BASV 316 R Lab Manual
George Self
2019-01-07
Preface
I have taught BASV 316, Introductory Methods of Analysis, online for the University of Arizona in Sierra Vista since 2010 and enjoy working with students on research methods. From the start, I wanted students to work with statistics as part of our studies and carry out the types of calculations that are discussed in the text. As I evaluated statistical software I had three criteria:
Open Educational Resource (OER). It is important to me that students use software that is available free of charge and is supported by the entire web community.
Platform. While most of my students use a Windows-based system, some use Macintosh and it was important to me to use software that is available for all of those platforms. As a bonus, most OER software is also available for the Linux system, though I am not aware of any of my students who are using Linux. Finally, I occasionally have students who are not able to load software on their personal computers (think: Chromebook) so I needed an online capability.
Longevity. I wanted a system that could be used in other college classes or in a business setting after graduation. That way, any time a student spends learning the software in my class will be an investment that can yield results for many years.
R (just a single letter, R) met those objectives and that is the software I chose to use. This manual started as a series of six lab exercises using R but has grown over the years to the ten topics covered in this edition. Moreover, R is a recognized standard for statistical analysis and could be easily used for even peer-reviewed published papers. It is my hope that students will find the labs instructive and they will then be able to use R for other classes.
This lab manual is written with Bookdown tools in RStudio. It is published under a Creative Commons 0 Universal license, essentially “public domain,” (see [Creative Commons License]) with a goal that other instructors can modify and use it to meet their own needs. The source can be found at GITHUB and I always welcome comments. Finally, it was written with base R (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.).
–George Self