I update the sdm code on https://www.biogeoinformatics.org. I felt this was overdue since it was written in 2016 by Babak Naimi and many changes have been made to the package since then making it difficult for new users to rely on the tutorial.

This analysis explores historical stock price trends using key technical indicators to assess market behavior. It includes: Daily Returns & Volatility: Understanding stock price fluctuations. Moving Averages (50-day & 200-day): Identifying market trends. Bollinger Bands: Detecting overbought and oversold conditions. Correlation Heatmap: Analyzing relationships between stock metrics. Key Insights: ✔ Moving averages help confirm trend direction. ✔ Bollinger Bands indicate potential price reversals. ✔ Correlation analysis aids in portfolio diversification. This structured approach provides valuable insights for traders and investors to enhance decision-making.

OHIO BRFSS Data Analysis

Implement an R function to generate a line graph depicting the trend of a time-series dataset, with separate lines for each group, utilizing ggplot2’s group aesthetic.

Write an R script to create a scatter plot, incorporating categorical analysis through color-coded data points representing different groups, using ggplot2.

Develop an R program to quickly explore a given dataset, including categorical analysis using the group by command, and visualize the findings using ggplot2 features.