# figure 2

data2 <- data%>%
rename(immunity = q8haveimmunity,
gshop = Adhere_shop_groceries,
oshop = Adhere_shop_other,
friends = Adhere_meet_friends,
outings = Going_out_total,
noworry = q9worry,
rself = q10arisk,
rothers = q10brisk,
covidknow = Sx_covid_nomissing,
think_covid = Ever_covid) %>%
select(immunity,
gshop,
oshop,
friends,
outings,
noworry,
rself,
rothers,
covidknow,
think_covid) %>%
mutate(think_covid = case_when(think_covid == 0 ~ "Think have not had COVID-19",
think_covid == 1 ~ "Think have had COVID-19"))

#Making individual datasets

#Count for thinking have had/have not had COVID-19
covid <- data2 %>%
select(immunity, think_covid) %>%
group_by(think_covid) %>%
count() %>%
rename(covidtotal = n)

#immunity
dimmunity <- data2 %>%
select(immunity, think_covid) %>%
filter(immunity == "5") %>%
mutate(immunity = case_when(immunity == 5 ~ "Strongly agree that they are immune")) %>%
group_by(think_covid, immunity) %>%
count() %>%
ungroup() %>%
rename("stronglyagree" = "n")

#trying to create third column for percentages

#left joining covid and dimmunity

dimmunity <- merge(x = dimmunity, y = covid, by = "think_covid") %>%
mutate(strongpercent = stronglyagree*100/covidtotal)

#groceryshopping percentage table

dgshop <- data2 %>%
select(gshop, think_covid) %>%
filter(gshop == 0) %>%
mutate(gshop = case_when(gshop == 0 ~ "Shopping for groceries/pharmacy two or more times")) %>%
group_by(think_covid, gshop) %>%
count() %>%
ungroup() %>%
rename("shopmorethan1" = "n")

dgshop <- merge(x = dgshop, y = covid, by = "think_covid") %>%
mutate(gshoppercent = shopmorethan1*100/covidtotal)

#othershopping

doshop <- data2 %>%
select(oshop, think_covid) %>%
filter(oshop == 0) %>%
mutate(oshop = case_when(oshop == 0 ~ "Shopping for items other than groceries/pharmacy")) %>%
group_by(think_covid, oshop) %>%
count() %>%
ungroup() %>%
rename("othershop" = "n")

doshop <- merge(x = doshop, y = covid, by = "think_covid") %>%
mutate(othershoppercent = othershop*100/covidtotal)

#friends

dfriends <- data2 %>%
select(friends, think_covid) %>%
filter(friends == 0) %>%
mutate(friends = case_when(friends == 0 ~ "Met up with friends/family they do not live with")) %>%
group_by(think_covid, friends) %>%
count() %>%
ungroup() %>%
rename("metfriends" = "n")

dfriends <- merge(x = dfriends, y = covid, by = "think_covid") %>%
mutate(metfriendspercent = metfriends*100/covidtotal)

#outings

doutings <- data2 %>%
select(outings, think_covid) %>%
filter(outings > 7) %>%
mutate(outings = case_when(outings > 7 ~ "Out-of-home activity, 8 or more outings")) %>%
group_by(think_covid, outings) %>%
count() %>%
ungroup() %>%
rename("moreoutings" = "n")

doutings <- merge(x = doutings, y = covid, by = "think_covid") %>%
mutate(more8outingspercent = moreoutings*100/covidtotal)

#noworry

dnoworry <- data2 %>%
select(noworry, think_covid) %>%
filter(noworry == 1) %>%
mutate(noworry = case_when(noworry == 1 ~ "Not worried at all")) %>%
group_by(think_covid, noworry) %>%
count() %>%
ungroup() %>%
rename("notworry" = "n")

dnoworry <- merge(x = dnoworry, y = covid, by = "think_covid") %>%
mutate(notworrypercent = notworry*100/covidtotal)

#risktoself

drself <- data2 %>%
select(rself, think_covid) %>%
filter(rself == 1) %>%
mutate(rself = case_when(rself == 1 ~ "Perceive no risk at all to themselves")) %>%
group_by(think_covid, rself) %>%
count() %>%
ungroup() %>%
rename("noriskself" = "n")

drself <- merge(x = drself, y = covid, by = "think_covid") %>%
mutate(noriskselfpercent = noriskself*100/covidtotal)

#risktoothers

drothers <- data2 %>%
select(rothers, think_covid) %>%
filter(rothers == 1) %>%
mutate(rothers = case_when(rothers == 1 ~ "Perceive no risk at all to others")) %>%
group_by(think_covid, rothers) %>%
count() %>%
ungroup() %>%
rename("noriskothers" = "n")

drothers <- merge(x = drothers, y = covid, by = "think_covid") %>%
mutate(noriskotherspercent = noriskothers*100/covidtotal)

#covidknowledge

dcovidknow <- data2 %>%
select(covidknow, think_covid) %>%
filter(covidknow == 0) %>%
mutate(covidknow = case_when(covidknow == 0 ~ "Did not identify common symptoms")) %>%
group_by(think_covid, covidknow) %>%
count() %>%
ungroup() %>%
rename("idcovidsym" = "n")

dcovidknow <- merge(x = dcovidknow, y = covid, by = "think_covid") %>%
mutate(idcovidsympercent = idcovidsym*100/covidtotal)

#attempting to merge data into one tibble

listdata2 <- list(dcovidknow,
drothers,
drself,
dnoworry,
doutings,
dfriends,
doshop,
dgshop,
dimmunity)

tdata2<- rbindlist(listdata2, use.name=FALSE)
tdata2<- subset(tdata2, select = -c(idcovidsym, covidtotal)) %>%
rename("groups"=covidknow) %>%
rename(Percentage = idcovidsympercent) %>%
group_by(think_covid)

#rearranging group variables

#attempting to convert table into plot

plotdata2 <- tdata2 %>% mutate(groups = fct_relevel(groups,
"Strongly agree that they are immune",
"Shopping for groceries/pharmacy two or more times",
"Shopping for items other than groceries/pharmacy",
"Met up with friends/family they do not live with",
"Out-of-home activity, 8 or more outings",
"Not worried at all",
"Perceive no risk at all to themselves",
"Perceive no risk at all to others",
"Did not identify common symptoms"))
plotdata2_new <- plotdata2
plotdata2_new$think_covid <- factor(plotdata2_new$think_covid,
levels = c("Think have not had COVID-19", "Think have had COVID-19" ))

plot2 <- ggplot(plotdata2_new, aes(fill=think_covid,
y= Percentage,
x= groups)) +
geom_bar(position ='dodge', stat='identity', width=0.5) +
scale_fill_grey()+ # scale_color_manual(labels = c("Think have not had COVID-19", "Think have had COVID-19"),
#values = c("black", "grey"))
theme(axis.text.x = element_text(angle = 45, vjust = 0.9, hjust = 1),
panel.background = element_rect(fill = "white", colour="white"),
panel.grid.major.y = element_line(size = 0.5, colour="grey"),
panel.grid.minor.y = element_line(size = 0.5, colour="grey"),
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
legend.position="bottom",
legend.title=element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_text(margin = margin(t = 0, r = 50, b = 0, l = 0))) +
scale_y_continuous(limits = c(0,60), breaks = c(0,10,20,30,40,50,60))