R For Data Science Import Tidy Transform Visualize And Model Data

R for Data Science

Learn how to use R to turn raw data into insight, knowledge, and understanding. 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, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

R for Data Science

Cover -- 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 Online Learning -- 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 Mappings -- Exercises -- 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.

R for Data Science