What This Document Is
This is an introductory guide to the R programming language, specifically tailored for students engaged in statistical computing and analysis. Developed for the University of Wisconsin-Madison’s STAT 849 course (Theory and Application of Regression and Analysis of Variance I), this material serves as a foundational resource for those new to R or seeking a refresher on its core functionalities. It focuses on establishing a working knowledge of the R environment and essential data handling techniques.
Why This Document Matters
This resource is invaluable for students and researchers who need to perform statistical analysis using R. It’s particularly helpful at the beginning of a course or project where R is the primary tool. Individuals with limited prior programming experience will find this a useful starting point, as it aims to build understanding from the ground up. Understanding the concepts presented here will streamline your ability to implement more advanced statistical methods later in your studies or research. It’s best utilized *before* diving into complex modeling or data manipulation tasks.
Common Limitations or Challenges
This guide focuses on the fundamental aspects of R and does not cover advanced topics such as creating custom functions, complex data visualizations, or specific statistical modeling techniques. It’s designed as a starting point and assumes further learning will be necessary to master R for specialized applications. It also doesn’t provide a comprehensive overview of all R packages available; rather, it concentrates on the base R environment. This material will not substitute for hands-on practice and experimentation.
What This Document Provides
* An overview of what R is and its capabilities within the field of statistical computing.
* Guidance on organizing and structuring data within the R environment.
* Explanations of how to access and modify variables stored in R.
* Methods for creating and working with subsets of data, including data frames.
* An introduction to handling missing data within datasets.
* Basic usage of R as a calculator for simple arithmetic operations.
* Information on assigning values to names and managing objects within the R workspace.
* An explanation of vectors and how to create and manipulate them.
