/BIO580-DWVR

BIO 580 – Data Wrangling and Visualization (in R)

Primary LanguageHTML

 

Course information
Instructor Dr. Jeremy Van Cleve
E-mail jvancleve@uky.edu
Phone (859) 218-3020
Office 216 THM
Office hours By appointment via e-mail
Credits 3
Class Time Tue/Thur 12:30pm-1:45pm
Class Location JSB 357
Website https://github.com/vancleve/BIO580-DWVR (github website)
https://uk.instructure.com/courses/2077222 (Canvas website)

Course description

The last 20 years have seen data science emerge as a field from data intensive work in other fields such as statistics, biology, physics, engineering, history, and journalism. This three-credit course aims to provide a brief introduction to data science and analyzing and visualizing data. As data science using R, Python, Julia, and other scripting languages has 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 in order to produce replicable and publishable reports.

Prerequisites:

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

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 introduction and interactive demonstration of the concepts and tools for that week on Tuesday followed by a lab where students apply the concepts and tools on Thursday. 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.

Required materials

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.

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.

  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.

    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 by first typing rm(list=ls()) into the R console and then compiling the file. This clear your workspace of previous variables 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.

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

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

Topic schedule

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

Week Class Dates Topic Link
1 01/09 & 01/11 Intro to the course and markdown, and Quarto html
2 01/16 & 01/18 Intro to R: data types, flow control, and functions html
3 01/23 & 01/25 Vectors, slicing, and map(ping) html
4 01/30 & 02/01 Getting data into R data.frames via files and spreadsheets html
5 02/06 & 02/08 Tidy Data html
6 02/13 & 02/15 Joins and databases html
7 02/20 & 02/22 Text manipulation and regular expressions html
8 02/27 & 02/29 Introduction to plotting and ggplot2 html
9 03/05 & 03/07 Plot types in ggplot2 html
03/12 & 03/14 Spring Break
10 03/19 & 03/21 Principles of displaying data & how to modify plots html
11 03/26 & 03/28 Colors and heat maps html
12 04/02 & 04/04 Networks html
13 04/09 & 04/11 Visualizing/analyzing lots of data html
14 04/16 Interactive plots html
04/18 Project presentations
15 04/23 Project presentations

Course policies

Academic policies

Please see https://www.uky.edu/universitysenate/acadpolicy for a full description of UK academic policies regarding excused absences, religious observances, accommodations due to disability, and non-discrimination and Title IX compliance.

Unexcused absences and late work

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

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.

The University of Kentucky Senate Council Statement on Diversity, Equity, and Inclusion includes important additional language and information that applies to this course. Please refer to to this link for the full statement.

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://www.uky.edu/covid-19/

University resources

Please see https://www.uky.edu/universitysenate/student-resources for a list of university resources including bias reporting, counseling, disability resources, libraries, and technology support.

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 university policies on academic offenses, https://www.uky.edu/universitysenate/ao, for official descriptions of cheating and plagarism and the processes and penalties for violations of the policies.