# Load necessary packages
library(dplyr)
library(ggplot2)
library(gridExtra)
library(knitr)

# Prompt the user to select a file (LongHFScores.csv <- available in the Supplementary Materials)
selected_file <- file.choose()

# Load the selected file (LongHFScores.csv <- available in the Supplementary Materials)
data <- read.csv(selected_file)

# Display the first few records from the loaded file
head(data)

# Function to obtain a specific statistical summary by subgroup and format it as a table
get_specific_summary <- function(data, condition, social_isolation) {
  filtered_data <- data %>%
    filter(CONDITION == condition, SOCIAL_ISOLATION == social_isolation)
  
  # Calculate statistical summary for each variable
  summary_specific <- as.numeric(summary(filtered_data$SPECIFIC_SCENARIOS))
  summary_screen <- as.numeric(summary(filtered_data$SCREEN_TIME))
  summary_total <- as.numeric(summary(filtered_data$TOTAL_SCORE))
  
  # Create a data frame for the summary table
  summary_table <- data.frame(
    Statistic = c("Min", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max"),
    SPECIFIC_SCENARIOS = summary_specific,
    SCREEN_TIME = summary_screen,
    TOTAL_SCORE = summary_total
  )
  
  # Display the formatted table
  cat("Summary for CONDITION =", condition, "and SOCIAL_ISOLATION =", social_isolation, "\n")
  print(kable(summary_table, format = "markdown"))
  cat("\n")
}

# Get statistical summary for each combination of CONDITION and SOCIAL_ISOLATION

# CONDITION 1
get_specific_summary(data, 1, 0)
get_specific_summary(data, 1, 1)

# CONDITION 2
get_specific_summary(data, 2, 0)
get_specific_summary(data, 2, 1)

# CONDITION 3
get_specific_summary(data, 3, 0)
get_specific_summary(data, 3, 1)

# Ensure necessary packages are loaded
library(dplyr)
library(ggplot2)
library(gridExtra)

# Set the number of bins for histograms
num_bins <- 7  # Adjust for between 7 to 10 intervals

# Create histograms with density curves for each variable before and during social isolation

# SCREEN_TIME before and during isolation
hist_screen_time_before <- ggplot(data %>% filter(SOCIAL_ISOLATION == 0), aes(x = SCREEN_TIME)) +
  geom_histogram(aes(y = after_stat(density)), bins = num_bins, fill = "blue", alpha = 0.4) +
  geom_density(alpha = 0.5, fill = "blue") +
  ggtitle("SCREEN_TIME Distribution (Before)") +
  xlab("Screen Time") +
  ylab("Density")

hist_screen_time_during <- ggplot(data %>% filter(SOCIAL_ISOLATION == 1), aes(x = SCREEN_TIME)) +
  geom_histogram(aes(y = after_stat(density)), bins = num_bins, fill = "blue", alpha = 0.4) +
  geom_density(alpha = 0.5, fill = "blue") +
  ggtitle("SCREEN_TIME Distribution (During)") +
  xlab("Screen Time") +
  ylab("Density")

# SPECIFIC_SCENARIOS before and during isolation
hist_specific_scenarios_before <- ggplot(data %>% filter(SOCIAL_ISOLATION == 0), aes(x = SPECIFIC_SCENARIOS)) +
  geom_histogram(aes(y = after_stat(density)), bins = num_bins, fill = "red", alpha = 0.4) +
  geom_density(alpha = 0.5, fill = "red") +
  ggtitle("SPECIFIC_SCENARIOS Distribution (Before)") +
  xlab("Specific Scenarios") +
  ylab("Density")

hist_specific_scenarios_during <- ggplot(data %>% filter(SOCIAL_ISOLATION == 1), aes(x = SPECIFIC_SCENARIOS)) +
  geom_histogram(aes(y = after_stat(density)), bins = num_bins, fill = "red", alpha = 0.4) +
  geom_density(alpha = 0.5, fill = "red") +
  ggtitle("SPECIFIC_SCENARIOS Distribution (During)") +
  xlab("Specific Scenarios") +
  ylab("Density")

# TOTAL_SCORE before and during isolation
hist_total_score_before <- ggplot(data %>% filter(SOCIAL_ISOLATION == 0), aes(x = TOTAL_SCORE)) +
  geom_histogram(aes(y = after_stat(density)), bins = num_bins, fill = "green", alpha = 0.4) +
  geom_density(alpha = 0.5, fill = "green") +
  ggtitle("TOTAL_SCORE Distribution (Before)") +
  xlab("Total Score") +
  ylab("Density")

hist_total_score_during <- ggplot(data %>% filter(SOCIAL_ISOLATION == 1), aes(x = TOTAL_SCORE)) +
  geom_histogram(aes(y = after_stat(density)), bins = num_bins, fill = "green", alpha = 0.4) +
  geom_density(alpha = 0.5, fill = "green") +
  ggtitle("TOTAL_SCORE Distribution (During)") +
  xlab("Total Score") +
  ylab("Density")

# Arrange all histograms in the same window
grid.arrange(
  hist_screen_time_before, hist_screen_time_during,
  hist_specific_scenarios_before, hist_specific_scenarios_during,
  hist_total_score_before, hist_total_score_during,
  ncol = 2
)