Course information

Instructor	Dr. Jeremy Van Cleve
E-mail	[email protected]
Phone	(859) 218-3020
Office	216 THM
Office hours	By appointment via e-mail
Credits	3
Class Time	Tuesday & Thursday 11-12:15pm
Class Location	JSB 347
Website	https://github.com/vancleve/BIO540-DWV (github website)
	https://uk.instructure.com/courses/2122860 (Canvas website)

Course description

Biologists working in the laboratory perform many essential biochemical tasks to prep and run molecular analyses on their specimens and samples. Similarly, biologists working on data they collected, or on aggregated data collected by many researchers, must perform essential tasks such as cleaning, reshaping, and transforming their data so that they can explore and visualize it. The interdisciplinary field of data science integrates tools from scientific computing, data visualization, communication, and other fields to help biologists and other knowledge workers perform these tasks and extract insights from data.

This three-credit course aims to provide a brief introduction to data science for biologists and to wrangling, transforming, exploring, and visualizing data via scripting languages such as R, Python, and Julia. Students will get an opportunity to wrangle, explore, and visualize datasets from a variety of fields in biology as well as datasets of their own choosing. Alongside the tools of data science, the course will also introduce the tools required to document, maintain, share, and replicate data analyses and visualizations. More broadly, these tools help constitute the paradigm of “literate programming” and aid in the production of “reproducible research” wherein replicable and publication quality research products are generate directly from underlying source files in one integrated workflow.

Prerequisites:

• Undergraduate: STA 296, STA 381, PSY 216, or equivalent statistics course.
• Graduate: any undergraduate statistics course.

Student learning outcomes

At the end of the semester, students will able to:

1. Execute commands in a scripting language such as R
2. Load tabular data from a variety of sources including text files, Excel, and databases.
3. Wrangle and manipulate data by slicing matrices and data tables with Boolean operators and regular expressions
4. Create visualizations using “grammar of graphics” plotting packages
5. Visualize multidimensional data grids of plots, networks, and other tools
6. Use principles of visual perception to design effective graphics
7. Create Quarto markdown documents that document and explain data exploration and visualization in a reproducible way

Course format

Each week will consist of a introduction and interactive demonstration of the concepts and tools for that week on Tuesday followed by interactive problem solving on Thursday where students apply the concepts and tools from Tuesday. 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.

Technology requirements

Please bring a laptop to class with a recent version of macOS/Windows/Linux. If you need to borrow a laptop, please contact the instructor who can help you obtain one for the semester.

The course will utilize a cloud server for R, RStudio, and other software that is hosted by the College of Arts and Sciences.

Access to the server occurs at https://rstudio.as.uky.edu/. Once an account has been created for you by the instructor, you can use you linkblue username and password to access the server.

Assessment

Attendance	20%	Two unexcused absences permitted without penalty
Lab work	40%	Submitted as markdown file (.qmd) before the beginning of the following week
		One unexcused missing lab permitted without penalty
Data visualization project	40%	Data analysis and figures using a dataset of your choice
		8-10 min presentation of data and figures
		Markdown document with data analysis and figures due on date of presentation

The assessment portion of the course has three components.

1. Class attendance. Two unexcused absences are permitted without penalty. Further unexcused absences cannot be made up and will count against class attendance according to their fraction of the total class meetings.

2. Completion of the lab problems that we begin in class. This must be turned in as a markdown document before class the following week. 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. Data visualization project and presentation. The last two weeks will be devoted to 8-10 minute presentations of five figures that present data from a 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!

   There is a ton of data out there so selecting a dataset can be challenging. There a few sites with lots of interesting data that may help get you started.

   • Kaggle. https://www.kaggle.com/datasets. Datasets for practicing machine
   • Our World in Data. https://ourworldindata.org/. Lots of health, demographic, and cultural data from around the world.
   • Dryad Data Platform. https://datadryad.org/. Repository freely reusable datasets from scientific publications focusing on ecology and evolution.
   • Zenodo. https://zenodo.org/. Open repository for code and data for science with more focus on physical sciences. Search only the datasets using this link.

   Selection of the dataset can be challenging so please do not hesitate to consult with the instructor for help.

Grading scale

Undergraduate	Graduate		Range
A	A	100%	to	90%
B	B	< 90%	to	80%
C	C	< 80%	to	70%
D	E	< 70%	to	60%
E	E	< 60%	to	0%

Midterm grades will be reported to all students by the date specified in Senate Rule 6.1.4.1.

Graduate students

Graduate students have an additional requirement for the data visualization project. The markdown document containing the data analysis and figures should be written as a short project report with the following sections:

1. Introduction: briefly describe the problem or topic the dataset addresses.
2. Data: describe how the data were originally collected and where or how you obtained them.
3. Methods: describe any data wrangling that was necessary before the data could be analyzed and visualized.
4. Analyses: add a brief narrative description of each analysis and figure and include a statement about what can be concluded from the figure.
5. Conclusion: describe briefly what was learned from the analysis and visualization and propose a few next steps.

The presentation will constitute 25% of the course grade and the written document 15% of the course grade for a total of 40% of the course grade for the data visualization project.

Tips for making sure everyone, including me, 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 putting in full directories like /home/jva38/class/week1/stuff.jpg and instead simply put stuff.jpg if its in the same directory as the .qmd file.
• Make sure your analyses run without errors and your .qmd can be compiled into a .html file successfully.

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, Grolemund, Garrett, and Mine Çetinkaya-Rundel. 2023. R for Data Science (2e). O’Reilly. < https://r4ds.hadley.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”.

• Chang, Winston. 2013. R Graphics Cookbook. O’Reilly
• Crawley, Michael J.. 2005. Statistics: An Introduction using R. Wiley
• Dalgaard, Peter. 2008. Introductory Statistics with R. Springer
• Gandrud, Christopher. 2015. Reproducible Research with R and R Studio. CRC Press.
• Kolaczyk, Eric D. and Csárdi, Gábor. 2020. Statistical Analysis of Network Data with R (2e). Springer
• Mailund, Thomas. 2017. Beginning Data Science in R. Apress
• Murrell, Paul. 2011. R Graphics. CRC Press
• 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
• Wilkinson, Leland. 2005. The Grammar of Graphics. Springer
• Zelterman, Daniel. 2015. Applied Multivariate Statistics with R. Springer

Internet

• Cheatsheets for RStudio and tidyverse packages like ggplot2 (https://posit.co/resources/cheatsheets/).
• 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
  • STAT 545: “Data wrangling, exploration, and analysis with R” by Jenny Bryan (Posit/RStudio): http://stat545.com/
  • Current UBC version of Stat 545:
  • https://stat545.stat.ubc.ca/course/
• 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	Topic	Link
1	08/27 & 08/29	Intro to the course and markdown, and Quarto	html
2	09/03 & 09/05	Intro to R: data types, flow control, and functions	html
3	09/10 & 09/12	Vectors, slicing, and map(ping)	html
4	09/17 & 09/19	Getting data into R data.frames via files and spreadsheets	html
5	09/24 & 09/26	Tidy Data	html
6	10/01 & 11/03	Joins and databases	html
7	10/08 & 10/10	Text manipulation and regular expressions	html
8	10/15 & 10/17	Introduction to plotting and ggplot2	html
9	10/22 & 10/24	Plot types in ggplot2	html
	10/29	Fall Break
10	10/31	Principles of displaying data & how to modify plots	html
	11/05	Election Day
	11/07	Principles of displaying data & how to modify plots
11	11/12 & 11/14	Colors and heat maps	html
12	11/19	Visualizing/analyzing lots of data	html
	11/21	Research data management (UK Data Librarian Isaac Wink)
13	11/26	Networks
	11/28	Thanksgiving Break
14	12/03	Project presentations
	12/05	Project presentations
	12/10	No class - JVC at Conference

Course policies

Academic policies

For full description of UK academic policies regarding excused absences and their verification, religious observances, prep days and reading days, accommodations due to disability, and non-discrimination and Title IX compliance, please see the link below.

https://provost.uky.edu/proposals/guidance-course-proposals/standard-academic-policy-statements

Unexcused absences and late work

Unexcused absences will result in a decrease in the attendance grade and cannot be made up. Unexcused or late work may be submitted but can be subject to a 5% penalty per day up to 20%.

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.

Please see the link below for the university policies on academic offenses for official descriptions of cheating and plagarism and the processes and penalties for violations of the policies.

https://provost.uky.edu/proposals/guidance-course-proposals/academic-offenses

Belonging and engagement

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.

For information about reporting bias, harassment, or other issues or seeking accommodations, please see https://ieeo.uky.edu/contact-us.

Face covering/distancing policy

• Transmission of COVID-19 and other airborne respiratory illnesses is an important issue, especially during fall and winter months. The instructor and students are entitled to practices that reduce transmission including, and not limited to, wearing a high-quality mask and social distancing.

• These transmission reducing practices are optional but the instructor encourages them. If COVID-19 or another airborne illness is a specific risk to a student or a student has any concerns about classroom policies, please contact the instructor as soon as possible.

University resources for students

For a list of university resources for students, please see the link below.

https://studentsuccess.uky.edu/get-help

Campus Emergency Response Guide

See the link below for resources on classroom or campus emergencies.

https://police.uky.edu/emergency-response-guide