davismillerbooks

📘 Mastering R: From Basics to Advanced

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🛍️ Buy the Book

Get your copy with comprehensive explanations and hands-on examples:
📘 Amazon Paperback
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📱 Kindle Edition
📚 Google Play Books
🎧 Audiobook Edition
🍎 Apple Book Edition
🌐 Books to Read

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🎯 What to Expect

In a world powered by data, R has become the essential language for statistical computing and data science. This book offers:

• 📊 Data Analysis from Scratch: Start from installation, syntax, and data structures.
• 💡 Practical Techniques: Apply real-world examples and case studies.
• 🤖 Machine Learning & Forecasting: With caret, mlr3, and ARIMA.
• 🧠 Advanced R Programming: Metaprogramming, profiling, parallel computing.
• 📦 Package Development & Reporting: Create libraries and reproducible reports with rmarkdown.
• 🛠️ Team Workflows & CI/CD: Version control and collaborative R development.

Whether you’re a student, researcher, or industry professional, this guide equips you with the skills and confidence to tackle complex data projects.

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📚 Table of Contents

📘 Chapter 1–10: R Basics & Core Concepts

• Introduction to R & the Ecosystem
• Installing R & RStudio
• Basic Syntax, Data Types, Variables
• Operators, Vectors, Lists
• Matrices, Arrays, Data Frames & Tibbles
• Factors and Categorical Data
• Data Input/Output
• Data Cleaning & Preprocessing

🧮 Chapter 11–16: Manipulation, Control Flow & Functions

• Base R Data Manipulation
• tidyverse with dplyr, tidyr
• Conditional Logic & Looping
• Writing Functions & Scope
• Debugging and Error Handling

📈 Chapter 17–21: Statistics, Simulations & Regression

• Descriptive & Exploratory Analysis
• Simulating Distributions & Random Data
• Hypothesis Testing, t-test, ANOVA
• Confidence Intervals & p-values
• Linear & Logistic Regression

⏱️ Chapter 22–25: Time Series & Visualization

• ARIMA and Forecasting Techniques
• Data Visualization in Base R
• ggplot2 Grammar of Graphics
• Dashboards & Interactivity with plotly and shiny

⚙️ Chapter 26–28: Advanced Programming & Packaging

• Vectorization, Profiling, Performance
• Object-Oriented Programming: S3, S4, R6
• Building & Documenting R Packages

🤖 Chapter 29–30: Machine Learning & Reproducibility

• Supervised/Unsupervised Learning
• caret, mlr3, and project case studies
• Literate Programming with R Markdown
• Dynamic and Interactive Reporting

📊 Chapter 31–32: Big Data, Parallelism & Collaboration

• Big Data Techniques and Databases
• Parallel Computing and future
• Git, GitHub, CI/CD with RStudio
• Best Practices for Teams and Projects

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💬 Final Thoughts

“R is not just a language; it’s a gateway to data fluency. This book helps you master it, one chapter at a time.”

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