R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

By Hadley Wickham and Garrett Grolemund · O'Reilly Media · 2017

A book that teaches how to do data science with R: getting data into R, into its most useful structure, and transforming, visualising and modelling it. It offers a practicum of skills for data science and the best practices for doing each of them with R, including the grammar of graphics, literate programming and reproducible research. The authors keep this first edition, published in January 2017, free online at r4ds.had.co.nz; the site notes that a second edition was published in June 2023.

On the syllabus

Exam PCPA — objectives A1–A4; the 1st edition (2017), with no chapters named.

Welcome

1 Introduction

The book's model of a data science project: import the data into R, tidy it (each column a variable, each row an observation), transform it, then iterate between visualisation and modelling, and finally communicate the results; tidying and transforming together are called wrangling.
Programming is a cross-cutting tool used in every part of the project.
1.1 What you will learn
1.2 How this book is organised
1.3 What you won’t learn
1.4 Prerequisites
1.5 Running R code
1.6 Getting help and learning more
1.7 Acknowledgements
1.8 Colophon

Explore

2 Introduction

3 Data visualisation

Teaches data visualisation with ggplot2, which implements the grammar of graphics, a coherent system for describing and building graphs.
3.1 Introduction
3.2 First steps
3.3 Aesthetic mappings
3.4 Common problems
3.5 Facets
3.6 Geometric objects
3.7 Statistical transformations
3.8 Position adjustments
3.9 Coordinate systems
3.10 The layered grammar of graphics

4 Workflow: basics

4.1 Coding basics
4.2 What’s in a name?
4.3 Calling functions
4.4 Exercises

5 Data transformation

5.1 Introduction
5.2 Filter rows with filter()
5.3 Arrange rows with arrange()
5.4 Select columns with select()
5.5 Add new variables with mutate()
5.6 Grouped summaries with summarise()
5.7 Grouped mutates (and filters)

6 Workflow: scripts

6.1 Running code
6.2 RStudio diagnostics
6.3 Exercises

7 Exploratory Data Analysis

Exploratory Data Analysis is an iterative cycle: generate questions about the data, search for answers by visualising, transforming and modelling it, and use what is learned to refine the questions.
Data cleaning is one application of EDA, which is always needed to investigate the quality of the data.
Outliers are unusual observations that don't fit the pattern: sometimes data entry errors, sometimes a sign of important new science.
Rather than dropping a whole row with an unusual value, the chapter recommends replacing the unusual value with a missing value.
7.1 Introduction
7.2 Questions
7.3 Variation
7.4 Missing values
7.5 Covariation
7.6 Patterns and models
7.7 ggplot2 calls
7.8 Learning more

8 Workflow: projects

8.1 What is real?
8.2 Where does your analysis live?
8.3 Paths and directories
8.4 RStudio projects
8.5 Summary

Wrangle

9 Introduction

10 Tibbles

10.1 Introduction
10.2 Creating tibbles
10.3 Tibbles vs. data.frame
10.4 Interacting with older code
10.5 Exercises

11 Data import

11.1 Introduction
11.2 Getting started
11.3 Parsing a vector
11.4 Parsing a file
11.5 Writing to a file
11.6 Other types of data

12 Tidy data

Three interrelated rules make a dataset tidy: each variable must have its own column, each observation its own row, and each value its own cell.
12.1 Introduction
12.2 Tidy data
12.3 Pivoting
12.4 Separating and uniting
12.5 Missing values
12.6 Case Study
12.7 Non-tidy data

13 Relational data

13.1 Introduction
13.2 nycflights13
13.3 Keys
13.4 Mutating joins
13.5 Filtering joins
13.6 Join problems
13.7 Set operations

14 Strings

14.1 Introduction
14.2 String basics
14.3 Matching patterns with regular expressions
14.4 Tools
14.5 Other types of pattern
14.6 Other uses of regular expressions
14.7 stringi

15 Factors

In R, factors are used to work with categorical variables, variables that have a fixed and known set of possible values.
15.1 Introduction
15.2 Creating factors
15.3 General Social Survey
15.4 Modifying factor order
15.5 Modifying factor levels

16 Dates and times

16.1 Introduction
16.2 Creating date/times
16.3 Date-time components
16.4 Time spans
16.5 Time zones

Program

17 Introduction

17.1 Learning more

18 Pipes

18.1 Introduction
18.2 Piping alternatives
18.3 When not to use the pipe
18.4 Other tools from magrittr

19 Functions

19.1 Introduction
19.2 When should you write a function?
19.3 Functions are for humans and computers
19.4 Conditional execution
19.5 Function arguments
19.6 Return values
19.7 Environment

20 Vectors

20.1 Introduction
20.2 Vector basics
20.3 Important types of atomic vector
20.4 Using atomic vectors
20.5 Recursive vectors (lists)
20.6 Attributes
20.7 Augmented vectors

21 Iteration

21.1 Introduction
21.2 For loops
21.3 For loop variations
21.4 For loops vs. functionals
21.5 The map functions
21.6 Dealing with failure
21.7 Mapping over multiple arguments
21.8 Walk
21.9 Other patterns of for loops

Model

22 Introduction

22.1 Hypothesis generation vs. hypothesis confirmation

23 Model basics

23.1 Introduction
23.2 A simple model
23.3 Visualising models
23.4 Formulas and model families
23.5 Missing values
23.6 Other model families

24 Model building

24.1 Introduction
24.2 Why are low quality diamonds more expensive?
24.3 What affects the number of daily flights?
24.4 Learning more about models

25 Many models

25.1 Introduction
25.2 gapminder
25.3 List-columns
25.4 Creating list-columns
25.5 Simplifying list-columns
25.6 Making tidy data with broom

Communicate

26 Introduction

27 R Markdown

27.1 Introduction
27.2 R Markdown basics
27.3 Text formatting with Markdown
27.4 Code chunks
27.5 Troubleshooting
27.6 YAML header
27.7 Learning more

28 Graphics for communication

Once the data is understood, plots made for exploration are reworked to communicate that understanding to an audience that will likely not share the analyst's background knowledge.
28.1 Introduction
28.2 Label
28.3 Annotations
28.4 Scales
28.5 Zooming
28.6 Themes
28.7 Saving your plots
28.8 Learning more

29 R Markdown formats

29.1 Introduction
29.2 Output options
29.3 Documents
29.4 Notebooks
29.5 Presentations
29.6 Dashboards
29.7 Interactivity
29.8 Websites
29.9 Other formats
29.10 Learning more

30 R Markdown workflow

Sources

R for Data Science, 1st edition (Hadley Wickham and Garrett Grolemund, 2017) — the authors' free online edition: the Welcome page (edition, publication date, description, authorship) and every chapter page, for the part and chapter titles and each chapter's numbered sections; the text of Chapters 1, 3, 7, 12, 15 and 28
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (Open Library) — the print edition's catalogue record: full title, O'Reilly Media, January 2017, ISBN 978-1491910399
CAS PCPA Exam & Project Content Outline, v.8 (September 2026) — the citation (1st edition, 2017) and Domain A