Key FeaturesLearn five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling) in-depth using real-world case studiesA unique book that teaches you the essential and fundamental concepts in statistical modeling and simulationThis book is written by the Amazon best-selling author of Learning Statistics (The easier Way) with RBook DescriptionData Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world.The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results.By the end of this book, you reader will get in touch with the software environment R. After getting background on popular methods in the area, you will see applications in R to better understand the methods as well as to gain experience when working on real-world data and real-world problems.What you will learnThe book aims to explore advanced R features to simulate data to extract insights from your data.Get to know the advanced features of R including high-performance computing and advanced data manipulationSee random number simulation used to simulate distributions, data sets, and populationsSimulate close-to-reality populations as the basis for agent-based micro-, model- and design-based simulationsApplications to design statistical solutions with R for solving scientific and real world problemsComprehensive coverage of several R statistical packages like boot, simPop, VIM, data.table, dplyr, parallel, StatDA, simecol, simecolModels, deSolve and many more.About the AuthorMatthias Templ is associated professor at the Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology (Austria). He is additionally employed as a scientist at the methods unit at Statistics Austria, and together with two colleagues, he owns the company called data-analysis OG. His main research interests are in the areas of imputation, statistical disclosure control, visualization, compositional data analysis, computational statistics, robustness teaching in statistics, and multivariate methods. In the last few years, Matthias has published more than 45 papers in well-known indexed scientific journals. He is the author and maintainer of several R packages for official statistics, such as the R package sdcMicro for statistical disclosure control, the VIM package for visualization and imputation of missing values, the simPop package for synthetic population simulation, and the robCompositions package for robust analysis of compositional data. In addition, he is the editor of the Austrian Journal of Statistics that is free of charge and open-access. The probability is high to find him at the top of a mountain in his leisure time.Table of ContentsIntroductionR and High-Performance ComputingThe Discrepancy between Pencil-Driven Theory and Data-Driven Computational SolutionsSimulation of Random NumbersMonte Carlo Methods for Optimization ProblemsProbability Theory Shown by SimulationResampling MethodsApplications of Resampling Methods and Monte Carlo TestsThe EM AlgorithmSimulation with Complex DataSystem Dynamics and Agent-Based Models