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The Resource Data wrangling with R, Bradley C. Boehmke
Data wrangling with R, Bradley C. Boehmke
Resource Information
The item Data wrangling with R, Bradley C. Boehmke represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.This item is available to borrow from all library branches.
Resource Information
The item Data wrangling with R, Bradley C. Boehmke represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.
This item is available to borrow from all library branches.
 Summary
 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 stepbystep tutorial approach and provide a solid foundation 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 In essence, the user will have the data wrangling toolbox required for modern day data analysis. Brad Boehmke, Ph. D., is an Operations Research Analyst at Headquarters Air Force Materiel Command, Studies and Analyses Division. He is also Assistant Professor in the Operational Sciences Department at the Air Force Institute of Technology. Dr. Boehmke's research interests are in the areas of cost analysis, economic modeling, decision analysis, and developing applied modeling applications through the R statistical language
 Language
 eng
 Extent
 1 online resource (xii, 238 pages).
 Note
 Title from PDF title page (viewed 10/26/17)
 Contents

 Preface; Who This Book Is for; What You€Need for€This Book; Reader Feedback; Contents; Part I: Introduction; Chapter 1: The Role of€Data Wrangling; Bibliography; Chapter 2: Introduction to€R; 2.1 Open Source; 2.2 Flexibility; 2.3 Community; Bibliography; Chapter 3: The Basics; 3.1 Installing R and€RStudio; 3.2 Understanding the€Console; 3.2.1 Script Editor; 3.2.2 Workspace Environment; 3.2.3 Console; 3.2.4 Misc. Displays; 3.2.5 Workspace Options and€Shortcuts; 3.3 Getting Help; 3.3.1 General Help; 3.3.2 Getting Help on€Functions; 3.3.3 Getting Help from€the€Web; 3.4 Working with€Packages
 3.4.1 Installing Packages3.4.2 Loading Packages; 3.4.3 Getting Help on€Packages; 3.4.4 Useful Packages; 3.5 Assignment and€Evaluation; 3.6 R as€a€Calculator; 3.6.1 Vectorization; 3.7 Styling Guide; 3.7.1 Notation and€Naming; 3.7.2 Organization; 3.7.3 Syntax; Part II: Working with Different Types of Data in R; Chapter 4: Dealing with€Numbers; 4.1 Integer vs. Double; 4.1.1 Creating Integer and€Double Vectors; 4.1.2 Converting Between Integer and€Double Values; 4.2 Generating Sequence of€Nonrandom Numbers; 4.2.1 Specifing Numbers Within a€Sequence; 4.2.2 Generating Regular Sequences
 4.3 Generating Sequence of€Random Numbers4.3.1 Uniform Numbers; 4.3.2 Normal Distribution Numbers; 4.3.3 Binomial Distribution Numbers; 4.3.4 Poisson Distribution Numbers; 4.3.5 Exponential Distribution Numbers; 4.3.6 Gamma Distribution Numbers; 4.4 Setting the€Seed for€Reproducible Random Numbers; 4.5 Comparing Numeric Values; 4.5.1 Comparison Operators; 4.5.2 Exact Equality; 4.5.3 Floating Point Comparison; 4.6 Rounding Numbers; Chapter 5: Dealing with€Character Strings; 5.1 Character String Basics; 5.1.1 Creating Strings; 5.1.2 Converting to€Strings; 5.1.3 Printing Strings
 5.1.4 Counting String Elements and€Characters5.2 String Manipulation with€Base R; 5.2.1 Case Conversion; 5.2.2 Simple Character Replacement; 5.2.3 String Abbreviations; 5.2.4 Extract/Replace Substrings; 5.3 String Manipulation with€stringr; 5.3.1 Basic Operations; 5.3.2 Duplicate Characters Within a€String; 5.3.3 Remove Leading and€Trailing Whitespace; 5.3.4 Pad a€String with€Whitespace; 5.4 Set Operations for€Character Strings; 5.4.1 Set Union; 5.4.2 Set Intersection; 5.4.3 Identifying Different Elements; 5.4.4 Testing for€Element Equality; 5.4.5 Testing for€Exact Equality
 5.4.6 Identifying If Elements Are Contained in€a€String5.4.7 Sorting a€String; Chapter 6: Dealing with€Regular Expressions; 6.1 Regex Syntax; 6.1.1 Metacharacters; 6.1.2 Sequences; 6.1.3 Character Classes; 6.1.4 POSIX Character Classes; 6.1.5 Quantifiers; 6.2 Regex Functions; 6.2.1 Main Regex Functions in€R; 6.2.1.1 Pattern Matching; 6.2.1.2 Pattern Replacement Functions; 6.2.1.3 Splitting Character Vectors; 6.2.2 Regex Functions in€stringr; 6.2.2.1 Detecting Patterns; 6.2.2.2 Locating Patterns; 6.2.2.3 Extracting Patterns; 6.2.2.4 Replacing Patterns; 6.2.2.5 String Splitting
 Isbn
 9783319455990
 Label
 Data wrangling with R
 Title
 Data wrangling with R
 Statement of responsibility
 Bradley C. Boehmke
 Subject

 Electronic books
 MATHEMATICS  Probability & Statistics  General
 Mathematical & statistical software
 Algorithms & data structures
 Business mathematics & systems
 Combinatorics & graph theory
 Mathematical statistics  Data processing
 Mathematical statistics  Data processing
 MATHEMATICS  Applied
 Probability & statistics
 Graphics programming
 Language
 eng
 Summary
 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 stepbystep tutorial approach and provide a solid foundation 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 In essence, the user will have the data wrangling toolbox required for modern day data analysis. Brad Boehmke, Ph. D., is an Operations Research Analyst at Headquarters Air Force Materiel Command, Studies and Analyses Division. He is also Assistant Professor in the Operational Sciences Department at the Air Force Institute of Technology. Dr. Boehmke's research interests are in the areas of cost analysis, economic modeling, decision analysis, and developing applied modeling applications through the R statistical language
 Cataloging source
 EBLCP
 http://library.link/vocab/creatorName
 Boehmke, Bradley C
 Dewey number
 519.5
 Index
 no index present
 LC call number
 QA276.45.R3
 Literary form
 non fiction
 Nature of contents
 dictionaries
 Series statement
 Use R!
 http://library.link/vocab/subjectName

 Mathematical statistics
 MATHEMATICS
 MATHEMATICS
 Mathematical statistics
 Probability & statistics
 Algorithms & data structures
 Business mathematics & systems
 Combinatorics & graph theory
 Graphics programming
 Mathematical & statistical software
 Label
 Data wrangling with R, Bradley C. Boehmke
 Note
 Title from PDF title page (viewed 10/26/17)
 Antecedent source
 file reproduced from an electronic resource
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents

 Preface; Who This Book Is for; What You€Need for€This Book; Reader Feedback; Contents; Part I: Introduction; Chapter 1: The Role of€Data Wrangling; Bibliography; Chapter 2: Introduction to€R; 2.1 Open Source; 2.2 Flexibility; 2.3 Community; Bibliography; Chapter 3: The Basics; 3.1 Installing R and€RStudio; 3.2 Understanding the€Console; 3.2.1 Script Editor; 3.2.2 Workspace Environment; 3.2.3 Console; 3.2.4 Misc. Displays; 3.2.5 Workspace Options and€Shortcuts; 3.3 Getting Help; 3.3.1 General Help; 3.3.2 Getting Help on€Functions; 3.3.3 Getting Help from€the€Web; 3.4 Working with€Packages
 3.4.1 Installing Packages3.4.2 Loading Packages; 3.4.3 Getting Help on€Packages; 3.4.4 Useful Packages; 3.5 Assignment and€Evaluation; 3.6 R as€a€Calculator; 3.6.1 Vectorization; 3.7 Styling Guide; 3.7.1 Notation and€Naming; 3.7.2 Organization; 3.7.3 Syntax; Part II: Working with Different Types of Data in R; Chapter 4: Dealing with€Numbers; 4.1 Integer vs. Double; 4.1.1 Creating Integer and€Double Vectors; 4.1.2 Converting Between Integer and€Double Values; 4.2 Generating Sequence of€Nonrandom Numbers; 4.2.1 Specifing Numbers Within a€Sequence; 4.2.2 Generating Regular Sequences
 4.3 Generating Sequence of€Random Numbers4.3.1 Uniform Numbers; 4.3.2 Normal Distribution Numbers; 4.3.3 Binomial Distribution Numbers; 4.3.4 Poisson Distribution Numbers; 4.3.5 Exponential Distribution Numbers; 4.3.6 Gamma Distribution Numbers; 4.4 Setting the€Seed for€Reproducible Random Numbers; 4.5 Comparing Numeric Values; 4.5.1 Comparison Operators; 4.5.2 Exact Equality; 4.5.3 Floating Point Comparison; 4.6 Rounding Numbers; Chapter 5: Dealing with€Character Strings; 5.1 Character String Basics; 5.1.1 Creating Strings; 5.1.2 Converting to€Strings; 5.1.3 Printing Strings
 5.1.4 Counting String Elements and€Characters5.2 String Manipulation with€Base R; 5.2.1 Case Conversion; 5.2.2 Simple Character Replacement; 5.2.3 String Abbreviations; 5.2.4 Extract/Replace Substrings; 5.3 String Manipulation with€stringr; 5.3.1 Basic Operations; 5.3.2 Duplicate Characters Within a€String; 5.3.3 Remove Leading and€Trailing Whitespace; 5.3.4 Pad a€String with€Whitespace; 5.4 Set Operations for€Character Strings; 5.4.1 Set Union; 5.4.2 Set Intersection; 5.4.3 Identifying Different Elements; 5.4.4 Testing for€Element Equality; 5.4.5 Testing for€Exact Equality
 5.4.6 Identifying If Elements Are Contained in€a€String5.4.7 Sorting a€String; Chapter 6: Dealing with€Regular Expressions; 6.1 Regex Syntax; 6.1.1 Metacharacters; 6.1.2 Sequences; 6.1.3 Character Classes; 6.1.4 POSIX Character Classes; 6.1.5 Quantifiers; 6.2 Regex Functions; 6.2.1 Main Regex Functions in€R; 6.2.1.1 Pattern Matching; 6.2.1.2 Pattern Replacement Functions; 6.2.1.3 Splitting Character Vectors; 6.2.2 Regex Functions in€stringr; 6.2.2.1 Detecting Patterns; 6.2.2.2 Locating Patterns; 6.2.2.3 Extracting Patterns; 6.2.2.4 Replacing Patterns; 6.2.2.5 String Splitting
 Dimensions
 unknown
 Extent
 1 online resource (xii, 238 pages).
 File format
 one file format
 Form of item
 online
 Isbn
 9783319455990
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Note
 SpringerLink
 Quality assurance targets
 unknown
 Reformatting quality
 unknown
 Specific material designation
 remote
 System control number

 (OCoLC)964404346
 (OCoLC)ocn964404346
 Label
 Data wrangling with R, Bradley C. Boehmke
 Note
 Title from PDF title page (viewed 10/26/17)
 Antecedent source
 file reproduced from an electronic resource
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents

 Preface; Who This Book Is for; What You€Need for€This Book; Reader Feedback; Contents; Part I: Introduction; Chapter 1: The Role of€Data Wrangling; Bibliography; Chapter 2: Introduction to€R; 2.1 Open Source; 2.2 Flexibility; 2.3 Community; Bibliography; Chapter 3: The Basics; 3.1 Installing R and€RStudio; 3.2 Understanding the€Console; 3.2.1 Script Editor; 3.2.2 Workspace Environment; 3.2.3 Console; 3.2.4 Misc. Displays; 3.2.5 Workspace Options and€Shortcuts; 3.3 Getting Help; 3.3.1 General Help; 3.3.2 Getting Help on€Functions; 3.3.3 Getting Help from€the€Web; 3.4 Working with€Packages
 3.4.1 Installing Packages3.4.2 Loading Packages; 3.4.3 Getting Help on€Packages; 3.4.4 Useful Packages; 3.5 Assignment and€Evaluation; 3.6 R as€a€Calculator; 3.6.1 Vectorization; 3.7 Styling Guide; 3.7.1 Notation and€Naming; 3.7.2 Organization; 3.7.3 Syntax; Part II: Working with Different Types of Data in R; Chapter 4: Dealing with€Numbers; 4.1 Integer vs. Double; 4.1.1 Creating Integer and€Double Vectors; 4.1.2 Converting Between Integer and€Double Values; 4.2 Generating Sequence of€Nonrandom Numbers; 4.2.1 Specifing Numbers Within a€Sequence; 4.2.2 Generating Regular Sequences
 4.3 Generating Sequence of€Random Numbers4.3.1 Uniform Numbers; 4.3.2 Normal Distribution Numbers; 4.3.3 Binomial Distribution Numbers; 4.3.4 Poisson Distribution Numbers; 4.3.5 Exponential Distribution Numbers; 4.3.6 Gamma Distribution Numbers; 4.4 Setting the€Seed for€Reproducible Random Numbers; 4.5 Comparing Numeric Values; 4.5.1 Comparison Operators; 4.5.2 Exact Equality; 4.5.3 Floating Point Comparison; 4.6 Rounding Numbers; Chapter 5: Dealing with€Character Strings; 5.1 Character String Basics; 5.1.1 Creating Strings; 5.1.2 Converting to€Strings; 5.1.3 Printing Strings
 5.1.4 Counting String Elements and€Characters5.2 String Manipulation with€Base R; 5.2.1 Case Conversion; 5.2.2 Simple Character Replacement; 5.2.3 String Abbreviations; 5.2.4 Extract/Replace Substrings; 5.3 String Manipulation with€stringr; 5.3.1 Basic Operations; 5.3.2 Duplicate Characters Within a€String; 5.3.3 Remove Leading and€Trailing Whitespace; 5.3.4 Pad a€String with€Whitespace; 5.4 Set Operations for€Character Strings; 5.4.1 Set Union; 5.4.2 Set Intersection; 5.4.3 Identifying Different Elements; 5.4.4 Testing for€Element Equality; 5.4.5 Testing for€Exact Equality
 5.4.6 Identifying If Elements Are Contained in€a€String5.4.7 Sorting a€String; Chapter 6: Dealing with€Regular Expressions; 6.1 Regex Syntax; 6.1.1 Metacharacters; 6.1.2 Sequences; 6.1.3 Character Classes; 6.1.4 POSIX Character Classes; 6.1.5 Quantifiers; 6.2 Regex Functions; 6.2.1 Main Regex Functions in€R; 6.2.1.1 Pattern Matching; 6.2.1.2 Pattern Replacement Functions; 6.2.1.3 Splitting Character Vectors; 6.2.2 Regex Functions in€stringr; 6.2.2.1 Detecting Patterns; 6.2.2.2 Locating Patterns; 6.2.2.3 Extracting Patterns; 6.2.2.4 Replacing Patterns; 6.2.2.5 String Splitting
 Dimensions
 unknown
 Extent
 1 online resource (xii, 238 pages).
 File format
 one file format
 Form of item
 online
 Isbn
 9783319455990
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Note
 SpringerLink
 Quality assurance targets
 unknown
 Reformatting quality
 unknown
 Specific material designation
 remote
 System control number

 (OCoLC)964404346
 (OCoLC)ocn964404346
Subject
 Algorithms & data structures
 Business mathematics & systems
 Combinatorics & graph theory
 Electronic books
 Graphics programming
 MATHEMATICS  Applied
 MATHEMATICS  Probability & Statistics  General
 Mathematical & statistical software
 Mathematical statistics  Data processing
 Mathematical statistics  Data processing
 Probability & statistics
Genre
Member of
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