ggplot(heatbeat_dataset,aes(x=gendar,fill=heart_risk))+geom_bar(postion="fill")+labs(title="heart risk distribution by gendar",x="gendar",y="proposition",fill="heart risk")+theme_minimal() ggplot(heartbeat_dataset,aes(x=resting_heart_rate,y=max_heart_rate,color=heart_risk))+geom_point(alpha=0.7)+labs(title="Resting vs Max Heart Rate by Risk Level",x="Resting Heart Rate",y="Max Heart Rate",color="heart Risk")+theme_minimal() ggplot(heartbeat_dataset,aes(x=heartrisk,y=Cholesteral,fill=heart_risk))+geom_boxplot()+labs(title="cholesteral Levels by Heart Risk Category",x="Heart Risk",y="cholesterol")+theme_minimal() ggplot(heartbeat_dataset,aes(x=bmi,fill=haert_risk))+geom_density(alpha=0.5)+labs(title="BMI Distribution by Heart Risk",x="BMI",y="density",fill="Heart Risk")+theme_minimal()

a simple ioslide presentation on hypothesis testing

We will use use a supervised machine learning linear regression program to predict metabolite concentration.

This R Markdown document demonstrates a basic example of data cleaning and visualization for healthcare quality metrics. A small synthetic dataset (claims) was created to represent ten patients, including their age and whether they were readmitted within 30 days. Data Cleaning: Duplicate records were removed using distinct(), ensuring each patient record is unique and ready for analysis. Summary Statistics: The dataset summary provides an overview of patient ages and readmission counts, which can highlight general trends in patient demographics. Visualization: The histogram visualizes the distribution of patient ages, with color coding by readmission status. The x-axis shows patient Age in years. The y-axis shows the number of patients in each age group. The colored bars indicate whether patients were readmitted within 30 days. This kind of visualization helps identify age groups with higher readmission rates and can be extended to larger, real-world healthcare datasets for performance monitoring and predictive analytics.