options(scipen = 999)

library(dplyr)

setwd("C:/Users/irina/Documents/DND/EOHI/eohi1")

df <- read.csv("ehi1.csv")

data <- df %>%
  select(eohiDGEN_mean, ehi_global_mean, demo_sex, demo_age_1, edu3, AOT_total, CRT_correct, CRT_int, bs_28, bs_easy, bs_hard, cal_selfActual, cal_global) %>%
  filter(demo_sex != "Prefer not to say")

print(colSums(is.na(data)))
print(sapply(data, class))

# Create dummy variable for sex (0 = Male, 1 = Female)
data$sex_dummy <- ifelse(data$demo_sex == "Female", 1, 0)

# Verify the dummy coding
print(table(data$demo_sex, data$sex_dummy))

#descriptives

# Descriptives for age
print(summary(data$demo_age_1))
print(sd(data$demo_age_1, na.rm = TRUE))

# Center demo_age_1 (subtract the mean)
data$age_centered <- data$demo_age_1 - mean(data$demo_age_1, na.rm = TRUE)

# Verify the centering
print(summary(data$age_centered))

# Descriptives for sex (frequency table)
print(table(data$demo_sex))
print(prop.table(table(data$demo_sex)))

# Descriptives for sex dummy variable
print(table(data$sex_dummy))

# Convert edu3 to numeric factor for correlations (1, 2, 3)
# First ensure edu3 is a factor, then convert to numeric
data$edu3 <- factor(data$edu3, levels = c("HS_TS", "C_Ug", "grad_prof"), ordered = TRUE)
data$edu_num <- as.numeric(data$edu3)

# Check the numeric conversion
print(table(data$edu_num, useNA = "ifany"))

# Verify the conversion
print(table(data$edu3, data$edu_num, useNA = "ifany"))

####correlation matrix ####

# Select numeric variables for correlation matrix
numeric_vars <- data %>%
  select(eohiDGEN_mean, ehi_global_mean, sex_dummy, age_centered, edu_num, AOT_total, CRT_correct, CRT_int, bs_28, bs_easy, bs_hard, cal_selfActual, cal_global)

# Create correlation matrix
cor_matrix <- cor(numeric_vars, use = "complete.obs")

# Print correlation matrix
print("Correlation Matrix:")
print(round(cor_matrix, 3))

# Save correlation matrix to CSV
write.csv(cor_matrix, "correlation_matrix.csv", row.names = TRUE)
print("Correlation matrix saved to correlation_matrix.csv")