R Data Science Quick Reference (eBook)

Table of Contents 4
About the Author 8
About the Technical Reviewer 9
Chapter 1: Introduction 10
Chapter 2: Importing Data: readr 13
Functions for Reading Data 14
File Headers 16
Column Types 19
String-based Column Type Specification 20
Function-based Column Type Specification 26
Parsing Time and Dates 30
Space-separated Columns 36
Functions for Writing Data 39
Chapter 3: Representing Tables: tibble 40
Creating Tibbles 40
Indexing Tibbles 45
Chapter 4: Reformatting Tables: tidyr 51
Tidy Data 51
Gather and Spread 52
Complex Column Encodings 57
Expanding, Crossing, and Completing 63
Missing Values 67
Nesting Data 72
Chapter 5: Pipelines: magrittr 76
The Problem with Pipelines 76
Pipeline Notation 79
Pipelines and Function Arguments 80
Function Composition 83
Other Pipe Operations 84
Chapter 6: Functional Programming: purrr 87
General Features of purrr Functions 88
Filtering 88
Mapping 90
Reduce and Accumulate 101
Partial Evaluation and Function Composition 105
Lambda Expressions 108
Chapter 7: Manipulating Data Frames: dplyr 112
Selecting Columns 112
Filter 120
Sorting 128
Modifying Data Frames 130
Grouping and Summarizing 136
Joining Tables 149
Income in Fictional Countries 158
Chapter 8: Working with Strings: stringr 164
Counting String Patterns 164
Splitting Strings 167
Capitalizing Strings 169
Wrapping, Padding, and Trimming 169
Detecting Substrings 174
Extracting Substrings 177
Transforming Strings 177
Chapter 9: Working with Factors: forcats 184
Creating Factors 184
Concatenation 186
Projection 189
Adding Levels 193
Reorder Levels 194
Chapter 10: Working with Dates: lubridate 197
Time Points 197
Time Zones 199
Time Intervals 201
Chapter 11: Working with Models: broom and modelr 206
broom 206
modelr 209
Chapter 12: Plotting: ggplot2 220
The Basic Plotting Components in ggplot2 220
Adding Components to Plot Objects 222
Adding Data 224
Adding Aesthetics 224
Adding Geometries 225
Facets 233
Adding Coordinates 237
Chapter 13: Conclusions 240
Index 241