.Rhistory

summarise(mean_correct = mean(sub_mean_correct),
sd_correct = sd(sub_mean_correct),
n_obs = length(sub_mean_correct),
sem = sd_correct / sqrt(n_obs),
ci = sem * 1.96)
sgf_sub_means
sgf_group_means
sgf_sub_means <- sgf |>
group_by(item, condition) |>
summarise(sub_mean_correct = mean(correct))
sgf_group_means <- sgf_sub_means |>
group_by(item, condition) |>
summarise(mean_correct = mean(sub_mean_correct),
sd_correct = sd(sub_mean_correct),
n_obs = length(sub_mean_correct),
sem = sd_correct / sqrt(n_obs),
ci = sem * 1.96)
sgf_sub_means
sgf_group_means
sgf_wide <- read_csv("www/data/sgf_wide.csv")
sgf_wide <- read_csv("www/data/sgf_wide.csv")
sgf_wide <- read_csv("/www/data/sgf_wide.csv")
sgf_wide <- read_csv("sgf_wide.csv")
sgf_wide <- read_csv("www/data/sgf_wide.csv")
sgf <- read_csv("www/data/stiller_scales_data.csv")
sgf_wide <- read_csv("sgf_wide.csv")
sgf_wide <- read_csv("www/data/sgf_wide.csv")
sgf_wide <- read_csv("www/data/sgf_wide.csv")
?pivot_wider
sgf
sgf_wide <- sgf |>
pivot_wider(names_from = "item",
values_from = "correct")
sgf_wide
library(tidyverse)
load("data/KenyaData.RData")
scale.means = function (df, ..., na.rm=FALSE) {
vars = unlist(list(...))
mean_vars = rowMeans(df[,vars], na.rm=na.rm)
return(mean_vars)
}
for (var in c(k1_df$sel.con, k1_df$sel.pers, k1_df$sel.com,
k1_df$sel.prob, k1_df$sel.bett)) {
var[var < 0] <- NA
}
k1_df$sel.score.z <- scale(k1_df$sel.score)
k1_df$sel.score.avg <- scale.means(k1_df, "sel.con", "sel.pers", "sel.com",
"sel.prob", "sel.bett", na.rm = T)
k1_df$sel.score <- scale(k1_df$sel.con) + scale(k1_df$sel.pers) +
scale(k1_df$sel.com) + scale(k1_df$sel.prob) + scale(k1_df$sel.bett)
k1_df$sel.score.z <- scale(k1_df$sel.score)
ggplot(k1_df, aes(x = ...)) +
geom_histogram()
ggplot(k1_df, aes(x = sel.score)) +
geom_histogram()
ggplot(k1_df, aes(x = sel.score), fill = treat) +
geom_histogram()
ggplot(k1_df, aes(x = sel.score), fill = treat) +
geom_histogram()
ggplot(k1_df, aes(x = sel.score, fill= factor(treat))) +
geom_histogram()
ggplot(k1_df, aes(x = vid.num, fill= factor(treat))) +
geom_histogram()
ggplot(k1_df, aes(x = sel.score, facet_wrap(~treat))) +
geom_histogram()
ggplot(k1_df, aes(x = sel.score), facet_wrap(~treat)) +
geom_histogram()
ggplot(k1_df, aes(x = sel.score)) + facet_wrap(~treat) +
geom_histogram()
ggplot(k1_df, aes(x = vid.num)) + facet_wrap(~treat) +
geom_histogram()
ggplot(k1_df, aes(x = sel.score)) + facet_wrap(~treat) +
geom_histogram()
ggplot(k1_df, aes(x = vid.num)) + facet_wrap(~treat) +
geom_histogram()
ggplot(k1_df, aes(x = sel.score)) + facet_wrap(~treat) +
geom_histogram(bins = 2)
ggplot(k1_df, aes(x = sel.score)) + facet_wrap(~treat) +
geom_histogram()
ggplot(k1_df, aes(x = sel.score)) + facet_wrap(~treat) +
geom_histogram(bins = 10)
ggplot(k1_df, aes(x = sel.score)) + facet_wrap(~treat) +
geom_histogram(bins = 5)
ggplot(k1_df, aes(x = sel.score)) + facet_wrap(~treat) +
geom_histogram(bins = 4)
ggplot(k1_df, aes(x = sel.score)) + facet_wrap(~treat) +
geom_histogram(bins = 30)
ggplot(k1_df, aes(x = sel.score)) + facet_wrap(~treat) +
geom_histogram(bins = 300)
ggplot(k1_df, aes(x = sel.score)) + facet_wrap(~treat) +
geom_histogram(bins = 50)
ggplot(k1_df, aes(x = sel.score)) + facet_wrap(~treat) +
geom_histogram(bins = 30)
?summarise
k1_df %>%
group_by(treat) %>%
summarise(VidNum = mean(vid.num)) %>%
ggplot(aes(x = treat, y = VidNum) + geom_col(position = "dodge"))
k1_df %>%
group_by(treat) %>%
summarise(VidNum = mean(vid.num)) %>%
ggplot(aes(x = treat, y = VidNum)) + geom_col(position = "dodge")
k1_df %>%
group_by(treat) %>%
summarise(VidNum = mean(vid.num)) %>%
ggplot(aes(x = treat, y = VidNum)) + geom_col(position = "dodge") + ylim(0.8,1.6)
k1_df %>%
group_by(treat) %>%
summarise(VidNum = mean(vid.num)) %>%
ggplot(aes(x = treat, y = VidNum)) + geom_col(position = "dodge")
k1_df %>%
group_by(treat) %>%
summarise(VidNum = mean(vid.num)) %>%
ggplot(aes(x = treat, y = VidNum)) + geom_col(position = "dodge") + coord_cartesian(ylim=c(0.8,1.6))
k1_df %>%
group_by(treat) %>%
summarise(VidNum = mean(vid.num)) %>%
ggplot(aes(x = treat, y = VidNum)) + geom_col(position = "dodge") + coord_cartesian(ylim=c(0.8,1.8))
k1_df %>%
group_by(treat) %>%
summarise(VidNum = mean(vid.num)) %>%
ggplot(aes(x = treat, y = VidNum)) + geom_col(position = "dodge") + coord_cartesian(ylim=c(0.8,2))
k1_df %>%
group_by(treat) %>%
summarise(VidNum = mean(vid.num)) %>%
ggplot(aes(x = treat, y = VidNum)) + geom_col(position = "dodge") + coord_cartesian(ylim=c(0.8,1.6))
k1_df %>%
group_by(treat) %>%
summarise(VidNum = mean(vid.num)) %>%
ggplot(aes(x = treat, y = VidNum)) + geom_col(position = "dodge") + coord_cartesian(ylim=c(0.9,1.6))
k1_df %>%
group_by(treat) %>%
summarise(VidNum = mean(vid.num)) %>%
ggplot(aes(x = treat, y = VidNum)) + geom_col(position = "dodge") + coord_cartesian(ylim=c(1,1.6))
k1_df_modified <- k1_df |> group_by(treat) %>%
summarise(VidNum = mean(vid.num), .groups = "keep") %>%
mutate(SEM = std.err(VidNum))
k1_df_modified <- k1_df |> group_by(treat) %>%
summarise(VidNum = mean(vid.num), .groups = "keep") %>%
mutate(SEM = std.err(k1_df$vid.num))
std.err <- function(x) sd(x)/sqrt(length(x))
k1_df_modified <- k1_df |> group_by(treat) %>%
summarise(VidNum = mean(vid.num), .groups = "keep") %>%
mutate(SEM = std.err(k1_df$vid.num))
ggplot(k1_df_modified, aes(x = treat, y = VidNum)) + geom_col(position = "dodge") + coord_cartesian(ylim=c(1,1.6))
ggplot(k1_df_modified, aes(x = treat, y = VidNum)) + geom_col(position = "dodge") + coord_cartesian(ylim=c(1,1.6)) + geom_errorbar()
ggplot(k1_df_modified, aes(x = treat, y = VidNum)) + geom_col(position = "dodge") + coord_cartesian(ylim=c(1,1.6)) + geom_errorbar(aes(ymin= VidNum - SEM, ymax = VidNum + SEM), position="dodge")
k1_df_modified <- k1_df |> group_by(treat) %>%
summarise(VidNum = mean(vid.num), .groups = "keep") %>%
mutate(SD = sd(k1_df$vid.num))
ggplot(k1_df_modified, aes(x = treat, y = VidNum)) + geom_col(position = "dodge") + coord_cartesian(ylim=c(1,1.6)) + geom_errorbar(aes(ymin= VidNum - 2*SD, ymax = VidNum + 2*SD), position="dodge")
k1_df %>%
group_by(treat) %>%
summarise(mean_vids = mean(vid.num),
se_vids = sd(vid.num) / sqrt(n()),
n = n()) %>%
ggplot(aes(x = treat, y = mean_vids)) +
geom_pointrange(aes(ymin = mean_vids - se_vids, ymax = mean_vids + se_vids))
k1_df %>%
group_by(treat) %>%
summarise(mean_sel = mean(sel.score.avg),
se_sel = sd(sel.score.avg) / sqrt(n()),
n = n()) %>%
ggplot(aes(x = treat, y = mean_sel)) +
geom_pointrange(aes(ymin = mean_sel - se_sel, ymax = mean_sel + se_sel))
k1_df %>%
group_by(treat) %>%
summarise(mean_vids = mean(vid.num),
se_vids = sd(vid.num) / sqrt(n()),
n = n()) %>%
ggplot(aes(x = treat, y = mean_vids)) +
geom_pointrange(aes(ymin = mean_vids - se_vids * 1.96, ymax = mean_vids + se_vids * 1.96))
k1_df %>%
group_by(treat) %>%
summarise(mean_vids = mean(vid.num),
se_vids = sd(vid.num) / sqrt(n()), # standard error
n = n()) %>%
ggplot(aes(x = treat, y = mean_vids, col = treat)) +
geom_pointrange(aes(ymin = mean_vids - se_vids * 1.96, ymax = mean_vids + se_vids * 1.96)) # Multiplying by 1.96 to approximate 95% confindence interval
# Set working directory
setwd("/Users/bendix/Desktop/krauss2003_rescue")
library("tidyverse")
library("qualtRics")
library("plyr")
# Set working directory
setwd("/Users/bendix/Desktop/krauss2003_rescue")
getwd()
data <- read_survey("./data/prescreening.csv")
view(data)
tidy_data = data |>
select("Prolific ID question", Finished, MontyHallQuestion) |>
filter(MontyHallQuestion == "No")
tidy_data = data |>
select(Q8, Finished, MontyHallQuestion) |>
filter(MontyHallQuestion == "No")
view(tidy_data)
paste0(tidy_data$Q8, collapse=",")
library(tidyverse)
library(metafor)
library(here)
theme_set(theme_bw())
#devtools::install_github("langcog/metalabr")
library(metalabr)
install.packages(metalabr)
install.packages("metalabr")
#devtools::install_github("langcog/metalabr")
library(metalabr)
library("tidyverse")
library("qualtRics")
library("plyr")
# Set working directory
setwd("/Users/bendix/Desktop/krauss2003_rescue")
getwd()
data <- read_survey("./data/Krauss & Wang replication pre-survey_December 4, 2023_15.41.csv")
view(data)
tidy_data = data |>
select(Q8, Finished, MontyHallQuestion) |>
filter(MontyHallQuestion == "No")
view(tidy_data)
paste0(letters[1:5], collapse=",")
paste0(tidy_data$G8, collapse=",")
paste0(tidy_data$Q8, collapse=",")
library("tidyverse")
library("qualtRics")
library("plyr")
# Set working directory
setwd("/Users/bendix/Desktop/krauss2003_rescue")
getwd()
data <- read_survey("./data/data_nonanon.csv")
view(data)
tidy_data = data |>
select("Prolific ID question", ResponseId, Finished, MontyHallQuestion) |>
mutate(switch = case_when(switch == 'switch to door 2' | switch == 'switch to the last remaining door' ~ TRUE,
TRUE ~ FALSE)) |>
dplyr::rename(wins_cases_if_stays = Q7, wins_cases_if_switches = Q9, already_familiar = Q14, already_knew_answer = Q15, own_answer = Q43) |>
mutate(already_familiar = case_when(already_familiar == 'I was familiar with this game' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(already_knew_answer = case_when(already_knew_answer == 'I already knew what the correct answer should be' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(own_answer = case_when(own_answer == 'I arrived at the answer on my own' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(control = case_when(is.na(wins_cases_if_stays) ~ TRUE,
TRUE ~ FALSE))
tidy_data = data |>
select("Prolific ID question", ResponseId, Finished, "Duration (in seconds)", Q7, Q9, Q10, Q20, Q14, Q15, Q43) |>
unite(switch, c(Q10, Q20), na.rm = T) |>
mutate(switch = case_when(switch == 'switch to door 2' | switch == 'switch to the last remaining door' ~ TRUE,
TRUE ~ FALSE)) |>
dplyr::rename(wins_cases_if_stays = Q7, wins_cases_if_switches = Q9, already_familiar = Q14, already_knew_answer = Q15, own_answer = Q43) |>
mutate(already_familiar = case_when(already_familiar == 'I was familiar with this game' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(already_knew_answer = case_when(already_knew_answer == 'I already knew what the correct answer should be' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(own_answer = case_when(own_answer == 'I arrived at the answer on my own' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(control = case_when(is.na(wins_cases_if_stays) ~ TRUE,
TRUE ~ FALSE))
view(tidy_data)
tidy_data = data |>
select(ResponseId, Finished, "Duration (in seconds)", Q7, Q9, Q10, Q20, Q14, Q15, Q43) |>
unite(switch, c(Q10, Q20), na.rm = T) |>
mutate(switch = case_when(switch == 'switch to door 2' | switch == 'switch to the last remaining door' ~ TRUE,
TRUE ~ FALSE)) |>
dplyr::rename(wins_cases_if_stays = Q7, wins_cases_if_switches = Q9, already_familiar = Q14, already_knew_answer = Q15, own_answer = Q43) |>
mutate(already_familiar = case_when(already_familiar == 'I was familiar with this game' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(already_knew_answer = case_when(already_knew_answer == 'I already knew what the correct answer should be' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(own_answer = case_when(own_answer == 'I arrived at the answer on my own' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(control = case_when(is.na(wins_cases_if_stays) ~ TRUE,
TRUE ~ FALSE))
view(tidy_data)
write.csv(tidy_data, "./data/experiment.csv")
library("tidyverse")
library("qualtRics")
library("plyr")
library("dplyr")
# Set working directory
setwd("/Users/bendix/Desktop/krauss2003_rescue")
getwd()
### load data
data <- read_survey("./data/experiment.csv")
### load data
data <- read_csv("./data/experiment.csv")
### load data
data <- read_csv("../data/experiment.csv")
view(data)
### load data
data <- read_survey("./data/data_nonanon.csv")
### load data
data <- read_survey("../data/data_nonanon.csv")
tidy_data = data |>
select(ResponseId, Finished, "Duration (in seconds)", Q7, Q9, Q10, Q20, Q14, Q15, Q43) |>
unite(switch, c(Q10, Q20), na.rm = T) |>
mutate(switch = case_when(switch == 'switch to door 2' | switch == 'switch to the last remaining door' ~ TRUE,
TRUE ~ FALSE)) |>
dplyr::rename(wins_cases_if_stays = Q7, wins_cases_if_switches = Q9, already_familiar = Q14, already_knew_answer = Q15, own_answer = Q43) |>
mutate(already_familiar = case_when(already_familiar == 'I was familiar with this game' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(already_knew_answer = case_when(already_knew_answer == 'I already knew what the correct answer should be' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(own_answer = case_when(own_answer == 'I arrived at the answer on my own' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(control = case_when(is.na(wins_cases_if_stays) ~ TRUE,
TRUE ~ FALSE))
relevant_tidy_data = tidy_data |>
filter("Duration (in seconds)" >= 60) |>
filter(Finished == T) |>
filter(already_familiar == F) |>
filter(already_knew_answer == F) |>
filter(own_answer == T) |>
select(ResponseId, control, switch)
relevant_tidy_data = relevant_tidy_data |>
mutate(correct_justification = "")
write.csv(relevant_tidy_data, "../data/experiment_to_be_coded.csv")
relevant_tidy_data = relevant_tidy_data[order(relevant_tidy_data$switch, decreasing = TRUE)]
view(relevant_tidy_data)
relevant_tidy_data = relevant_tidy_data[order(relevant_tidy_data$switch)]
relevant_tidy_data = relevant_tidy_data[order(relevant_tidy_data$switch),]
view(relevant_tidy_data)
write.csv(relevant_tidy_data, "../data/experiment_to_be_coded.csv")
tidy_data = data |>
select(ResponseId, Finished, "Duration (in seconds)", Q7, Q9, Q10, Q20, Q14, Q15, Q43) |>
unite(switch, c(Q10, Q20), na.rm = T) |>
mutate(switch = case_when(switch == 'switch to door 2' | switch == 'switch to the last remaining door' ~ TRUE,
TRUE ~ FALSE)) |>
dplyr::rename(wins_cases_if_stays = Q7, wins_cases_if_switches = Q9, already_familiar = Q14, already_knew_answer = Q15, own_answer = Q43) |>
mutate(already_familiar = case_when(already_familiar == 'I was familiar with this game' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(already_knew_answer = case_when(already_knew_answer == 'I already knew what the correct answer should be' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(own_answer = case_when(own_answer == 'I arrived at the answer on my own' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(control = case_when(is.na(wins_cases_if_stays) ~ TRUE,
TRUE ~ FALSE))
view(tidy_data)
relevant_tidy_data <- read_csv("../data/pilotB_coded.csv")
table <- with(
relevant_tidy_data,
table(correct_justification, control))
table
relevant_tidy_data <- read_csv("../data/experiment_coded.csv")
table <- with(
relevant_tidy_data,
table(correct_justification, control))
table
proportions = data.frame(Condition=c("Control","Experimental"),
Proportion=c((table[2, 2]/
(table[2, 2] + table[1, 2])), # the first proportion is that of correct justifications given in control condition
(table[2,1]/
(table[2, 1] + table[1, 1])))) # the second proportion is that of correct justifications given in experimental condition
proportions
view(proportions)
table <- with(
relevant_tidy_data,
table(switch, control))
table
proportions = data.frame(Condition=c("Control","Experimental"),
Proportion=c((table[2, 2]/
(table[2, 2] + table[1, 2])), # the first proportion is that of correct justifications given in control condition
(table[2,1]/
(table[2, 1] + table[1, 1])))) # the second proportion is that of correct justifications given in experimental condition
view(proportions)
table <- with(
relevant_tidy_data,
table(correct_justification, control))
table
proportions = data.frame(Condition=c("Control","Experimental"),
Proportion=c((table[2, 2]/
(table[2, 2] + table[1, 2])), # the first proportion is that of correct justifications given in control condition
(table[2,1]/
(table[2, 1] + table[1, 1])))) # the second proportion is that of correct justifications given in experimental condition
view(proportions)
library("pwr")
h <- ES.h(
proportions$Proportion[1], # the first proportion is that of correct justifications given in control condition
proportions$Proportion[2] # the second proportion is that of correct justifications given in experimental condition
)
h
h <- ES.h(
proportions$Proportion[2], # the second proportion is that of correct justifications given in experimental condition
proportions$Proportion[1] # the first proportion is that of correct justifications given in control condition
)
h
fisher_exact_test <- fisher.test(as.matrix(table))
fisher_exact_test
library(ggplot2)
plot <- ggplot(data=proportions, aes(x=Condition, y=(Proportion)*100)) +
geom_bar(stat="identity") +
scale_x_discrete(breaks=c("Control", "Experimental"),
labels=c("Control", "Experimental")) + ylim(0, 100) +
labs(title = "Pilot B results", x = "Condition", y = "Percentage correct (%)")
plot
plot <- ggplot(data=proportions, aes(x=Condition, y=(Proportion)*100)) +
geom_bar(stat="identity") +
scale_x_discrete(breaks=c("Control", "Experimental"),
labels=c("Control", "Experimental")) + ylim(0, 100) +
labs(title = "Replication results", x = "Condition", y = "Percentage correct (%)")
plot
proportions_original_study = data.frame(Condition=c("Control","Experimental"),
Proportion=c(.3, .38))
plot_original_study <- ggplot(data=proportions_original_study, aes(x=Condition, y=(Proportion)*100)) +
geom_bar(stat="identity") +
scale_x_discrete(breaks=c("Control","Experimental"),
labels=c("Control", "Experimental")) + ylim(0, 100) +
labs(title = "Original study results", x = "Condition", y = "Percentage correct (%)")
plot_original_study
plot
proportions_original_study = data.frame(Condition=c("Control","Experimental"),
Proportion=c(.3, .38))
plot_original_study <- ggplot(data=proportions_original_study, aes(x=Condition, y=(Proportion)*100)) +
geom_bar(stat="identity") +
scale_x_discrete(breaks=c("Control","Experimental"),
labels=c("Control", "Experimental")) + ylim(0, 100) +
labs(title = "Original study results", x = "Condition", y = "Percentage correct (%)")
plot_original_study
proportions_original_study = data.frame(Condition=c("Control","Experimental"),
Proportion=c(.03, .38))
plot_original_study <- ggplot(data=proportions_original_study, aes(x=Condition, y=(Proportion)*100)) +
geom_bar(stat="identity") +
scale_x_discrete(breaks=c("Control","Experimental"),
labels=c("Control", "Experimental")) + ylim(0, 100) +
labs(title = "Original study results", x = "Condition", y = "Percentage correct (%)")
plot_original_study
plot
plot_original_study
plot
table_switch <- with(
relevant_tidy_data,
table(switch, control))
chisq_test <- chisq.test(table_switch)
chisq_test
table_switch
# after coding answers, load file
relevant_tidy_data <- read_csv("../data/experiment_coded.csv")
# generate contingency table that displays how many participants in the control and experimental condition provided a correct justification
table <- with(
relevant_tidy_data,
table(correct_justification, control))
table
proportions = data.frame(Condition=c("Control","Experimental"),
Proportion=c((table[2, 2]/
(table[2, 2] + table[1, 2])), # the first proportion is that of correct justifications given in control condition
(table[2,1]/
(table[2, 1] + table[1, 1])))) # the second proportion is that of correct justifications given in experimental condition
view(proportions)
# perform Cohen's h test
library("pwr")
h <- ES.h(
proportions$Proportion[2], # the first proportion is that of correct justifications given in experimental condition
proportions$Proportion[1] # the second proportion is that of correct justifications given in control condition
)
h
# perform Fisher's exact test
fisher_exact_test <- fisher.test(as.matrix(table))
fisher_exact_test
# generate contingency table that displays how many participants in the control and experimental condition indicated that the contestant should switch (i.e., gave a correct answer)
table_switch <- with(
relevant_tidy_data,
table(switch, control))
table_switch
# Chi squared test for rate of switch choices
chisq_test <- chisq.test(table_switch)
chisq_test
# Plots of results
library(ggplot2)
# Plot from replication study
plot <- ggplot(data=proportions, aes(x=Condition, y=(Proportion)*100)) +
geom_bar(stat="identity") +
scale_x_discrete(breaks=c("Control", "Experimental"),
labels=c("Control", "Experimental")) + ylim(0, 100) +
labs(title = "Replication results", x = "Condition", y = "Percentage correct (%)")
plot
# Plot from the original study
proportions_original_study = data.frame(Condition=c("Control","Experimental"),
Proportion=c(.03, .38))
plot_original_study <- ggplot(data=proportions_original_study, aes(x=Condition, y=(Proportion)*100)) +
geom_bar(stat="identity") +
scale_x_discrete(breaks=c("Control","Experimental"),
labels=c("Control", "Experimental")) + ylim(0, 100) +
labs(title = "Original study results", x = "Condition", y = "Percentage correct (%)")
plot_original_study
# Set working directory
setwd("/Users/bendix/Desktop/krauss2003_rescue")
getwd()
### load data
data <- read_survey("../data/data_nonanon.csv")
tidy_data = data |>
select(ResponseId, Finished, "Duration (in seconds)", Q7, Q9, Q10, Q20, Q14, Q15, Q43) |>
unite(switch, c(Q10, Q20), na.rm = T) |>
mutate(switch = case_when(switch == 'switch to door 2' | switch == 'switch to the last remaining door' ~ TRUE,
TRUE ~ FALSE)) |>
dplyr::rename(wins_cases_if_stays = Q7, wins_cases_if_switches = Q9, already_familiar = Q14, already_knew_answer = Q15, own_answer = Q43) |>
mutate(already_familiar = case_when(already_familiar == 'I was familiar with this game' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(already_knew_answer = case_when(already_knew_answer == 'I already knew what the correct answer should be' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(own_answer = case_when(own_answer == 'I arrived at the answer on my own' ~ TRUE,
TRUE ~ FALSE)) |>
mutate(control = case_when(is.na(wins_cases_if_stays) ~ TRUE,
TRUE ~ FALSE))
relevant_tidy_data = tidy_data |>
filter("Duration (in seconds)" >= 60) |>
filter(Finished == T) |>
filter(already_familiar == F) |>
filter(already_knew_answer == F) |>
filter(own_answer == T) |>
select(ResponseId, control, switch)
relevant_tidy_data = relevant_tidy_data |>
mutate(correct_justification = "")
relevant_tidy_data = relevant_tidy_data[order(relevant_tidy_data$switch),]