| Descript |
1 online resource (579 pages) |
| Content |
text txt |
| Media |
computer c |
| Carrier |
online resource cr |
| Contents |
Intro -- Copyright -- Table of Contents -- 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 -- Part I. Whole Game -- Chapter 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 -- Chapter 2. Workflow: Basics -- Coding Basics -- Comments -- What's in a Name? -- Calling Functions -- Exercises -- Summary -- Chapter 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 -- Chapter 4. Workflow: Code Style -- Names -- Spaces -- Pipes -- ggplot2 -- Sectioning Comments -- Exercises -- Summary -- Chapter 5. Data Tidying -- Introduction -- Prerequisites -- Tidy Data -- Exercises -- Lengthening Data -- Data in Column Names -- How Does Pivoting Work? -- Many Variables in Column Names. |
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Data and Variable Names in the Column Headers -- Widening Data -- How Does pivot_wider() Work? -- Summary -- Chapter 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 -- Chapter 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 -- Chapter 8. Workflow: Getting Help -- Google Is Your Friend -- Making a reprex -- Investing in Yourself -- Summary -- Part II. Visualize -- Chapter 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 -- Chapter 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 -- Chapter 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 -- Part III. Transform -- Chapter 12. Logical Vectors -- Introduction -- Prerequisites. |
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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 -- Chapter 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 -- Chapter 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 -- Chapter 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. |
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Creating a Pattern with Code -- Exercises -- Regular Expressions in Other Places -- Tidyverse -- Base R -- Summary -- Chapter 16. Factors -- Introduction -- Prerequisites -- Factor Basics -- General Social Survey -- Exercise -- Modifying Factor Order -- Exercises -- Modifying Factor Levels -- Exercises -- Ordered Factors -- Summary -- Chapter 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 -- Chapter 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 -- Chapter 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 -- Part IV. Import -- Chapter 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 -- Chapter 21. Databases -- Introduction -- Prerequisites -- Database Basics -- Connecting to a Database. |
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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 -- Chapter 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 -- Chapter 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 -- Chapter 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 -- Part V. Program -- Chapter 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 -- Chapter 26. Iteration -- Introduction -- Prerequisites -- Modifying Multiple Columns. |
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Selecting Columns with .cols. |
| Alt author |
Çetinkaya-Rundel, Mine.
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Grolemund, Garrett.
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| ISBN |
9781492097372 (electronic bk.) |
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