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R in 24 Hours, Sams Teach Yourself

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R in 24 Hours, Sams Teach Yourself

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  • Copyright 2016
  • Dimensions: 7" x 9-1/8"
  • Pages: 624
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-428882-3
  • ISBN-13: 978-0-13-428882-6

In just 24 lessons of one hour or less, Sams Teach Yourself R in 24 Hours helps you learn all the R skills you need to solve a wide spectrum of real-world data analysis problems. You’ll master the entire data analysis workflow, learning to build code that’s efficient, reproducible, and suitable for sharing with others.

This book’s straightforward, step-by-step approach teaches you how to import, manipulate, summarize, model, and plot data with R; formalize your analytical code; and build powerful R packages using current best practices.

Practical, hands-on examples show you how to apply what you learn.
Quizzes and exercises help you test your knowledge and stretch your skills.

Learn How To

  • Install, configure, and explore the R environment, including RStudio
  • Use basic R syntax, objects, and packages
  • Create and manage data structures, including vectors, matrices, and arrays
  • Understand lists and data frames
  • Work with dates, times, and factors
  • Use common R functions, and learn to write your own
  • Import and export data and connect to databases and spreadsheets
  • Use the popular tidyr, dplyr and data.table packages
  • Write more efficient R code with profiling, vectorization, and initialization
  • Plot data and extend your graphical capabilities with ggplot2 and Lattice graphics
  • Develop common types of models
  • Construct high-quality packages, both simple and complex
  • Write R classes: S3, S4, and Reference Classes
  • Use R to generate dynamic reports
  • Build web applications with Shiny

Register your book at informit.com/register for convenient access to updates and corrections as they become available.

This book’s source code can be found at http://www.mango-solutions.com/wp/teach-yourself-r-in-24-hours-book/.

Sample Content

Table of Contents

Preface     xii
HOUR 1: The R Community     1
A Concise History of R     1
The R Community     3
R Development     7
Summary     8
Q&A     8
Workshop     9
Activities     9
HOUR 2: The R Environment     11
Integrated Development Environments     11
R Syntax     14
R Objects     16
Using R Packages     23
Internal Help     28
Summary     29
Q&A     30
Workshop     30
Activities     32
HOUR 3: Single-Mode Data Structures     33
The R Data Types     33
Vectors, Matrices, and Arrays     34
Vectors     35
Matrices     49
Arrays     58
Relationship Between Single-Mode Data Objects     60
Summary     62
Q&A     62
Workshop     63
Activities     64
HOUR 4: Multi-Mode Data Structures     67
Multi-Mode Structures     67
Lists     68
Data Frames     86
Exploring Your Data     93
Summary     98
Q&A     98
Workshop     100
Activities     101
HOUR 5: Dates, Times, and Factors     103
Working with Dates and Times     103
The lubridate Package     107
Working with Categorical Data     108
Summary     112
Q&A     112
Workshop     113
Activities     114
HOUR 6: Common R Utility Functions     115
Using R Functions     115
Functions for Numeric Data     117
Logical Data     121
Missing Data     122
Character Data     123
Summary     125
Q&A     126
Workshop     126
Activities     127
HOUR 7: Writing Functions: Part I     129
The Motivation for Functions     129
Creating a Simple Function     130
The If/Else Structure     136
Summary     146
Q&A     147
Workshop     148
Activities     149
HOUR 8: Writing Functions: Part II     151
Errors and Warnings     151
Checking Inputs     155
The Ellipsis     157
Checking Multivalue Inputs     162
Using Input Definition     164
Summary     168
Q&A     168
Workshop     170
Activities     171
HOUR 9: Loops and Summaries     173
Repetitive Tasks     173
The “apply” Family of Functions     181
The apply Function     183
The lapply Function     195
The sapply Function     204
The tapply Function     208
Summary     213
Q&A     213
Workshop     214
Activities     216
HOUR 10: Importing and Exporting     217
Working with Text Files     217
Relational Databases     223
Working with Microsoft Excel     226
Summary     231
Q&A     232
Workshop     232
Activities     233
HOUR 11: Data Manipulation and Transformation     235
Sorting     236
Appending     237
Merging     238
Duplicate Values     241
Restructuring     242
Data Aggregation     249
Summary     258
Q&A     258
Workshop     259
Activities     259
HOUR 12: Efficient Data Handling in R     261
dplyr: A New Way of Handling Data     261
Efficient Data Handling with data     table     273
Summary     282
Q&A     283
Workshop     283
Activities     284
HOUR 13: Graphics     287
Graphics Devices and Colors     287
High-Level Graphics Functions     289
Low-Level Graphics Functions     298
Graphical Parameters     304
Controlling the Layout     305
Summary     308
Q&A     309
Workshop     309
Activities     311
HOUR 14: The ggplot2 Package for Graphics     313
The Philosophy of ggplot2     313
Quick Plots and Basic Control     314
Changing Plot Types     317
Aesthetics     320
Paneling (a     k     a Faceting)     328
Custom Plots     333
Themes and Layout     338
The ggvis Evolution     342
Summary     342
Q&A     343
Workshop     343
Activities     344
HOUR 15: Lattice Graphics     345
The History of Trellis Graphics     345
The Lattice Package     346
Creating a Simple Lattice Graph     346
Graph Options     356
Multiple Variables     358
Groups of Data     360
Using Panels     362
Controlling Styles     372
Summary     376
Q&A     377
Workshop     378
Activities     378
HOUR 16: Introduction to R Models and Object Orientation     379
Statistical Models in R     379
Simple Linear Models     380
Assessing a Model in R     382
Multiple Linear Regression     391
Interaction Terms     396
Factor Independent Variables     398
Variable Transformations     402
R and Object Orientation     405
Summary     407
Q&A     408
Workshop     408
Activities     409
HOUR 17: Common R Models     411
Generalized Linear Models     411
Nonlinear Models     423
Survival Analysis     430
Time Series Analysis     441
Summary     452
Q&A     452
Workshop     452
Activities     453
HOUR 18: Code Efficiency     455
Determining Efficiency     455
Initialization     458
Vectorization     459
Using Alternative Functions     462
Managing Memory Usage     463
Integrating with C++     464
Summary     468
Q&A     469
Workshop     469
Activities     470
HOUR 19: Package Building     471
Why Build an R Package?     471
The Structure of an R Package     472
Code Quality     476
Automated Documentation with roxygen2     477
Building a Package with devtools     482
Summary     485
Q&A     485
Workshop     486
Activities     487
HOUR 20: Advanced Package Building     489
Extending R Packages     489
Developing a Test Framework     490
Including Data in Packages     494
Including a User Guide     496
Code Using Rcpp     501
Summary     502
Q&A     502
Workshop     503
Activities     504
HOUR 21: Writing R Classes     505
What Is a Class?     505
Creating a New S3 Class     509
Generic Functions and Methods     511
Inheritance in S3     516
Documenting S3     518
Limitations of S3     518
Summary     519
Q&A     519
Workshop     520
Activities     520
HOUR 22: Formal Class Systems     523
S4     523
Reference Classes     535
R6 Classes     542
Other Class Systems     544
Summary     544
Q&A     545
Workshop     545
Activities     546
HOUR 23: Dynamic Reporting     547
What Is Dynamic Reporting?     547
An Introduction to knitr     548
Simple Reports with RMarkdown     548
Reporting with LaTeX     553
Summary     557
Q&A     558
Workshop     558
Activities     559
HOUR 24: Building Web Applications with Shiny     561
A Simple Shiny Application     561
Reactive Functions     566
Interactive Documents     569
Sharing Shiny Applications     570
Summary     571
Q&A     571
Workshop     571
Activities     572
APPENDIX: Installation     573
Installing R     573
Installing Rtools for Windows     575
Installing the RStudio IDE     577
Index     579


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