# Load required packages
library(psych)
library(Hmisc)
library(corrplot)
library(openxlsx)

# Specify variables
variables <- c("GAD", "GDS", "WEMWBS", "CFRS", "PCA_RC1", "PCA_RC2", 
               "BERQ_ActivelyApproach", "BERQ_Distraction", "BERQ_Withdrawal",  
               "BERQ_SocialSupport", "BERQ_Ignoring", "CERQ_SelfBlame", 
               "CERQ_Acceptance", "CERQ_Rumination", "CERQ_Refocus", 
               "CERQ_Planning", "CERQ_Reappraisal", "CERQ_Perspective", 
               "CERQ_Catastrophising", "CERQ_OtherBlame",
               "Age", "Education", "UPDRS_Motor", "PD_Duration", "MoCA_Total")

# Subset the dataframe
df_subset <- df2[, variables]

# Calculate the Spearman correlation matrix and p-values
cor_results <- rcorr(as.matrix(df_subset), type = "spearman")

# Extract the correlation coefficients and p-values
cor_matrix <- cor_results$r
p_matrix <- cor_results$P

# Define the number of tests for Bonferroni correction 
n_tests <- sum(upper.tri(cor_matrix))
alpha_bonferroni <- 0.05 / n_tests

# Create a matrix to store both correlation coefficients and significance asterisks
combined_matrix <- matrix("", ncol = length(variables), nrow = length(variables))
colnames(combined_matrix) <- variables
rownames(combined_matrix) <- variables

# Fill the matrix with correlation coefficients and significance asterisks
for (i in 1:nrow(cor_matrix)) {
  for (j in 1:ncol(cor_matrix)) {
    if (!is.na(p_matrix[i, j])) {
      # Determine significance level based on p-value
      significance <- ifelse(p_matrix[i, j] <= 0.001, "***",
                             ifelse(p_matrix[i, j] <= 0.01, "**",
                                    ifelse(p_matrix[i, j] <= 0.05, "*", "")))
      
      # Store the correlation coefficient rounded to 3 decimal places and significance level in the matrix
      combined_matrix[i, j] <- sprintf("%.3f%s", round(cor_matrix[i, j], 3), significance)
    }
  }
}
# Convert the matrix to a data frame 
Q1B_Correlations <- as.data.frame(combined_matrix)

# Print the combined matrix
print(Q1B_Correlations)

# Define the file path to save the Excel file
file_path <- "~/Desktop/Q1B_Correlations.xlsx"

# Write the combined matrix to an Excel file
write.xlsx(Q1B_Correlations, file_path)