 0.29 54.51 0.19
## 2: Business assistant 4.36 39.94 0.31 55.23 0.17
## 3: Govt/state employee 2.95 41.63 0.11 55.31 0
## 4: Private employee 4.97 42.08 0.22 52.51 0.22
## 5: Student 1.48 49.06 0.24 49.21 0
## 6: House manager 6 33.51 0.53 59.57 0.38
## 7: Infant/elderly 11.35 26.88 0.88 60.15 0.74
## 8: Unemployed 4.1 44.38 0.65 50.85 0.03
## 9: Unknown 7.71 56.84 0 35.45 0
## Income.Q 1 7.01 39.01 0.37 53.29 0.31
## Income.Q 2 6.17 37.49 0.28 55.34 0.72
## Income.Q 3 5.69 41.13 0.27 52.8 0.11
## Income.Q 4 5.18 40.96 0.33 53.47 0.05
## Income.Q 5 3.79 41.54 0.23 54.36 0.08
Table4.5
## 1: Beer 2: Spirits (white) 3: Spirits (red) 4: Wine 5: Mixed 6: Other
## Region
## Bangkok 49.19 6.61 37.81 0.87 4.28 1.25
## Central 38.08 14.96 39.91 0.86 2.83 3.35
## North 31.35 37.89 23.66 0.51 1.23 5.37
## North-east 36.94 43.87 11.52 0.49 2.29 4.88
## South 47.71 18.94 26.34 0.72 3.32 2.96
## Area
## Urban 42.44 17.51 33.01 1.01 3.05 2.98
## Rural 35.16 36.3 21.28 0.39 2.13 4.75
## Sex
## female 53.29 12.72 15.82 2.72 10.34 5.11
## male 34.76 31.74 28.98 0.17 0.66 3.7
## Age.15 - 19 55.77 16.1 21.15 0.96 4.58 1.45
## Age.20 - 24 54.77 16.13 23.4 0.94 4.3 0.46
## Age.25 - 44 45.32 20.76 28.26 0.71 3.13 1.82
## Age.45 - 59 28.02 37.04 27.24 0.38 1.49 5.84
## Age.60+ 15.38 49.73 21.41 0.86 0.5 12.11
## Marital
## Single 48.51 19.14 26.18 0.82 4.22 1.13
## Married 35.19 30.69 26.74 0.57 2.04 4.77
## Previously married 33.2 33.63 24.73 0.92 1.45 6.07
## Education
## Primary 26.95 44.93 20.16 0.23 0.94 6.79
## Secondary 48.01 18.52 27.11 0.77 3.45 2.13
## Vocational 46.26 11.98 36.65 0.45 3.41 1.25
## Tertiary 47.49 4.67 39.28 2.28 5.37 0.91
## Other 29.13 43.31 27.56 0 0 0
## Occupation
## Agricultural 28.27 47.77 16.53 0.12 1.13 6.17
## Technical 48.95 12.81 33.28 0.28 2.02 2.66
## Commercial 40.22 23.66 29.33 0.71 2.96 3.13
## Professional 43.69 11.08 38.01 2.29 3.51 1.41
## Not working 41.38 21.96 26.47 1.33 4.52 4.33
## Occupational_Gp
## 1: Employer 30.57 37.66 24.23 0.42 1.36 5.78
## 2: Business assistant 41.55 31.28 20.01 0.58 2.55 4.04
## 3: Govt/state employee 40.69 14.98 37.52 0.83 4.51 1.48
## 4: Private employee 43.26 22.71 28.2 0.66 2.5 2.67
## 5: Student 65.77 6.59 19.04 1.28 6.88 0.44
## 6: House manager 46.71 12.21 25.31 2.12 9.13 4.52
## 7: Infant/elderly 16.51 47.67 23.27 1.1 0.75 10.7
## 8: Unemployed 34.47 23.24 36.75 1 1.85 2.71
## 9: Unknown 39.19 33.47 20.39 0 6.2 0.75
## Income.Q 1 34.73 37.14 19.53 0.64 2.22 5.75
## Income.Q 2 33.08 41.42 17.36 0.28 1.42 6.44
## Income.Q 3 32.31 41.27 18.94 0.66 2.59 4.23
## Income.Q 4 40.98 26.12 26.84 0.54 2.52 2.99
## Income.Q 5 43.74 11.19 37.97 0.99 3.19 2.91
Table5.1
## Percent
## Temple 1.38
## School 0.28
## Clinic 0.13
## Govt. 0.25
## Dorm 0.56
## Pump 1.90
## Park 0.61
## Factory 0.54
## Any illegal place 4.50
Table5.2
## Temple School Clinic Gov. Dorm Pump Park Factory Any
## Sex "----" "----" "----" "----" "----" "----" "----" "----" "----"
## female:d11Yes "1.01" "0.25" "0.16" "0.17" "0.54" "1.04" "0.36" "0.39" "3.07"
## male:d11Yes "1.47" "0.28" "0.12" "0.27" "0.56" "2.1" "0.68" "0.57" "4.84"
## Age_group "----" "----" "----" "----" "----" "----" "----" "----" "----"
## 15 - 19:d11Yes "1.07" "0.39" "0.25" "0.41" "1.27" "0.67" "0.57" "0.06" "3.63"
## 20 - 24:d11Yes "1.11" "0.08" "0" "0.09" "2.09" "1.34" "0.91" "0.39" "4.51"
## 25 - 44:d11Yes "1.3" "0.36" "0.12" "0.2" "0.51" "2.56" "0.58" "0.68" "5.08"
## 45 - 59:d11Yes "1.74" "0.27" "0.11" "0.37" "0.15" "1.55" "0.63" "0.47" "4.26"
## 60+:d11Yes "1.11" "0.11" "0.3" "0.26" "0.13" "1.07" "0.46" "0.42" "2.99"
## Drinker "----" "----" "----" "----" "----" "----" "----" "----" "----"
## 3: Current regular:d11Yes "2.07" "0.38" "0.18" "0.29" "0.72" "2.65" "0.8" "0.86" "6.23"
## 4: Current social:d11Yes "0.84" "0.2" "0.09" "0.23" "0.43" "1.31" "0.47" "0.28" "3.14"
## Income "----" "----" "----" "----" "----" "----" "----" "----" "----"
## Q 1:d11Yes "1.07" "0.35" "0.05" "0.06" "0.93" "0.9" "0.8" "0.24" "3.62"
## Q 2:d11Yes "1.75" "0.41" "0.11" "0.27" "0.45" "1.4" "0.63" "0.25" "3.87"
## Q 3:d11Yes "2.16" "0.24" "0.15" "0.25" "0.58" "1.07" "0.72" "0.44" "4.58"
## Q 4:d11Yes "1.34" "0.25" "0.21" "0.28" "0.32" "2.02" "0.48" "0.73" "4.46"
## Q 5:d11Yes "0.8" "0.24" "0.07" "0.28" "0.72" "2.91" "0.61" "0.66" "5.07"
Table5.3
## Percent
## Temple 3.87
## School 1.02
## Clinic 0.27
## Govt. 1.11
## Dorm 0.75
## Pump 1.44
## Park 0.96
## Plant 0.63
## Path 5.47
## Any 11.09
Table5.4
## Temple School Clinic Govt. Dorm Pump Park Plant Path Any
## Sex "----" "----" "----" "----" "----" "----" "----" "----" "----" "----"
## female:d21Yes "2.24" "0.56" "0.18" "0.76" "0.67" "0.61" "0.43" "0.38" "2.56" "6.45"
## male:d21Yes "4.26" "1.12" "0.29" "1.19" "0.77" "1.63" "1.08" "0.69" "6.17" "12.21"
## Age_group "----" "----" "----" "----" "----" "----" "----" "----" "----" "----"
## 15 - 19:d21Yes "4.88" "1.28" "0" "0.74" "3.34" "1.9" "1.72" "0.84" "9.16" "15.93"
## 20 - 24:d21Yes "3.34" "0.79" "0.07" "0.75" "2.66" "1" "1.21" "0.24" "9.12" "14.87"
## 25 - 44:d21Yes "3.58" "1.02" "0.31" "1.12" "0.57" "1.62" "0.85" "0.74" "5.26" "10.76"
## 45 - 59:d21Yes "4.39" "1.07" "0.39" "1.37" "0.23" "1.43" "1.14" "0.7" "4.76" "10.7"
## 60+:d21Yes "3.8" "0.94" "0.06" "0.84" "0.13" "0.94" "0.37" "0.29" "3.42" "8.17"
## Drinker "----" "----" "----" "----" "----" "----" "----" "----" "----" "----"
## 15 - 19:d29Yes "5.4" "1.17" "0.35" "1.19" "0.76" "2.06" "1.29" "0.79" "9.16" "14.22"
## 20 - 24:d29Yes "2.67" "0.89" "0.21" "1.04" "0.74" "0.95" "0.69" "0.5" "9.12" "8.64"
## 25 - 44:d29Yes "5.4" "1.17" "0.35" "1.19" "0.76" "2.06" "1.29" "0.79" "5.26" "14.22"
## 45 - 59:d29Yes "2.67" "0.89" "0.21" "1.04" "0.74" "0.95" "0.69" "0.5" "4.76" "8.64"
## 60+:d29Yes "5.4" "1.17" "0.35" "1.19" "0.76" "2.06" "1.29" "0.79" "3.42" "14.22"
## Income "----" "----" "----" "----" "----" "----" "----" "----" "----" "----"
## Q 1:d21Yes "3.54" "1.29" "0.05" "1.06" "1.41" "0.55" "1.3" "0.25" "4.81" "9.7"
## Q 2:d21Yes "4.22" "1.17" "0.14" "1.06" "0.85" "1.22" "0.76" "0.14" "5.23" "10.47"
## Q 3:d21Yes "5.7" "1.22" "0.22" "1.19" "0.64" "0.92" "1.36" "0.33" "6.53" "13.44"
## Q 4:d21Yes "3.19" "0.98" "0.28" "1.09" "0.58" "1.5" "0.64" "1.1" "6.01" "11.25"
## Q 5:d21Yes "3.23" "0.74" "0.43" "1.11" "0.73" "2.11" "0.99" "0.75" "4.55" "10.11"
Table5.5
## d42 > 1 & d42 < 10TRUE
## White whiskey 9.39
## Red whiskey 4.65
## Any type 12.26
Table5.6
## Legal Illegal
## Sex
## female 93.22 6.78
## male 86.43 13.57
## Age_group
## 15 - 19 88.45 11.55
## 20 - 24 92.85 7.15
## 25 - 44 88.22 11.78
## 45 - 59 86.12 13.88
## 60+ 85.05 14.95
## Drinker
## 3: Current regular 83.89 16.11
## 4: Current social 90.76 9.24
## Income
## Q 1 89.14 10.86
## Q 2 85.07 14.93
## Q 3 85.05 14.95
## Q 4 88.77 11.23
## Q 5 89.49 10.51
Table5.7
## female male female male
## age_15_17TRUE 5.00 5.43 1.20 1.92
## age_group215-19 7.82 8.61 2.95 4.16
## age_group220+ 92.18 91.39 97.05 95.84
T57Total
## a4female a4male a4female a4male
## "28,923,809" "27,024,420" " 3,071,845" "12,825,419"
Table5.8
## Total a4female a4male
## 15-17 9.71 12.99699 87.00301
## 15-19 13.60 14.52176 85.47824
## 20+ 29.74 19.51919 80.48081
Table5.9
## female male female male
## drinker41: Never drinker 96.34 81.58 93.72 73.77
## drinker42: Ex drinker 1.12 1.64 2.28 3.31
## drinker43: Current regular 0.12 3.68 0.42 6.14
## drinker44: Current social 2.43 13.10 3.59 16.78
Table5.10
## Age15_17 age_group215-19 age_group220+
## Urban 4.41 7.38 92.62
## Rural 5.88 8.88 91.12
## Bangkok 2.98 5.32 94.68
## Central 4.23 7.01 92.99
## North 5.14 8.27 91.73
## North-east 7.10 10.31 89.69
## South 5.95 9.45 90.55
Table5.11
## 15-17 female male 15-19 female male 20+ female male
## Beer 13.68 4.32 15.08 17.92 3.93 20.30 30.98 12.10 35.56
## Spirits (white) 3.53 0.74 3.95 3.28 0.30 3.79 9.16 3.54 10.52
## Spirits (red) 2.95 1.07 3.23 4.41 4.46 4.40 8.33 2.29 9.79
## Spirits (other) 0.00 0.01 0.00 0.00 0.00 0.00 0.31 0.18 0.34
## Wine 0.00 0.02 0.00 0.08 0.54 0.00 0.25 0.17 0.27
## Wine cooler 0.11 0.76 0.01 0.28 0.39 0.26 0.15 0.28 0.11
## Other 0.04 0.00 0.04 0.04 0.01 0.04 0.08 0.01 0.10
Table6.1
## prob.annoyTRUE
## Annoy 4.41
## Abuse 1.27
## Violence 0.11
## Money 2.02
## Work 1.83
## Any problem 7.04
Table6.2
## Annoy Abuse Violence Money Work Any problem
## female 4.31 0.90 0.07 1.19 0.58 5.68
## male 4.51 1.66 0.15 2.92 3.17 8.49
## 15 - 19 4.05 0.71 0.20 1.64 0.99 5.69
## 20 - 24 4.18 1.26 0.04 1.75 1.89 6.96
## 25 - 44 4.75 1.27 0.15 2.38 2.24 7.88
## 45 - 59 4.69 1.66 0.07 2.33 2.17 7.63
## 60+ 3.67 0.94 0.08 1.26 0.97 5.36
## 1: Never drinker 3.81 0.57 0.05 0.90 0.52 4.84
## 2: Ex drinker 3.12 0.94 0.09 1.21 1.19 4.92
## 3: Current regular 9.18 4.73 0.50 8.24 8.45 19.73
## 4: Current social 3.98 1.33 0.04 1.91 1.94 6.92
## Q 1 3.89 1.07 0.10 1.62 0.88 5.68
## Q 2 5.33 1.42 0.13 2.60 1.99 8.33
## Q 3 4.71 1.55 0.11 2.25 2.14 7.68
## Q 4 4.57 1.55 0.09 2.31 2.00 7.73
## Q 5 3.71 0.81 0.11 1.44 2.09 5.96
Table6.3
## Percent
## Drives (Among current drinkers) 73.12
## ...while drinking (Among everyone - for comparison) 12.50
## ...while drinking (Among current drinkers) 55.57
## ......regularly 7.83
## ......occasionally 47.74
## Breathalysed (among current drink drivers) 6.93
## Injured while drink driving 5.10
## Injured by others drink driving (Among everyone) 1.10
## ...while in the car 1.01
## ...while walking 0.02
Table6.4
## DUI Accident Other Other DUI
## female 39.03 1.51 0.81 0.62
## male 58.58 5.53 1.42 1.16
## 15 - 19 65.17 6.74 1.13 0.87
## 20 - 24 61.27 5.30 1.53 1.35
## 25 - 44 54.84 4.83 1.08 0.90
## 45 - 59 54.65 5.00 1.08 0.84
## 60+ 50.22 5.70 0.96 0.72
## 1: Never drinker NA NA 0.75 0.57
## 2: Ex drinker NA NA 1.34 1.04
## 3: Current regular 65.37 6.83 1.85 1.61
## 4: Current social 47.45 3.12 1.58 1.31
## Q 1 50.32 7.14 1.00 0.79
## Q 2 60.31 5.69 1.29 1.01
## Q 3 62.35 5.82 1.11 0.98
## Q 4 56.60 5.37 1.10 0.92
## Q 5 49.12 3.31 1.03 0.75