Description
Book SynopsisMake 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 dashb
Table of ContentsIntroduction 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
