R for Data Science

Cover -- Copyright -- Table of Contents -- Preface -- What You Will Learn -- How This Book Is Organized -- What You Won't Learn -- Big Data -- Python, Julia, and Friends -- Nonrectangular Data -- Hypothesis Confirmation -- Prerequisites -- R -- RStudio -- The Tidyverse -- Other Packages -- Running R Code -- Getting Help and Learning More -- Acknowledgments -- Online Version -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Part I. Explore -- Chapter 1. Data Visualization with ggplot2 -- Introduction -- Prerequisites -- First Steps -- The mpg Data Frame -- Creating a ggplot -- A Graphing Template -- Exercises -- Aesthetic Mappings -- Exercises -- Common Problems -- Facets -- Exercises -- Geometric Objects -- Exercises -- Statistical Transformations -- Exercises -- Position Adjustments -- Exercises -- Coordinate Systems -- Exercises -- The Layered Grammar of Graphics -- Chapter 2. Workflow: Basics -- Coding Basics -- What's in a Name? -- Calling Functions -- Exercises -- Chapter 3. Data Transformation with dplyr -- Introduction -- Prerequisites -- nycflights13 -- dplyr Basics -- Filter Rows with filter() -- Comparisons -- Logical Operators -- Missing Values -- Exercises -- Arrange Rows with arrange() -- Exercises -- Select Columns with select() -- Exercises -- Add New Variables with mutate() -- Useful Creation Functions -- Exercises -- Grouped Summaries with summarize() -- Combining Multiple Operations with the Pipe -- Missing Values -- Counts -- Useful Summary Functions -- Grouping by Multiple Variables -- Ungrouping -- Exercises -- Grouped Mutates (and Filters) -- Exercises -- Chapter 4. Workflow: Scripts -- Running Code -- RStudio Diagnostics -- Exercises -- Chapter 5. Exploratory Data Analysis -- Introduction -- Prerequisites -- Questions -- Variation -- Visualizing Distributions.