1st Edition

An Introduction to R and Python for Data Analysis A Side-By-Side Approach

By Taylor R. Brown Copyright 2023
    266 Pages 13 Color & 8 B/W Illustrations
    by Chapman & Hall

    266 Pages 13 Color & 8 B/W Illustrations
    by Chapman & Hall

    An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously. As both R and Python can be used in similar manners, it is useful and efficient to learn both at the same time, helping lecturers and students to teach and learn more, save time, whilst reinforcing the shared concepts and differences of the systems. This tandem learning is highly useful for students, helping them to become literate in both languages, and develop skills which will be handy after their studies. This book presumes no prior experience with computing, and is intended to be used by students from a variety of backgrounds. The side-by-side formatting of this book helps introductory graduate students quickly grasp the basics of R and Python, with the exercises providing helping them to teach themselves the skills they will need upon the completion of their course, as employers now ask for competency in both R and Python. Teachers and lecturers will also find this book useful in their teaching, providing a singular work to help ensure their students are well trained in both computer languages. All data for exercises can be found here: https://github.com/tbrown122387/r_and_python_book/tree/master/data. Instructors can access the solutions manual via the book's website.

    Key features: 

    - Teaches R and Python in a "side-by-side" way. 

    - Examples are tailored to aspiring data scientists and statisticians, not software engineers. 

    - Designed for introductory graduate students.

    - Does not assume any mathematical background.

    1. Introduction  2. Basic Types  3. R vectors versus Numpy arrays and Pandas’ Series  4. Numpy ndarrays Versus R’s matrix and array Types  5. R’s lists Versus Python’s lists and dicts  6. Functions  7. Categorical Data  8. Data Frames  Part 1. Introducing the Basics  10. Using Third-Party Code  11. Control Flow  12. Reshaping and Combining Data Sets  13. Visualization  Part 2. Common Tasks and Patterns  14. An Introduction to Object-Oriented Programming  15. An Introduction to Functional Programming


    Taylor R. Brown is an assistant professor of statistics at the University of Virginia. His research interests include state space models, particle filtering, and Markov chain Monte Carlo algorithms. He obtained his Ph.D. in statistics from the University of Virginia.

    “The book is written in an engaging, collaborative style that makes it enjoyable to read. It maintains its formality without creating a barrier between the reader and the content. The inclusion of numerous practical exercises allows readers to deepen their understanding, adhering to the principle that hands-on experience and experimentation are key to mastering a language.[…]
    This book is an excellent resource for individuals who wish to learn both languages concurrently or for those who are familiar with one language and wish to refresh their knowledge while learning another.”
    - Daniel Fischer in International Statistical Review, February 2024

    "[This book] is a welcome new educational resource, designed for graduate students, newcomers to programming, and those in the field of data science and statistics. Its dual-language approach, offering side-by-side instruction in both R and Python, sets it apart in the literature. [...] This book is ideally suited as a course text at either the undergraduate or the graduate level and is a nice choice for instructors. It can be used for self-study or as a comprehensive guide for a full course. Its integration with a GitHub repository further enhances its practicality. In conclusion, this book stands out for its innovative duallanguage instruction, practical approach, and accessibility to beginners."
    - Gabriel Wallin in The American Statistician, April 2024