D2MR Syllabus

Winter 2025 - PSYC/CHDV/MAPS/MACS 30550

Course Description

This course tackles the basic skills needed to build an integrated research report with the R programming language. We will cover every step from data to manuscript including: Using R’s libraries to clean up and re-format messy datasets, preparing data sets for analysis, running statistical tools, generating clear and attractive figures and tables, and knitting those bits of code together with your manuscript writing. The result will be a reproducible, open-science friendly report that you can easily update after finishing data collection or receiving comments from readers. Never copy-paste your way through a table again! The R universe is large, so this course will focus specifically on: The core R libraries, the tidyverse library, and R Markdown. Students will also learn about the use of GitHub for version control.

Overview

Can’t find what you’re looking for? Try the FAQ page for answers to common questions about the course, the final research project, and the mini-projects. If you still can’t find what you need, please reach out to Dr. Dowling or your TA.

Professor

• Dr. Natalie Dowling
• Email: ndowling@uchicago.edu
• GitHub: @nrdowling
• Office: 1155 Building, Room 404

Section 1

• Weekly meetings: Mondays and Fridays 1:30pm - 2:250pm
• 1155 E 60th St, Room 289B
• TA: Mian Li
  • Email: lim1an@uchicago.edu
  • GitHub: @lim1ian

Section 2

• Weekly meetings: Mondays and Fridays 3:00pm - 4:20pm
• 1155 E 60th St, Room 289B
• TA: Yuchen Jin
  • Email: yuchenjin@uchicago.edu
  • GitHub: @regenchen

Office hours

Students are welcome to attend any office hours for help with ongoing work or general support, regardless of section. For discussion about grades, you should meet with your section TA or Dr. Dowling. TAs cannot discuss grades with students outside of their section.

• Dr. Dowling: Thursday 2pm - 4pm; 1155 E 60th St, Room 404
  • sign up via GCal or email me to request an alternative time
• Mian Li: Friday 9am - 10:30am; 1155 E 60th St, MAPSS 4th Floor Lounge (no signup required)
• Yuchen Jin: Monday 4:30pm - 6pm; 1155 E 60th St, MAPSS 4th Floor Lounge (no signup required)

Hubs

• Course site & syllabus (you are here!)
• Slack workspace
• Canvas
  • Canvas is used minimally in this course. It is primarily used to post grades. A few administrative tasks may be completed through Canvas, but the majority of the course content will be accessed through this site, GitHub, and Slack.
• Centralized assessment repo
  • All mini-projects will be organized and assessed through your personal fork of this repo
  • Updated with materials from class meetings and demos throughout the quarter
• Example repo: schelling-games
  • Fork this repo to poke around a (mostly) functional GitHub pages site created entirely using R Markdown. Most of the .Rmd and .R files include extensive commenting, including some ideas for how you can practice coding by revising or adding to those R scripts directly.

Communication

View the full communication policy.

• Slack: We will use Slack for most course communication. You should have received an invitation to join the workspace. If you did not, please email Dr. Dowling.
  • Channels: The Slack has separate channels for different topics as well as channels for each section. You can also create your own channels for group work or other purposes. Please use the appropriate channel for your question or comment! This keeps the workspace organized and helps others with similar questions (or answers!) find what they need.
  • Direct messages: Do not send direct messages to Dr. Dowling or the TAs unless they explicitly ask to you do so. If you have a question, it is likely that others do too! This is the point of Slack, so please ask questions in the appropriate channel. You may DM other students, but please be respectful and professional in your communication.
• Email: Use email for private communication with Dr. Dowling or your TA. You should expect a response within about 2 days. If you have not received a response within 3 days please send a follow-up email.

Course materials

View the full resources page.

• Most required readings will be drawn from one of the following great (and free! and online!) resources:
  • R for Data Science by Hadley Wickham
  • R Markdown for Scientists by Nicholas Tierney
  • Also check out R Markdown: The Definitive Guide by Yihui Xie, J. J. Allaire, and Garrett Grolemund
• Additional resources can be found on the resources page

Grading

View the grading policies for complete details on the course grading system and graded components.

View the assessment overview for detailed information about objectives-based grading, the course objectives, and how standards are assessed.

The course is graded on a 100-point scale, with the following breakdown:

Component	Objectives	Engagement	Total
Mini-projects	40	10	50
Research project	30	10	40
Community engagement	0	10	10
Total	70	30	100

This course uses objectives-based¹ grading. This means you earn your grade by demonstrating you have met class learning objectives.

Attendance & late work

View the full attendance and late work policy.

Attendance

You are expected to attend all class meetings. Attendance is not a component of your grade, but missing class will mean missing both content and critical course information. Being absent from class does not excuse you from the responsibility of knowing what was covered in your absence, including any announcements or changes to the syllabus.

The engagement grade is not an attendance grade. Do not come to class if you are ill.

Late Work

You can turn in any work for assessment at any time during the quarter. You have a full quarter to plan ahead, budget your time, and submit work to count towards the 90 points of objectives-based assessments (50pt mini-projects + 40 research project).

This class has exactly two deadlines, both of which are absolutely firm:

1. Assessment mini-projects must be submitted on or before Wednesday of 9th week
2. Final drafts of research projects must be submitted on or before Wednesday of finals (10th) week

Extensions will not be granted outside of truly exceptional circumstances.

Academic Integrity & Artificial Intelligence

View the full AI*2 policy.

Academic Integrity

Students in this course are expected to follow UChicago’s Academic Honesty & Plagiarism policy. To add clarity to this general policy, in this class I am using Oxford University’s explanation of plagiarism:

Plagiarism is presenting work or ideas from another source as your own, with or without consent of the original author, by incorporating it into your work without full acknowledgement. All published and unpublished material, whether in manuscript, printed or electronic form, is covered under this definition, as is the use of material generated wholly or in part through use of artificial intelligence (save when use of Artificial Intelligence - AI for assessment has received prior authorisation e.g. as a reasonable adjustment for a student’s disability). Plagiarism can also include re-using your own work without citation.

In From Data to Manuscript in R, you may use AI tools for coding, but not for writing. This means you may use AI to generate and revise code without penalty.

You may not submit any AI-generated written work, including but not limited to written responses to questions, narrative text of your research project, or project reflections. This includes using AI to generate text that you then revise or edit.

Plagiarized written work, whether from human or AI creators, will receive a non-negotiable 0.

Artificial Intelligence

Use of AI is permitted in this course, with limitations.

LLMs have become ubiquitous in programming and data analysis. They are powerful tools that can help you learn, but they can also be used to cheat. It is up to you to use them responsibly, and to understand the difference between using AI productively and counter-productively.

Although I expect all the work you submit, including code, to be your own, I will not be checking code (and only code) for plagiarism. I strongly encourage you to treat LLMs as learning tools and to not fall into the trap of using them as content generators.

Use of AI for any written work is strictly prohibited. Specifically, you many not use LLMs to generate anything other than code. This includes but is not limited to written components of mini-projects and the narrative text of your research project (lit review, discussion, etc.). You will receive a 0 for any assignment that includes AI-generated text.

Course schedule

Refer to the site home page for calendar updates (including new and updated links, files, etc.) throughout the quarter.

Slides and other shared materials will typically be posted to the day’s schedule the morning of class and remain accessible through the end of the academic year.

Footnotes

1. You’ll hear me use “objectives-based” and “standards-based” interchangeably, though that’s not technically correct. The assessment overview has more explanation.