{"product_id":"r-projects-for-dummies-isbn-9781119446187","title":"R Projects For Dummies","description":"\u003cp\u003e\u003cb\u003eMake the most of R’s extensive toolset\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eR Projects For Dummies\u003c\/i\u003e 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!\u003c\/p\u003e \u003cp\u003eR 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.\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eAppropriate for R users at all levels\u003c\/li\u003e \u003cli\u003eHelps R programmers plan and complete their own projects\u003c\/li\u003e \u003cli\u003eFocuses on R functions and packages\u003c\/li\u003e \u003cli\u003eShows how to carry out complex analyses by just entering a few commands\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eIf you’re brand new to R or just want to brush up on your skills, \u003ci\u003eR Projects For Dummies\u003c\/i\u003e will help you complete your projects with ease.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAbout This Book 2\u003c\/p\u003e \u003cp\u003ePart 1: The Tools of the Trade 2\u003c\/p\u003e \u003cp\u003ePart 2: Interacting with a User 2\u003c\/p\u003e \u003cp\u003ePart 3: Machine Learning 2\u003c\/p\u003e \u003cp\u003ePart 4: Large(ish) Data Sets 2\u003c\/p\u003e \u003cp\u003ePart 5: Maps and Images 2\u003c\/p\u003e \u003cp\u003ePart 6: The Part of Tens 3\u003c\/p\u003e \u003cp\u003eWhat You Can Safely Skip 3\u003c\/p\u003e \u003cp\u003eFoolish Assumptions 3\u003c\/p\u003e \u003cp\u003eIcons Used in This Book 3\u003c\/p\u003e \u003cp\u003eBeyond the Book 4\u003c\/p\u003e \u003cp\u003eWhere to Go from Here 4\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: the Tools of the Trade 5\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: R: What It Does and How It Does It 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting R 7\u003c\/p\u003e \u003cp\u003eGetting RStudio 8\u003c\/p\u003e \u003cp\u003eA Session with R 11\u003c\/p\u003e \u003cp\u003eThe working directory 11\u003c\/p\u003e \u003cp\u003eGetting started 12\u003c\/p\u003e \u003cp\u003eR Functions 15\u003c\/p\u003e \u003cp\u003eUser-Defined Functions 16\u003c\/p\u003e \u003cp\u003eComments 18\u003c\/p\u003e \u003cp\u003eR Structures 18\u003c\/p\u003e \u003cp\u003eVectors 18\u003c\/p\u003e \u003cp\u003eNumerical vectors 19\u003c\/p\u003e \u003cp\u003eMatrices 21\u003c\/p\u003e \u003cp\u003eLists 24\u003c\/p\u003e \u003cp\u003eData frames 25\u003c\/p\u003e \u003cp\u003eOf for Loops and if Statements 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Working with Packages 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eInstalling Packages 31\u003c\/p\u003e \u003cp\u003eExamining Data 33\u003c\/p\u003e \u003cp\u003eHeads and tails 33\u003c\/p\u003e \u003cp\u003eMissing data 33\u003c\/p\u003e \u003cp\u003eSubsets 34\u003c\/p\u003e \u003cp\u003eR Formulas 35\u003c\/p\u003e \u003cp\u003eMore Packages 36\u003c\/p\u003e \u003cp\u003eExploring the tidyverse 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Getting Graphic 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTouching Base 43\u003c\/p\u003e \u003cp\u003eHistograms 44\u003c\/p\u003e \u003cp\u003eDensity plots 45\u003c\/p\u003e \u003cp\u003eBar plots 47\u003c\/p\u003e \u003cp\u003eGrouping the bars 49\u003c\/p\u003e \u003cp\u003eQuick Suggested Project 51\u003c\/p\u003e \u003cp\u003ePie graphs 53\u003c\/p\u003e \u003cp\u003eScatterplots 53\u003c\/p\u003e \u003cp\u003eScatterplot matrix 55\u003c\/p\u003e \u003cp\u003eBox plots 56\u003c\/p\u003e \u003cp\u003eGraduating to ggplot2 57\u003c\/p\u003e \u003cp\u003eHow it works 58\u003c\/p\u003e \u003cp\u003eHistograms 59\u003c\/p\u003e \u003cp\u003eBar plots 61\u003c\/p\u003e \u003cp\u003eGrouped bar plots 62\u003c\/p\u003e \u003cp\u003eGrouping yet again 64\u003c\/p\u003e \u003cp\u003eScatterplots 67\u003c\/p\u003e \u003cp\u003eThe plot thickens 68\u003c\/p\u003e \u003cp\u003eScatterplot matrix 72\u003c\/p\u003e \u003cp\u003eBox plots 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Interacting with a User 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Working with a Browser 79\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting Your Shine On 79\u003c\/p\u003e \u003cp\u003eCreating Your First shiny Project 80\u003c\/p\u003e \u003cp\u003eThe user interface 83\u003c\/p\u003e \u003cp\u003eThe server 84\u003c\/p\u003e \u003cp\u003eFinal steps 85\u003c\/p\u003e \u003cp\u003eGetting reactive 86\u003c\/p\u003e \u003cp\u003eWorking with ggplot 89\u003c\/p\u003e \u003cp\u003eChanging the server 90\u003c\/p\u003e \u003cp\u003eA few more changes 92\u003c\/p\u003e \u003cp\u003eGetting reactive with ggplot 94\u003c\/p\u003e \u003cp\u003eAnother shiny Project 96\u003c\/p\u003e \u003cp\u003eThe base R version 97\u003c\/p\u003e \u003cp\u003eThe ggplot version 104\u003c\/p\u003e \u003cp\u003eSuggested Project 106\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Dashboards — How Dashing! 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe shinydashboard Package 107\u003c\/p\u003e \u003cp\u003eExploring Dashboard Layouts 108\u003c\/p\u003e \u003cp\u003eGetting started with the user interface 109\u003c\/p\u003e \u003cp\u003eBuilding the user interface: Boxes, boxes, boxes 110\u003c\/p\u003e \u003cp\u003eLining up in columns 117\u003c\/p\u003e \u003cp\u003eA nice trick: Keeping tabs 121\u003c\/p\u003e \u003cp\u003eSuggested project: Add statistics 125\u003c\/p\u003e \u003cp\u003eSuggested project: Place valueBoxes in tabPanels 126\u003c\/p\u003e \u003cp\u003eWorking with the Sidebar 126\u003c\/p\u003e \u003cp\u003eThe user interface 128\u003c\/p\u003e \u003cp\u003eThe server 131\u003c\/p\u003e \u003cp\u003eSuggested project: Relocate the slider 133\u003c\/p\u003e \u003cp\u003eInteracting with Graphics 135\u003c\/p\u003e \u003cp\u003eClicks, double-clicks, and brushes — oh, my! 135\u003c\/p\u003e \u003cp\u003eWhy bother with all this? 138\u003c\/p\u003e \u003cp\u003eSuggested project: Experiment with airquality 141\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3: Machine Learning 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6: Tools and Data for Machine Learning Projects 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe UCI (University of California-Irvine) ML Repository 146\u003c\/p\u003e \u003cp\u003eDownloading a UCI dataset 146\u003c\/p\u003e \u003cp\u003eCleaning up the data 148\u003c\/p\u003e \u003cp\u003eExploring the data 150\u003c\/p\u003e \u003cp\u003eExploring relationships in the data 152\u003c\/p\u003e \u003cp\u003eIntroducing the Rattle package 157\u003c\/p\u003e \u003cp\u003eUsing Rattle with iris 159\u003c\/p\u003e \u003cp\u003eGetting and (further) exploring the data 159\u003c\/p\u003e \u003cp\u003eFinding clusters in the data 162\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7: Decisions, Decisions, Decisions 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDecision Tree Components 167\u003c\/p\u003e \u003cp\u003eRoots and leaves 168\u003c\/p\u003e \u003cp\u003eTree construction 168\u003c\/p\u003e \u003cp\u003eDecision Trees in R 169\u003c\/p\u003e \u003cp\u003eGrowing the tree in R 169\u003c\/p\u003e \u003cp\u003eDrawing the tree in R 171\u003c\/p\u003e \u003cp\u003eDecision Trees in Rattle 173\u003c\/p\u003e \u003cp\u003eCreating the tree 174\u003c\/p\u003e \u003cp\u003eDrawing the tree 175\u003c\/p\u003e \u003cp\u003eEvaluating the tree 176\u003c\/p\u003e \u003cp\u003eProject: A More Complex Decision Tree 177\u003c\/p\u003e \u003cp\u003eThe data: Car evaluation 177\u003c\/p\u003e \u003cp\u003eData exploration 179\u003c\/p\u003e \u003cp\u003eBuilding and drawing the tree 180\u003c\/p\u003e \u003cp\u003eEvaluating the tree 181\u003c\/p\u003e \u003cp\u003eQuick suggested project: Understanding the complexity parameter 181\u003c\/p\u003e \u003cp\u003eSuggested Project: Titanic 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8: Into the Forest, Randomly 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGrowing a Random Forest 185\u003c\/p\u003e \u003cp\u003eRandom Forests in R 187\u003c\/p\u003e \u003cp\u003eBuilding the forest 187\u003c\/p\u003e \u003cp\u003eEvaluating the forest 189\u003c\/p\u003e \u003cp\u003eA closer look 190\u003c\/p\u003e \u003cp\u003ePlotting error 191\u003c\/p\u003e \u003cp\u003ePlotting importance 193\u003c\/p\u003e \u003cp\u003eProject: Identifying Glass 194\u003c\/p\u003e \u003cp\u003eThe data 194\u003c\/p\u003e \u003cp\u003eGetting the data into Rattle 195\u003c\/p\u003e \u003cp\u003eExploring the data 196\u003c\/p\u003e \u003cp\u003eGrowing the random forest 198\u003c\/p\u003e \u003cp\u003eVisualizing the results 198\u003c\/p\u003e \u003cp\u003eSuggested Project: Identifying Mushrooms 200\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9: Support Your Local Vector 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSome Data to Work With 201\u003c\/p\u003e \u003cp\u003eUsing a subset 202\u003c\/p\u003e \u003cp\u003eDefining a boundary 202\u003c\/p\u003e \u003cp\u003eUnderstanding support vectors 203\u003c\/p\u003e \u003cp\u003eSeparability: It’s Usually Nonlinear 205\u003c\/p\u003e \u003cp\u003eSupport Vector Machines in R 207\u003c\/p\u003e \u003cp\u003eWorking with e1071 207\u003c\/p\u003e \u003cp\u003eWorking with kernlab 212\u003c\/p\u003e \u003cp\u003eProject: House Parties 214\u003c\/p\u003e \u003cp\u003eReading in the data 216\u003c\/p\u003e \u003cp\u003eExploring the data 217\u003c\/p\u003e \u003cp\u003eCreating the SVM 218\u003c\/p\u003e \u003cp\u003eEvaluating the SVM 220\u003c\/p\u003e \u003cp\u003eSuggested Project: Titanic Again 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10: K-Means Clustering 221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHow It Works 221\u003c\/p\u003e \u003cp\u003eK-Means Clustering in R 223\u003c\/p\u003e \u003cp\u003eSetting up and analyzing the data 223\u003c\/p\u003e \u003cp\u003eUnderstanding the output 224\u003c\/p\u003e \u003cp\u003eVisualizing the clusters 225\u003c\/p\u003e \u003cp\u003eFinding the optimum number of clusters 226\u003c\/p\u003e \u003cp\u003eQuick suggested project: Adding the sepals 229\u003c\/p\u003e \u003cp\u003eProject: Glass Clusters 231\u003c\/p\u003e \u003cp\u003eThe data 231\u003c\/p\u003e \u003cp\u003eStarting Rattle and exploring the data 232\u003c\/p\u003e \u003cp\u003ePreparing to cluster 233\u003c\/p\u003e \u003cp\u003eDoing the clustering 234\u003c\/p\u003e \u003cp\u003eGoing beyond Rattle 234\u003c\/p\u003e \u003cp\u003eSuggested Project: A Few Quick Ones 235\u003c\/p\u003e \u003cp\u003eVisualizing data points and clusters 235\u003c\/p\u003e \u003cp\u003eThe optimum number of clusters 236\u003c\/p\u003e \u003cp\u003eAdding variables 236\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11: Neural Networks 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNetworks in the Nervous System 237\u003c\/p\u003e \u003cp\u003eArtificial Neural Networks 238\u003c\/p\u003e \u003cp\u003eOverview 238\u003c\/p\u003e \u003cp\u003eInput layer and hidden layer 239\u003c\/p\u003e \u003cp\u003eOutput layer 240\u003c\/p\u003e \u003cp\u003eHow it all works 240\u003c\/p\u003e \u003cp\u003eNeural Networks in R 241\u003c\/p\u003e \u003cp\u003eBuilding a neural network for the iris data frame 241\u003c\/p\u003e \u003cp\u003ePlotting the network 243\u003c\/p\u003e \u003cp\u003eEvaluating the network 244\u003c\/p\u003e \u003cp\u003eQuick suggested project: Those sepals 245\u003c\/p\u003e \u003cp\u003eProject: Banknotes 245\u003c\/p\u003e \u003cp\u003eThe data 245\u003c\/p\u003e \u003cp\u003eTaking a quick look ahead 246\u003c\/p\u003e \u003cp\u003eSetting up Rattle 247\u003c\/p\u003e \u003cp\u003eEvaluating the network 249\u003c\/p\u003e \u003cp\u003eGoing beyond Rattle: Visualizing the network 249\u003c\/p\u003e \u003cp\u003eSuggested Projects: Rattling Around 251\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4: Large(ish) Data Sets 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12: Exploring Marketing 255\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProject: Analyzing Retail Data 255\u003c\/p\u003e \u003cp\u003eThe data 256\u003c\/p\u003e \u003cp\u003eRFM in R 257\u003c\/p\u003e \u003cp\u003eEnter Machine Learning 265\u003c\/p\u003e \u003cp\u003eK-means clustering 265\u003c\/p\u003e \u003cp\u003eWorking with Rattle 267\u003c\/p\u003e \u003cp\u003eDigging into the clusters 268\u003c\/p\u003e \u003cp\u003eThe clusters and the classes 270\u003c\/p\u003e \u003cp\u003eQuick suggested project 271\u003c\/p\u003e \u003cp\u003eSuggested Project: Another Data Set 272\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13: From the City That Never Sleeps 275\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExamining the Data Set 275\u003c\/p\u003e \u003cp\u003eWarming Up 276\u003c\/p\u003e \u003cp\u003eGlimpsing and viewing 276\u003c\/p\u003e \u003cp\u003ePiping, filtering, and grouping 277\u003c\/p\u003e \u003cp\u003eVisualizing 279\u003c\/p\u003e \u003cp\u003eJoining 280\u003c\/p\u003e \u003cp\u003eQuick Suggested Project: Airline names 283\u003c\/p\u003e \u003cp\u003eProject: Departure Delays 283\u003c\/p\u003e \u003cp\u003eAdding a variable: weekday 283\u003c\/p\u003e \u003cp\u003eQuick Suggested Project: Analyze weekday differences 284\u003c\/p\u003e \u003cp\u003eDelay, weekday, and airport 285\u003c\/p\u003e \u003cp\u003eDelay and flight duration 287\u003c\/p\u003e \u003cp\u003eSuggested Project: Delay and Weather 289\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 5: Maps and Images 291\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14: All Over the Map 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProject: The Airports of Wisconsin 293\u003c\/p\u003e \u003cp\u003eDispensing with the preliminaries 293\u003c\/p\u003e \u003cp\u003eGetting the state geographic data 294\u003c\/p\u003e \u003cp\u003eGetting the airport geographic data 295\u003c\/p\u003e \u003cp\u003ePlotting the airports on the state map 298\u003c\/p\u003e \u003cp\u003eQuick Suggested Project: Another source of airport geographic info 299\u003c\/p\u003e \u003cp\u003eSuggested Project 1: Map Your State 299\u003c\/p\u003e \u003cp\u003eSuggested Project 2: Map the Country 299\u003c\/p\u003e \u003cp\u003ePlotting the state capitals 301\u003c\/p\u003e \u003cp\u003ePlotting the airports 302\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15: Fun with Pictures 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePolishing a Picture: It’s magick! 305\u003c\/p\u003e \u003cp\u003eReading the image 306\u003c\/p\u003e \u003cp\u003eRotating, flipping, and flopping 307\u003c\/p\u003e \u003cp\u003eAnnotating 308\u003c\/p\u003e \u003cp\u003eCombining transformations 309\u003c\/p\u003e \u003cp\u003eQuick suggested project: Three F’s 309\u003c\/p\u003e \u003cp\u003eCombining images 310\u003c\/p\u003e \u003cp\u003eAnimating 311\u003c\/p\u003e \u003cp\u003eMaking your own morphs 312\u003c\/p\u003e \u003cp\u003eProject: Two Legends in Search of a Legend 313\u003c\/p\u003e \u003cp\u003eGetting Stan and Ollie 313\u003c\/p\u003e \u003cp\u003eCombining the boys with the background 314\u003c\/p\u003e \u003cp\u003eExplaining image_apply() 314\u003c\/p\u003e \u003cp\u003eGetting back to the animation 316\u003c\/p\u003e \u003cp\u003eSuggested Project: Combine an Animation with a Plot 316\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 6: the Part of Tens 319\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16: More Than Ten Packages for Your R Projects 321\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMachine Learning 321\u003c\/p\u003e \u003cp\u003eDatabases 322\u003c\/p\u003e \u003cp\u003eMaps 322\u003c\/p\u003e \u003cp\u003eImage Processing 324\u003c\/p\u003e \u003cp\u003eText Analysis 324\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17: More than Ten Useful Resources 327\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eInteracting with Users 327\u003c\/p\u003e \u003cp\u003eMachine Learning 328\u003c\/p\u003e \u003cp\u003eDatabases 328\u003c\/p\u003e \u003cp\u003eMaps and Images 329\u003c\/p\u003e \u003cp\u003eIndex 331\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eJoseph Schmuller, PhD,\u003c\/b\u003e is a veteran of more than 25 years in Information Technology. He is the author of several books, including \u003ci\u003eStatistical Analysis with R For Dummies\u003c\/i\u003e and four editions of \u003ci\u003eStatistical Analysis with Excel For Dummies.\u003c\/i\u003e In addition, he has written numerous articles and created online coursework for Lynda.com.   \u003c\/p\u003e\u003cul\u003e \t\u003cli\u003eLearn a wide range of R applications\u003c\/li\u003e \t\u003cli\u003eWork through R projects and sharpen your skillset\u003c\/li\u003e \t\u003cli\u003eUnderstand how to execute your own R projects\/li\u0026gt; \u003c\/li\u003e\n\u003c\/ul\u003e  \u003cp\u003e\u003cb\u003eLearn R with practical data projects\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eWhy 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eInside…\u003c\/b\u003e \t\u003c\/p\u003e\u003cul\u003e \t\u003cli\u003eDownload and install R and  RStudio\u003csup\u003e®\u003c\/sup\u003e\n\u003c\/li\u003e \t\u003cli\u003eUse packages and examine data\u003c\/li\u003e \t\u003cli\u003eCreate interactive applications\u003c\/li\u003e \t\u003cli\u003eSee how to use decision trees\u003c\/li\u003e \t\u003cli\u003eApply neural networks\u003c\/li\u003e \t\u003cli\u003eExplore datasets\u003c\/li\u003e \t\u003cli\u003eBuild maps that show data\u003c\/li\u003e \t\u003cli\u003eTransform and combine images\u003c\/li\u003e \u003c\/ul\u003e","brand":"For Dummies","offers":[{"title":"Default Title","offer_id":47989902442725,"sku":"NP9781119446187","price":29.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119446187.jpg?v=1761785853","url":"https:\/\/k12savings.com\/products\/r-projects-for-dummies-isbn-9781119446187","provider":"K12savings","version":"1.0","type":"link"}