process_data.R

# Load required libraries
library(relaimpo)
library(readxl)
library(readr)
library(lavaan)
library(leaps)
library(dplyr)
library(tidyr)

# Logging function
log_message <- function(message, output_text_file) {
  cat(message, "\n")
  write(message, file = output_text_file, append = TRUE)
}

# Trust Driver analysis function
trust_driver_analysis <- function(model_formula, data, output_text_file, csv_file) {
  tryCatch({
    # Fit linear regression model
    model <- lm(model_formula, data = data)

    # Calculate relative importance using the lmg method
    calc_relaimpo <- calc.relimp(model, type = "lmg", rela = TRUE)
    # Calculate average importance
    average_importance <- mean(calc_relaimpo$lmg)

    # Open the output text file in append mode to add this model's output
    file_conn <- file(output_text_file, open = "a")
    # Capture output to include in the text file
    full_output <- capture.output({
      print("Trust Driver Analysis:\n")
      print(calc_relaimpo)
      cat("\nAverage Importance: ", average_importance, "\n")
    })
    # Write output to text file
    writeLines(full_output, file_conn)
    close(file_conn)

    # Create data frame of predictor names and their importance
    results <- data.frame(Predictor = names(calc_relaimpo$lmg), Importance = calc_relaimpo$lmg)

    # Save results to CSV file
    write.csv(results, file = csv_file, row.names = FALSE)
  }, error = function(e) {
    log_message(paste("Error in trust_driver_analysis:", e$message), output_text_file)
  })
}

# Trust Builder Analysis function
trust_builder_analysis <- function(data, data_headers, output_text_file, csv_file) {
  tryCatch({
    # Map the questions to column names
    question_to_column <- setNames(as.list(data_headers[1, ]), as.character(data_headers[2, ]))

    # Number of important statements to be selected
    p <- 6

    # Define the list of column names
    bucket_columns <- c("Stability", "Development", "Relationship", "Benefit", "Vision", "Competence")

    # Select columns based on the predefined list
    bucket <- data %>% select(all_of(bucket_columns))

    # Select all columns from the consumer dataframe that contain "TB" in their names and assign them to the variable TB
    TB <- data %>% select(contains("TB"))

    # Initialize a matrix with 37 rows and 6 columns, filled with NA values
    coef <- matrix(NA, ncol = 6, nrow = 37)

    # Initialize an empty list to store the predictors for each bucket column
    bucket_predictors <- list()

    # Loop over each of the 6 columns
    for (i in 1:6) {
      # Extract the i-th column from 'bucket' as a matrix and assign it to 'y'
      y <- as.matrix(pull(bucket[, i]))

      # Convert 'TB' dataframe to a matrix and assign it to 'x'
      x <- as.matrix(TB)

      # Perform best subset regression using 'x' as predictors and 'y' as the response variable
      fit <- regsubsets(x, y, nbest = 1, nvmax = p)

      # Summarize the regression subsets
      fit_sum <- summary(fit)

      # Store the coefficients of the best model in the i-th column of 'coef' matrix
      coef[, i] <- fit_sum$outmat[p, ]

      # Print the predictors used in the best model
      predictors <- names(which(fit_sum$outmat[p, ] == "*"))

      # Append the predictors to the bucket_predictors list
      bucket_predictors[[bucket_columns[i]]] <- predictors
    }

    # Create the desired output format as model
    model_str <- sapply(names(bucket_predictors), function(col) {
      paste(col, "~", paste(bucket_predictors[[col]], collapse = "+"))
    })

    # Prepend the Trust x and y to model_str
    model_str <- c("Trust ~ Stability + Development + Relationship + Benefit + Vision + Competence", model_str)

    # Fit the model using sem() function
    fit <- sem(model_str, data = data)
    fit_summary <- summary(fit, standardized = TRUE, fit.measures = TRUE, rsquare = TRUE)

    # Make it percentages
    output <- fit_summary$pe[fit_summary$pe$op == "~", c("lhs", "rhs", "std.all")]

    # Define the function to convert std.all to percentages
    convert_to_percentage <- function(df) {
      df %>%
        group_by(lhs) %>%
        mutate(abs_std = abs(std.all),
              sum_abs_std = sum(abs_std),
              percent_std = (abs_std / sum_abs_std) * 100) %>%
        select(-abs_std, -sum_abs_std) %>%
        ungroup()
    }

    # Convert the estimates to percentages
    percentage_output <- convert_to_percentage(output)

    # Extract TB column names
    tb_column_names <- colnames(TB)

    # Convert std.all to a wide format dataframe
    percentage_output_wide <- percentage_output %>%
      pivot_wider(names_from = lhs, values_from = percent_std) %>%
      rename_with(~ gsub("std.all\\.", "", .), starts_with("std.all"))

    # Create a new dataframe with TB columns and percentage estimates
    result_df <- data.frame(TB = tb_column_names)

    # Merge the result_df with percentage_estimates_wide
    result_df <- left_join(result_df, percentage_output_wide, by = c("TB" = "rhs"))

    # Fill NA values with 0 to ensure proper representation
    result_df[is.na(result_df)] <- 0

    # Add corresponding messages of TB as a new column
    result_df$Message <- sapply(result_df$TB, function(tb_col) question_to_column[[tb_col]])

    # Convert 'TB' column to a factor with the correct order
    result_df$TB <- factor(result_df$TB, levels = paste0("TB", 1:37))

    # Exclude 'est' and 'Trust' columns and merge rows by 'TB'
    result_df <- result_df %>%
      select(-std.all, -Trust) %>%
      group_by(TB) %>%
      summarise(across(everything(), ~ if(is.numeric(.)) sum(., na.rm = TRUE) else first(.))) %>%
      arrange(TB)

    # Reorder columns to have Message as the second column
    result_df <- result_df %>%
      select(TB, Message, everything())

    # Open the output text file in append mode to add this model's output
    file_conn <- file(output_text_file, open = "a")

    # Capture output to include in the text file
    full_output <- capture.output({
      print("Trust Builder Analysis:\n")
      print("Data header mapping:\n")
      print(question_to_column)
      print("Buckets:\n")
      print(bucket)
      print("Messages:\n")
      print(TB)
      print("Coefficients matrix (coef:\n")
      print(coef)
      print("Model:\n")
      cat(model_str, sep = "\n")
      print("Fit summary:\n")
      print(fit_summary)
      print("Output:\n")
      print(output)
      print("Output in percentage (%):\n")
      print(percentage_output)
      print("result_df:\n")
      print(result_df)
    })
    # Write output to text file
    writeLines(full_output, file_conn)
    close(file_conn)

    # Create data frame of predictor names and their importance
    results <- data.frame(result_df)

    # Save results to CSV file
    write.csv(results, file = csv_file, row.names = FALSE)
  }, error = function(e) {
    log_message(paste("Error in trust_builder_analysis:", e$message), output_text_file)
  })
}

# Read command-line arguments
args <- commandArgs(trailingOnly = TRUE)
input_file <- args[1]
output_text_file <- args[2]  # Base path for output text and CSV files
csv_output_path_trust <- args[3]
csv_output_path_nps <- args[4]
csv_output_path_loyalty <- args[5]
csv_output_path_consideration <- args[6]
csv_output_path_satisfaction <- args[7]
csv_output_path_trustbuilder <- args[8]
nps_present <- as.logical(tolower(args[9]))  # Expecting "TRUE" or "FALSE" as the argument
loyalty_present <- as.logical(tolower(args[10]))
consideration_present <- as.logical(tolower(args[11]))
satisfaction_present <- as.logical(tolower(args[12]))
trustbuilder_present <- as.logical(tolower(args[13]))

# Log the starting of the script
log_message("Starting Trust Driver and Builder Analysis Script.", output_text_file)

########## Trust Driver Analysis ######################

# Load the trust driver dataset (CSV or Excel)
data_driver <- NULL
if (grepl(".xlsx", input_file)) {
  # Load the Excel file with the fourth row as the header
  data_driver <- read_excel(input_file, sheet = "Driver", skip = 3)
}

# Process the Trust model
trust_driver_analysis(
  Trust ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
  data_driver,
  output_text_file,
  csv_output_path_trust)

# Conditionally process the NPS model
if (nps_present) {
  trust_driver_analysis(
    NPS ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
    data_driver,
    output_text_file,
    csv_output_path_nps)
}

# Conditionally process the Loyalty model
if (loyalty_present) {
  trust_driver_analysis(
    Loyalty ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
    data_driver,
    output_text_file,
    csv_output_path_loyalty)
}

# Conditionally process the Consideration model
if (consideration_present) {
  trust_driver_analysis(
    Consideration ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
    data_driver,
    output_text_file,
    csv_output_path_consideration)
}

# Conditionally process the Satisfaction model
if (satisfaction_present) {
  trust_driver_analysis(
    Satisfaction ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
    data_driver,
    output_text_file,
    csv_output_path_satisfaction)
}

########## Trust Builder Analysis ######################

if (trustbuilder_present) {
  data_builder <- NULL

  if (grepl(".xlsx", input_file)) {
    # Read the 4th and 5th rows as header mapping
    data_builder_headers <- read_excel(input_file, sheet = "Builder", skip = 3, n_max = 2)  
    # Read the rest of the data, skipping the first 5 rows (to start from row 6)
    data_builder_rows <- read_excel(input_file, sheet = "Builder", skip = 5)
  }

  # Process the Builder model
  trust_builder_analysis(data_builder_rows, data_builder_headers, output_text_file, csv_output_path_trustbuilder)

}

# Log the ending of the script
log_message("Trust Driver and Builder Analysis Script Completed.", output_text_file)