rm(list=ls())

library(tidyverse)
library(broom)
library(lsr)
library(psych)
library(pwr2)
library(car)



dat<- read_csv("MSc dissertation Volna_20 July 2021_23.40.csv")

vaccines <- dat %>%
  select( Gender, Age, `6x2 time_Page Submit`, `choice6x2`, `choice6x2 time_Last Click`,
         `6x6a time_Page Submit`,`choice6x6`, `choice6x6 time_Last Click`,
         `2x6 time_Page Submit`, `coice2x6`, `choice2x6 time_Last Click`, `2x2 time_Page Submit`,
         `choice2x2`, `choice 2x2 time_Last Click`, Q17, Q18, Q19) 
 

vaccines1 <- vaccines %>% 
  gather(condition, time, `6x2 time_Page Submit`, `6x6a time_Page Submit`,`2x6 time_Page Submit`, 
         `2x2 time_Page Submit`)
 

vaccines2 <- vaccines1 %>%
  gather(choice, vaccine, `choice6x2`, `choice6x6`, `coice2x6`, `choice2x2` )
  

vaccines3 <- vaccines2 %>%
  gather(choicetime, tchoice, `choice6x2 time_Last Click`, `choice6x6 time_Last Click`,
         `choice2x6 time_Last Click`, `choice 2x2 time_Last Click`)
 
  
 

vaccines.noNA <- na.omit(vaccines3) 


names(vaccines.noNA)[names(vaccines.noNA) == "Q17"] <- "satisfaction"
names(vaccines.noNA)[names(vaccines.noNA) == "Q18"] <- "confidence"
names(vaccines.noNA)[names(vaccines.noNA) == "Q19"] <- "regret"

vaccines.noNA$condition <- recode_factor(vaccines.noNA$condition, `6x2 time_Page Submit` = "high info\\low opt", 
                                         `6x6a time_Page Submit` ="high info\\high opt", 
                                         `2x6 time_Page Submit` = "low info\\high opt",
                                         `2x2 time_Page Submit` = "low info\\low opt")

vaccines.noNA$Gender <- recode_factor(vaccines.noNA$Gender, `1` = "male", 
                                      `2` = "female", 
                                      `3` = "non-binary/thirdgender",
                                      `4` = "prefernottosay")

vaccines.noNA <- vaccines.noNA[which(vaccines.noNA$Age >= 18),]
summary(vaccines.noNA)


vaccines.noNA.s <- vaccines.noNA %>% 
  separate(condition, into = c('Information','Options'),sep = '\\\\')

vaccines.noNA.s$Information<- as.factor(vaccines.noNA.s$Information)
vaccines.noNA.s$Options<- as.factor(vaccines.noNA.s$Options)


head(vaccines.noNA.s)

summary(vaccines.noNA.s)

vaccines.means.t <- vaccines.noNA.s%>%
  group_by(Information, Options)%>%
  summarise(mean = mean(tchoice, na.rm = TRUE),
            sd = sd(tchoice, na.rm = TRUE))
(vaccines.means.t)

vaccines.means.t.i <- vaccines.noNA.s%>%
  group_by(Information)%>%
  summarise(mean = mean(tchoice, na.rm = TRUE),
            sd = sd(tchoice, na.rm = TRUE))
(vaccines.means.t.i)

vaccines.means.t.o <- vaccines.noNA.s%>%
  group_by(Options)%>%
  summarise(mean = mean(tchoice, na.rm = TRUE),
            sd = sd(tchoice, na.rm = TRUE))
(vaccines.means.t.o)

vaccines.means.s <- vaccines.noNA.s%>%
  group_by(Information, Options)%>%
  summarise(mean = mean(satisfaction, na.rm = TRUE),
            sd = sd(satisfaction, na.rm = TRUE))
(vaccines.means.s)

vaccines.means.s.i <- vaccines.noNA.s%>%
  group_by(Information)%>%
  summarise(mean = mean(satisfaction, na.rm = TRUE),
            sd = sd(satisfaction, na.rm = TRUE))
(vaccines.means.s.i)

vaccines.means.s.o <- vaccines.noNA.s%>%
  group_by(Options)%>%
  summarise(mean = mean(satisfaction, na.rm = TRUE),
            sd = sd(satisfaction, na.rm = TRUE))
(vaccines.means.s.o)

vaccines.means.c <- vaccines.noNA.s%>%
  group_by(Information, Options)%>%
  summarise(mean = mean(confidence, na.rm = TRUE),
            sd = sd(confidence, na.rm = TRUE))
(vaccines.means.c)

vaccines.means.c.o <- vaccines.noNA.s%>%
  group_by(Options)%>%
  summarise(mean = mean(confidence, na.rm = TRUE),
            sd = sd(confidence, na.rm = TRUE))
(vaccines.means.c.o)

vaccines.means.c.i <- vaccines.noNA.s%>%
  group_by(Information)%>%
  summarise(mean = mean(confidence, na.rm = TRUE),
            sd = sd(confidence, na.rm = TRUE))
(vaccines.means.c.i)

vaccines.means.r <- vaccines.noNA.s%>%
  group_by(Information, Options)%>%
  summarise(mean = mean(regret, na.rm = TRUE),
    sd = sd(regret, na.rm = TRUE))
  
(vaccines.means.r)

vaccines.means.r.i <- vaccines.noNA.s%>%
  group_by(Information)%>%
  summarise(mean = mean(regret, na.rm = TRUE),
            sd = sd(regret, na.rm = TRUE))

(vaccines.means.r.i)

vaccines.means.r.o <- vaccines.noNA.s%>%
  group_by(Options)%>%
  summarise(mean = mean(regret, na.rm = TRUE),
            sd = sd(regret, na.rm = TRUE))

(vaccines.means.r.o)

reasoning<- dat%>%
  select(why, Age, Gender)

reasoning.noNA <- na.omit(reasoning)

ggplot(vaccines.noNA.s, aes(x = Information, y = tchoice)) + 
  geom_boxplot() +
  theme_bw()+
  xlab("Information")+
  ylab("time")

ggplot(vaccines.noNA.s, aes(x = Options, y = tchoice)) + 
  geom_boxplot() +
  theme_bw()+
  xlab("Options")+
  ylab("time")

ggplot(vaccines.noNA.s, aes(x = Information, y = satisfaction)) + 
  geom_boxplot() +
  theme_bw()+
  xlab("Information")+
  ylab("satisfaction")

ggplot(vaccines.noNA.s, aes(x = Options, y = satisfaction)) + 
  geom_boxplot() +
  theme_bw()+
  xlab("Options")+
  ylab("satisfaction")


ggplot(vaccines.noNA.s, aes(x = Information, y = confidence)) + 
  geom_boxplot() +
  theme_bw()+
  xlab("information")+
  ylab("confidence")

ggplot(vaccines.noNA.s, aes(x = Options, y = confidence)) + 
  geom_boxplot() +
  theme_bw()+
  xlab("options")+
  ylab("confidence")

ggplot(vaccines.noNA.s, aes(x = Information, y = regret)) + 
  geom_boxplot() +
  theme_bw()+
  xlab("information")+
  ylab("regret")

ggplot(vaccines.noNA.s, aes(x = Options, y = regret)) + 
  geom_boxplot() +
  theme_bw()+
  xlab("Options")+
  ylab("regret")

ggplot(vaccines.noNA, aes(x = condition, y = satisfaction)) + 
  geom_point() +
  theme_bw()+
  xlab("")+
  ylab("satisfaction")

ggplot(vaccines.noNA, aes(x = condition, y = confidence)) + 
  geom_point() +
  theme_bw()+
  xlab("condition")+
  ylab("confidence")

ggplot(vaccines.noNA, aes(x = condition, y = regret)) + 
  geom_point() +
  theme_bw()+
  xlab("condtion")+
  ylab("regret")


time.aov <- aov(tchoice ~ Information*Options, data = vaccines.noNA.s)
summary(time.aov)
etaSquared(time.aov)

satisfaction.aov <- aov(satisfaction ~ Information*Options, data = vaccines.noNA.s)
summary(satisfaction.aov)
etaSquared(satisfaction.aov)
TukeyHSD(satisfaction.aov)

confidence.aov <- aov(confidence ~ Information*Options, data = vaccines.noNA.s)
summary(confidence.aov)
etaSquared(confidence.aov)

regret.aov <- aov(regret ~ Information*Options, data = vaccines.noNA.s)
summary(regret.aov)
etaSquared(regret.aov)

vaccines.noNA$Age <- as.numeric(vaccines.noNA$Age)

mean(vaccines.noNA$Age)

ggplot(vaccines.noNA, aes(x = satisfaction)) + 
  geom_histogram()

ggplot(vaccines.noNA, aes(x = confidence)) + 
  geom_histogram()

ggplot(vaccines.noNA, aes(x = regret)) + 
  geom_histogram()

qqPlot(vaccines.noNA$satisfaction)
qqPlot(vaccines.noNA$confidence)
qqPlot(vaccines.noNA$regret)

ggplot(vaccines.noNA, aes(x = regret, y = satisfaction)) +
  geom_point()+
  geom_smooth(method = "lm")+
  theme_bw()+
  xlab("regret")+
  ylab("satisfaction")

ggplot(vaccines.noNA, aes(x = confidence, y = satisfaction)) +
  geom_point()+
  geom_smooth(method = "lm")+
  theme_bw()+
  xlab("confidence")+
  ylab("satisfaction")

ggplot(vaccines.noNA, aes(x = confidence, y = regret)) +
  geom_point()+
  geom_smooth(method = "lm")+
  theme_bw()+
  xlab("confidence")+
  ylab("regret")  

cor.test(vaccines.noNA$satisfaction, vaccines.noNA$regret, method = "spearman")

cor.test(vaccines.noNA$satisfaction, vaccines.noNA$confidence, method = "spearman")

cor.test(vaccines.noNA$regret, vaccines.noNA$confidence, method = "spearman")

satisfaction.means <- vaccines.noNA.s%>%
  summarise(mean = mean(satisfaction, na.rm = TRUE),
            sd = sd(satisfaction, na.rm = TRUE))
(satisfaction.means)

confidence.means <- vaccines.noNA.s%>%
  summarise(mean = mean(confidence, na.rm = TRUE),
            sd = sd(confidence, na.rm = TRUE))
(confidence.means)

regret.means <- vaccines.noNA.s%>%
  summarise(mean = mean(regret, na.rm = TRUE),
            sd = sd(regret, na.rm = TRUE))
(regret.means)

vaccines.noNA <- vaccines.noNA[which(vaccines.noNA$Age >= 18),]
summary(vaccines.noNA)

vaccines.noNA %>% summarise(mean = mean(Age),
                     sd = sd(Age))

vaccines.noNA$Gender <- as.factor(vaccines.noNA$Gender)

vaccines.noNA %>% 
  count(male, female, non-binary/thirdgender, prefernottosay)

table( factor(vaccines.noNA$Gender,labels=c("male", "female", "non-binary/thirdgender", "prefernottosay")) ) 

reasoning.noNA$Age <- as.numeric(reasoning.noNA$Age)
reasoning.noNA$Gender<- as.factor(reasoning.noNA$Gender)

reasoning.noNA <- reasoning.noNA[which(reasoning.noNA$Age >= 18),]

reasoning.noNA %>% summarise(mean = mean(Age),
                            sd = sd(Age))

summary(reasoning.noNA)

reasoning.noNA$Gender <- recode_factor(reasoning.noNA$Gender, `1` = "male", 
                                       `2` = "female", 
                                       `3` = "non-binary/thirdgender",
                                       `4` = "prefernottosay")


reasoning.noNA$Gender <- as.factor(reasoning.noNA$Gender)



table( factor(reasoning.noNA$Gender,labels=c("male", "female", "non-binary/thirdgender", "prefernottosay")) )