Coverart for item
The Resource R for data science : import, tidy, transform, visualize, and model data, Hadley Wickham & Garrett Grolemund

R for data science : import, tidy, transform, visualize, and model data, Hadley Wickham & Garrett Grolemund

Label
R for data science : import, tidy, transform, visualize, and model data
Title
R for data science
Title remainder
import, tidy, transform, visualize, and model data
Statement of responsibility
Hadley Wickham & Garrett Grolemund
Creator
Contributor
Author
Subject
Language
eng
Cataloging source
N$T
http://library.link/vocab/creatorName
Wickham, Hadley
Dewey number
004
Illustrations
illustrations
Index
index present
LC call number
QA76
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
Grolemund, Garrett
http://library.link/vocab/subjectName
  • Electronic data processing
  • R (Computer program language)
  • Databases
  • Big data
  • COMPUTERS / Computer Literacy
  • COMPUTERS / Computer Science
  • COMPUTERS / Data Processing
  • COMPUTERS / Hardware / General
  • COMPUTERS / Information Technology
  • COMPUTERS / Machine Theory
  • COMPUTERS / Reference
  • Big data
  • Databases
  • Electronic data processing
  • R (Computer program language)
Label
R for data science : import, tidy, transform, visualize, and model data, Hadley Wickham & Garrett Grolemund
Link
Instantiates
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 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
Control code
ocn966429425
Dimensions
unknown
Edition
First edition.
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9781491910368
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
Stock number
17A40AC7-2948-4F90-9C18-207341CE0160
System control number
  • (Sirsi) 1718358
  • (OCoLC)966429425
Label
R for data science : import, tidy, transform, visualize, and model data, Hadley Wickham & Garrett Grolemund
Link
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 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
Control code
ocn966429425
Dimensions
unknown
Edition
First edition.
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9781491910368
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
Stock number
17A40AC7-2948-4F90-9C18-207341CE0160
System control number
  • (Sirsi) 1718358
  • (OCoLC)966429425

Library Locations

    • Merrimack Valley Library ConsortiumBorrow it
      4 High Street, Suite 175, North Andover, MA, 01845, US
      42.7009413 -71.1255084
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