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E-Book

$19.99

R Projects For Dummies

Joseph Schmuller

ISBN: 978-1-119-44617-0 January 2018 360 Pages

Description

Make the most of R’s extensive toolset

R Projects For Dummies offers a unique learn-by-doing approach. You will increase the depth and breadth of your R skillset by completing a wide variety of projects. By using R’s graphics, interactive, and machine learning tools, you’ll learn to apply R’s extensive capabilities in an array of scenarios. The depth of the project experience is unmatched by any other content online or in print. And you just might increase your statistics knowledge along the way, too!

R is a free tool, and it’s the basis of a huge amount of work in data science. It's taking the place of costly statistical software that sometimes takes a long time to learn. One reason is that you can use just a few R commands to create sophisticated analyses. Another is that easy-to-learn R graphics enable you make the results of those analyses available to a wide audience.

This book will help you sharpen your skills by applying them in the context of projects with R, including dashboards, image processing, data reduction, mapping, and more.

  • Appropriate for R users at all levels
  • Helps R programmers plan and complete their own projects
  • Focuses on R functions and packages
  • Shows how to carry out complex analyses by just entering a few commands

If you’re brand new to R or just want to brush up on your skills, R Projects For Dummies will help you complete your projects with ease.

Introduction 1

About This Book 2

Part 1: The Tools of the Trade 2

Part 2: Interacting with a User 2

Part 3: Machine Learning 2

Part 4: Large(ish) Data Sets 2

Part 5: Maps and Images 2

Part 6: The Part of Tens 3

What You Can Safely Skip 3

Foolish Assumptions 3

Icons Used in This Book 3

Beyond the Book 4

Where to Go from Here 4

Part 1: The Tools of the Trade 5

Chapter 1: R: What It Does and How It Does It 7

Getting R 7

Getting RStudio 8

A Session with R 11

The working directory 11

Getting started 12

R Functions 15

User-Defined Functions 16

Comments 18

R Structures 18

Vectors 18

Numerical vectors 19

Matrices 21

Lists 24

Data frames 25

Of for Loops and if Statements 28

Chapter 2: Working with Packages 31

Installing Packages 31

Examining Data 33

Heads and tails 33

Missing data 33

Subsets 34

R Formulas 35

More Packages 36

Exploring the tidyverse 37

Chapter 3: Getting Graphic 43

Touching Base 43

Histograms 44

Density plots 45

Bar plots 47

Grouping the bars 49

Quick Suggested Project 51

Pie graphs 53

Scatterplots 53

Scatterplot matrix 55

Box plots 56

Graduating to ggplot2 57

How it works 58

Histograms 59

Bar plots 61

Grouped bar plots 62

Grouping yet again 64

Scatterplots 67

The plot thickens 68

Scatterplot matrix 72

Box plots 73

Part 2: Interacting with a User 77

Chapter 4: Working with a Browser 79

Getting Your Shine On 79

Creating Your First shiny Project 80

The user interface 83

The server 84

Final steps 85

Getting reactive 86

Working with ggplot 89

Changing the server 90

A few more changes 92

Getting reactive with ggplot 94

Another shiny Project 96

The base R version 97

The ggplot version 104

Suggested Project 106

Chapter 5: Dashboards — How Dashing! 107

The shinydashboard Package 107

Exploring Dashboard Layouts 108

Getting started with the user interface 109

Building the user interface: Boxes, boxes, boxes 110

Lining up in columns 117

A nice trick: Keeping tabs 121

Suggested project: Add statistics 125

Suggested project: Place valueBoxes in tabPanels 126

Working with the Sidebar 126

The user interface 128

The server 131

Suggested project: Relocate the slider 133

Interacting with Graphics 135

Clicks, double-clicks, and brushes — oh, my! 135

Why bother with all this? 138

Suggested project: Experiment with airquality 141

Part 3: Machine Learning 143

Chapter 6: Tools and Data for Machine Learning Projects 145

The UCI (University of California-Irvine) ML Repository 146

Downloading a UCI dataset 146

Cleaning up the data 148

Exploring the data 150

Exploring relationships in the data 152

Introducing the Rattle package 157

Using Rattle with iris 159

Getting and (further) exploring the data 159

Finding clusters in the data 162

Chapter 7: Decisions, Decisions, Decisions 167

Decision Tree Components 167

Roots and leaves 168

Tree construction 168

Decision Trees in R 169

Growing the tree in R 169

Drawing the tree in R 171

Decision Trees in Rattle 173

Creating the tree 174

Drawing the tree 175

Evaluating the tree 176

Project: A More Complex Decision Tree 177

The data: Car evaluation 177

Data exploration 179

Building and drawing the tree 180

Evaluating the tree 181

Quick suggested project: Understanding the complexity parameter 181

Suggested Project: Titanic 182

Chapter 8: Into the Forest, Randomly 185

Growing a Random Forest 185

Random Forests in R 187

Building the forest 187

Evaluating the forest 189

A closer look 190

Plotting error 191

Plotting importance 193

Project: Identifying Glass 194

The data 194

Getting the data into Rattle 195

Exploring the data 196

Growing the random forest 198

Visualizing the results 198

Suggested Project: Identifying Mushrooms 200

Chapter 9: Support Your Local Vector 201

Some Data to Work With 201

Using a subset 202

Defining a boundary 202

Understanding support vectors 203

Separability: It’s Usually Nonlinear 205

Support Vector Machines in R 207

Working with e1071 207

Working with kernlab 212

Project: House Parties 214

Reading in the data 216

Exploring the data 217

Creating the SVM 218

Evaluating the SVM 220

Suggested Project: Titanic Again 220

Chapter 10: K-Means Clustering 221

How It Works 221

K-Means Clustering in R 223

Setting up and analyzing the data 223

Understanding the output 224

Visualizing the clusters 225

Finding the optimum number of clusters 226

Quick suggested project: Adding the sepals 229

Project: Glass Clusters 231

The data 231

Starting Rattle and exploring the data 232

Preparing to cluster 233

Doing the clustering 234

Going beyond Rattle 234

Suggested Project: A Few Quick Ones 235

Visualizing data points and clusters 235

The optimum number of clusters 236

Adding variables 236

Chapter 11: Neural Networks 237

Networks in the Nervous System 237

Artificial Neural Networks 238

Overview 238

Input layer and hidden layer 239

Output layer 240

How it all works 240

Neural Networks in R 241

Building a neural network for the iris data frame 241

Plotting the network 243

Evaluating the network 244

Quick suggested project: Those sepals 245

Project: Banknotes 245

The data 245

Taking a quick look ahead 246

Setting up Rattle 247

Evaluating the network 249

Going beyond Rattle: Visualizing the network 249

Suggested Projects: Rattling Around 251

Part 4: Large(ish) Data Sets 253

Chapter 12: Exploring Marketing 255

Project: Analyzing Retail Data 255

The data 256

RFM in R 257

Enter Machine Learning 265

K-means clustering 265

Working with Rattle 267

Digging into the clusters 268

The clusters and the classes 270

Quick suggested project 271

Suggested Project: Another Data Set 272

Chapter 13: From the City That Never Sleeps 275

Examining the Data Set 275

Warming Up 276

Glimpsing and viewing 276

Piping, filtering, and grouping 277

Visualizing 279

Joining 280

Quick Suggested Project: Airline names 283

Project: Departure Delays 283

Adding a variable: weekday 283

Quick Suggested Project: Analyze weekday differences 284

Delay, weekday, and airport 285

Delay and flight duration 287

Suggested Project: Delay and Weather 289

Part 5: Maps and Images 291

Chapter 14: All Over the Map 293

Project: The Airports of Wisconsin 293

Dispensing with the preliminaries 293

Getting the state geographic data 294

Getting the airport geographic data 295

Plotting the airports on the state map 298

Quick Suggested Project: Another source of airport geographic info 299

Suggested Project 1: Map Your State 299

Suggested Project 2: Map the Country 299

Plotting the state capitals 301

Plotting the airports 302

Chapter 15: Fun with Pictures 305

Polishing a Picture: It’s magick! 305

Reading the image 306

Rotating, flipping, and flopping 307

Annotating 308

Combining transformations 309

Quick suggested project: Three F’s 309

Combining images 310

Animating 311

Making your own morphs 312

Project: Two Legends in Search of a Legend 313

Getting Stan and Ollie 313

Combining the boys with the background 314

Explaining image_apply() 314

Getting back to the animation 316

Suggested Project: Combine an Animation with a Plot 316

Part 6: The Part of Tens 319

Chapter 16: More Than Ten Packages for Your R Projects 321

Machine Learning 321

Databases 322

Maps 322

Image Processing 324

Text Analysis 324

Chapter 17: More than Ten Useful Resources 327

Interacting with Users 327

Machine Learning 328

Databases 328

Maps and Images 329

Index 331