Simulation for Data Science with R

Harness actionable insights from your data with computational statistics and simulations using R

About This Book

• Learn five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling) in-depth using real-world case studies
• A unique book that teaches you the essential and fundamental concepts in statistical modeling and simulation
• This book is written by the Amazon best-selling author of Learning Statistics (The easier Way) with R

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.

What You Will Learn

• The 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 manipulation
• See random number simulation used to simulate distributions, data sets, and populations
• Simulate close-to-reality populations as the basis for agent-based micro-, model- and design-based simulations
• Applications to design statistical solutions with R for solving scientific and real world problems
• Comprehensive coverage of several R statistical packages like boot, simPop, VIM, data.table, dplyr, parallel, StatDA, simecol, simecolModels, deSolve and many more.

In Detail

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.

Style and approach

This 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.

從您的數據中利用R進行計算統計和模擬，獲得可操作的洞察力

關於本書

- 使用真實案例研究深入學習五種不同的模擬技術（蒙特卡羅、離散事件模擬、系統動力學、基於代理的建模和重抽樣）
- 一本獨特的書籍，教授您統計建模和模擬的基本概念
- 本書由暢銷書《以R學習統計學（更簡單的方式）》的亞馬遜暢銷作者撰寫

適合閱讀對象

本書適合熟悉計算方法的使用者。如果您想學習R的高級功能，包括計算密集型的蒙特卡羅方法以及統計模擬的計算工具，那麼本書適合您。需要對R編程有良好的了解。

您將學到什麼

- 本書旨在探索高級R功能，以模擬數據並從中提取洞察力。
- 瞭解R的高級功能，包括高性能計算和高級數據操作。
- 使用隨機數模擬來模擬分佈、數據集和人口。
- 模擬接近現實的人口，作為基於代理的微觀、模型和設計模擬的基礎。
- 使用R設計統計解決方案，解決科學和現實世界問題。
- 全面介紹多個R統計包，如boot、simPop、VIM、data.table、dplyr、parallel、StatDA、simecol、simecolModels、deSolve等。

詳細內容

《R數據科學》旨在教您如何利用R強大的套件生態系統開始進行數據科學任務。當R與數據科學結合使用時，R成為最廣泛使用的編程語言，可以解決現實世界中各種數據集所涉及的複雜性。

本書將為用戶提供統計模擬的計算和方法論框架。通過本書，您將熟悉R軟件環境。在瞭解計算統計領域中流行方法的背景之後，您將在R中看到一些應用，以更好地理解這些方法，並獲得處理真實世界數據和問題的經驗。本書有助於揭示複雜系統中的大規模模式，其中相互依賴性和變異性至關重要。有效的模擬由準確反映真實物理人口的數據生成過程驅動。您將學習如何計劃和結構化模擬項目，以幫助決策過程以及結果的呈現。

通過閱讀本書，您將熟悉R軟件環境。在瞭解該領域中流行方法的背景之後，您將在R中看到一些應用，以更好地理解這些方法，並在處理真實世界數據和問題時獲得經驗。

風格和方法

本書以實用、實踐的方式解釋統計計算方法，提供使用這些方法的建議，並提供計算工具，幫助您解決統計模擬和計算密集型方法中的常見問題。