Details

R Projects For Dummies


R Projects For Dummies


1. Aufl.

von: Joseph Schmuller

20,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 18.01.2018
ISBN/EAN: 9781119446163
Sprache: englisch
Anzahl Seiten: 360

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Beschreibungen

<p><b>Make the most of R’s extensive toolset</b></p> <p><i>R Projects For Dummies</i> 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!</p> <p>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.</p> <p>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.</p> <ul> <li>Appropriate for R users at all levels</li> <li>Helps R programmers plan and complete their own projects</li> <li>Focuses on R functions and packages</li> <li>Shows how to carry out complex analyses by just entering a few commands</li> </ul> <p>If you’re brand new to R or just want to brush up on your skills, <i>R Projects For Dummies</i> will help you complete your projects with ease.</p>
<p><b>Introduction</b><b> 1</b></p> <p>About This Book 2</p> <p>Part 1: The Tools of the Trade 2</p> <p>Part 2: Interacting with a User 2</p> <p>Part 3: Machine Learning 2</p> <p>Part 4: Large(ish) Data Sets 2</p> <p>Part 5: Maps and Images 2</p> <p>Part 6: The Part of Tens 3</p> <p>What You Can Safely Skip 3</p> <p>Foolish Assumptions 3</p> <p>Icons Used in This Book 3</p> <p>Beyond the Book 4</p> <p>Where to Go from Here 4</p> <p><b>Part 1: The Tools of the Trade</b><b> 5</b></p> <p><b>Chapter 1: R: What It Does and How It Does It</b><b> 7</b></p> <p>Getting R 7</p> <p>Getting RStudio 8</p> <p>A Session with R 11</p> <p>The working directory 11</p> <p>Getting started 12</p> <p>R Functions 15</p> <p>User-Defined Functions 16</p> <p>Comments 18</p> <p>R Structures 18</p> <p>Vectors 18</p> <p>Numerical vectors 19</p> <p>Matrices 21</p> <p>Lists 24</p> <p>Data frames 25</p> <p>Of for Loops and if Statements 28</p> <p><b>Chapter 2: Working with Packages</b><b> 31</b></p> <p>Installing Packages 31</p> <p>Examining Data 33</p> <p>Heads and tails 33</p> <p>Missing data 33</p> <p>Subsets 34</p> <p>R Formulas 35</p> <p>More Packages 36</p> <p>Exploring the tidyverse 37</p> <p><b>Chapter 3: Getting Graphic</b><b> 43</b></p> <p>Touching Base 43</p> <p>Histograms 44</p> <p>Density plots 45</p> <p>Bar plots 47</p> <p>Grouping the bars 49</p> <p>Quick Suggested Project 51</p> <p>Pie graphs 53</p> <p>Scatterplots 53</p> <p>Scatterplot matrix 55</p> <p>Box plots 56</p> <p>Graduating to ggplot2 57</p> <p>How it works 58</p> <p>Histograms 59</p> <p>Bar plots 61</p> <p>Grouped bar plots 62</p> <p>Grouping yet again 64</p> <p>Scatterplots 67</p> <p>The plot thickens 68</p> <p>Scatterplot matrix 72</p> <p>Box plots 73</p> <p><b>Part 2: Interacting with a User </b><b>77</b></p> <p><b>Chapter 4: Working with a Browser</b><b> 79</b></p> <p>Getting Your Shine On 79</p> <p>Creating Your First shiny Project 80</p> <p>The user interface 83</p> <p>The server 84</p> <p>Final steps 85</p> <p>Getting reactive 86</p> <p>Working with ggplot 89</p> <p>Changing the server 90</p> <p>A few more changes 92</p> <p>Getting reactive with ggplot 94</p> <p>Another shiny Project 96</p> <p>The base R version 97</p> <p>The ggplot version 104</p> <p>Suggested Project 106</p> <p><b>Chapter 5: Dashboards — How Dashing!</b><b> 107</b></p> <p>The shinydashboard Package 107</p> <p>Exploring Dashboard Layouts 108</p> <p>Getting started with the user interface 109</p> <p>Building the user interface: Boxes, boxes, boxes 110</p> <p>Lining up in columns 117</p> <p>A nice trick: Keeping tabs 121</p> <p>Suggested project: Add statistics 125</p> <p>Suggested project: Place valueBoxes in tabPanels 126</p> <p>Working with the Sidebar 126</p> <p>The user interface 128</p> <p>The server 131</p> <p>Suggested project: Relocate the slider 133</p> <p>Interacting with Graphics 135</p> <p>Clicks, double-clicks, and brushes — oh, my! 135</p> <p>Why bother with all this? 138</p> <p>Suggested project: Experiment with airquality 141</p> <p><b>Part 3: Machine Learning</b><b> 143</b></p> <p><b>Chapter 6: Tools and Data for Machine Learning Projects</b><b> 145</b></p> <p>The UCI (University of California-Irvine) ML Repository 146</p> <p>Downloading a UCI dataset 146</p> <p>Cleaning up the data 148</p> <p>Exploring the data 150</p> <p>Exploring relationships in the data 152</p> <p>Introducing the Rattle package 157</p> <p>Using Rattle with iris 159</p> <p>Getting and (further) exploring the data 159</p> <p>Finding clusters in the data 162</p> <p><b>Chapter 7: Decisions, Decisions, Decisions </b><b>167</b></p> <p>Decision Tree Components 167</p> <p>Roots and leaves 168</p> <p>Tree construction 168</p> <p>Decision Trees in R 169</p> <p>Growing the tree in R 169</p> <p>Drawing the tree in R 171</p> <p>Decision Trees in Rattle 173</p> <p>Creating the tree 174</p> <p>Drawing the tree 175</p> <p>Evaluating the tree 176</p> <p>Project: A More Complex Decision Tree 177</p> <p>The data: Car evaluation 177</p> <p>Data exploration 179</p> <p>Building and drawing the tree 180</p> <p>Evaluating the tree 181</p> <p>Quick suggested project: Understanding the complexity parameter 181</p> <p>Suggested Project: Titanic 182</p> <p><b>Chapter 8: Into the Forest, Randomly</b><b> 185</b></p> <p>Growing a Random Forest 185</p> <p>Random Forests in R 187</p> <p>Building the forest 187</p> <p>Evaluating the forest 189</p> <p>A closer look 190</p> <p>Plotting error 191</p> <p>Plotting importance 193</p> <p>Project: Identifying Glass 194</p> <p>The data 194</p> <p>Getting the data into Rattle 195</p> <p>Exploring the data 196</p> <p>Growing the random forest 198</p> <p>Visualizing the results 198</p> <p>Suggested Project: Identifying Mushrooms 200</p> <p><b>Chapter 9: Support Your Local Vector</b><b> 201</b></p> <p>Some Data to Work With 201</p> <p>Using a subset 202</p> <p>Defining a boundary 202</p> <p>Understanding support vectors 203</p> <p>Separability: It’s Usually Nonlinear 205</p> <p>Support Vector Machines in R 207</p> <p>Working with e1071 207</p> <p>Working with kernlab 212</p> <p>Project: House Parties 214</p> <p>Reading in the data 216</p> <p>Exploring the data 217</p> <p>Creating the SVM 218</p> <p>Evaluating the SVM 220</p> <p>Suggested Project: Titanic Again 220</p> <p><b>Chapter 10: K-Means Clustering</b><b> 221</b></p> <p>How It Works 221</p> <p>K-Means Clustering in R 223</p> <p>Setting up and analyzing the data 223</p> <p>Understanding the output 224</p> <p>Visualizing the clusters 225</p> <p>Finding the optimum number of clusters 226</p> <p>Quick suggested project: Adding the sepals 229</p> <p>Project: Glass Clusters 231</p> <p>The data 231</p> <p>Starting Rattle and exploring the data 232</p> <p>Preparing to cluster 233</p> <p>Doing the clustering 234</p> <p>Going beyond Rattle 234</p> <p>Suggested Project: A Few Quick Ones 235</p> <p>Visualizing data points and clusters 235</p> <p>The optimum number of clusters 236</p> <p>Adding variables 236</p> <p><b>Chapter 11: Neural Networks</b><b> 237</b></p> <p>Networks in the Nervous System 237</p> <p>Artificial Neural Networks 238</p> <p>Overview 238</p> <p>Input layer and hidden layer 239</p> <p>Output layer 240</p> <p>How it all works 240</p> <p>Neural Networks in R 241</p> <p>Building a neural network for the iris data frame 241</p> <p>Plotting the network 243</p> <p>Evaluating the network 244</p> <p>Quick suggested project: Those sepals 245</p> <p>Project: Banknotes 245</p> <p>The data 245</p> <p>Taking a quick look ahead 246</p> <p>Setting up Rattle 247</p> <p>Evaluating the network 249</p> <p>Going beyond Rattle: Visualizing the network 249</p> <p>Suggested Projects: Rattling Around 251</p> <p><b>Part 4: Large(ish) Data Sets</b><b> 253</b></p> <p><b>Chapter 12: Exploring Marketing</b><b> 255</b></p> <p>Project: Analyzing Retail Data 255</p> <p>The data 256</p> <p>RFM in R 257</p> <p>Enter Machine Learning 265</p> <p>K-means clustering 265</p> <p>Working with Rattle 267</p> <p>Digging into the clusters 268</p> <p>The clusters and the classes 270</p> <p>Quick suggested project 271</p> <p>Suggested Project: Another Data Set 272</p> <p><b>Chapter 13: From the City That Never Sleeps</b><b> 275</b></p> <p>Examining the Data Set 275</p> <p>Warming Up 276</p> <p>Glimpsing and viewing 276</p> <p>Piping, filtering, and grouping 277</p> <p>Visualizing 279</p> <p>Joining 280</p> <p>Quick Suggested Project: Airline names 283</p> <p>Project: Departure Delays 283</p> <p>Adding a variable: weekday 283</p> <p>Quick Suggested Project: Analyze weekday differences 284</p> <p>Delay, weekday, and airport 285</p> <p>Delay and flight duration 287</p> <p>Suggested Project: Delay and Weather 289</p> <p><b>Part 5: Maps and Images</b><b> 291</b></p> <p><b>Chapter 14: All Over the Map</b><b> 293</b></p> <p>Project: The Airports of Wisconsin 293</p> <p>Dispensing with the preliminaries 293</p> <p>Getting the state geographic data 294</p> <p>Getting the airport geographic data 295</p> <p>Plotting the airports on the state map 298</p> <p>Quick Suggested Project: Another source of airport geographic info 299</p> <p>Suggested Project 1: Map Your State 299</p> <p>Suggested Project 2: Map the Country 299</p> <p>Plotting the state capitals 301</p> <p>Plotting the airports 302</p> <p><b>Chapter 15: Fun with Pictures</b><b> 305</b></p> <p>Polishing a Picture: It’s magick! 305</p> <p>Reading the image 306</p> <p>Rotating, flipping, and flopping 307</p> <p>Annotating 308</p> <p>Combining transformations 309</p> <p>Quick suggested project: Three F’s 309</p> <p>Combining images 310</p> <p>Animating 311</p> <p>Making your own morphs 312</p> <p>Project: Two Legends in Search of a Legend 313</p> <p>Getting Stan and Ollie 313</p> <p>Combining the boys with the background 314</p> <p>Explaining image_apply() 314</p> <p>Getting back to the animation 316</p> <p>Suggested Project: Combine an Animation with a Plot 316</p> <p><b>Part 6: The Part of Tens</b><b> 319</b></p> <p><b>Chapter 16: More Than Ten Packages for Your R Projects</b><b> 321</b></p> <p>Machine Learning 321</p> <p>Databases 322</p> <p>Maps 322</p> <p>Image Processing 324</p> <p>Text Analysis 324</p> <p><b>Chapter 17: More than Ten Useful Resources</b><b> 327</b></p> <p>Interacting with Users 327</p> <p>Machine Learning 328</p> <p>Databases 328</p> <p>Maps and Images 329</p> <p>Index 331</p>
<p><b>Joseph Schmuller, PhD,</b> is a veteran of more than 25 years in Information Technology. He is the author of several books, including <i>Statistical Analysis with R For Dummies</i> and four editions of <i>Statistical Analysis with Excel For Dummies.</i> In addition, he has written numerous articles and created online coursework for Lynda.com.
<ul> <li>Learn a wide range of R applications</li> <li>Work through R projects and sharpen your skillset</li> <li>Understand how to execute your own R projects/li> </ul> <p><b>Learn R with practical data projects</b> <p>Why spend weeks learning a complex, costly statistical software package? With the help of this book, you can quickly master R, the free data science toolkit that some of the world's top companies use. These projects give you hands-on experience in using R to create interactive applications, use machine learning methods, and process images. You'll learn the skills you'll need to work with R's extensive toolset and understand exactly how to apply R in projects you'll encounter on the job. <p><b>Inside…</b> <ul> <li>Download and install R and RStudio<sup>®</sup></li> <li>Use packages and examine data</li> <li>Create interactive applications</li> <li>See how to use decision trees</li> <li>Apply neural networks</li> <li>Explore datasets</li> <li>Build maps that show data</li> <li>Transform and combine images</li> </ul>

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