ENVIRONMENTAL DATA ANALYSIS USING R

MODULE 1: Statistics (Theory) & R Fundamentals 

Statistical fundamentals • Measures of central tendency and dispersion • Normal distribution and assumptions • Parametric vs. non-parametric tests • Correlation and regression concepts • P-values and hypothesis testing framework.  Introduction to R and  Positron IDE with GitHub Copilot AI • Data types and structures • Importing Excel/CSV files • Tidyverse and pipe operator (|>) • Data cleaning and transformation • Creating variables with mutate() • Grouping and summarizing data • Custom functions and loops.

MODULE 2: Data Visualization – Static & Interactive
Grammar of graphics with ggplot2 • Time series, histograms, and scatter plots • Publication-quality figure customization • Interactive plots with Plotly • Interactive maps using Leaflet • Visualising administrative boundaries 

MODULE 3: Data Analysis & Hypothesis Testing
Descriptive statistics by groups • Normality testing • Non-parametric tests • Seasonal pattern analysis • Correlation analysis  • Simple Linear Regression  • Interpreting statistical outputs

MODULE 4: Advanced Analysis – PCA & Geospatial Data
Principal Component Analysis for Multivariate data • Interpreting PCA biplots • Downloading CHIRPS satellite rainfall data • Raster manipulation with terra.

MODULE 5: AI-assisted Coding 

Introduction to AI-assisted development environments • GitHub Copilot/ GPT models / Claude Models setup and workflow integration • Code completion and intelligent suggestions with Copilot/Agents • AI-powered chat for code explanation and refactoring