03922nam 2200589 450 991081379320332120231222054547.01-119-08007-X1-119-08006-11-119-08005-3(CKB)4330000000008388(Au-PeEL)EBL5114535(CaPaEBR)ebr11461101(OCoLC)1009305288(CaSebORM)9781119080022(MiAaPQ)EBC5114535(PPN)242972950(EXLCZ)99433000000000838820171128h20172017 uy 0engurcnu||||||||rdacontentrdamediardacarrierA data scientist's guide to acquiring, cleaning and managing data in R /by Samuel Buttrey, Naval Postgraduate School, California, United States, Lyn R. Whitaker, Naval Postgraduate School, California, United States1st editionHoboken, New Jersey :Wiley,2017.©20171 online resource (284 pages)THEi Wiley ebooks1-119-08002-9 Includes bibliographical references and index.The only how-to guide offering a unified, systemic approach to acquiring, cleaning, and managing data in R Every experienced practitioner knows that preparing data for modeling is a painstaking, time-consuming process. Adding to the difficulty is that most modelers learn the steps involved in cleaning and managing data piecemeal, often on the fly, or they develop their own ad hoc methods. This book helps simplify their task by providing a unified, systematic approach to acquiring, modeling, manipulating, cleaning, and maintaining data in R. Starting with the very basics, data scientists Samuel E. Buttrey and Lyn R. Whitaker walk readers through the entire process. From what data looks like and what it should look like, they progress through all the steps involved in getting data ready for modeling.  They describe best practices for acquiring data from numerous sources; explore key issues in data handling, including text/regular expressions, big data, parallel processing, merging, matching, and checking for duplicates; and outline highly efficient and reliable techniques for documenting data and recordkeeping, including audit trails, getting data back out of R, and more. The only single-source guide to R data and its preparation, it describes best practices for acquiring, manipulating, cleaning, and maintaining data Begins with the basics and walks readers through all the steps necessary to get data ready for the modeling process Provides expert guidance on how to document the processes described so that they are reproducible Written by seasoned professionals, it provides both introductory and advanced techniques Features case studies with supporting data and R code, hosted on a companion website A Data Scientist's Guide to Acquiring, Cleaning and Managing Data in R is a valuable working resource/bench manual for practitioners who collect and analyze data, lab scientists and research associates of all levels of experience, and graduate-level data mining students.THEi Wiley ebooks.Database designComputer programsData editingComputer programsInput design, ComputerR (Computer program language)Database designComputer programs.Data editingComputer programs.Input design, Computer.R (Computer program language).005.74/3Buttrey Samuel1706167Whitaker Lyn R.MiAaPQMiAaPQMiAaPQBOOK9910813793203321A data scientist's guide to acquiring, cleaning and managing data in R4093383UNINA03453nam 2200649 a 450 991082165610332120240516091741.01-283-40462-197866134046261-118-30044-01-118-30042-4(CKB)2670000000133768(EBL)827062(OCoLC)769343032(SSID)ssj0000571061(PQKBManifestationID)11377245(PQKBTitleCode)TC0000571061(PQKBWorkID)10611491(PQKB)10529157(MiAaPQ)EBC827062(Au-PeEL)EBL827062(CaPaEBR)ebr10521404(CaONFJC)MIL340462(PPN)183678869(EXLCZ)99267000000013376820110915d2012 uy 0engur|n|---|||||txtccrNonlinearity, complexity and randomness in economics towards algorithmic foundations for economics /edited by Stefano Zambelli and Donald A.R. Georg1st ed.Chichester John Wiley & Sons20121 online resource (255 p.)Surveys of Recent Research in EconomicsDescription based upon print version of record.1-4443-5031-5 Includes bibliographical references and index.Nonlinearity, Complexity and Randomnessin Economics; Contents; Notes on Contributors; 1. Introduction; 2. Towards an Algorithmic Revolution in Economic Theory; 3. An Algorithmic Information-Theoretic Approach to the Behaviour of Financial Markets; 4. Complexity and Randomness in Mathematics: Philosophical Reflections on the Relevance for Economic Modelling; 5. Behavioural Complexity; 6. Bounded Rationality and the Emergence of Simplicity Amidst Complexity; 7. Emergent Complexity in Agent-Based Computational Economics; 8. Non-Linear Dynamics, Complexity and Randomness: Algorithmic Foundations9. Stock-Flow Interactions, Disequilibrium Macroeconomics and the Role of Economic Policy10. Equilibrium Versus Market Efficiency: Randomness versus Complexity in Finance Markets; 11. Flexible Accelerator Economic Systems as Coupled Oscillators; 12. Shifting Sands: Non-Linearity, Complexity and Randomness in Economics; IndexNonlinearity, Complexity and Randomness in Economics presents a variety of papers by leading economists, scientists, and philosophers who focus on different aspects of nonlinearity, complexity and randomness, and their implications for economics. A theme of the book is that economics should be based on algorithmic, computable mathematical foundations. Features an interdisciplinary collection of papers by economists, scientists, and philosophersPresents new approaches to macroeconomic modelling, agent-based modelling, financial markets, and emergent complexity<bSurveys of Recent Research in EconomicsEconomics, MathematicalEconometricsEconomics, Mathematical.Econometrics.330.01/519Zambelli Stefano125402George Donald A. R.1953-121522MiAaPQMiAaPQMiAaPQBOOK9910821656103321Nonlinearity, complexity and randomness in economics3961124UNINA