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Beyond Spreadsheets with R /

With Beyond Spreadsheets with R you'll learn how to go from raw data to meaningful insights using R and RStudio. Each carefully crafted chapter covers a unique way to wrangle data, from understanding individual values to interacting with complex collections of data, including data you scrape fr...

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Detalles Bibliográficos
Autor principal:	Carroll, Jon (Autor)
Autor Corporativo:	Safari, an O'Reilly Media Company
Formato:	Electrónico eBook
Idioma:	Inglés
Publicado:	Manning Publications, 2019.
Edición:	1st edition.
Temas:	R (Computer program language) Data mining. R (Langage de programmation) Exploration de données (Informatique) Data mining
Acceso en línea:	Texto completo (Requiere registro previo con correo institucional)

Tabla de Contenidos:

Intro
Titlepage
Copyright
preface
acknowledgments
about this book
Who needs this book?
How to read this book
Formatting
Structure
Getting started
Where to find more help
More about this book
Book forum
about the author
about the cover illustration
Chapter 1: Introducing data and the R language
1.1 Data: What, where, how?
1.1.1 What is data?
1.1.2 Seeing the world as data sources
1.1.3 Data munging
1.1.4 What you can do with well-handled data
1.1.5 Data as an asset
1.1.6 Reproducible research and version control
1.2 Introducing R
1.2.1 The origins of R
1.2.2 What R is and what it isn't
1.3 How R works
1.4 Introducing RStudio
1.4.1 Working with R within RStudio
1.4.2 Built-in packages (data and functions)
1.4.3 Built-in documentation
1.4.4 Vignettes
1.5 Try it yourself
Terminology
Summary
Chapter 2: Getting to know R data types
2.1 Types of data
2.1.1 Numbers
2.1.2 Text (strings)
2.1.3 Categories (factors)
2.1.4 Dates and times
2.1.5 Logicals
2.1.6 Missing values
2.2 Storing values (assigning)
2.2.1 Naming data (variables)
2.2.2 Unchanging data
2.2.3 The assignment operators (&lt
vs. =)
2.3 Specifying the data type
2.4 Telling R to ignore something
2.5 Try it yourself
Terminology
Summary
Chapter 3: Making new data values
3.1 Basic mathematics
3.2 Operator precedence
3.3 String concatenation (joining)
3.4 Comparisons
3.5 Automatic conversion (coercion)
3.6 Try it yourself
Terminology
Summary
Chapter 4: Understanding the tools you'll use: Functions
4.1 Functions
4.1.1 Under the hood
4.1.2 Function template
4.1.3 Arguments
4.1.4 Multiple arguments
4.1.5 Default arguments
4.1.6 Argument name matching
4.1.7 Partial matching
4.1.8 Scope.
4.2 Packages
4.2.1 Installing packages
4.2.2 How does R (not) know about this function?
4.2.3 Namespaces
4.3 Messages, warnings, and errors, oh my!
4.3.1 Creating messages, warnings, and errors
4.3.2 Diagnosing messages, warnings, and errors
4.4 Testing
4.5 Project: Generalizing a function
4.6 Try it yourself
Terminology
Summary
Chapter 5: Combining data values
5.1 Simple collections
5.1.1 Coercion
5.1.2 Missing values
5.1.3 Attributes
5.1.4 Names
5.2 Sequences
5.2.1 Vector functions
5.2.2 Vector math operations
5.3 Matrices
5.3.1 Naming dimensions
5.4 Lists
5.5 data.frames
5.6 Classes
5.6.1 The tibble class
5.6.2 Structures as function arguments
5.7 Try it yourself
Terminology
Summary
Chapter 6: Selecting data values
6.1 Text processing
6.1.1 Text matching
6.1.2 Substrings
6.1.3 Text substitutions
6.1.4 Regular expressions
6.2 Selecting components from structures
6.2.1 Vectors
6.2.2 Lists
6.2.3 Matrices
6.3 Replacing values
6.4 data.frames and dplyr
6.4.1 dplyr verbs
6.4.2 Non-standard evaluation
6.4.3 Pipes
6.4.4 Subsetting data.frame the hard way
6.5 Replacing NA
6.6 Selecting conditionally
6.7 Summarizing values
6.8 A worked example: Excel vs. R
6.9 Try it yourself
6.9.1 Solutions
no peeking
Terminology
Summary
Chapter 7: Doing things with lots of data
7.1 Tidy data principles
7.1.1 The working directory
7.1.2 Stored data formats
7.1.3 Reading data into R
7.1.4 Scraping data
7.1.5 Inspecting data
7.1.6 Dealing with odd values in data (sentinel values)
7.1.7 Converting to tidy data
7.2 Merging data
7.3 Writing data from R
7.4 Try it yourself
Terminology
Summary
Chapter 8: Doing things conditionally: Control structures
8.1 Looping.
8.1.1 Vectorization
8.1.2 Tidy repetition: Looping with purrr
8.1.3 for loops
8.2 Wider and narrower loop scope
8.2.1 while loops
8.3 Conditional evaluation
8.3.1 if conditions
8.3.2 ifelse conditions
8.4 Try it yourself
Terminology
Summary
Chapter 9: Visualizing data: Plotting
9.1 Data preparation
9.1.1 Tidy data, revisited
9.1.2 Importance of data types
9.2 ggplot2
9.2.1 General construction
9.2.2 Adding points
9.2.3 Style aesthetics
9.2.4 Adding lines
9.2.5 Adding bars
9.2.6 Other types of plots
9.2.7 Scales
9.2.8 Facetting
9.2.9 Additional options
9.3 Plots as objects
9.4 Saving plots
9.5 Try it yourself
Terminology
Summary
Chapter 10: Doing more with your data with extensions
10.1 Writing your own packages
10.1.1 Creating a minimal package
10.1.2 Documentation
10.2 Analyzing your package
10.2.1 Unit testing
10.2.2 Profiling
10.3 What to do next?
10.3.1 Regression
10.3.2 Clustering
10.3.3 Working with maps
10.3.4 Interacting with APIs
10.3.5 Sharing your package
10.4 More resources
Terminology
Summary
Appendix A: Installing R
Windows
Mac
Linux
From source
Appendix B: Installing RStudio
Installing RStudio
Packages used in this book
Appendix C: Graphics in base R
Index
List of Figures
List of Tables
List of Listings.

Beyond Spreadsheets with R /

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