Mastering Scientific Computing with R

Employ professional quantitative methods to answer scientific questions with a powerful open source data analysis environment

About This Book

• Perform publication-quality science using R
• Use some of R’s most powerful and least known features to solve complex scientific computing problems
• Learn how to create visual illustrations of scientific results

Who This Book Is For

If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.

In Detail

With this book, you will learn not just about R, but how to use R to answer conceptual, scientific, and experimental questions.

Beginning with an overview of fundamental R concepts, you’ll learn how R can be used to achieve the most commonly needed scientific data analysis tasks: testing for statistically significant differences between groups and model relationships in data. You will delve into linear algebra and matrix operations with an emphasis not on the R syntax, but on how these operations can be used to address common computational or analytical needs. This book also covers the application of matrix operations for the purpose of finding structure in high-dimensional data using the principal component, exploratory factor, and confirmatory factor analysis in addition to structural equation modeling. You will also master methods for simulation and learn about an advanced analytical method.

Table of Contents

Chapter 1: Programming with R
Chapter 2: Statistical Methods with R
Chapter 3: Linear Models
Chapter 4: Nonlinear Methods
Chapter 5: Linear Algebra
Chapter 6: Principal Component Analysis and the Common Factor Model
Chapter 7: Structural Equation Modeling and Confirmatory Factor Analysis
Chapter 8: Simulations
Chapter 9: Optimization
Chapter 10: Advanced Data Management