setwd("D:/PRAK ANDAT") getwd()

#read in your data library(readr) data <- read_csv2("depression_anxiety_data.csv", col_types = "ccccinnccccccncnncccccc") names(data) <-make.names(names(data)) names(data)

#check the packaging nrow(data) ncol(data) str(data)

#Look at the top and the bottom of your data head(data[, c(11,13)]) tail(data[, c(11,13)])

head(table(data$depression_diagnosis)) head(table(data$gad_score)) library(dplyr) data %>% filter(is.na(depression_diagnosis)) %>% select(depression_diagnosis, gad_score) data_clean <- data %>% filter(!is.na(depression_diagnosis)) unique(data_clean$depression_diagnosis)

#Validate with at least one external data source data_clean$depression_diagnosis <- tolower(data_clean$depression_diagnosis) == "true" summary(data_clean$depression_diagnosis) data_clean$gad_score <- as.numeric(data_clean$gad_score) summary(data_clean$gad_score) sd(data_clean$gad_score) quantile(as.numeric(data_clean$gad_score, seq(0, 1, 0.1)))

#make a plot data_clean$depression_diagnosis <- ifelse(data_clean$depression_diagnosis == TRUE, 1, 0) par(las = 2, mar = c(10, 4, 2, 2), cex.axis = 0.8) boxplot(gad_score ~ depression_diagnosis, data = data_clean, range = 0, ylab = "GAD Score", xlab = "Depression Diagnosis (0 = No, 1 = Yes)")

#Try the easy solution first data_clean%>% group_by(depression_diagnosis) %>% summarize(mean_gad = mean(gad_score, na.rm = TRUE), median_gad = median(gad_score, na.rm = TRUE))

#Model as expetation

data_clean$depression_diagnosis <- as.factor(data_clean$depression_diagnosis) data_clean$gad_score <- as.numeric(data_clean$gad_score) logistic_model <- glm(depression_diagnosis ~ gad_score, data = data_clean, family = binomial) summary(logistic_model)

#Comparing Model Expectations to Reality

library(ggplot2) histogram <- ggplot(data_clean, aes(x = gad_score, fill = factor(depression_diagnosis))) + geom_histogram(aes(y = after_stat(density)), position = "identity", binwidth = 1, alpha = 0.5, color = "black") + stat_function(fun = dnorm, args = list(mean = mean(data_clean$gad_score, na.rm = TRUE), sd = sd(data_clean$gad_score, na.rm = TRUE)), color = "red", linewidth = 1) + scale_fill_manual(values = c("0" = "blue", "1" = "green")) + labs(title = "Distribusi GAD Score dengan Kurva Normal dan Diagnosis Depresi", x = "GAD Score", y = "Kepadatan", fill = "Diagnosis Depresi") + theme_minimal() print(histogram)

#Pendeteksian Normalitas library(nortest) lillie.test(model.resid) #Normalitas dengan plot qqnorm(model.resid,main='Normal QQ plot') qqline(model.resid)