Course Structure

This comprehensive R programming course is organized into three levels:

📚 Beginner Level

Duration: 2-3 weeks (24 hours)
Location: beginner/
Prerequisites: None
Focus: R fundamentals

Lessons

  1. 01-introduction.Rmd - Getting started with R and RStudio
  2. 02-data-types.Rmd - Vectors, lists, matrices, data frames, factors
  3. 03-control-structures.R - If/else, loops, functions
  4. 04-data-input-output.R - Reading and writing data files

Learning Objectives

  • Understand R and RStudio interface
  • Master basic data types and structures
  • Write control flow statements
  • Read and write data files
  • Write basic R functions

Exercises

  • 10 practice exercises in exercises/beginner-exercises.R
  • Complete solutions in solutions/beginner-solutions.R

Key Topics

  • Variables and assignment
  • Data structures (vectors, matrices, lists, data frames)
  • Indexing and subsetting
  • If/else statements and loops
  • Functions and R programming basics
  • File I/O operations

📊 Intermediate Level

Duration: 3-4 weeks (38 hours)
Location: intermediate/
Prerequisites: Completed Beginner Level
Focus: Data manipulation and visualization

Lessons

  1. 01-data-manipulation.Rmd - dplyr for data manipulation
  2. 02-data-reshaping.R - tidyr for reshaping data
  3. 03-visualization.Rmd - ggplot2 for visualization
  4. 04-descriptive-statistics.R - Summary statistics

Learning Objectives

  • Manipulate data using dplyr
  • Reshape data between wide and long formats
  • Create publication-quality visualizations
  • Calculate descriptive statistics
  • Perform exploratory data analysis

Exercises

  • 10 practical exercises in exercises/intermediate-exercises.R
  • Complete solutions with visualizations in solutions/intermediate-solutions.R

Key Topics

  • dplyr verbs (filter, select, mutate, summarize, group_by)
  • Pipe operator %>%
  • tidyr functions (pivot_longer, pivot_wider, separate, unite)
  • ggplot2 grammar of graphics
  • Creating various plot types (scatter, bar, histogram, box plots)
  • Descriptive statistics and correlation

🎯 Advanced Level

Duration: 4-5 weeks (56 hours)
Location: advanced/
Prerequisites: Completed Intermediate Level
Focus: Statistical analysis and modeling

Lessons

  1. 01-probability-distributions.Rmd - Normal, t, chi-square, F distributions
  2. 02-hypothesis-testing.Rmd - t-tests, ANOVA, non-parametric tests
  3. 03-linear-regression.Rmd - Simple and multiple regression
  4. 04-advanced-anova.R - One-way, two-way, repeated measures ANOVA
  5. 05-model-selection.R - Model selection and validation

Learning Objectives

  • Understand probability distributions
  • Perform hypothesis tests (t-tests, ANOVA, chi-square)
  • Build and interpret regression models
  • Conduct ANOVA analyses
  • Select and validate statistical models
  • Check model assumptions

Exercises

  • 10 comprehensive exercises in exercises/advanced-exercises.R
  • Complete statistical reports in solutions/advanced-solutions.R

Key Topics

  • Probability distributions in R
  • Hypothesis testing workflow
  • Type I and Type II errors
  • p-values and interpretation
  • Linear regression (simple and multiple)
  • Model diagnostics (assumptions checking)
  • ANOVA (one-way, two-way, repeated measures)
  • Post-hoc tests (Tukey, Bonferroni)
  • Effect sizes (eta-squared, Cohen's d)
  • Model selection (AIC, BIC, stepwise)
  • Cross-validation
  • Regularization methods

Course Materials

File Formats

  • .Rmd - R Markdown files (rich content with code, text, and output)
  • .R - R script files (pure code with comments)
  • .md - Markdown documentation files
  • .csv - Sample datasets for practice

Supporting Files

  • install-packages.R - Install all required packages
  • README.md - Main project documentation
  • COURSE_OVERVIEW.md - This file
  • datasets/ - Sample datasets for exercises

Getting Started

1. Install R and RStudio

2. Install Required Packages

Open RStudio and run:

source("install-packages.R")

3. Start Learning

  • Begin with beginner/README.md
  • Follow the lessons in order
  • Complete exercises before checking solutions
  • Practice with provided datasets

Learning Path

BEGINNER
   ↓ (Complete beginner level)
INTERMEDIATE
   ↓ (Complete intermediate level)
ADVANCED
   ↓ (Complete advanced level)
STATISTICAL CONSULTING READY

Progression Guide

After Beginner Level: - You can write basic R programs - You understand data structures - You can read and manipulate data

After Intermediate Level: - You can clean and analyze real-world data - You can create professional visualizations - You can perform exploratory data analysis

After Advanced Level: - You can conduct statistical tests - You can build and interpret models - You can perform regression and ANOVA - You can write statistical reports

Course Features

✅ Comprehensive coverage - From basics to advanced statistics
✅ Multiple formats - R Markdown, R scripts, and documentation
✅ Practical exercises - Real-world problems with solutions
✅ Sample datasets - Ready-to-use data for practice
✅ Step-by-step - Progressive difficulty across levels
✅ Theory + Practice - Both concepts and implementation
✅ Statistical focus - Hypothesis testing, regression, ANOVA
✅ Model diagnostics - Assumptions checking and validation

Estimated Completion Time

  • Total: 118 hours (15-20 weeks part-time)
  • Beginner: 24 hours
  • Intermediate: 38 hours
  • Advanced: 56 hours

Resources

Additional Learning Resources

Package Documentation

Tips for Success

  1. Practice regularly - Code along with every example
  2. Complete exercises - Don't skip the practice problems
  3. Read solutions - Learn from complete solutions
  4. Experiment - Modify code to see what happens
  5. Use help - ?function_name is your friend
  6. Join communities - R help mailing lists, Stack Overflow
  7. Build projects - Apply skills to real problems
  8. Review regularly - Revisit previous lessons

Assessment

Self-Assessment Questions

Beginner: - Can I create and manipulate vectors and data frames? - Can I write a function? - Can I read CSV files?

Intermediate: - Can I manipulate data with dplyr? - Can I create publication-quality plots? - Can I reshape data from wide to long?

Advanced: - Can I perform hypothesis tests? - Can I build and diagnose regression models? - Can I conduct ANOVA and interpret results?

Support

For questions or issues: - Review the solutions files - Check R documentation: ?function_name - Consult the lesson material - Practice with sample datasets - Refer to external resources

Course Completion

Upon completing all three levels, you will: - ✓ Be proficient in R programming - ✓ Understand statistical analysis in R - ✓ Be able to conduct hypothesis tests - ✓ Be capable of building regression models - ✓ Know how to perform ANOVA analyses - ✓ Be able to create statistical reports - ✓ Be ready for statistical consulting work

Good luck with your R learning journey!