The Book of R
A First Course in Programming and Statistics
By: Tilman M. Davies
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.
AI Overview
"The Book of R: A First Course in Programming and Statistics" by Tilman M. Davies is a comprehensive guide to the R programming language, specifically designed for beginners in statistical analysis. Here is a detailed overview of the book:
Key Themes
- Programming Fundamentals: The book covers the basics of programming in R, including how to write data frames, create functions, and use variables, statements, and control structures.
- Statistics and Probability: It delves into statistical concepts and probability theory, providing a solid foundation for data scientists and analysts.
- Data Structures: The book explains data structures such as lists and data frames, which are essential for handling and manipulating data in R.
- Statistical Testing and Modeling: It includes detailed chapters on statistical testing and modeling, which are crucial for data analysis and interpretation.
Plot Summary
The book is structured to provide a gradual introduction to both programming and statistical concepts. It starts with the basics of R programming, moving on to more advanced topics like data manipulation, visualization, and statistical analysis. The sections on statistics and probability are particularly comprehensive, covering topics such as probability distributions, sampling distributions, confidence intervals, hypothesis testing, and analysis of variance.
Critical Reception
The book has received positive reviews for its clarity and comprehensive coverage of both programming and statistical concepts. Here are some key points from the critical reception:
- Practical Approach: Reviewers have praised the book for its practical approach, making it easy for beginners to learn and apply R in real-world scenarios.
- Mathematical Foundations: The book is commended for not shying away from mathematical foundations, which is essential for data science and machine learning.
- Comprehensive Coverage: The detailed chapters on statistics and probability have been particularly appreciated by instructors and students alike, as they fill gaps in introductory data science and machine learning courses.
Overall, "The Book of R" by Tilman M. Davies is a highly recommended resource for anyone looking to learn the R programming language and statistical analysis, especially those new to these fields. Its comprehensive coverage and practical approach make it an excellent choice for both beginners and those looking to deepen their understanding of R and statistical concepts.