As an open source computing environment, R is rapidly becoming the lingua franca of the statistical computing community. R's powerful base functions, powerful statistical tools, open source nature, and avid user community have led to R having an expansive library of powerful, cutting-edge quantitative methods not yet available to users of other high-cost statistical programs.
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, including data types, R program flow, and basic coding techniques, you'll learn how R can be used to achieve the most commonly needed scientific data analysis tasks, including testing for statistically significant differences between groups and model relationships in data. You will also learn parametric and nonparametric techniques for both difference testing and relationship modeling.
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 a 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, learn about an advanced analytical method, and finish by going to the next level with advanced data management focused on dealing with messy and problematic datasets that serious analysts deal with daily.
By the end of this book, you will be able to undertake publication-quality data analysis in R.
Chapter 1, Programming with R, presents an overview of how data is stored and accessed in R. Then, we will go over how to load data into R using built-in functions and useful packages for easy import from Excel worksheets. We will also cover how to use flow control statements and functions to reduce complexity and help you program more efficiently.
Chapter 2, Statistical Methods with R, presents an overview of how to summarize your data and get useful statistical information for downstream analysis. We will show you how to plot and get statistical information from probability distributions and how to test the fit of your sample distribution to well-defined probability distributions.
Chapter 3, Linear Models, covers linear models, which are probably the most commonly used statistical methods to study the relationships between variables. The Generalized linear model section will delve into a bit more detail than typical R books, discussing the nature of link functions and canonical link functions.
Chapter 4, Nonlinear Methods, reviews applications of nonlinear methods in R using both parametric and nonparametric methods for both theory-driven and exploratory analysis.
Chapter 5, Linear Algebra, covers algebra techniques in R. We will also learn linear algebra operations including transposition, inversion, matrix multiplication, and a number of matrix transformations.
Chapter 6, Principal Component Analysis and the Common Factor Model, helps you understand the application of linear algebra to covariance and correlation matrices. We will cover how to use PCA to account for total variance in a set of variables and how to use EFA to model common variance among these variables in R.
Chapter 7, Structural Equation Modeling and Confirmatory Factor Analysis, covers the fundamental ideas underlying structural equation modeling, which are often overlooked in other books discussing SEM in R, and then delve into how SEM is done in R.
Chapter 8, Simulations, explains how to perform basic sample simulations and how to use simulations to answer statistical problems. We will also learn how to use R to generate random numbers, and how to simulate random variables from several common probability distributions.
Chapter 9, Optimization, explores a variety of methods and techniques to optimize a variety of functions. We will also cover how to use a wide range of R packages and functions to set up, solve, and visualize different optimization problems.
Chapter 10, Advanced Data Management, walks you through the basic techniques for data handling and some basic memory management considerations.
The software that we require for this book is R Version 3.0.1 or higher, OpenMx Version 1.4, and RStudio.
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 start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "You can also retrieve additional information on the objects stored in your environment using the str() function."
A block of code is set as follows:
> integer_vector <- c(1L, 2L, 12L, 29L) > integer_vector [1] 1 2 12 29
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "To install R on Windows, click on Download R for Windows, and then click on base for the download link and installation instructions."
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