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Book Outline:

Part I: Introduction to R Programming

Chapter 1: Introduction to R and RStudio

• Installing R and RStudio
• Navigating the RStudio interface
• Basic R syntax: Variables, data types, operators, and functions
• Writing and running R scripts
• Key libraries for data science in R

Chapter 2: Data Structures and Basic Operations in R

• Vectors, Matrices, Lists, and Data Frames
• Indexing, subsetting, and manipulating data
• Importing and exporting data (CSV, Excel, SPSS, etc.)
• Basic exploratory analysis (summary statistics, structure, and head/tail functions)

Chapter 3: Data Manipulation with dplyr and tidyr

• Filtering, arranging, mutating, and summarizing data
• Grouped operations and pipelines using %>%
• Reshaping data with pivot_longer() and pivot_wider()
• Joining datasets: inner, outer, left, and right joins
• Hands-on data cleaning exercise

Chapter 4: Data Visualization with ggplot2

• Basic plots: Histograms, scatter plots, bar charts, and box plots
• Advanced visualizations: Heatmaps, faceted plots, and density plots
• Customizing plots with themes, annotations, and labels
• Interactive visualizations with plotly
• Case study: Visualizing relationships in a real dataset

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Part II: Expanded Statistics

Chapter 5: Descriptive Statistics and Exploratory Data Analysis (EDA)

• Measures of central tendency and variability
• Identifying outliers and missing data
• Visualizing distributions and relationships (e.g., correlation plots)
• Preparing datasets for statistical analysis

Chapter 6: Hypothesis Testing and Statistical Inference

• Introduction to hypothesis testing
• One-sample and two-sample t-tests
• Paired t-tests and their applications
• Chi-square tests for independence
• Non-parametric tests: Wilcoxon and Mann-Whitney U tests

Chapter 7: Advanced Statistical Methods

• Analysis of Variance (ANOVA): One-way and two-way
• Post hoc tests (e.g., Tukey’s HSD)
• Simple and multiple linear regression analysis
• Logistic regression for binary outcomes
• Case study: Predicting outcomes using regression models

Chapter 8: Multivariate Statistical Techniques

• Principal Component Analysis (PCA) for dimensionality reduction
• Factor analysis and interpretation of factors
• Cluster analysis: k-means and hierarchical clustering
• Case study: Clustering research observations

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Part III: Machine Learning

Chapter 9: Introduction to Machine Learning in R

• Overview of supervised and unsupervised learning
• Data preprocessing: Scaling, normalization, and feature engineering
• Splitting datasets into training, testing, and validation sets
• Implementing cross-validation and hyperparameter tuning

Chapter 10: Supervised Learning - Classification Models

• Decision trees and random forests
• k-Nearest Neighbors (k-NN)
• Support Vector Machines (SVM)
• Performance metrics: Confusion matrix, accuracy, precision, recall, F1 score
• Case study: Classifying research observations

Chapter 11: Supervised Learning - Regression Models

• Advanced regression techniques: Ridge, Lasso, and Elastic Net
• Regression trees and boosting methods (e.g., XGBoost, LightGBM)
• Hands-on project: Regression modeling for real-world research data

Chapter 12: Unsupervised Learning - Clustering

• k-means and hierarchical clustering revisited
• Density-based clustering (DBSCAN) and Gaussian Mixture Models
• Evaluating clustering performance
• Case study: Discovering patterns in research datasets

Chapter 13: Advanced Machine Learning Techniques

• Ensemble learning: Bagging and Boosting
• Neural networks basics with R (e.g., keras and tensorflow packages)
• Time series forecasting using ARIMA and Prophet
• Hands-on project: Applying advanced ML techniques

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Part IV: Reproducible Research and Project Work

Chapter 14: Reproducible Research with R Markdown and Shiny

• Creating dynamic reports with R Markdown
• Exporting to PDF, Word, and HTML
• Introduction to Shiny apps for interactive research tools
• Best practices for reproducible research workflows

Chapter 15: Final Project Presentation and Advanced Applications

• Students present their final projects, integrating statistical or machine learning methods
• Feedback and discussion on projects
• Exploring advanced R tools for domain-specific applications
• Course summary and future learning resources