R in 24 Hours, Sams Teach Yourself

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/.

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