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Simulation for Data Science with R
Harness actionable insights from your data with computational statistics and simulations using R
About This BookLearn 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 simulationWho This Book Is ForThis book is for users who are familiar with computational methods. If you want to learn about the advanced features of R, including the computer-intense Monte-Carlo methods as well as computational tools for statistical simulation, then this book is for you. Good knowledge of R programming is assumed/required.
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.In DetailData 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.
Style and approachThis book takes a practical, hands-on approach to explain the statistical computing methods, gives advice on the usage of these methods, and provides computational tools to help you solve common problems in statistical simulation and computer-intense methods.
About This BookLearn 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 simulationWho This Book Is ForThis book is for users who are familiar with computational methods. If you want to learn about the advanced features of R, including the computer-intense Monte-Carlo methods as well as computational tools for statistical simulation, then this book is for you. Good knowledge of R programming is assumed/required.
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.In DetailData 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.
Style and approachThis book takes a practical, hands-on approach to explain the statistical computing methods, gives advice on the usage of these methods, and provides computational tools to help you solve common problems in statistical simulation and computer-intense methods.
400 pages, Kindle Edition
Published June 30, 2016
7 people are currently reading
5 people want to read
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Displaying 1 of 1 review
December 24, 2018
While this book has some helpful information, it is a hot mess.
It reads like it was first translated into English after first being translated into Chinese. Most of it is OK to read, but some of it is very confusing. The formatting is a bit off. The in-line math does not always display correctly. The quote marks in the reference sections are someone shown as footers. However, these are relatively minor annoyances. The R code is the real issue.
The R code shown in the text is not indented so it can sometimes be hard to tell when the text ends and the code begins (the code is in a different font, but it doesn't stand out the way it should). The code output is shown in the same font and color as the R code, which makes it hard to see what is going on.
The assortment of packages used in the book is a bit random. The author does not specify the packages used at the start of the book. There is some use of the dyplr package and the pipe is sometimes used, but not consistently. Sometimes base R is used for graphics and sometimes ggplot2 is used. The author would have been better off sticking with one set of packages for data manipulation and graphics.
The book is taxing to read. Someone should write a better version of the book as the material covered is handy to have around. It is just too difficult to use this book as a guide.
It reads like it was first translated into English after first being translated into Chinese. Most of it is OK to read, but some of it is very confusing. The formatting is a bit off. The in-line math does not always display correctly. The quote marks in the reference sections are someone shown as footers. However, these are relatively minor annoyances. The R code is the real issue.
The R code shown in the text is not indented so it can sometimes be hard to tell when the text ends and the code begins (the code is in a different font, but it doesn't stand out the way it should). The code output is shown in the same font and color as the R code, which makes it hard to see what is going on.
The assortment of packages used in the book is a bit random. The author does not specify the packages used at the start of the book. There is some use of the dyplr package and the pipe is sometimes used, but not consistently. Sometimes base R is used for graphics and sometimes ggplot2 is used. The author would have been better off sticking with one set of packages for data manipulation and graphics.
The book is taxing to read. Someone should write a better version of the book as the material covered is handy to have around. It is just too difficult to use this book as a guide.
Displaying 1 of 1 review