The content in this guidebook was created by me and revised by my graduate program mentor, Dr. Keith Lohse (https://github.com/keithlohse). This repository is currently a work in progress.
The purpose of this guidebook is to provide a brief introduction to performing statistical analysis in R, and it is intended for a reader who 1) Is knowledgeable about statistical analysis and is selecting the appropriate statistical model for whatever research question they would like to answer, and 2) Is unsure of how to perform the analysis in R, and is unfamiliar with programming languages in general.
We tried to make this book as brief as possible while still providing all of the necessary information to get up and running with R. The book is sequential and builds on knowledge from prior chapters, and, as such, we recommended reading it in order. Chapters 1 and 2 will cover downloading and describing R and the RStudio IDE, as well as programming basics, such as variable types and data types, objects and assignments, and functions and arguments. Chapters 3 and 4 will cover installing/loading packages and reading/writing files. Chapters 5 and 6 will cover descriptive statistics, visualizing data, and formatting data. Chapters 7-13 will cover various statistical models from the General Linear Model framework.