R: Recipes for Analysis, Visualization and Machine Learning by Yu-Wei, Chiu (David Chiu)

Key FeaturesProficiently analyze data and apply machine learning techniquesGenerate visualizations, develop interactive visualizations and applications to understand various data exploratory functions in RConstruct a predictive model by using a variety of machine learning packagesBook DescriptionThe R language is a powerful, open source, functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. Well start off with data analysis – this will show you ways to use R to generate professional analysis reports. Well then move on to visualizing our data – this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, well move into the world of machine learning – this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:R Data Analysis Cookbook by Viswa Viswanathan and Shanthi ViswanathanR Data Visualization Cookbook by Atmajitsinh GohilMachine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu)What you will learnGet data into your R environment and prepare it for analysisPerform exploratory data analyses and generate meaningful visualizations of the dataGenerate various plots in R using the basic R plotting techniquesCreate presentations and learn the basics of creating apps in R for your audienceCreate and inspect the transaction dataset, performing association analysis with the Apriori algorithmVisualize associations in various graph formats and find frequent itemset using the ECLAT algorithmBuild, tune, and evaluate predictive models with different machine learning packagesIncorporate R and Hadoop to solve machine learning problems on big dataAbout the AuthorViswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. After completing his PhD in Artificial Intelligence, Viswa spent a decade in academia and then switched to a leadership position in the software industry for another decade during which he worked for Infosys, Igate, and Starbase. He embraced academia once again in 2001.Viswa has taught extensively in fields ranging from operations research, computer science, software engineering, management information systems, and enterprise systems. In addition to university teaching, Viswa has conducted training programs for industry professionals and has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education. He has authored a book titled Data Analytics with R:A hands-on approach.Viswa thoroughly enjoys hands-on software development and has single-handedly conceived, architected, developed, and deployed several web-based applications.Apart from his deep interest in technical fields such as data analytics, artificial intelligence, computer science, and software engineering, Viswa harbors a deep interest in education with special emphasis on the roots of learning and methods to foster deeper learning. He has done research in this area and hopes to pursue the subject further.Viswa would like to express deep gratitude to professors Amitava Bagchi and Anup Sen, who were inspirational forces during his early research career. He is also grateful to several extremely intelligent colleagues, notable among them being Rajesh Venkatesh, Dan Richner, and Sriram Bala, who significantly shaped his thinking. His aunt, Analdavalli; his sister, Sankari; and his wife, Shanthi, taught him much about hard work, and even the little he has absorbed has helped him immensely. His sons, Nitin and Siddarth, have helped with numerous insightful comments on various topics.Shanthi Viswanathan is an experienced technologist who has delivered technology management and enterprise architecture consulting to many enterprise customers. She has worked for Infosys Technologies, Oracle Corporation, and Accenture. As a consultant, Shanthi has helped several large organizations such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE, among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling. When she is not in front of her Mac, Shanthi spends time hiking in the suburbs of NY/NJ, working in the garden, and teaching yoga.Shanthi would like to thank her husband, Viswa, for all the great discussions on numerous topics during their hikes together and for exposing her to R and Java. She would also like to thank her sons, Nitin and Siddarth, for getting her into the data analytics world.Atmajitsinh Gohil works as a senior consultant at a consultancy firm in New York City. After graduating, he worked in the financial industry as a Fixed Income Analyst. He writes about data manipulation, data exploration, visualization, and basic R plotting functions on his blog at http://datavisualizationineconomics.blogspot.com.Atmajitsinh has a masters degree in Financial Economics from the State University of New York (SUNY), Buffalo. He also graduated with a master of arts degree in Economics from University of Pune, India. He loves to read blogs on data visualization and loves to go out on hikes in his free time.Yu-Wei, Chiu (David Chiu) is the founder of LargitData (www.LargitData.com). He has previously worked for Trend Micro as a software engineer with the responsibility of building big data platforms for business intelligence and customer relationship management systems. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer and has delivered lectures on Python, R, Hadoop, and tech talks at a variety of conferences.In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook (Packt Publishing). Please visit his personal website for more information at www.ywchiu.com.Table of ContentsA Simple Guide to RPractical Machine Learning with RAcquire and Prepare the Ingredients – Your DataWhats in There? – Exploratory Data AnalysisWhere Does It Belong? – ClassificationGive Me a Number – RegressionCan You Simplify That? – Data Reduction TechniquesLessons from History – Time Series AnalysisIts All About Your Connections – Social Network AnalysisPut Your Best Foot Forward – Document and Present Your AnalysisWork Smarter, Not Harder – Efficient and Elegant R CodeWhere in the World? – Geospatial AnalysisPlaying Nice – Connecting to Other SystemsBasic and Interactive PlotsHeat Maps and DendrogramsMapsThe Pie Chart and Its AlternativesAdding the Third DimensionData in Higher DimensionsVisualizing Continuous DataVisualizing Text and XKCD-style PlotsCreating Applications in RData Exploration with RMS TitanicR and StatisticsUnderstanding Regression AnalysisClassification (I) – Tree, Lazy, and ProbabilisticClassification (II) – Neural Network and SVMModel EvaluationEnsemble LearningClusteringAssociation Analysis and Sequence MiningDimension ReductionBig Data Analysis (R and Hadoop)Resources for R and Machine LearningDataset – Survival of Passengers on the TitanicBibliography