R for Data Science Import Tidy Transform Visualize and Model Data 1st Edition by Hadley Wickham, Garrett Grolemund 1491910399 978-1491910399

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ISBN 10: 1491910399

ISBN 13: 978-1491910399

Author: Hadley Wickham, Garrett Grolemund

 

R for Data Science Import Tidy Transform Visualize and Model Data 1st Table of contents:

 

I. Explore

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

2. Workflow: Basics

Coding Basics

What’s in a Name?

Calling Functions

Exercises

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

4. Workflow: Scripts

Running Code

RStudio Diagnostics

Exercises

5. Exploratory Data Analysis

Introduction

Prerequisites

Questions

Variation

Visualizing Distributions

Typical Values

Unusual Values

Exercises

Missing Values

Exercises

Covariation

A Categorical and Continuous Variable

Exercises

Two Categorical Variables

Exercises

Two Continuous Variables

Exercises

Patterns and Models

ggplot2 Calls

Learning More

6. Workflow: Projects

What Is Real?

Where Does Your Analysis Live?

Paths and Directories

RStudio Projects

Summary

II. Wrangle

7. Tibbles with tibble

Introduction

Prerequisites

Creating Tibbles

Tibbles Versus data.frame

Printing

Subsetting

Interacting with Older Code

Exercises

8. Data Import with readr

Introduction

Prerequisites

Getting Started

Compared to Base R

Exercises

Parsing a Vector

Numbers

Strings

Factors

Dates, Date-Times, and Times

Exercises

Parsing a File

Strategy

Problems

Other Strategies

Writing to a File

Other Types of Data

9. Tidy Data with tidyr

Introduction

Prerequisites

Tidy Data

Exercises

Spreading and Gathering

Gathering

Spreading

Exercises

Separating and Pull

Separate

Unite

Exercises

Missing Values

Exercises

Case Study

Exercises

Nontidy Data

10. Relational Data with dplyr

Introduction

Prerequisites

nycflights13

Exercises

Keys

Exercises

Mutating Joins

Understanding Joins

Inner Join

Outer Joins

Duplicate Keys

Defining the Key Columns

Exercises

Other Implementations

Filtering Joins

Exercises

Join Problems

Set Operations

11. Strings with stringr

Introduction

Prerequisites

String Basics

String Length

Combining Strings

Subsetting Strings

Locales

Exercises

Matching Patterns with Regular Expressions

Basic Matches

Exercises

Anchors

Exercises

Character Classes and Alternatives

Exercises

Repetition

Exercises

Grouping and Backreferences

Exercises

Tools

Detect Matches

Exercises

Extract Matches

Exercises

Grouped Matches

Exercises

Replacing Matches

Exercises

Splitting

Exercises

Find Matches

Other Types of Pattern

Exercises

Other Uses of Regular Expressions

stringi

Exercises

12. Factors with forcats

Introduction

Prerequisites

Creating Factors

General Social Survey

Exercises

Modifying Factor Order

Exercises

Modifying Factor Levels

Exercises

13. Dates and Times with lubridate

Introduction

Prerequisites

Creating Date/Times

From Strings

From Individual Components

From Other Types

Exercises

Date-Time Components

Getting Components

Rounding

Setting Components

Exercises

Time Spans

Durations

Periods

Intervals

Summary

Exercises

Time Zones

III. Program

14. Pipes with magrittr

Introduction

Prerequisites

Piping Alternatives

Intermediate Steps

Overwrite the Original

Function Composition

Use the Pipe

When Not to Use the Pipe

Other Tools from magrittr

15. Functions

Introduction

Prerequisites

When Should You Write a Function?

Exercises

Functions Are for Humans and Computers

Exercises

Conditional Execution

Conditions

Multiple Conditions

Code Style

Exercises

Function Arguments

Choosing Names

Checking Values

Dot-Dot-Dot (…)

Lazy Evaluation

Exercises

Return Values

Explicit Return Statements

Writing Pipeable Functions

Environment

16. Vectors

Introduction

Prerequisites

Vector Basics

Important Types of Atomic Vector

Logical

Numeric

Character

Missing Values

Exercises

Using Atomic Vectors

Coercion

Test Functions

Scalars and Recycling Rules

Naming Vectors

Subsetting

Exercises

Recursive Vectors (Lists)

Visualizing Lists

Subsetting

Lists of Condiments

Exercises

Attributes

Augmented Vectors

Factors

Dates and Date-Times

Tibbles

Exercises

17. Iteration with purrr

Introduction

Prerequisites

For Loops

Exercises

For Loop Variations

Modifying an Existing Object

Looping Patterns

Unknown Output Length

Unknown Sequence Length

Exercises

For Loops Versus Functionals

Exercises

The Map Functions

Shortcuts

Base R

Exercises

Dealing with Failure

Mapping over Multiple Arguments

Invoking Different Functions

Walk

Other Patterns of For Loops

Predicate Functions

Reduce and Accumulate

Exercises

IV. Model

18. Model Basics with modelr

Introduction

Prerequisites

A Simple Model

Exercises

Visualizing Models

Predictions

Residuals

Exercises

Formulas and Model Families

Categorical Variables

Interactions (Continuous and Categorical)

Interactions (Two Continuous)

Transformations

Exercises

Missing Values

Other Model Families

19. Model Building

Introduction

Prerequisites

Why Are Low-Quality Diamonds More Expensive?

Price and Carat

A More Complicated Model

Exercises

What Affects the Number of Daily Flights?

Day of Week

Seasonal Saturday Effect

Computed Variables

Time of Year: An Alternative Approach

Exercises

Learning More About Models

20. Many Models with purrr and broom

Introduction

Prerequisites

gapminder

Nested Data

List-Columns

Unnesting

Model Quality

Exercises

List-Columns

Creating List-Columns

With Nesting

From Vectorized Functions

From Multivalued Summaries

From a Named List

Exercises

Simplifying List-Columns

List to Vector

Unnesting

Exercises

Making Tidy Data with broom

V. Communicate

21. R Markdown

Introduction

Prerequisites

R Markdown Basics

Exercises

Text Formatting with Markdown

Exercises

Code Chunks

Chunk Name

Chunk Options

Table

Caching

Global Options

Inline Code

Exercises

Troubleshooting

YAML Header

Parameters

Bibliographies and Citations

Learning More

22. Graphics for Communication with ggplot2

Introduction

Prerequisites

Label

Exercises

Annotations

Exercises

Scales

Axis Ticks and Legend Keys

Legend Layout

Replacing a Scale

Exercises

Zooming

Themes

Saving Your Plots

Figure Sizing

Other Important Options

Learning More

23. R Markdown Formats

Introduction

Output Options

Documents

Notebooks

Presentations

Dashboards

Interactivity

htmlwidgets

Shiny

Websites

Other Formats

Learning More

24. R Markdown Workflow

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