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

Description

Learn how to use R to turn data into insight, knowledge, and understanding. Ideal for current and aspiring data scientists, this book introduces you to doing data science with R and RStudio, as well as the tidyverse—a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Each section in this edition includes exercises to help you practice what you've learned along the way. Updated for the latest tidyverse best practices, new chapters dive deeper into visualization and data wrangling, show you how to get data from spreadsheets, databases, and websites, and help you make the most of new programming tools. You'll learn how to: Visualize-create plots for data exploration and communication of results Transform-discover types of variables and the tools you can use to work with them Import-get data into R and in a form convenient for analysis Program-learn R tools for solving data problems with greater clarity and ease Communicate-integrate prose, code, and results with Quarto logo Hello, Welcome to EPUB Reader Click button to select your book Open EPUB book This Online Web App is made by Neo Reader for experimental purpose, it is a very simple EPUB Reader. We recommend you try our Neo Reader for better experience. Take a look now neat reader pc AD Ultimate EPUB Reader Totally free to try Support multiple file types, such as EPUB, MOBI, AZW3, AZW, PDF and TXT. Learn more about Neo Reader General Ebook Solution Introduction Preface to the Second Edition What You Will Learn How This Book Is Organized What You Won’t Learn Modeling Big Data Python, Julia, and Friends Prerequisites R RStudio The Tidyverse Other Packages Running R Code Other Conventions Used in This Book O’Reilly 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 What’s 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 Two Categorical Variables Two Numerical Variables 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 About the Authors