Beyond spreadsheets with R : a beginner's guide to R and RStudio / Dr. Jonathan Carroll.

Format:

Book

Author/Creator:

Carroll, Jon, author.

Language:

English

Subjects (All):

Data mining.

Physical Description:

1 online resource (352 pages)

Edition:

1st edition

Place of Publication:

Shelter Island, New York : Manning, [2019]

System Details:

text file

Summary:

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.

Contents:

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

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

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

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

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

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

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

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

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

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.

Notes:

Description based on print version record.

Includes index.

ISBN:

9781638356080

1638356084

OCLC:

1260345860