How to Use This Book

Who this book is for

This book assumes no prior programming experience. If you’ve never written a line of code before, you are in the right place — we start from installing R and progress gradually. Readers with programming experience in other languages (Python, MATLAB, SAS, SPSS, or Stata) will find the book familiar; feel free to skim Chapters 1–3 and focus on the tidyverse workflow starting in Chapter 5.

A basic familiarity with algebra and descriptive statistics (means, medians, percentages) is helpful but not required. We introduce more advanced statistical ideas gently as they come up.

What you will be able to do

By the end of this book, you will be able to:

  • Install and configure R, RStudio, and R packages on your own computer.
  • Create and manipulate R’s core data structures: atomic vectors, matrices, lists, data frames, and tibbles.
  • Import data from CSV, Excel, SPSS, SAS, and Stata files.
  • Produce publication-quality static and interactive graphics with ggplot2 and plotly.
  • Wrangle and tidy data using the dplyr and tidyr verbs (filter(), select(), mutate(), summarize(), pivot_longer(), etc.).
  • Work with strings and regular expressions.
  • Apply fundamental statistical methods (distributions, hypothesis tests, linear regression).
  • Write your own functions, handle errors, and organize code with control flow.
  • Produce reproducible reports in R Markdown and Quarto.

How chapters are organized

Most chapters follow the same structure:

  1. A short chapter overview with learning objectives and a roadmap of the sections.
  2. Sections that introduce concepts step-by-step with runnable code.
  3. Caution boxes that highlight common pitfalls or subtle points.
  4. Exercises at the end of every section, designed for active practice.

Chapters 1 through 7 form a sequential foundation — please read them in order. From Chapter 8 onward, the chapters can be read more independently, though the tidyverse chapters (8–10) are the most useful next step after the visualization chapters.

Conventions

  • Package names appear in bold (e.g., r02pro, ggplot2).
  • Inline code, function names, and variables appear in monospace font (e.g., 1 + 2, mean()). We always include parentheses after a function name (e.g., ggplot()).
  • Datasets used throughout the book come from the companion r02pro package. Before running any example that uses data, make sure to load the package with library(r02pro).
  • Cross-references to other sections use numbered links (e.g., Section 1.1).

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