setwd("C:/Users/irina/Documents/DND/EOHI/eohi2")

options(scipen = 999)

df <- read.csv("eohi2.csv")
library(psych)
library(dplyr)
library(knitr)

# Your named variable sets (replace df with your actual dataframe name)
present_pref_vars <- c("present_pref_read", "present_pref_music", "present_pref_tv", "present_pref_nap", "present_pref_travel")
past_5_pref_vars  <- c("past_5_pref_read", "past_5_pref_music", "past_5_pref_TV", "past_5_pref_nap", "past_5_pref_travel")
past_10_pref_vars <- c("past_10_pref_read", "past_10_pref_music", "past_10_pref_TV", "past_10_pref_nap", "past_10_pref_travel")
fut_5_pref_vars   <- c("fut_5_pref_read", "fut_5_pref_music", "fut_5_pref_TV", "fut_5_pref_nap", "fut_5_pref_travel")
fut_10_pref_vars  <- c("fut_10_pref_read", "fut_10_pref_music", "fut_10_pref_TV", "fut_10_pref_nap", "fut_10_pref_travel")

present_pers_vars <- c("present_pers_extravert", "present_pers_critical", "present_pers_dependable", "present_pers_anxious", "present_pers_complex")
past_5_pers_vars  <- c("past_5_pers_extravert", "past_5_pers_critical", "past_5_pers_dependable", "past_5_pers_anxious", "past_5_pers_complex")
past_10_pers_vars <- c("past_10_pers_extravert", "past_10_pers_critical", "past_10_pers_dependable", "past_10_pers_anxious", "past_10_pers_complex")
fut_5_pers_vars   <- c("fut_5_pers_extravert", "fut_5_pers_critical", "fut_5_pers_dependable", "fut_5_pers_anxious", "fut_5_pers_complex")
fut_10_pers_vars  <- c("fut_10_pers_extravert", "fut_10_pers_critical", "fut_10_pers_dependable", "fut_10_pers_anxious", "fut_10_pers_complex")

present_val_vars <- c("present_val_obey", "present_val_trad", "present_val_opinion", "present_val_performance", "present_val_justice")
past_5_val_vars  <- c("past_5_val_obey", "past_5_val_trad", "past_5_val_opinion", "past_5_val_performance", "past_5_val_justice")
past_10_val_vars <- c("past_10_val_obey", "past_10_val_trad", "past_10_val_opinion", "past_10_val_performance", "past_10_val_justice")
fut_5_val_vars   <- c("fut_5_val_obey", "fut_5_val_trad", "fut_5_val_opinion", "fut_5_val_performance", "fut_5_val_justice")
fut_10_val_vars  <- c("fut_10_val_obey", "fut_10_val_trad", "fut_10_val_opinion", "fut_10_val_performance", "fut_10_val_justice")

all_scales <- list(
  "Present_Preferences" = present_pref_vars,
  "Past5_Preferences"   = past_5_pref_vars,
  "Past10_Preferences"  = past_10_pref_vars,
  "Fut5_Preferences"    = fut_5_pref_vars,
  "Fut10_Preferences"   = fut_10_pref_vars,
  "Present_Personality" = present_pers_vars,
  "Past5_Personality"   = past_5_pers_vars,
  "Past10_Personality"  = past_10_pers_vars,
  "Fut5_Personality"    = fut_5_pers_vars,
  "Fut10_Personality"   = fut_10_pers_vars,
  "Present_Values"      = present_val_vars,
  "Past5_Values"        = past_5_val_vars,
  "Past10_Values"       = past_10_val_vars,
  "Fut5_Values"         = fut_5_val_vars,
  "Fut10_Values"        = fut_10_val_vars
)

# Reliability analysis loop
alpha_results <- list()
total_results <- data.frame()
item_stats_results <- data.frame()
alpha_drop_results <- data.frame()

for(scale_name in names(all_scales)) {
  vars <- all_scales[[scale_name]]
  scale_data <- df %>% select(all_of(vars))
  
  # Only run if there is more than one column present
  if(ncol(scale_data) > 1) {
    alpha_out <- psych::alpha(scale_data, check.keys = TRUE)
    alpha_results[[scale_name]] <- alpha_out
    
    # Print full output for diagnostics
    cat("\n----------", scale_name, "----------\n")
    print(alpha_out$total)
    print(alpha_out$item.stats)
    print(alpha_out$alpha.drop)
    
    # Prepare results for CSV export
    scale_total <- alpha_out$total
    scale_items <- alpha_out$item.stats
    scale_alpha_drop <- alpha_out$alpha.drop
    
    # Add scale name to each row
    scale_total$Scale <- scale_name
    scale_items$Scale <- scale_name
    scale_alpha_drop$Scale <- scale_name
    
    # Combine results by type
    total_results <- rbind(total_results, scale_total)
    item_stats_results <- rbind(item_stats_results, scale_items)
    alpha_drop_results <- rbind(alpha_drop_results, scale_alpha_drop)
  }
}

# Create HTML report
html_content <- "<!DOCTYPE html>
<html>
<head>
    <title>Reliability Analysis Results</title>
    <style>
        body { font-family: Arial, sans-serif; margin: 20px; }
        h1 { color: #2c3e50; }
        h2 { color: #34495e; border-bottom: 2px solid #3498db; padding-bottom: 5px; }
        table { border-collapse: collapse; width: 100%; margin: 20px 0; }
        th, td { border: 1px solid #ddd; padding: 8px; text-align: left; }
        th { background-color: #3498db; color: white; }
        tr:nth-child(even) { background-color: #f2f2f2; }
        .scale-section { margin: 30px 0; }
        .summary-stats { background-color: #ecf0f1; padding: 15px; border-radius: 5px; }
    </style>
</head>
<body>
    <h1>Reliability Analysis Results</h1>
    <p>Generated on: " %+% Sys.time() %+% "</p>"

# Add total statistics
html_content <- html_content %+% "<h2>Overall Scale Statistics</h2>
    <div class='summary-stats'>
    <table>
    <tr><th>Scale</th><th>Raw Alpha</th><th>Std Alpha</th><th>G6(SMC)</th><th>Average r</th><th>S/N</th><th>ASE</th><th>Mean</th><th>SD</th><th>Median r</th></tr>"

for(i in 1:nrow(total_results)) {
  row <- total_results[i,]
  html_content <- html_content %+% sprintf("<tr><td>%s</td><td>%.4f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td></tr>",
    row$Scale, row$raw_alpha, row$std.alpha, row$G6.smc, row$average_r, row$S.N, row$ase, row$mean, row$sd, row$median_r)
}

html_content <- html_content %+% "</table></div>"

# Add item statistics
html_content <- html_content %+% "<h2>Item Statistics</h2>
    <table>
    <tr><th>Scale</th><th>Item</th><th>n</th><th>Raw r</th><th>Std r</th><th>r.cor</th><th>r.drop</th><th>Mean</th><th>SD</th></tr>"

for(i in 1:nrow(item_stats_results)) {
  row <- item_stats_results[i,]
  html_content <- html_content %+% sprintf("<tr><td>%s</td><td>%s</td><td>%.0f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td></tr>",
    row$Scale, rownames(row), row$n, row$raw.r, row$std.r, row$r.cor, row$r.drop, row$mean, row$sd)
}

html_content <- html_content %+% "</table>"

# Add alpha if dropped
html_content <- html_content %+% "<h2>Alpha If Item Dropped</h2>
    <table>
    <tr><th>Scale</th><th>Item</th><th>Raw Alpha</th><th>Std Alpha</th><th>G6(SMC)</th><th>Average r</th><th>S/N</th><th>Alpha SE</th><th>Var r</th><th>Med r</th></tr>"

for(i in 1:nrow(alpha_drop_results)) {
  row <- alpha_drop_results[i,]
  html_content <- html_content %+% sprintf("<tr><td>%s</td><td>%s</td><td>%.4f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td><td>%.4f</td></tr>",
    row$Scale, rownames(row), row$raw_alpha, row$std.alpha, row$G6.smc, row$average_r, row$S.N, row$alpha.se, row$var.r, row$med.r)
}

html_content <- html_content %+% "</table>
</body>
</html>"

# Write HTML file
writeLines(html_content, "reliability_analysis_report.html")


# Check for reversed items
cat("\n=== REVERSED ITEMS CHECK ===\n")
for(scale_name in names(all_scales)) {
  vars <- all_scales[[scale_name]]
  scale_data <- df %>% select(all_of(vars))
  
  if(ncol(scale_data) > 1) {
    alpha_out <- alpha_results[[scale_name]]
    
    # Check if any items were reversed by looking at the keys
    if(!is.null(alpha_out$keys)) {
      keys_df <- as.data.frame(alpha_out$keys)
      reversed_items <- rownames(keys_df)[keys_df[,1] < 0]
      if(length(reversed_items) > 0) {
        cat(scale_name, "reversed items:", paste(reversed_items, collapse = ", "), "\n")
      } else {
        cat(scale_name, ": No items reversed\n")
      }
    } else {
      cat(scale_name, ": No keys available\n")
    }
  }
}