Beyond Spreadsheets with R

Name: Beyond Spreadsheets with R | A beginner's guide to R and RStudio
Brand: Manning;Pearson Professional
Price: 39.71 EUR
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A beginner's guide to R and RStudio

Jonathan Carroll(Autor*in)

Manning;Pearson Professional (Verlag)

1. Auflage

Erschienen am 10. Dezember 2018

352 Seiten

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Beschreibung

Summary

Beyond Spreadsheets with R shows you how to take raw data and transform it for use in computations, tables, graphs, and more. 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 using R and RStudio.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Spreadsheets are powerful tools for many tasks, but if you need to interpret, interrogate, and present data, they can feel like the wrong tools for the task. That's when R programming is the way to go. The R programming language provides a comfortable environment to properly handle all types of data. And within the open source RStudio development suite, you have at your fingertips easy-to-use ways to simplify complex manipulations and create reproducible processes for analysis and reporting.

About the Book

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.

What's inside

How to start programming with R and RStudio
Understanding and implementing important R structures and operators
Installing and working with R packages
Tidying, refining, and plotting your data

About the Reader

If you're comfortable writing formulas in Excel, you're ready for this book.

About the Author

Dr Jonathan Carroll is a data science consultant providing R programming services. He holds a PhD in theoretical physics.

Table of Contents

Introducing data and the R language
Getting to know R data types
Making new data values
Understanding the tools you'll use: Functions
Combining data values
Selecting data values
Doing things with lots of data
Doing things conditionally: Control

structures

Visualizing data: Plotting
Doing more with your data with extensions

Weitere Details

Weitere Ausgaben

Person

Inhalt

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 (&- 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

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