Harness actionable insights from your data with computational
statistics and simulations using R
Key Features:
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Learn five different simulation techniques (Monte Carlo, Discrete
Event Simulation, System Dynamics, Agent-Based Modeling, and
Resampling) in-depth using real-world case studies
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A unique book that teaches you the essential and fundamental concepts
in statistical modeling and simulation
Book Description:
Data 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 Learn:
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The book aims to explore advanced R features to simulate data to
extract insights from your data.
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Get to know the advanced features of R including high-performance
computing and advanced data manipulation
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See random number simulation used to simulate distributions, data
sets, and populations
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Simulate close-to-reality populations as the basis for agent-based
micro-, model- and design-based simulations
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Applications to design statistical solutions with R for solving
scientific and real world problems
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Comprehensive coverage of several R statistical packages like boot,
simPop, VIM, data.table, dplyr, parallel, StatDA, simecol,
simecolModels, deSolve and many more.
Who this book is for:
This 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.
