Data Wrangling with R

This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques.

This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author’s goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned:

• How to work with different types of data such as numerics, characters, regular expressions, factors, and dates
• The difference between different data structures and how to create, add additional components to, and subset each data structure
• How to acquire and parse data from locations previously inaccessible
• How to develop functions and use loop control structures to reduce code redundancy
• How to use pipe operators to simplify code and make it more readable
• How to reshape the layout of data and manipulate, summarize, and join data sets

Table of Contents

Part I: Introduction
Chapter 1: The Role of Data Wrangling
Chapter 2: Introduction to R
Chapter 3: The Basics

Part II: Working with Different Types of Data in R
Chapter 4: Dealing with Numbers
Chapter 5: Dealing with Character Strings
Chapter 6: Dealing with Regular Expressions
Chapter 7: Dealing with Factors
Chapter 8: Dealing with Dates

Part III: Managing Data Structures in R
Chapter 9: Data Structure Basics
Chapter 10: Managing Vectors
Chapter 11: Managing Lists
Chapter 12: Managing Matrices
Chapter 13: Managing Data Frames
Chapter 14: Dealing with Missing Values

Part IV: Importing, Scraping, and Exporting Data with R
Chapter 15: Importing Data
Chapter 16: Scraping Data
Chapter 17: Exporting Data

Part V: Creating Efficient and Readable Code in R
Chapter 18: Functions
Chapter 19: Loop Control Statements
Chapter 20: Simplify Your Code with %>%

Part VI: Shaping and Transforming Your Data with R
Chapter 21: Reshaping Your Data with tidyr
Chapter 22: Transforming Your Data with dplyr