Course information

Instructor	Dr. Jeremy Van Cleve
E-mail	jvancleve@uky.edu
Phone	(859) 218-3020
Office	216 THM
Office hours	By appointment
Class Time	M 1 – 3 PM
Class Location	JSB 357
Website	https://github.com/vancleve/BIO621-DWVR (github website)
	https://uk.instructure.com/courses/2041707 (Canvas website)

Course description

The last 20 years have seen the R programming language rise in popularity from a language used and developed primarily by statisticians to one used and developed by anyone interested in analyzing and visualizing data from scientists and engineers to historians and journalists. This two-credit seminar aims to provide a brief introduction (i.e., a crash course) to using R for analyzing and visualizing data. As R and other scripting languages have become more popular, so have the tools required to document, maintain, share, and replicate analyses and visualization. These activities constitute “literate programming” and “reproducible research”, and we will use some of these tools (particularly Quarto).

Prerequisites: None.

Student learning outcomes

1. Execute commands in R
2. Create Quarto markdown documents that use R to explain and reproduce analyses
3. Wrangle and manipulate data by slicing matrices and by using the dplyr, tidyr, and other tidyverse packages
4. Plotting using the ggplot2 package
5. Visualize multidimensional data using 2D/3D plots, networks, and other tools
6. Create easily reproducible publication quality figures without expensive applications

Course format

Each week will consist of a short introduction and interactive demonstration of the concepts and tools for that week followed by a short lab where students apply the concepts and tools. There may be preliminary readings to do before class for some weeks (see “Topic schedule” below and check back for updates); please make sure to do those so that we make the most of time in class.

Assessment

Attendance	20%	Two absences permitted without penalty
Lab work	40%	Submitted as markdown file (.qmd) before next class
		One missing lab permitted without penalty
Project Presentation	40%	8-10 min presentation with figures and data analysis
		Data/markdown doc/slides due on date of presentation

The assessment portion of the course has three components.

1. Class attendance.
2. Completion of the lab component that we begin in class. This must be turned in as a markdown document. If there are datasets that are required for the analysis (other than datasets provided as part of the lab or lecture), these should be provided along with the Quarto markdown file (with last name qmd) by adding all the files to a single compressed zip file. The qmd or zip file should then be uploaded to the Canvas course website.
3. Project presentation. The last two classes will be devoted to 8-10 minute presentations of five figures that present data from one or two datasets of your choice. The figures should be “publication quality” in terms of aesthetics (labeling, font size, colors, etc) but do not need a caption (that’s what the talk is for!). The markdown source code and any necessary data files must be submitted to the Canvas website as a zip file; compiling the markdown file (with Quarto) should produce the figures as they were presented during the lightning talk. If you want a challenge, you can even write your slides in markdown too!

Tips for making sure I can run your R code.

• For every assignment, create a separate folder for each assignment and put the .qmd file and all the other necessary files (data files, images, etc) in that folder.
• Zip the contents of that folder (or the folder itself) and submit that to Canvas.
• Use relative directories when pointing to files. Relative directories begin simply the name of the file or subdirectory of the current directory (I use relative directories in all the course .qmd files). That is, avoid directories like C:\Documents\student\R\stuff\stuff.jpg and instead simply put stuff.jpg if its in the same directory as the .qmd.
• Make sure your analyses run without errors and your .qmd can be compiled into a .html file successfully by first typing rm(list=ls()) into the R console and then compiling the file. This will start your workspace from scratch and is also a good way of preventing the problem where an analysis worked when you closed R but now doesn’t when you reopen it.

Getting help (i.e., uh, how do I…?)

Classmates and instructor

1. Please start a discussion on the Canvas website. This will allow everyone to benefit from the questions and answers posed. I will monitor this discussion and post replies as necessary. Please also post your own replies too!
2. Instructor office hours.

Internet

1. Stack Overflow (http://stackoverflow.com/). Programming and developer Q&A site. Search as normal for keywords, add tags enclosed in square brackets, e.g. [ggplot] or [git], to restrict results to the library or language you want answers in.
2. Cross Validated (http://stats.stackexchange.com/). A site in the same family as Stack Overflow. Focused on conceptual and procedural questions in statistics (less on implementation in R or other languages).
3. Google. The oldie but the goodie.

Useful resources

Books

There are some recent books on data science and visualization (all written in RMarkdown, which is a predecessor and alternative to Quarto) that cover much of the material in the course.

• Wickham, Hadley and Grolemund, Garrett. 2017. R for Data Science. O’Reilly. http://r4ds.had.co.nz/
• Wilke, Claus O. 2018. Fundamentals of Data Visualization. https://clauswilke.com/dataviz/
• Healy, Kieran. 2018. Data Visualization: A Practical Introduction. http://socviz.co/
• Ismay, Chester and Kim, Albert Y. 2018. An Introduction to Statistical and Data Sciences via R. https://moderndive.com/
• Silge, Julia and Robinson, David. 2018. Text Mining with R: A Tidy Approach. https://www.tidytextmining.com/

If you want to become an R wizard in the style of Hadley Wickham, this book is for you.

• Wickham, Hadley. 2019. Advanced R. https://adv-r.hadley.nz/

The following are some popular books on R. PDFs are available for “check out” on the Canvas website under “Modules: References”.

• Crawley, Michael J.. 2005. Statistics: An Introduction using R. Wiley
• Dalgaard, Peter. 2008. Introductory Statistics with R. Springer
• Murrell, Paul. 2011. R Graphics. CRC Press
• Chang, Winston. 2013. R Graphics Cookbook. O’Reilly
• Gandrud, Christopher. 2015. Reproducible Research with R and R Studio. CRC Press.
• Zelterman, Daniel. 2015. Applied Multivariate Statistics with R. Springer
• Phillips, Nathaniel. 2016. YaRrr! The Pirate’s Guide to R.
• Wickham, Hadley. 2016. ggplot2. Springer
• Wickham, Hadley and Grolemund, Garrett. 2017. R for Data Science. O’Reilly
• Mailund, Thomas. 2017. Beginning Data Science in R. Apress

Internet

• RStudio Cheatsheets (https://www.rstudio.com/resources/cheatsheets/). Cheatsheets for working with ggplot2, R Markdown, and other R packages.
• Quarto documentation. https://quarto.org/docs/guide/
• FlowingData (http://flowingdata.com/). Articles, examples, and tutorials on data visualization by Nathan Yau.
• Other data visualization and wrangling courses:
  • “Visualizing Data” by Chris Adolph (UWashington): http://faculty.washington.edu/cadolph/index.php?page=22
  • “Data wrangling, exploration, and analysis with R” by Jenny Bryan (UBC): http://stat545.com/
• DataCamp interactive courses. http://www.datacamp.com

Topic schedule

The following is the preliminary schedule of topics and will be adjusted as the semester progress.

Week	Class Dates (W)	Topic	Link
1	08/22	Intro to course and markdown, and Quarto	html
2	08/29	Intro to R: data types, flow control, and functions	html
	09/05	No class (Labor Day)
3	09/12	Vectors, slicing, and map(ping)	html
4	09/19	Getting data into R with data.frames	html
5	09/26	Tidy Data	html
6	10/03	Introduction to plotting and ggplot2	html
7	10/10	Plot types in ggplot2	html
	10/17	No class (JVC at international conference)
	10/24	No class (Fall Break)
8	10/31	Principles of displaying data & how to modify plots	html
9	11/07	Text manipulation: regular expressions	html
10	11/14	Colors and heat maps	html
11	11/21	Visualizing lots of data	html
12	11/28	Networks	html
13	12/05	Project Presentations

Course policies

Please see https://www.uky.edu/universitysenate/acadpolicy for a full description of UK academic policies.

Diversity and Inclusion

Members of the course are entitled to learn from each other in an open and welcoming environment regardless of their racial, ethnic, gender, and sexual identities. Conduct that is not respectful of these identities or of the national origin, religion, and political beliefs students and instructors will not be tolerated. Please report any concerning conduct to the instructor.

Face Covering/Distancing Policy

• Transmission of COVID-19 is still prevalent in Kentucky. The instructor and students are entitled to practices that reduce transmission including, and not limited to, wearing a high-quality mask and social distancing.

• In accordance with University guidelines, these transmission reducing practices are optional but the instructor encourages them. If COVID-19 is a specific risk to a student or a student has any concerns about classroom COVID-19 policies, please contact the instructor as soon as possible.

• More information about COVID-19 and UK can be found here: https://coronavirus.uky.edu/

Excused Absences

Students need to notify the instructor of absences prior to class when possible. Senate Rule Senate Rules 5.2.5.2.1 defines the following as acceptable reasons for excused absences: (a) significant illness, (b) death of a family member, (c) trips for members of student organizations sponsored by an educational unit, trips for University classes, and trips for participation in intercollegiate athletic events, (d) major religious holidays, (e) interviews for graduate/professional school or full-time employment post-graduation, and (f) other circumstances found to fit “reasonable cause for nonattendance” by the professor.

Students anticipating an absence for a major religious holiday are responsible for notifying the instructor in writing of anticipated absences due to their observance of such holidays no later than the last day in the semester to add a class. Information regarding major religious holidays may be obtained through the Ombud (859-257-3737, https://www.uky.edu/ombud/religious-observances). Students are expected to withdraw from the class if more than 20% of the classes scheduled for the semester are missed (excused or unexcused) per university policy.

Verification of Absences

Students may be asked to verify their absences in order for them to be considered excused. Senate Rule 5.2.5.2.1 states that faculty have the right to request appropriate verification when students claim an excused absence due to: significant illness; death in the household, trips for classes, trips sponsored by an educational unit and trips for participation related to intercollegiate athletic events; and interviews for full-time job opportunities after graduation and interviews for graduate and professional school.

Academic Integrity

Per university policy, students shall not plagiarize, cheat, or falsify or misuse academic records. Students are expected to adhere to University policy on cheating and plagiarism in all courses. The minimum penalty for a first offense is a zero on the assignment on which the offense occurred. If the offense is considered severe or the student has other academic offenses on their record, more serious penalties, up to suspension from the university may be imposed.

Plagiarism and cheating are serious breaches of academic conduct. Each student is advised to become familiar with the various forms of academic dishonesty as explained in the Code of Student Rights and Responsibilities. Complete information can be found at the following website: https://www.uky.edu/ombud/. A plea of ignorance is not acceptable as a defense against the charge of academic dishonesty. It is important that you review this information as all ideas borrowed from others need to be properly credited.

Section 6.3 “Academic Offenses and Procedures” of the Senate Rules lays out UK’s policy on academic integrity and says the following about plagarism and

Accommodations due to disability

In accordance with federal law, if you have a documented disability that requires academic accommodations, please inform me as soon as possible. In order to receive accommodations in a course, you must provide me with a Letter of Accommodation from the Disability Resource Center (DRC).

The DRC coordinates campus disability services available to students with disabilities. It is located on the corner of Rose Street and Huguelet Drive in the Multidisciplinary Science Building, Suite 407. You can reach them via phone at (859) 257-2754, via email (drc@uky.edu) or visit their website (https://www.uky.edu/DisabilityResourceCenter/). DRC accommodations are not retroactive and should therefore be established with the DRC as early in the semester as is feasible.