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home	R for non-Technical Researchers

Part 1: Foundations of R Programming and Data Handling

Chapter 1: Getting Started with R and RStudio

• Installing and setting up R and RStudio.
• Exploring the RStudio interface: Source, Console, Environment, and Plots panes.
• Writing and executing R scripts.
• Overview of R libraries and packages.

Chapter 2: Data Structures in R

• Understanding vectors, matrices, lists, and data frames.
• Indexing, subsetting, and modifying data structures.
• Handling Errors Gracefully
• Packages and Libraries in R.
• Importing and exporting data (CSV, Excel, SPSS).
• Basic exploratory analysis: summary statistics and data visualization.

Chapter 3: Data Manipulation with dplyr and tidyr

• Filtering, arranging, and summarizing data.
• Grouped operations with the %>% pipeline.
• Reshaping data: pivoting and joining datasets.
• Hands-on data cleaning and preparation techniques.

Chapter 4: Data Visualization with ggplot2

• Creating basic visualizations: bar plots, histograms, scatter plots, and box plots.
• Customizing plots: themes, annotations, and advanced styling.
• Advanced visualizations: heatmaps, faceting, and density plots.
• Exporting publication-quality plots.

Part 2: Statistical Analysis for Research

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

• Central tendency, variability, and distribution.
• Identifying missing data and outliers.
• Visualizing relationships with correlation plots and pairplots.

Chapter 6: Hypothesis Testing and Statistical Inference

• Basics of hypothesis testing: p-values and confidence intervals.
• One-sample, two-sample, and paired t-tests.
• Chi-square tests for categorical data.
• Non-parametric tests: Wilcoxon and Mann-Whitney U tests.

Chapter 7: Advanced Statistical Techniques

• ANOVA (one-way and two-way) and post hoc testing.
• Linear regression: simple and multiple.
• Logistic regression for binary outcomes.
• Case study: Predicting outcomes using regression models.

Chapter 8: Multivariate Statistical Methods

• Principal Component Analysis (PCA) for dimensionality reduction.
• Factor analysis for latent variable identification.
• Clustering methods: k-means and hierarchical clustering.
• Case study: Identifying patterns in complex datasets.

Part 3: Machine Learning with R

Chapter 9: Introduction to Machine Learning in R

• Supervised vs. unsupervised learning: An overview.
• Preprocessing data: scaling, normalization, and feature engineering.
• Splitting datasets into training, testing, and validation sets.
• Cross-validation and hyperparameter tuning.

Chapter 10: Classification Models

• Decision trees and random forests.
• k-Nearest Neighbors (k-NN).
• Support Vector Machines (SVMs).
• Evaluating classification performance: confusion matrix, precision, recall, and F1 score.
• Case study: Classifying observations in a research dataset.

Chapter 11: Regression Models

• Ridge, Lasso, and Elastic Net regression.
• Boosting methods: Gradient Boosting, XGBoost.
• Evaluating regression models: RMSE, MAE, and R².
• Hands-on project: Regression modeling on real-world data.

Chapter 12: Clustering Techniques

• Advanced clustering: DBSCAN and Gaussian Mixture Models.
• Evaluating clustering performance metrics.
• Case study: Grouping data points for pattern discovery.

Chapter 13: Advanced Machine Learning Techniques

• Ensemble methods: Bagging and Boosting.
• Introduction to neural networks using R (keras and tensorflow).
• Time series forecasting with ARIMA and Prophet.
• Hands-on project: Applying advanced ML techniques to research data.

Part 4: Reproducible Research and Applications

Chapter 14: Reproducible Research with R Markdown, Shiny, and LaTeX

• Writing dynamic documents with R Markdown.
• Exporting to PDF, Word, and HTML.
• Integrating LaTeX for equations and professional formatting.
• Introduction to Shiny for creating interactive dashboards and tools.
• Best practices for reproducibility in research.

Chapter 15: Putting It All Together: Final Projects and Advanced Tools

• Building a complete research project in R: Data wrangling, analysis, and visualization.
• Presenting results with R Markdown and Shiny.
• Overcoming challenges in domain-specific research applications.
• Future directions and advanced resources for learning R.

Appendices:

• Appendix A: Installation and Setup Guide for R and RStudio.
• Appendix B: Glossary of R Functions and Commands.
• Appendix C: Recommended R Packages and Libraries.
• Appendix D: Common Errors in R and How to Fix Them.