Get over the hump in learning the R programming language and become proficient in using R for a variety of purposes, including wrangling data, generating plots, conducting analyses, and generating reports.
Learn to conduct reproducible research and to reproduce research of others.
Get experience making brief presentations on research to an interdisciplinary audience, and to gain a broad sense for the types of work that graduate student peers are conducting.
Make progress on own research by tackling complex tasks with R
Class sessions will consist of live-coding sessions led by the instructor, as well as individual and group exercises.
Each class session will have an acommpanying web module that provides details and codes. These are designed to be guides that will help you retain the information in the future. Compiled together, this will essentially be your textbook.
Most weeks, we will start a task during the Thursday session, which will then turn into a homework assignment. Sometimes this will entail generating and submitting code scripts. Other times, it will require you to prepare data, look up literature, or other tasks.
Three times during the semester, we will have project presentation sessions. See below for details.
The last part of the semester will mostly be dedicated to conducting independent research projects.
Students will be evaluated based on 4 criteria. (1) Participation [15 points], (2) Presentations [15 points], (3) Take-home assignments [20 points], (4) Project Proposal & Progress Reports [20 points], (5) Independent Project Final Products [30 points].
Here are the basic expectations for each of the above categories:
Participation: Students are expected to attend in-person sessions, but accommodations will be made for illness (including illness of dependents), conflicts with research or teaching needs (e.g., field research trip), etc. Students are asked to notify the instructor of upcoming conflicts ahead of time. Conflicts that would require the student to miss more than 4 sessions over the course of the semester should be discussed with the instructor at the beginning of the semester.
Presentations: Three times during the semester, we will have project presentation sessions. In the first session near the beginning of the semester, each student will present a brief overview of their research theme. This can be very broad description of the theme or a detailed account of a specific project. In the second session in the middle of the semester, students will present a ‘progress report’–a description of the project that they settled on, and where they are on the project. In the third session, the students will present their final products.
Take-home assignments: There will be take-home assignments over the course of the semester, which are designed to guide you through some independent exploration of material covered in lecture, and to prepare you towards the beginning of your independent project. These sill be posted via Canvas. You will have 5 or 7 days to complete the assignment.
Project Proposal & Progress Reports: The student will develop a project proposal in two steps: submission of an initial project idea, and submission of a 2-page project proposal that includes objectives and description of the dataset (i.e., how it was collected). During the last third of the semester, students will submit progress reports every 2 weeks.
Independent Project Final Products: At the end of the semester, students will submit a final report (4-5 pages), the data file, and the R script(s).
You will spend several weeks during the course conducting independent projects. We will have regular class during this time, but I will be available to help you with your R codes. This is an opportunity for you to work on something new that will further your research goals. I will leave the format fairly open, but here are some suggestions:
Conduct new analyses of your own data
Re-create models and/or figures from a publication in your field (I recommend this approach—it really helps you understand concepts at a new level).
Conduct meta-analyses or re-analysis of publically available data.
You will submit a brief plan (< 2 pages) of your project during the 8th week of the course. However, you can start your project earlier than that. You will submit a final report, along with the dataset and R scripts needed to reproduce the results.
Students with disabilities are encouraged to contact the instructor for a confidential discussion of their individual needs for academic accommodation. It is the policy of the University of Nebraska-Lincoln to provide flexible and individualized accommodation to students with documented disabilities that may affect their ability to fully participate in course activities or to meet course requirements. To receive accommodation services, students must be registered with the Services for Students with Disabilities (SSD) office, 132 Canfield Administration, 472-3787 voice or TTY.
Week 1 (Aug 22, 24): Intros & Getting started with R; File Organization & Importing Data;
Week 2 (Aug 31): No class on Tuesday (29th); Data Formats
Week 3 (Sep 5, 7): Some simple statistics & Plots
Week 4 (Sep 14): No class on Tuesday (12th); Presentations 1: Project Ideas (Thursday)
Week 5 (Sep 19, 21): Data Wrangling & Plotting in Tidyverse Part ; Creating reports
Week 6 (Sep 26, 28): apply functions, loops, statements, custom functions
Week 7 (Oct 3, 5): Resampling & bootstrapping
Week 8 (Oct 10, 12): Simulations, stochasticity, Submit proposal for independent project
Week 9 (Oct 19): Fall Break (Tuesday 17th); Presentations 2: Project Plans (Thursday)
Week 10 (Oct 24, 26): Batch processing
Week 11 (Oct 31, Nov 2): Worked examples, independent projects
Week 12 (Nov 7, 9): Worked examples, independent projects
Week 13 (Nov 14, 16): Independent projects
Week 14 (Nov 21, 23): No class for Thanksgiving
Week 15 (Nov 28, Nov 30): Independent projects
Week 16 (Dec 5, 7): Presentations 3: Independent projects presentation
Please do the following things before we meet for the first class.
Download and Install R and RStudio
Desktop
Go to https://posit.co/download/rstudio-desktop/
and follow directions for downloading and installing R and RStudio If
you already have R, upgrade to the latest version. It will make things
easier to have everyone on the same version.
Get a GitHub account: Go to https://github.com/ and create an account. Send me an email with your account name and email so I can add you to the class “GitHub Organization”.