BIO 770 – Data Wrangling and Visualization Using R – Fall 2018
|Instructor||Dr. Jeremy Van Cleve|
|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)|
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
Student learning outcomes
- Execute commands in R
- Create R Markdown documents that explain and reproduce analyses
- Wrangle and manipulate data by slicing matrices and by using the
tidyr, and other
- Plotting using the
- Visualize multidimensional data using 2D/3D plots, networks, and other tools
- Create easily reproducible publication quality figures without expensive applications
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.
|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.
- Class attendance.
- 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
zipfile should then be uploaded to the Canvas course website: https://uk.instructure.com/courses/1843807.
- 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
zipfile; 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
.Rmdand 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.jpgand instead simply put
stuff.jpgif its in the same directory as the
- Make sure your analyses run without errors and your
.Rmdcan be knit into a
.htmlfile 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
- 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!
- Instructor office hours.
- 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.
- 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).
- Google. The oldie but the goodie.
There are some recent books on data science and visualization (all
RMarkdown!) that cover much of the material in the course.
- Wickham, Hadley and Grolemund, Garrett. 2016. R for Data Science. O’Reilly. http://r4ds.had.co.nz/
- Wilke, Claus O. 2018. Fundamentals of Data Visualization. https://serialmentor.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/
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
- RStudio Cheatsheets
(https://www.rstudio.com/resources/cheatsheets/). Cheatsheets for
ggplot2, R Markdown, and other R packages.
- Try R (http://tryr.codeschool.com/). An interactive online R tutorial.
- 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
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|
|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|
|6||10/3||Introduction to plotting and
|7||10/10||Plot types in
|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|
|11/21||No class||Thanksgiving Break|
Students need to notify the professor or instructor of absences prior to class when possible. Senate Rule 188.8.131.52 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 184.108.40.206 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.
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: firstname.lastname@example.org