# Mixed ANOVA Analysis for Past vs Future Differences
# EOHI Experiment Data Analysis

# Load required libraries
library(tidyverse)
library(ez)
library(car)
library(nortest)      # For normality tests

# Read the data
data <- read.csv("eohi1/exp1.csv")

# Display basic information about the dataset
cat("Dataset dimensions:", dim(data), "\n")
cat("Number of participants:", length(unique(data$pID)), "\n")

# Check experimental conditions
cat("\nExperimental conditions:\n")
table(data$GROUP, data$TASK_DO, data$TEMPORAL_DO)

# Prepare data for mixed ANOVA
# We'll reshape the data to have Past and Future as separate rows for each participant

# Create a function to reshape data for a specific domain
reshape_domain_data <- function(data, domain_name) {
  past_col <- paste0("NPastDiff_", domain_name)
  fut_col <- paste0("NFutDiff_", domain_name)
  
  # Check if columns exist
  if (!(past_col %in% colnames(data)) || !(fut_col %in% colnames(data))) {
    cat("Warning: Columns", past_col, "or", fut_col, "not found\n")
    return(NULL)
  }
  
  # Create long format data
  past_data <- data %>%
    select(pID, ResponseId, GROUP, TASK_DO, TEMPORAL_DO, ITEM_DO, COC_DO, 
           demo_sex, demo_age_1, AOT_total, CRT_correct, all_of(past_col)) %>%
    mutate(TimePerspective = "Past",
           Difference = .data[[past_col]]) %>%
    select(-all_of(past_col))
  
  fut_data <- data %>%
    select(pID, ResponseId, GROUP, TASK_DO, TEMPORAL_DO, ITEM_DO, COC_DO,
           demo_sex, demo_age_1, AOT_total, CRT_correct, all_of(fut_col)) %>%
    mutate(TimePerspective = "Future", 
           Difference = .data[[fut_col]]) %>%
    select(-all_of(fut_col))
  
  combined_data <- rbind(past_data, fut_data) %>%
    mutate(TimePerspective = as.factor(TimePerspective),
           pID = as.factor(pID))
  
  return(combined_data)
}

# Define domains to analyze
domains <- c("pref_read", "pref_music", "pref_tv", "pref_nap", "pref_travel",
             "pers_extravert", "pers_critical", "pers_dependable", "pers_anxious", "pers_complex",
             "val_obey", "val_trad", "val_opinion", "val_performance", "val_justice",
             "life_ideal", "life_excellent", "life_satisfied", "life_important", "life_change")

# Run mixed ANOVA for each domain
results_list <- list()

for (domain in domains) {
  cat("\n", "="*60, "\n")
  cat("ANALYZING DOMAIN:", toupper(domain), "\n")
  cat("="*60, "\n")
  
  # Reshape data for this domain
  domain_data <- reshape_domain_data(data, domain)
  
  if (is.null(domain_data)) {
    next
  }
  
  # Check for missing values
  missing_count <- sum(is.na(domain_data$Difference))
  if (missing_count > 0) {
    cat("Warning:", missing_count, "missing values found for", domain, "\n")
    domain_data <- domain_data[!is.na(domain_data$Difference), ]
  }
  
  # Descriptive statistics
  cat("\nDescriptive Statistics:\n")
  desc_stats <- domain_data %>%
    group_by(TimePerspective) %>%
    summarise(
      n = n(),
      mean = mean(Difference, na.rm = TRUE),
      sd = sd(Difference, na.rm = TRUE),
      median = median(Difference, na.rm = TRUE),
      .groups = 'drop'
    )
  print(desc_stats)
  
  # Effect size (Cohen's d)
  past_diff <- domain_data$Difference[domain_data$TimePerspective == "Past"]
  fut_diff <- domain_data$Difference[domain_data$TimePerspective == "Future"]
  
  pooled_sd <- sqrt(((length(past_diff) - 1) * var(past_diff) + 
                     (length(fut_diff) - 1) * var(fut_diff)) / 
                    (length(past_diff) + length(fut_diff) - 2))
  
  cohens_d <- (mean(past_diff) - mean(fut_diff)) / pooled_sd
  
  cat("\nCohen's d (Past vs Future):", round(cohens_d, 5), "\n")
  
  # Mixed ANOVA using ezANOVA
  tryCatch({
    # Simple repeated measures ANOVA (TimePerspective as within-subjects)
    anova_result <- ezANOVA(
      data = domain_data,
      dv = Difference,
      wid = pID,
      within = TimePerspective,
      between = c(GROUP, TASK_DO),
      type = 3,
      detailed = TRUE
    )
    
    cat("\nMixed ANOVA Results:\n")
    print(anova_result)
    
    # Store results
    results_list[[domain]] <- list(
      descriptive = desc_stats,
      cohens_d = cohens_d,
      anova = anova_result
    )
    
    # Post-hoc comparisons if significant
    if (!is.null(anova_result$ANOVA)) {
      if (any(anova_result$ANOVA$p < 0.05, na.rm = TRUE)) {
        cat("\nSignificant effects found! Post-hoc analysis:\n")
        
        # Pairwise comparisons for TimePerspective
        if ("TimePerspective" %in% anova_result$ANOVA$Effect && 
            anova_result$ANOVA$p[anova_result$ANOVA$Effect == "TimePerspective"] < 0.05) {
          
          # Simple t-test for comparison
          t_test_result <- t.test(past_diff, fut_diff, paired = TRUE)
          cat("\nPaired t-test (Past vs Future):\n")
          cat("t =", round(t_test_result$statistic, 3), 
              ", df =", t_test_result$parameter, 
              ", p =", round(t_test_result$p.value, 5), "\n")
        }
      }
    }
    
  }, error = function(e) {
    cat("Error in ANOVA for", domain, ":", e$message, "\n")
    
    # Fallback to simple paired t-test
    t_test_result <- t.test(past_diff, fut_diff, paired = TRUE)
    cat("\nFallback: Paired t-test (Past vs Future):\n")
    cat("t =", round(t_test_result$statistic, 3), 
        ", df =", t_test_result$parameter, 
        ", p =", round(t_test_result$p.value, 5), "\n")
    
    results_list[[domain]] <- list(
      descriptive = desc_stats,
      cohens_d = cohens_d,
      t_test = t_test_result
    )
  })
}

# Summary of all results
cat("\n", "="*80, "\n")
cat("SUMMARY OF ALL DOMAINS\n")
cat("="*80, "\n")

summary_df <- data.frame(
  Domain = character(),
  Past_Mean = numeric(),
  Future_Mean = numeric(),
  Cohen_d = numeric(),
  Significant = logical(),
  stringsAsFactors = FALSE
)

for (domain in names(results_list)) {
  result <- results_list[[domain]]
  
  past_mean <- result$descriptive$mean[result$descriptive$TimePerspective == "Past"]
  fut_mean <- result$descriptive$mean[result$descriptive$TimePerspective == "Future"]
  cohens_d <- result$cohens_d
  
  # Check if significant (p < 0.05)
  significant <- FALSE
  if (!is.null(result$anova) && !is.null(result$anova$ANOVA)) {
    if ("TimePerspective" %in% result$anova$ANOVA$Effect) {
      p_val <- result$anova$ANOVA$p[result$anova$ANOVA$Effect == "TimePerspective"]
      significant <- !is.na(p_val) && p_val < 0.05
    }
  } else if (!is.null(result$t_test)) {
    significant <- result$t_test$p.value < 0.05
  }
  
  summary_df <- rbind(summary_df, data.frame(
    Domain = domain,
    Past_Mean = round(past_mean, 3),
    Future_Mean = round(fut_mean, 3),
    Cohen_d = round(cohens_d, 5),
    Significant = significant
  ))
}

# Sort by effect size (absolute value)
summary_df <- summary_df[order(abs(summary_df$Cohen_d), decreasing = TRUE), ]

print(summary_df)

# Create visualization
library(ggplot2)

# Prepare data for plotting
plot_data <- summary_df %>%
  mutate(
    Effect_Size = abs(Cohen_d),
    Direction = ifelse(Cohen_d > 0, "Past > Future", "Future > Past"),
    Domain_Type = case_when(
      grepl("pref_", Domain) ~ "Preferences",
      grepl("pers_", Domain) ~ "Personality", 
      grepl("val_", Domain) ~ "Values",
      grepl("life_", Domain) ~ "Life Satisfaction",
      TRUE ~ "Other"
    )
  )

# Effect size plot
p1 <- ggplot(plot_data, aes(x = reorder(Domain, Effect_Size), y = Effect_Size, 
                           fill = Direction, alpha = Significant)) +
  geom_col() +
  coord_flip() +
  scale_alpha_manual(values = c(0.5, 1), name = "Significant\n(p < 0.05)") +
  scale_fill_manual(values = c("Past > Future" = "#E74C3C", "Future > Past" = "#3498DB")) +
  labs(
    title = "Effect Sizes: Past vs Future Differences",
    subtitle = "Absolute Cohen's d values across domains",
    x = "Domain",
    y = "|Cohen's d|",
    fill = "Direction"
  ) +
  theme_minimal() +
  theme(axis.text.y = element_text(size = 8))

print(p1)

# Mean differences plot
plot_data_long <- summary_df %>%
  select(Domain, Past_Mean, Future_Mean) %>%
  pivot_longer(cols = c(Past_Mean, Future_Mean), 
               names_to = "TimePerspective", 
               values_to = "Mean_Difference") %>%
  mutate(TimePerspective = gsub("_Mean", "", TimePerspective))

p2 <- ggplot(plot_data_long, aes(x = reorder(Domain, Mean_Difference), 
                                 y = Mean_Difference, 
                                 fill = TimePerspective)) +
  geom_col(position = "dodge") +
  coord_flip() +
  scale_fill_manual(values = c("Past" = "#E74C3C", "Future" = "#3498DB")) +
  labs(
    title = "Mean Differences by Time Perspective",
    subtitle = "Past vs Future difference scores",
    x = "Domain", 
    y = "Mean Difference Score",
    fill = "Time Perspective"
  ) +
  theme_minimal() +
  theme(axis.text.y = element_text(size = 8))

print(p2)

cat("\nAnalysis complete! Check the plots and summary table above.\n")
cat("Key findings:\n")
cat("- Domains with largest effect sizes:", paste(head(summary_df$Domain, 3), collapse = ", "), "\n")
cat("- Number of significant differences:", sum(summary_df$Significant), "out of", nrow(summary_df), "\n")