R for data science : import, tidy, transform, visualize, and model data

"This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"--

Online resource; title from PDF title page (EBSCO, viewed December 22, 2016)

Includes bibliographical references and index

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 Safari; 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 MappingsExercises; 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; Typical Values; Unusual Values; Exercises; Missing Values; Exercises; Covariation

A Categorical and Continuous VariableExercises; Two Categorical Variables; Exercises; Two Continuous Variables; Exercises; Patterns and Models; ggplot2 Calls; Learning More; Chapter 6. Workflow: Projects; What Is Real?; Where Does Your Analysis Live?; Paths and Directories; RStudio Projects; Summary; Part II. Wrangle; Chapter 7. Tibbles with tibble; Introduction; Prerequisites; Creating Tibbles; Tibbles Versus data.frame; Printing; Subsetting; Interacting with Older Code; Exercises; Chapter 8. Data Import with readr; Introduction; Prerequisites; Getting Started; Compared to Base R; Exercises

Parsing a VectorNumbers; Strings; Factors; Dates, Date-Times, and Times; Exercises; Parsing a File; Strategy; Problems; Other Strategies; Writing to a File; Other Types of Data; Chapter 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; Chapter 10. Relational Data with dplyr; Introduction; Prerequisites; nycflights13; Exercises; Keys; Exercises; Mutating Joins; Understanding Joins; Inner Join

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