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)

MARC

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520 |a 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 from the web. You'll build on simple programming techniques like loops and conditionals to create your own custom functions. You'll come away with a toolkit of strategies for analyzing and visualizing data of all sorts. 
542 |f © 2019 Manning Publications Co. All rights reserved.  |g 2019 
550 |a Made available through: Safari, an O'Reilly Media Company. 
505 0 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a R (Computer program language) 
650 0 |a Data mining. 
650 6 |a R (Langage de programmation) 
650 6 |a Exploration de données (Informatique) 
650 7 |a R (Computer program language)  |2 fast 
650 7 |a Data mining  |2 fast 
710 2 |a Safari, an O'Reilly Media Company. 
856 4 0 |u https://learning.oreilly.com/library/view/~/9781617294594/?ar  |z Texto completo (Requiere registro previo con correo institucional) 
994 |a 92  |b IZTAP 

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