R for Data Science. 2nd Edition Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund

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Spis treści książki

• Introduction
  • Preface to the Second Edition
  • What You Will Learn
  • How This Book Is Organized
  • What You Wont Learn
    • Modeling
    • Big Data
    • Python, Julia, and Friends
  • Prerequisites
    • R
    • RStudio
    • The Tidyverse
    • Other Packages
  • Running R Code
  • Other Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
  • Online Edition
• I. Whole Game
• 1. Data Visualization
  • Introduction
    • Prerequisites
  • First Steps
    • The penguins Data Frame
    • Ultimate Goal
    • Creating a ggplot
    • Adding Aesthetics and Layers
    • Exercises
  • ggplot2 Calls
  • Visualizing Distributions
    • A Categorical Variable
    • A Numerical Variable
    • Exercises
  • Visualizing Relationships
    • A Numerical and a Categorical Variable
    • Two Categorical Variables
    • Two Numerical Variables
    • Three or More Variables
    • Exercises
  • Saving Your Plots
    • Exercises
  • Common Problems
  • Summary
• 2. Workflow: Basics
  • Coding Basics
  • Comments
  • Whats in a Name?
  • Calling Functions
  • Exercises
  • Summary
• 3. Data Transformation
  • Introduction
    • Prerequisites
    • nycflights13
    • dplyr Basics
  • Rows
    • filter()
    • Common Mistakes
    • arrange()
    • distinct()
    • Exercises
  • Columns
    • mutate()
    • select()
    • rename()
    • relocate()
    • Exercises
  • The Pipe
  • Groups
    • group_by()
    • summarize()
    • The slice_ Functions
    • Grouping by Multiple Variables
    • Ungrouping
    • .by
    • Exercises
  • Case Study: Aggregates and Sample Size
  • Summary
• 4. Workflow: Code Style
  • Names
  • Spaces
  • Pipes
  • ggplot2
  • Sectioning Comments
  • Exercises
  • Summary
• 5. Data Tidying
  • Introduction
    • Prerequisites
  • Tidy Data
    • Exercises
  • Lengthening Data
    • Data in Column Names
    • How Does Pivoting Work?
    • Many Variables in Column Names
    • Data and Variable Names in the Column Headers
  • Widening Data
    • How Does pivot_wider() Work?
  • Summary
• 6. Workflow: Scripts and Projects
  • Scripts
    • Running Code
    • RStudio Diagnostics
    • Saving and Naming
  • Projects
    • What Is the Source of Truth?
    • Where Does Your Analysis Live?
    • RStudio Projects
    • Relative and Absolute Paths
  • Exercises
  • Summary
• 7. Data Import
  • Introduction
    • Prerequisites
  • Reading Data from a File
    • Practical Advice
    • Other Arguments
    • Other File Types
    • Exercises
  • Controlling Column Types
    • Guessing Types
    • Missing Values, Column Types, and Problems
    • Column Types
  • Reading Data from Multiple Files
  • Writing to a File
  • Data Entry
  • Summary
• 8. Workflow: Getting Help
  • Google Is Your Friend
  • Making a reprex
  • Investing in Yourself
  • Summary
• II. Visualize
• 9. Layers
  • Introduction
    • Prerequisites
  • Aesthetic Mappings
    • Exercises
  • Geometric Objects
    • Exercises
  • Facets
    • Exercises
  • Statistical Transformations
    • Exercises
  • Position Adjustments
    • Exercises
  • Coordinate Systems
    • Exercises
  • The Layered Grammar of Graphics
  • Summary
• 10. Exploratory Data Analysis
  • Introduction
    • Prerequisites
  • Questions
  • Variation
    • Typical Values
    • Unusual Values
    • Exercises
  • Unusual Values
    • Exercises
  • Covariation
    • A Categorical and a Numerical Variable
      • Exercises
    • Two Categorical Variables
      • Exercises
    • Two Numerical Variables
      • Exercises
  • Patterns and Models
  • Summary
• 11. Communication
  • Introduction
    • Prerequisites
  • Labels
    • Exercises
  • Annotations
    • Exercises
  • Scales
    • Default Scales
    • Axis Ticks and Legend Keys
    • Legend Layout
    • Replacing a Scale
    • Zooming
    • Exercises
  • Themes
    • Exercises
  • Layout
    • Exercises
  • Summary
• III. Transform
• 12. Logical Vectors
  • Introduction
    • Prerequisites
  • Comparisons
    • Floating-Point Comparison
    • Missing Values
    • is.na()
    • Exercises
  • Boolean Algebra
    • Missing Values
    • Order of Operations
    • %in%
    • Exercises
  • Summaries
    • Logical Summaries
    • Numeric Summaries of Logical Vectors
    • Logical Subsetting
    • Exercises
  • Conditional Transformations
    • if_else()
    • case_when()
    • Compatible Types
    • Exercises
  • Summary
• 13. Numbers
  • Introduction
    • Prerequisites
  • Making Numbers
  • Counts
    • Exercises
  • Numeric Transformations
    • Arithmetic and Recycling Rules
    • Minimum and Maximum
    • Modular Arithmetic
    • Logarithms
    • Rounding
    • Cutting Numbers into Ranges
    • Cumulative and Rolling Aggregates
    • Exercises
  • General Transformations
    • Ranks
    • Offsets
    • Consecutive Identifiers
    • Exercises
  • Numeric Summaries
    • Center
    • Minimum, Maximum, and Quantiles
    • Spread
    • Distributions
    • Positions
    • With mutate()
    • Exercises
  • Summary
• 14. Strings
  • Introduction
    • Prerequisites
  • Creating a String
    • Escapes
    • Raw Strings
    • Other Special Characters
    • Exercises
  • Creating Many Strings from Data
    • str_c()
    • str_glue()
    • str_flatten()
    • Exercises
  • Extracting Data from Strings
    • Separating into Rows
    • Separating into Columns
    • Diagnosing Widening Problems
  • Letters
    • Length
    • Subsetting
    • Exercises
  • Non-English Text
    • Encoding
    • Letter Variations
    • Locale-Dependent Functions
  • Summary
• 15. Regular Expressions
  • Introduction
    • Prerequisites
  • Pattern Basics
  • Key Functions
    • Detect Matches
    • Count Matches
    • Replace Values
    • Extract Variables
    • Exercises
  • Pattern Details
    • Escaping
    • Anchors
    • Character Classes
    • Quantifiers
    • Operator Precedence and Parentheses
    • Grouping and Capturing
    • Exercises
  • Pattern Control
    • Regex Flags
    • Fixed Matches
  • Practice
    • Check Your Work
    • Boolean Operations
    • Creating a Pattern with Code
    • Exercises
  • Regular Expressions in Other Places
    • Tidyverse
    • Base R
  • Summary
• 16. Factors
  • Introduction
    • Prerequisites
  • Factor Basics
  • General Social Survey
    • Exercise
  • Modifying Factor Order
    • Exercises
  • Modifying Factor Levels
    • Exercises
  • Ordered Factors
  • Summary
• 17. Dates and Times
  • Introduction
    • Prerequisites
  • Creating Date/Times
    • During Import
    • From Strings
    • From Individual Components
    • From Other Types
    • Exercises
  • Date-Time Components
    • Getting Components
    • Rounding
    • Modifying Components
    • Exercises
  • Time Spans
    • Durations
    • Periods
    • Intervals
    • Exercises
  • Time Zones
  • Summary
• 18. Missing Values
  • Introduction
    • Prerequisites
  • Explicit Missing Values
    • Last Observation Carried Forward
    • Fixed Values
    • NaN
  • Implicit Missing Values
    • Pivoting
    • Complete
    • Joins
    • Exercises
  • Factors and Empty Groups
  • Summary
• 19. Joins
  • Introduction
    • Prerequisites
  • Keys
    • Primary and Foreign Keys
    • Checking Primary Keys
    • Surrogate Keys
    • Exercises
  • Basic Joins
    • Mutating Joins
    • Specifying Join Keys
    • Filtering Joins
    • Exercises
  • How Do Joins Work?
    • Row Matching
    • Filtering Joins
  • Non-Equi Joins
    • Cross Joins
    • Inequality Joins
    • Rolling Joins
    • Overlap Joins
    • Exercises
  • Summary
• IV. Import
• 20. Spreadsheets
  • Introduction
  • Excel
    • Prerequisites
    • Getting Started
    • Reading Excel Spreadsheets
    • Reading Worksheets
    • Reading Part of a Sheet
    • Data Types
    • Writing to Excel
    • Formatted Output
    • Exercises
  • Google Sheets
    • Prerequisites
    • Getting Started
    • Reading Google Sheets
    • Writing to Google Sheets
    • Authentication
    • Exercises
  • Summary
• 21. Databases
  • Introduction
    • Prerequisites
  • Database Basics
  • Connecting to a Database
    • In This Book
    • Load Some Data
    • DBI Basics
  • dbplyr Basics
  • SQL
    • SQL Basics
    • SELECT
    • FROM
    • GROUP BY
    • WHERE
    • ORDER BY
    • Subqueries
    • Joins
    • Other Verbs
    • Exercises
  • Function Translations
  • Summary
• 22. Arrow
  • Introduction
    • Prerequisites
  • Getting the Data
  • Opening a Dataset
  • The Parquet Format
    • Advantages of Parquet
    • Partitioning
    • Rewriting the Seattle Library Data
  • Using dplyr with Arrow
    • Performance
    • Using dbplyr with Arrow
  • Summary
• 23. Hierarchical Data
  • Introduction
    • Prerequisites
  • Lists
    • Hierarchy
    • List Columns
  • Unnesting
    • unnest_wider()
    • unnest_longer()
    • Inconsistent Types
    • Other Functions
    • Exercises
  • Case Studies
    • Very Wide Data
    • Relational Data
    • Deeply Nested
    • Exercises
  • JSON
    • Data Types
    • jsonlite
    • Starting the Rectangling Process
    • Exercises
  • Summary
• 24. Web Scraping
  • Introduction
    • Prerequisites
  • Scraping Ethics and Legalities
    • Terms of Service
    • Personally Identifiable Information
    • Copyright
  • HTML Basics
    • Elements
    • Attributes
  • Extracting Data
    • Find Elements
    • Nesting Selections
    • Text and Attributes
    • Tables
  • Finding the Right Selectors
  • Putting It All Together
    • Star Wars
    • IMDb Top Films
  • Dynamic Sites
  • Summary
• V. Program
• 25. Functions
  • Introduction
    • Prerequisites
  • Vector Functions
    • Writing a Function
    • Improving Our Function
    • Mutate Functions
    • Summary Functions
    • Exercises
  • Data Frame Functions
    • Indirection and Tidy Evaluation
    • When to Embrace?
    • Common Use Cases
    • Data Masking Versus Tidy Selection
    • Exercises
  • Plot Functions
    • More Variables
    • Combining with Other Tidyverse Packages
    • Labeling
    • Exercises
  • Style
    • Exercises
  • Summary
• 26. Iteration
  • Introduction
    • Prerequisites
  • Modifying Multiple Columns
    • Selecting Columns with .cols
    • Calling a Single Function
    • Calling Multiple Functions
    • Column Names
    • Filtering
    • across() in Functions
    • Versus pivot_longer()
    • Exercises
  • Reading Multiple Files
    • Listing Files in a Directory
    • Lists
    • purrr::map() and list_rbind()
    • Data in the Path
    • Save Your Work
    • Many Simple Iterations
    • Heterogeneous Data
    • Handling Failures
  • Saving Multiple Outputs
    • Writing to a Database
    • Writing CSV Files
    • Saving Plots
  • Summary
• 27. A Field Guide to Base R
  • Introduction
    • Prerequisites
  • Selecting Multiple Elements with [
    • Subsetting Vectors
    • Subsetting Data Frames
    • dplyr Equivalents
    • Exercises
  • Selecting a Single Element with $ and [[
    • Data Frames
    • Tibbles
    • Lists
    • Exercises
  • Apply Family
  • for Loops
  • Plots
  • Summary
• VI. Communicate
• 28. Quarto
  • Introduction
    • Prerequisites
  • Quarto Basics
    • Exercises
  • Visual Editor
    • Exercises
  • Source Editor
    • Exercises
  • Code Chunks
    • Chunk Label
    • Chunk Options
    • Global Options
    • Inline Code
    • Exercises
  • Figures
    • Figure Sizing
    • Other Important Options
    • Exercises
  • Tables
    • Exercises
  • Caching
    • Exercises
  • Troubleshooting
  • YAML Header
    • Self-Contained
    • Parameters
    • Bibliographies and Citations
  • Workflow
  • Summary
• 29. Quarto Formats
  • Introduction
  • Output Options
  • Documents
  • Presentations
  • Interactivity
    • htmlwidgets
    • Shiny
  • Websites and Books
  • Other Formats
  • Summary
• Index

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