Syllabus: R as a Research Tool (BIOL 6325)

11:00-12:20 T TH, Biology room 405

Wednesdays 12:00-1:30 PM or by appointment. Building ESB2, room 402B. I can meet via zoom as well. Email me for advice or to set up a meeting. Programming is difficult and I will help you.

This is a workshop course in which I will teach the computer language “R”, an open-source, interactive software package specifically designed for scientific numerical computation. R is a language designed around a core set of statistical libraries and offers advanced statistical capabilities. The language also offers very good graphical capabilities for exploring data or preparing figures for presentations and publication. Additionally, the course will teach basic computer programming principles that can apply to other computer languages such as Python or C++. This is not a statistics course, but is aimed at teaching you to use R for data science. Therefore, I will cover visualization, data shaping, and model fitting in addition to the traditional programming subjects. My goal is to provide graduate students with tools to become better users of computers — programming allows a scientist to use the computer to answer the questions that are most important to the researcher and not be limited by pre-packaged tools.

There are no prerequisites. However, if you have gaps in your knowledge regarding basic operating system use (especially file paths and file naming) you will need to do some extra work. I will help you of course, but this is a programming course, not a course in basic computer use.

The overarching goal of this course is to produce scientists who can efficiently and accurately investigate data and do so in an open and reproducible workflow.

After completion of the course students will be able to

• use the R console as a calculator and assign variables.
• use and understand the R data storage objects (vectors, matrices, dataframes) and the differences among data types such as real numbers, integers, character strings and booleans.
• write functions in R and break a large problem into a set of functions.
• be fluent in some programming concepts especially important in data analysis and be able to solve problems in a modular programming style.
• have some familiarity but not necessarily fluency with other programming concepts such as functional programming style, object-oriented programming, recursion, and regular expressions.
• understand and be able to use debugging methods.
• use a text editor and produce a plain-text workflow.
• search for and install domain-specific R packages and be able to use the documentation of such packages.
• take a new data set and efficiently investigate it for patterns using data reshaping techniques and exploratory visualization.
• engage in good code and data organization practices and use a consistent programming style.
• understand the basics of reproducible research including passing familiarity with version control and RMarkdown so that the student could go on learn more about these subjects with some existing knowledge.

There will be no exams. Evaluations will be based solely on completion of weekly assignments. I will provide detailed feedback on the code students turn in each week.

The final grade will be based on the average of the weekly programming assignments (see “assignments”, below). There are 12 assignments each worth 20 points. I will drop the lowest score. Grading framework: A => 90%; B => 80%; C 70%; D => 60%; F < 60%

http://r-research-tool.schwilk.org

You will need to install on your own computer:

1. Download and install R, https://www.r-project.org/.
2. Download and install RStudio, http://www.rstudio.com/download.
3. Install required packages:

pkgs <- c("tidyverse", "Lahman", "lubridate", "maps", "nlme", "pbkrtest", "RColorBrewer",
"scales")
install.packages(pkgs)

There is no textbook and there are no required outside readings. See the website for recommendations on supplementary material.

It is the student’s responsibility to conduct him/herself in a civil manner while in the classroom. Please consult the university policy on and academic honesty (OP 34.12) and civility.

I do not tolerate any plagiarism. You must write your own code. If you use code (even just for ideas) from any other resource such as a classmate, a friend, or the internet, you must carefully cite that in your code comments. I will teach you how to use the internet to help you find solutions, but do not blindly google or skim StackOverflow! You are meant to complete the assignments using what you have learned up to that point in the course and the course progression is designed with that in mind. Plagiarism will result in a written report submitted to the Office of Student Conduct and to the chair of Biological Sciences.

So called “AI” tools based on large language models (LLMs) such as ChatGPT are increasingly popular for quick code generation. These are generators of plausible sounding text (or plausible looking code) with no necessary relationship to truth. In other words, large language models are “bullshit generators”. I have tested such tools and they will lead you astray and produce buggy code. Use of any such gnerative AI in this course is prohibited. Submission of AI-generated content (i.e., information, text, or images) as your own work is a violation of academic integrity and may result in referral to the Office of Student Conduct.

Texas Tech Policies Concerning Academic Honesty, Special Accommodations for Students with Disabilities, Student Absences for Observance of Religious Holy Days, Accommodations for Pregnant Students, and other policies may be found on at this link: https://www.depts.ttu.edu/tlpdc/RequiredSyllabusStatements.php.

Attendance is required to perform well in this course. Please contact me if you need to miss class or if you need flexibility in any assignment due dates. I will endeavor to help you out. I understand that you all have your own research, potentially field work to conduct, not to mention illness which can affect us all.

• Discrimination, harassment, and sexual violence - https://www.depts.ttu.edu/tlpdc/DiscriminationHarrasmentSexualViolenceStatement.pdf
• Civility in the classroom - https://www.depts.ttu.edu/tlpdc/CivilityInTheClassroomStatement.pdf
• Plagiarism - https://www.depts.ttu.edu/tlpdc/PlagiarismStatement.pdf
• Student support - https://www.depts.ttu.edu/tlpdc/StudentSupportStatement.pdf
• Food insecurity - https://www.depts.ttu.edu/tlpdc/FoodInsecurityStatement.pdf