# Load ggplot2 for visualization library(ggplot2) # Plot blood pressure over time by treatment group ggplot(longitudinal_data, aes(x = Time, y = Blood_Pressure, color = Treatment)) + geom_line(aes(group = Patient_ID), alpha = 0.3) + # Individual patient lines stat_summary(fun = mean, geom = "line", size = 1.2, aes(group = Treatment)) + # Mean line labs(title = "Blood Pressure over Time by Treatment Group", x = "Time", y = "Blood Pressure") + theme_minimal()

# Fit the Kaplan-Meier model, stratifying by 'sex' > km_fit <- survfit(surv_object ~ Smoking_Status, data = clinical_survival_data) > > View(km_fit) > > # Plot the Kaplan-Meier survival curve > ggsurvplot(km_fit, data = clinical_survival_data, + pval = TRUE, # Add p-value for log-rank test + conf.int = TRUE, # Show confidence intervals + xlab = "Time in Days", # Label for x-axis + ylab = "Survival Probability", # Label for y-axis + legend.title = "Smoking Status", # Title for the legend + legend.labs = c("Nonsmoker", "Smoker"), # Labels for each group + risk.table = TRUE, # Show risk table below the plot + risk.table.height = 0.25, # Adjust the height of the risk table + ggtheme = theme_minimal()) # Use a minimal theme > >

# Load ggplot2 library(ggplot2) # Create a contingency table and convert it to a data frame for ggplot contingency_table <- table(clinical_data$Smoking_Status, clinical_data$Diabetes) plot_data <- as.data.frame(contingency_table) colnames(plot_data) <- c("Smoking_Status", "Diabetes", "Count") # Plot ggplot(plot_data, aes(x = Smoking_Status, y = Count, fill = Diabetes)) + geom_bar(stat = "identity", position = "fill") + labs(title = "Proportion of Diabetes Status by Smoking Status", x = "Smoking Status", y = "Proportion") + scale_y_continuous(labels = scales::percent) + theme_minimal() + theme(legend.position = "top")

# Create a mosaic plot from the contingency table mosaicplot(contingency_table, main = "Mosaic Plot of Smoking Status and Diabetes", xlab = "Smoking Status", ylab = "Diabetes", color = TRUE)

# Scatter plot with trend line ggplot(clinical_data, aes(x = Age, y = Cholesterol, color = Smoking_Status)) + geom_point() + geom_smooth(method = "lm", se = FALSE) + labs(title = "Age vs Cholesterol with Trend Line", x = "Age (years)", y = "Cholesterol (mg/dL)")