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BIO 770 – Data Wrangling and Visualization Using R – Fall 2018

 

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 W 1 – 2 PM
Class Location JSB 103
Website https://github.com/vancleve/BIO770-DWVR (github website)
https://uk.instructure.com/courses/1918933 (Canvas website)

Course description

The last 15 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 one-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 become more popular, so do tools the tools required to document, maintain, share, and replicate analyses and visualization. These activities constitute the notions of “literate programming” and “reproducible research”, and we will use some of these tools (particularly R Markdown).

Prerequisites: None.

Student learning outcomes

  1. Execute commands in R
  2. Create R Markdown documents that 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 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); please make sure to do those so that we make the most of time in class.

Assessment

Attendance 20% One absence permitted without penalty
Lab work 40% Submitted as R Markdown before next class
One missing lab permitted without penalty
Lightning talk 40% 4 min presentation and source for all analyses
Due by end of last day of class (12/7)

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 an R Markdown document. If there are datasets that are required for the analysis (other than datasets provided as part of the lab), these should be provided along with the R Markdown file by adding all the files to a single compressed zip file. The Rmd or zip file should then be uploaded to the Canvas course website: https://uk.instructure.com/courses/1843807.
  3. Lightning talk final presentation. The last two classes will be devoted to short four minute presentations of three figures that present data from a single dataset 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 R Markdown source code and any necessary data files must be submitted to the Canvas website as a zip file; compiling the R Markdown file should produce the figures as they were presented during the lightning talk. If you want a challenge, you can even write your slides in R Markdown too!

Tips for making sure I can run your R code.

  • Create a separate folder for each assignment and put the .Rmd and all the 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 .Rmd). 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 .Rmd.
  • Make sure your analyses run without errors and your .Rmd can be knit into a .html file successfully by first typing rm(list=ls()) and then knitting 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. 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!) that cover much of the material in the course.

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. http://nathanieldphillips.com/thepiratesguidetor/
  • Wickham, Hadley. 2016. ggplot2. Springer

Internet

Topic schedule

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

Week Class Dates (W) Topic Notes
1 8/29 Intro to course and R Markdown Install R & RStudio before class
(Installation instructions)
2 9/5 Intro to R: data types, flow control, and functions
3 9/12 Vectors, slicing, and map(ping)
4 9/19 Getting data into R with data.frames
5 9/26 Tidy Data
6 10/3 Introduction to plotting and ggplot2
7 10/10 Plot types in ggplot2
8 10/17 Principles of displaying data & how to modify plots
9 10/24 Text manipulation: regular expressions
10 10/31 Colors and heat maps
11 11/7 Visualizing lots of data
12 11/14 Networks
11/21 No class Thanksgiving Break
13 11/28 Lighting talks
14 12/5 Lighting talks

Course policies

Excused Absences

Students need to notify the professor or instructor of absences prior to class when possible. Senate Rule 5.2.4.2 defines the following as acceptable reasons for excused absences: (a) serious illness, (b) illness or death of family member, (c) University-related trips, (d) major religious holidays, and (e) 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, http://www.uky.edu/Ombud/ForStudents_ExcusedAbsences.php). 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 5.2.4.2 states that faculty have the right to request “appropriate verification” when students claim an excused absence because of illness or death in the family. Appropriate notification of absences due to university-related trips is required prior to the absence.

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: http://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.

Please see Section 6.3 “Academic Offenses and Procedures” of the University Senate Rules for UK’s policy on academic integrity

Accommodations due to disability

If you have a documented disability that requires academic accommodations, please see me as soon as possible during scheduled office hours. In order to receive accommodations in this course, you must provide me with a Letter of Accommodation from the Disability Resource Center for coordination of campus disability services available to students with disabilities.

Disability Resource Center: http://www.uky.edu/StudentAffairs/DisabilityResourceCenter/index.html. Suite 407 Multidisciplinary Science Building (physical address: 725 Rose Street); Phone: 257‐2754; Email: dtbeach1@uky.edu

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Seminar course on using R for wrangling and visualizing data

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