Program Structure

Module 1: R Setup & Programming Basics

• Installing R & RStudio, projects, working directories, package basics.
• R syntax: objects, assignment, operators, help system, best practices.
• Core data types: numeric, character, logical, factors.
• Vectors and indexing: slicing, filtering, basic vectorized operations.

Module 2: Data Structures & Control Flow

• Lists, matrices, data frames, tibbles—when to use what.
• Control flow: if/else, for, while, apply family (apply/lapply/sapply).
• Writing clean scripts: style, naming, comments, and simple automation.
• Common errors and how to read them (practical debugging mindset).

Module 3: Importing, Cleaning & Preparing Data

• Import/export: CSV, Excel (conceptual), TXT, and basic file handling.
• Data quality checks: structure, summaries, duplicates, outliers.
• Missing values: detection, strategies, and safe replacements.
• Type conversions: dates, factors, numeric parsing, and encodings.

Module 4: Data Wrangling with tidyverse

• dplyr essentials: filter, select, mutate, arrange, summarize, group_by.
• Joins and merges: left/right/inner/full joins and key hygiene.
• tidyr essentials: pivot_longer/pivot_wider, separate/unite.
• Practical workflows: transforming messy datasets into analysis-ready tables.

Module 5: Visualization with ggplot2 (Beginner to Pro)

• Grammar of graphics: aesthetics, geoms, scales, themes (intuitive approach).
• Charts that matter: distributions, comparisons, trends, relationships.
• Facets, annotations, legends, and layout for publication-style figures.
• Exporting plots and figure best practices (clarity, readability, integrity).

Module 6: Statistics in R (Core Toolkit)

• Descriptive stats and distributions (mean/median/variance, normality concepts).
• Hypothesis testing: t-test, chi-square, ANOVA (interpretation-focused).
• Correlation and simple linear regression; diagnostics basics.
• Confidence intervals, p-values, effect size thinking (avoid misuse).

Module 7: Modeling & Intro Machine Learning Workflows

• Regression extensions: multiple regression and categorical predictors.
• Train/test split, cross-validation concepts (intro-level, practical).
• Classification basics (logistic regression, simple models).
• Model evaluation: accuracy, precision/recall, RMSE; avoiding leakage.

Module 8: Advanced R (Functions, Debugging, Performance)

• Writing reusable functions, default arguments, and input validation.
• Functional programming mindset: purrr basics (or apply alternatives).
• Debugging tools: traceback, browser(), breakpoints, profiling basics.
• Performance tips: vectorization, memory awareness, faster data handling (conceptual).

Module 9: Reproducible Reporting & Project Delivery

• RMarkdown/Quarto reports: narrative + code + outputs.
• Parameterised reports for automation (generate reports for different datasets).
• Project structure: folders, scripts, data, outputs, README.
• Sharing results: exporting tables, plots, and a final analysis package.

Final Project

• Choose a dataset (research/public/industry) and define an analysis question.
• Perform end-to-end workflow: import → clean → transform → visualize → model (optional) → interpret.
• Create a final report with key insights, charts, and reproducible code.
• Deliverables: R project folder + report (PDF/HTML) + cleaned dataset + plots.