Introduction to D2M-R

syllabus, workflow overview

2026-01-06

Course

From Data to Manuscript in R - Quarter 1

• PSYC/MAPS/MACS/CHDV 30550
• Course website: d2m-r.github.io
• Textbook: Data to Manuscript in R
  • In early development!!

People

Professor: Dr. Natalie Dowling

• Preferred name: Natalie or Dr. Dowling
• Pronouns: she/her
• Email: ndowling@uchicago.edu
• GitHub: @nrdowling
• Office hours:
  • 1155 E 60th St, Rm 181
  • Thursdays 2-4pm by appointment - book here
  • Email to set up an alternative meeting time

Teaching Assistant: Qilong Bi

• Preferred name: Qilong
• Pronouns: he/him
• Email: bql20@uchicago.edu
• GitHub: @QilongBi
• Office hours:
  • location TBA
  • Tuesdays 2-3:30pm (open)

I genuinely don’t care if you call me Natalie or Dr. Dowling. I’m perfectly happy with either, so do whatever feels comfortable for you.

Syllabus

Expectations & Course Goals

1. Develop a reproducible and transparent research workflow.
2. Practice accountability & time management.
3. Engage with your research communities.
4. Develop problem-solving and independent learning skills.

1. Develop a reproducible and transparent research workflow. Plan, execute, and document a data analysis project from start to finish including data acquisition, cleaning, analysis, visualization, and reporting.
2. Practice accountability & time management. Set realistic goals, create a plan to achieve them, monitor your progress, and adjust as needed.
3. Engage with your research communities. Collaborate effectively with peers, contribute to group learning, and seek help when needed.
4. Develop problem-solving and independent learning skills. Develop specific strategies for debugging code, troubleshooting errors, asking good questions, and learning new skills independently using the myriad resources available online from R and open-science communities.

D2M-R will give you:

• A basic knowledge of how to use GitHub, R, RStudio, Quarto/RMarkdown, and the tidyverse packages
• Strategies for how to identify and resolve bugs in the data-to-manuscript process
• Supports for establishing and maintaining a workflow within your project
• Tools to implement your own data analysis and visualization plans

D2M-R will not give you:

• Comprehensive expertise in any of the tools or packages or depth in packages outside the tidyverse
• Instructors or classmates who solve your technical problems for you
• Accountability hand-holding or objectively “correct” ways of working
• New knowledge of statistics or research design practices

Course Philosophy

This class is for you.

• Gain foundational skills that everything else is built on
• Apply those skills designed around your individual priorities, interests, and goals

Four key principles

• Standards-based grading
• Multiple pathways to demonstrate course objectives
• Tiered credit for mastery levels
• Individualized plans and pacing

This course uses a standards-based grading system designed to support your skill development while accommodating diverse backgrounds, interests, and paces of learning. The system is built around the following principles:

• Standards-based: Your grade reflects your progress on a set of clearly defined core technical skills, interchangeably referred to as “standards” and “core objectives.”
• Multiple pathways: For most standards, you may choose from more structured activities, less structured / group activities, and minimally structured or self-designed small projects. You will show your mastery in smaller assignments and an integrative data project, both of which can be tailored to your interests and developed over the quarter.
• Tiered credit: Each standard is scored using a 4 / 5 / 6 point rubric. Higher scores reflect more independent and expansive use of the skill and allow you to reach your grade goals more quickly. Lower grades may be replaced by subsequent higher grades as you develop stronger command of the skills. All three levels represent success, and earning mostly 4s does not preclude getting an A in the class.
• Individualized: Beyond mastering technical skills that form the foundation for data analysis in R, you will also earn points for accountability, community engagement, and optional enrichment activities, with many ways to earn credit in each of these categories. There are no top-down deadlines for completing assignments to meet core standards. This structure allows you to set your own priorities based on your individual interests and goals.
• No fixed interim deadlines: You may complete assignments on your own schedule within the quarter, with are some exceptions. There are fixed deadlines for creating and maintaining the schedule itself (the accountability plan), and there are several hard deadlines based around course logistics (e.g., end of quarter submissions, allowances for timely feedback).
• Planning is part of the grade: The Accountability & Reflection component ensures that you set a realistic plan and revisit it as your research and other commitments evolve.
• Early completion is allowed: You can, in principle, earn all 80 points for core standards mastery and the integrative data project and another 10 points for enrichment early in the quarter. However, you must still maintain an active accountability plan and community engagement throughout the quarter.

D2M-R Workflow

1. Create and sync a GitHub repo and R project
2. Begin a full manuscript in a Quarto notebook (.qmd)
3. Use R scripts and chunks to read in, clean, and wrangle data
4. Source .R scripts for “background” needs
5. Analyze and visualize your data within your Quarto notebook
6. Maintain a BibTeX file with all manuscript citations
7. Compose the manuscript narrative in your notebook
8. 1-click knit your manuscript to a publishable file format
9. Dynamically update and maintain your manuscript

1. Create a GitHub repo and corresponding R Project that are always synced
2. Create an Quarto notebook (.qmd) that will become your full, complete, and final manuscript
3. Use .R scripts to wrangle and export data into intermediate datasets that you can then quickly read-in to your manuscript in a code chunk
4. Create and source .R scripts for “background” needs like functions and style
5. Analyze and visualize your data in code chunks within your Quarto notebook
6. Create and maintain a .bib file with all the material you may cite in your manuscript using BibTeX and R citation syntax
7. Write the narrative of your manuscript in your single, dedicated notebook file
8. Knit your manuscript to a publishable file format using pre-defined formatting and citation style
9. Dynamically update and maintain your manuscript

Quarter 1

1. Create and sync a GitHub repo and R project
2. Begin a full manuscript in a Quarto notebook (.qmd)
3. Use R scripts and chunks to read in, clean, and wrangle data
4. Source .R scripts for “background” needs
5. First steps: Analyze and visualize your data within your Quarto notebook
6. Maintain a BibTeX file with all manuscript citations
7. Compose the manuscript narrative in your notebook
8. 1-click knit your manuscript to a publishable file format
9. Dynamically update and maintain your manuscript

Course Policies: Overview

Review the syllabus on the course site for full policies!

Communication: Slack

• Slack participation is required
  • Your first stop for support
• Use the appropriate channels
• Intended for peer collaboration
  • Minimal instructor involvement
  • Peer support is part of your grade
• Direct messages:
  • Do not direct message instructors (including TAs).
  • DMing with other students is permitted but not required

• Slack participation is required
• Link to join our Slack channel is on Canvas
• THE place outside of class to get help with nearly anything
• Use the appropriate channel to request help
  • Take your best guess if you’re not sure where it goes
• Intended for peer collaboration
  • Instructors will moderate and answer questions when needed, after students have responded
  • Collaborative engagement is part of your grade; you are expected to both seek and offer support to your classmates
• You can DM and/or start private channels with classmates, but you are not required/expected to participate in private messaging
• Do not direct message instructors (including TAs).
  • Email us.
  • We will not respond to Slack DMs.

Communication: Email vs. Slack

Email

• Private communication with professor/TAs
• Topics that concern you and only you
• Accommodation requests

Slack

• Class-wide communication with instructors and students
• Topics that may concern others, e.g.:
  • Questions about course content
  • Clarifications about grading policies
  • Requests for support

Email:

• Private communication with professors or TAs
• Use for personal matters, scheduling, or other private issues
  • Topics that concern you and only you
• Requests for accommodations

Slack:

• Class-wide communication with instructors and students

• Topics that may concern others, e.g.:

  • Questions about course content
  • Clarifications about course policies
  • Requests for troubleshooting and debugging support
  • Really anything that might help another student if they saw it!

• Optionally: Private communication with other students

• Dr. Dowling’s office hours are Thursdays 2-4pm by appointment

  • Appointments are required

• TA office hours information

• Call me either Natalie or Dr/Professor Dowling

• You do not need to let us know if you will be missing class

  • But you can reach out if you feel behind due to absences

• Be cautious about disclosing personal information over email/Slack

  • Remember you never need to share details about your health!

• Stressed about emails? The student toolkit has a guide to help you out

  • Side note: We can tell when you use chatGPT. It really doesn’t look good. We’d rather get a kind of weird email from you than one of those “I kindly request” emails.

• Please do not line up to ask questions at the end of class.

  • Those “1-minute” questions add up
  • That’s what Slack is for

Climate

• View the class climate policy here.
• Basically, just be cool, ok?
• If you have problems with other students or the TA:
  • Contact me first
  • Escalate to UChicago CARES
• If you have a problem with me:
  • Discuss it with me directly if you feel comfortable doing so
  • Bring it to UChicago CARES
• You can always go to CARES without going through me or anyone else first.

• View the class climate policy here.
• But really just…be cool, ok?
• We will all be kind, respectful, responsive, and supportive to one another in the classroom, on Slack, and all other interaction.
• If you have problems with other students or the TA:
  • Contact me first (if possible)
  • If we can’t resolve it contact UChicago CARES
• If you have a problem with me
  • Discuss it with me directly if and only if you feel comfortable doing so
    • I never want to be the cause of discomfort for anyone! If you have feedback that can help me be a better instructor, I want to hear it.
  • Bring it to UChicago CARES
• You can always go to CARES without going through anyone else first, but my goal is to create a classroom climate where you feel comfortable talking to me about ways to create a more welcoming and respectful environment.

Accessibility & Accommodation

• Requests for formal accommodations should go through Student Disability Services
  • Graduate students need to request accommodation letters be sent to me
  • You never need to disclose personal details to me or anyone outside SDS
• Meet with me in office hours for informal accommodation requests
• You will factor your own accommodations into your accountability plan

Note: All arrangements must align with SDS policies for equity. Informal accommodations cannot disadvantage students with formal accommodations.

• Requests for formal accommodations should go through Student Disability Services
  • SDS will send undergraduate accommodation letters to me automatically
  • Graduate students need to ask SDS to send me a letter
  • You never need to disclose personal details to me or anyone outside SDS
• If you have an informal request for an accommodation, with me in office hours
  • We can almost always work something out, but we need to follow SDS policies
• After sending your letter, schedule a meeting with me to make an accommodation plan
  • If you do need accommodations, you will factor your accommodation plan into your accountability plan (coming up in a couple slides)

Disclosing personal information

You are never required to disclose personal information to me or any instructors. These guidelines are for all classes at UChicago, not just this one.

You do not need to:

• Share details about your health, disability, or reasoning for accommodations
• Get a doctor’s note for absences
  • Your instructor is not allowed to ask for one!
• Defend your need for accommodations

You are an adult with priorities and the capacity for cost/benefit decision-making. You can decide what you want to share and with whom. If you need to miss class because you have terrible period cramps, are having a panic attack, are recovering from a migraine, think your roommate might have COVID, need to cram for an exam for a different class, are shamefully hung over, or just really don’t wanna come to campus, you do not need to explain yourself to me or anyone else. Just take care of yourself, and take responsibility for effects on your learning and grades.

This doesn’t mean there are never consequences for missing class. You are responsible for keeping up with the material and assignments (per your accommodations, if applicable), and are subject to penalties defined by the syllabus.

Academic Integrity: Overview

• View the academic integrity policy here.
• Plagiarism is bad. Cheating is bad. Don’t do it.
  • Why are you even here?
• All written/textual content in your submitted assignments must be fully produced by you
• Code may be produced with AI (or human) assistance with a full disclosure statement
• Disclosure statements are required for all submitted work

Academic Integrity: Grading Penalties

You will not be penalized for (with disclosure):

• Using AI to help you with code
• Using code snippets from print or online resources
• Co-writing code with classmates/other humans
• Unintentionally crossing the line with AI if it is fully disclosed
  • I will ask to you revise and resubmit without penalty

You will be penalized for:

• Failing to disclose any of the above
• Using AI to generate text for ANY submitted work
• Using text written by any human other than you, including classmates
  • Exceptions for some group projects may be granted, but must be requested in advance

Submissions that do not follow these guidelines will receive a non-negotiable 0.

Academic Integrity: You & AI

The AIlephant in the room. This is DALL-E’s best attempt at “An elephant with the letters A and I on its ears.” Not very impressive, but I can’t blame it too much. It wasn’t a good pun to begin with.

To see GPT’s coding skills in action, watch DougDoug force it to create Snake 2 (with mixed success).

You should only be taking this class if you want to gain practical skills that will be useful to you outside of the class.

If you don’t care enough to actually learn the material yourself, this probably isn’t the right class for you.

My personal AI philosophy & Official Class Policy

You should only be taking this class if you want to gain practical skills that will be useful to you outside of the class.

Hopefully you are using the class to make progress on a research project you are invested in. If you do not use AI wisely, you may not be “cheating” in a way that will hurt your grade, but you will be cheating yourself out of the reason you joined the class. If you don’t care enough to actually learn the material yourself, this probably isn’t the right class for you.

You can read my guide for what I personally believe are ethical and constructive uses of AI in higher education.

Assessment & Grading

Grading Overview

Earn up to 100 points across five categories:

Points	Category	Description
10	Accountability	Create and maintain a personalized accountability plan
10	Community Engagement	Participate actively in class and on Slack
60	Core Standards Mastery	Demonstrate proficiency on 12 core technical skills (standards)
20	Integrative Data Project	Complete a comprehensive data analysis project demonstrating mastery of all core standards
((10))	Enrichment	Optional activities to deepen learning and supplement core work

Because enrichment points are optional, it is possible to earn more than 100 raw points across categories. Final course grades are capped at 100.

You start at 0 points and work up to 100. You don’t start at 100 and lose points. Meeting minimum requirements means you will pass the class. Earning an A takes more than that.

Accountability Plan (10pt)

You create an assignment schedule for yourself.

• Defining your own goals is part of the research workflow and an explicit learning objective and part of your grade
• SDS approved accommodations for extensions are built into your plan
• You will create a personal timeline and accountability plan for the quarter

Accountability is 10% of your final grade.

• Meeting the 3 checkpoint deadlines for the plan itself (meta-deadlines); Mondays of Weeks 2, 5, and 8
• Meeting the deadlines you set for yourself
• Reflecting on your progress and making adjustments
• Suggesting a grade (of 10)

• No fixed interim deadlines: You may complete assignments on your own schedule within the quarter, with are some exceptions. There are fixed deadlines for creating and maintaining the schedule itself (the accountability plan), and there are several hard deadlines based around course logistics (e.g., end of quarter submissions, allowances for timely feedback).

• Planning is part of the grade: The Accountability & Reflection component ensures that you set a realistic plan and revisit it as your research and other commitments evolve.

• Early completion is allowed: You can, in principle, earn all 80 points for core standards mastery and the integrative data project and another 10 points for enrichment early in the quarter. However, you must still maintain an active accountability plan and community engagement throughout the quarter.

• You create an assignment schedule for yourself. With very few exceptions, there are no top-down deadlines in this class. Why?

  • This class is about developing and maintaining a research workflow
  • A major part of that is learning how to set and meet your own deadlines
  • Making your own plan and sticking to it is part of the syllabus
    • This means that “reasonable accommodations” must not prevent you from reaching this objective

• You will create a personal timeline and accountability plan for the quarter

  • Initial plans are due by Monday of Week 2
  • Revised plans and reflections are due by Monday of Week 5
  • Final reflections are due Monday of Week 8

• You can base your plan on the suggested timeline in the assignment instructions

  • Use the suggested plan without changes (great option of the idea of designing your own schedule feels overwhelming)
  • Start with this plan and make adjustments
  • Start from scratch!

• If your SDS accommodations include extended deadlines, you will factor those into the plan

  • i.e. We are using this plan to agree on extensions now. I will not grant extensions to the planned extensions as we go.

• 10% of your final grade is based on your accountability plan, including:

  • Meeting the actual deadlines that I have set for the plan itself
  • Meeting the deadlines you set for yourself
  • Reflecting on your progress and making adjustments to your plan
  • Suggesting a grade (of 10) for how well you think you followed your plan

Community Engagement (10pt)

Attending class and paying attention are basic expectations. Your engagement grade is based on how you show investment in the course and your peers, both in and out of class.

• In-class:
  • Ask and answer questions during lecture
  • Use workshop time wisely and generously
  • Participate in group activities
• Outside the classroom:
  • Be an active participant on Slack
  • Seek support from peers and instructors when needed
  • Collaborate with peers
  • Complete collaborative assignments

Aside from the basic expectation that you’ll attend class and pay attention, this can look different for everyone. The community engagement grade is not about meeting the minimum requirement (e.g., attending class!). It’s about demonstrating a high level of investment in the course and your peers. This is not an attendance grade.

Here’s a non-exhaustive list of things that can contribute to the community engagement grade:

1. In-classroom:
   • Make active contributions in a large group setting by asking/answering questions during lecture
   • Use workshop time wisely and generously, seeking and offering support from/to classmates with debugging and troubleshooting
   • Participating in group activities
   • Generally paying attention and staying focused during class time
2. Outside the classroom:
   • Be an active participant on the class Slack channel, both asking and answering questions
   • Get support from classmates, TAs, and the professor
   • Team up with a partner or small group to meet for working groups outside of class
   • Take on one or more of the optional collaborative assignments in the assessment menu

Core Standards Mastery (60pt)

Demonstrate proficiency on 12 core technical skills (standards) selecting from a project menu.

• Structured assignments that guide you through skills step-by-step
• Semi-structured assignments that provide a clear framework with room for creativity
• Unstructured assignments that give suggested approaches to meet objectives
• Group assignments
• “Off-the-menu” Self-designed small projects

Core Standards Mastery (60pt)

The 12 core skills for the first quarter are:

1. RStudio + Quarto workflow
2. GitHub repositories and version control
3. Base R syntax and data structures
4. Control flow (if/else, loops)
5. Defining functions in Base R
6. Importing data with tidyverse tools
7. Data manipulation with dplyr and pipelines
8. Tidy data structure
9. Reshaping data with tidyr
10. Character strings with stringr
11. Factors with forcats
12. Basic reporting: plots and descriptives in Quarto

You earn points by demonstrating each of 12 specific learning objectives

1. RStudio + Quarto workflow
   • Create an RStudio project for course work, use relative paths, and knit simple Quarto documents that combine code and narrative.
   • Additional guidelines for RStudio/Quarto Workflow objective
2. GitHub repositories and version control
   • Create and maintain a GitHub repo with README, .gitignore, and a commit history that reflects real work over time.
   • Additional guidelines for GitHub/Version Control objective
3. Base R syntax and data structures
   • Create, index, and manipulate vectors, lists, and data frames, including handling missing data.
4. Control flow (if/else, loops)
   • Use if/else and loops to branch logic and automate repetitive tasks in multiple contexts.
5. Defining functions in Base R
   • Define and use functions with arguments and return values with reusable code logic in multiple contexts.
6. Importing data with tidyverse tools
   • Import datasets with readr and related packages, handling paths and basic options.
7. Data manipulation with dplyr and pipelines
   • Use multiple dplyr verbs (filter, select, mutate, summarize, arrange, group_by) in chained pipelines.
8. Tidy data structure
   • Diagnose untidy data and reshape to a tidy format, with explanation.
9. Reshaping data with tidyr
   • Use multiple tidyr functions (pivot_longer/pivot_wider/separate/unite) to reshape data.
10. Character strings with stringr
    • Use stringr functions (e.g., str_detect, str_replace, str_extract) to solve text cleaning or extraction tasks.
11. Factors with forcats
    • Recode, reorder, and/or lump factor levels to prepare categorical variables for analysis or plotting.
12. Basic reporting: plots and descriptives in Quarto
    • Create simple ggplot2 plots and compute/report basic descriptive statistics (means, medians, counts, proportions) in a Quarto document with clear captions and narrative.

Core Standards Mastery (60pt)

Each standard is scored on a 4 / 5 / 6 point scale based on the quality and independence of your demonstration of the skill.

Points	Label	Description
No grade	No demonstration	You have not yet demonstrated this skill.
4	Meets Expectations	You demonstrate the skill correctly in structured or familiar contexts.
5	Exceeds Expectations	You apply the skill correctly and independently in a novel context.
6	Outstanding	You use the skill in an especially expansive way.

Each standard is scored on a 4 / 5 / 6 point scale based on the quality and independence of your demonstration of the skill.

Points	Label	Description
No grade	No demonstration	You have not yet demonstrated this skill.
4	Meets Expectations	You demonstrate the skill correctly in structured or familiar contexts (for example, following a guided assignment or closely modeled example).
5	Exceeds Expectations	You apply the skill correctly and independently in a novel context, with clear, well-chosen code that would generalize to new datasets or problems.
6	Outstanding	You use the skill in an especially expansive way: combining it with other skills, applying
it to a complex or less-structured task, or going beyond the scope of in-class examples.

All three levels (4, 5, 6) are successful demonstrations. If you do not demonstrate a skill at least at the 4-point level, you will not earn credit for that standard.

Earning an average of 5 points per standard will earn you all 60 points. An average lower than 5 points can still result in an ‘A’ grade for the class if you complete enrichment activities.

Each assignment will receive a score for each standard it addresses based on this rubric. Individual assignments may not be resubmitted for a higher score, but you may improve your score for a standard by submitting new assignments that demonstrate that standard at a higher level.

Higher scores always replace lower scores for core standards, so after earning a high score with an impressive assignment, subsequent assignments that use the skill at a more basic level won’t lower your score.

Access and Complete Core Projects

1. Visit the menu of core standards assignments here.
2. Select assignments that address the standards you want to demonstrate.
3. Use the links on the menu to create a repo within the D2M-R GitHub organization by either:
   1. Accepting the GitHub classroom invitation (if available)
   2. Creating a private repo from scratch and adding in the associated files from the student hub repo
4. Complete the assignment according to the instructions in the assignment’s README file or Quarto notebook.
5. Complete the corresponding submission document (student-hub/templates)
6. Finalize your assignment with a GitHub pull request.
7. Submit to the grading queue with the “Submit” form on the website.

There are extremely detailed instructions for how do do each step of this process in the student-hub repo, on each submission document, and on the website. Start here.

We’ll do a demo of what this looks like later.

Integrative Data Project (20pt)

The final project is a data analysis report created with RStudio and maintained in a GitHub repo that includes:

• Data import
• Data wrangling/cleaning
• Basic descriptive statistics
• Simple data visualization
• Narrative markdown text explaining your choices and findings

Integrative Data Project (20pt)

Earn up to 20 points on the data project:

Points	Component	Description
12	Core standards	Demonstrate all 12 core standards at 4pt-level or above
2	Reproducibility	Code is fully reproducible, with no errors
2	Documentation	Code is clearly commented with explanatory Quarto narrative text
2	Cohesion	Data wrangling, statistics, and plots are clearly connected with narrative text
2	Presentation	Quarto document is well-formatted and knits without error

Submit your data project up to 3 times to get early feedback and/or complete the requirement early.

“Above and beyond” demonstrations of core standards or exceptional quality may receive bonus points at the grader’s discretion, but the project cannot contribute more than 20 points to the final grade. In other words, outstanding use of one standard might earn you a point that compensates for weaker presentation or documentation, but not a point of extra credit.

You may submit the data project up to 3 times throughout the quarter. With each draft, you will also include a self-assessment, indicating which standards you believe you have demonstrated and how well you have met each of the grading components above. After scoring your work, your grader will provide feedback as a response to your self-assessment to help you improve your project for future submissions.

You are allowed – and encouraged! – to submit partial drafts of your project to confirm that you are on the right track before submitting a full draft. You aren’t expected to be able to demonstrate a skill before we cover it in class, so early submissions may only demonstrate a subset of the standards. That’s fine! Submitting a draft that shows only a few objectives will let you confirm that you are in fact meeting expectations for those objectives and will give you guidance on where to go next.

Grades for resubmissions will replace previous scores. Your grade for the data project will be the score earned on your last submission before the end-of-quarter deadline.

Enrichment (up to 10pt)

10 optional points: customize your curriculum to your own interests

You can earn 100 points without any enrichment points.

Examples include:

• Debugging journals that document how you worked through specific problems
• Short write‑ups teaching a peer how to do something (e.g., connecting RStudio and GitHub)
• Reflections on responsible and productive use of AI tools in your workflow
• Evidence of sustained collaboration on GitHub (e.g., issues, pull requests, code review)
• Contributions to open‑source R packages or communities (e.g., GitHub issues, Stack Overflow answers)

Where do I find them?

For the most part it’s up to you to design your own enrichment activities. Figuring out for yourself what would be most useful for your own learning is part of the point of this category. If you are totally lost as to where to start, there are a few unstructured activities available in the student-hub repo.

Enrichment activities are not part of the base structure of the course grade. Instead, they give you a way to recover or add points if you choose a more structured path or if some standards land at the 4‑point level.

This is effectively up to 10 points of extra credit, but I don’t like thinking about these skills or these kinds of experiences as “extra.” I believe this category is as valuable as the core skills, but necessarily fuzzier and more open-ended. These 10 optional points are a way for you to customize your curriculum in this class to your own interests and goals.

Examples include:

• Debugging journals that document how you worked through specific problems
• Short write‑ups teaching a peer how to do something (e.g., connecting RStudio and GitHub)
• Simple documentation for your own utility functions
• Reflections on responsible and productive use of AI tools in your workflow
• Evidence of sustained collaboration on GitHub (e.g., issues, pull requests, code review)
• Short explorations of advanced R packages or techniques beyond the core curriculum
• Identifying a need in your own research workflow and designing a solution using R
• Discovering and effectively making use of specialized R packages for your specific needs
• Mini‑projects that apply course skills to your own research questions (must be different from your final data project)
• Contributions to open‑source R packages or communities (e.g., GitHub issues, Stack Overflow answers)

This is not a comprehensive list, and really the more creative and customized you approach this, the more useful it will be for your own learning (and the easier it will be to earn points). If you have an idea for an enrichment activity that isn’t listed here, send an email to Dr. Dowling to confirm that it is appropriate before you start working on it.

You may submit evidence for enrichment points up to 5 times during the quarter. Evidence may be in the form of a dedicated project (e.g., a debugging journal) or as a companion to another assignment (e.g., writing up how you used AI tools to help solve a problem in a core standards assignment). Points will be awarded based on overall quality and depth, which may look like creativity, independent learning, clear communication, thoughtful self-evaluation, or other dimensions depending on the nature of the activity. Your submission will include an opportunity for you to explain how the activity contributed to your learning and how many points you believe you have earned.

LET’S DO SOME STUFF!

Get Yourself Sorted

Use your common sense and the many resources on the course website to get the following done:

1. Make your GitHub account, apply for the education account.
2. Install R and RStudio.
3. Connect GitHub and RStudio using the guide on the course site.

Example Assignment

In the meantime, while you figure yourself out, I’m going to demo the workflow of an example assignment.

Follow along if you’re ready to go, otherwise just watch me and do it on your own later.

1. Go to the assignment menu and click the GitHub classroom invite for the example assignment.
2. Accept the assignment and create your GitHub repo.
3. Clone the repo to your computer using RStudio.
4. Open the example R script in RStudio and follow the instructions to complete the assignment.
   • Even if you don’t have the github/RStudio thing sorted yet, you should open RStudio at this point and try out the interface.
5. Submit the assignment for (fake) grading with a pull request and the class submission form.

If you’re not sure how to do something, figure it out!

That’s the point. Practice finding resources and solving problems on your own. If you get stuck, ask for help on Slack. Thursday we’ll talk about specific problem-solving and debugging strategies.