book

Beyond Spreadsheets with R

Name: Beyond Spreadsheets with R
Author: Jonathan Carroll
ISBN: 9781617294594

by Jonathan Carroll

January 2019

Beginner to intermediate

352 pages

9h 8m

English

Manning Publications

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Titlepage
Copyright
preface
acknowledgments
about this book
Who needs this book?How to read this bookFormattingStructureGetting startedWhere to find more helpMore about this bookBook 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 sources1.1.3 Data munging1.1.4 What you can do with well-handled data1.1.5 Data as an asset1.1.6 Reproducible research and version control1.2 Introducing R1.2.1 The origins of R1.2.2 What R is and what it isn’t1.3 How R works1.4 Introducing RStudio1.4.1 Working with R within RStudio1.4.2 Built-in packages (data and functions)1.4.3 Built-in documentation1.4.4 Vignettes1.5 Try it yourselfTerminologySummary
Chapter 2: Getting to know R data types
2.1 Types of data2.1.1 Numbers2.1.2 Text (strings)2.1.3 Categories (factors)2.1.4 Dates and times2.1.5 Logicals2.1.6 Missing values2.2 Storing values (assigning)2.2.1 Naming data (variables)2.2.2 Unchanging data2.2.3 The assignment operators (<- vs. =)2.3 Specifying the data type2.4 Telling R to ignore something2.5 Try it yourselfTerminologySummary
Chapter 3: Making new data values
3.1 Basic mathematics3.2 Operator precedence3.3 String concatenation (joining)3.4 Comparisons3.5 Automatic conversion (coercion)3.6 Try it yourselfTerminologySummary

Chapter 4: Understanding the tools you’ll use: Functions
4.1 Functions4.1.1 Under the hood4.1.2 Function template4.1.3 Arguments4.1.4 Multiple arguments4.1.5 Default arguments4.1.6 Argument name matching4.1.7 Partial matching4.1.8 Scope4.2 Packages4.2.1 Installing packages4.2.2 How does R (not) know about this function?4.2.3 Namespaces4.3 Messages, warnings, and errors, oh my!4.3.1 Creating messages, warnings, and errors4.3.2 Diagnosing messages, warnings, and errors4.4 Testing4.5 Project: Generalizing a function4.6 Try it yourselfTerminologySummary
Chapter 5: Combining data values
5.1 Simple collections5.1.1 Coercion5.1.2 Missing values5.1.3 Attributes5.1.4 Names5.2 Sequences5.2.1 Vector functions5.2.2 Vector math operations5.3 Matrices5.3.1 Naming dimensions5.4 Lists5.5 data.frames5.6 Classes5.6.1 The tibble class5.6.2 Structures as function arguments5.7 Try it yourselfTerminologySummary
Chapter 6: Selecting data values
6.1 Text processing6.1.1 Text matching6.1.2 Substrings6.1.3 Text substitutions6.1.4 Regular expressions6.2 Selecting components from structures6.2.1 Vectors6.2.2 Lists6.2.3 Matrices6.3 Replacing values6.4 data.frames and dplyr6.4.1 dplyr verbs6.4.2 Non-standard evaluation6.4.3 Pipes6.4.4 Subsetting data.frame the hard way6.5 Replacing NA6.6 Selecting conditionally6.7 Summarizing values6.8 A worked example: Excel vs. R6.9 Try it yourself6.9.1 Solutions — no peekingTerminologySummary
Chapter 7: Doing things with lots of data
7.1 Tidy data principles7.1.1 The working directory7.1.2 Stored data formats7.1.3 Reading data into R7.1.4 Scraping data7.1.5 Inspecting data7.1.6 Dealing with odd values in data (sentinel values)7.1.7 Converting to tidy data7.2 Merging data7.3 Writing data from R7.4 Try it yourselfTerminologySummary
Chapter 8: Doing things conditionally: Control structures
8.1 Looping8.1.1 Vectorization8.1.2 Tidy repetition: Looping with purrr8.1.3 for loops8.2 Wider and narrower loop scope8.2.1 while loops8.3 Conditional evaluation8.3.1 if conditions8.3.2 ifelse conditions8.4 Try it yourselfTerminologySummary
Chapter 9: Visualizing data: Plotting
9.1 Data preparation9.1.1 Tidy data, revisited9.1.2 Importance of data types9.2 ggplot29.2.1 General construction9.2.2 Adding points9.2.3 Style aesthetics9.2.4 Adding lines9.2.5 Adding bars9.2.6 Other types of plots9.2.7 Scales9.2.8 Facetting9.2.9 Additional options9.3 Plots as objects9.4 Saving plots9.5 Try it yourselfTerminologySummary
Chapter 10: Doing more with your data with extensions
10.1 Writing your own packages10.1.1 Creating a minimal package10.1.2 Documentation10.2 Analyzing your package10.2.1 Unit testing10.2.2 Profiling10.3 What to do next?10.3.1 Regression10.3.2 Clustering10.3.3 Working with maps10.3.4 Interacting with APIs10.3.5 Sharing your package10.4 More resourcesTerminologySummary
Appendix A: Installing R
WindowsMacLinuxFrom source
Appendix B: Installing RStudio
Installing RStudioPackages used in this book
Appendix C: Graphics in base R
Index
List of Figures
List of TablesList of Listings

Content preview from Beyond Spreadsheets with R

1 Introducing data and the R language

This chapter covers

Why data analysis is important
How to make your analysis robust
How and why R works with data
RStudio: Your interface to R

You have your data, and you want to start doing something awesome with it, right? Brilliant! I promise you, we’ll get to that as soon as we can. But first, let’s take a step back. Telling you to dive right in now would be like handing you a pile of different timbers, pointing you toward the workshop, and telling you to make some furniture. It’s a good idea to first understand both the materials and the tools you’re about to use.

We’ll go through what data means in general — to you and to those who may potentially inherit your data — because if you don’t fully ...

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Beyond Spreadsheets with R

by Jonathan Carroll

1 Introducing data and the R language

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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