Milestone 3: Final Report

June 3, 2026

NoteMilestone Details

Due: Wednesday, June 3 before class (Session 18) Submit: Zip file containing .qmd, rendered .html, and dataset(s) on Canvas Points: 60

Overview

The final report is the culmination of your project. It’s a polished, reproducible Quarto document that imports your data, explores your research questions through visualizations, and tells a clear story about what you found. This is meant to be something you could show to a future employer, a graduate admissions committee, or a research advisor.

What to submit

Upload a zip file to Canvas containing:

  1. Your Quarto source file (.qmd)
  2. The rendered HTML output (.html)
  3. Your dataset file(s) (.csv, .xlsx, etc.)

Everything needed to reproduce your report should be in the zip. Your .qmd file must render cleanly from a fresh R session — I will re-render it to verify, so if I don’t have your data, I can’t confirm it works.

ImportantInclude your data!

If your .qmd calls read_csv("my_data.csv"), that file needs to be in your submission. The easiest approach: put your .qmd and data file(s) in the same folder, zip the whole folder, and upload it.

Report structure

Your report should include the following sections:

1. Introduction

  • What is your dataset? Where does it come from?
  • What research questions are you exploring?
  • Why are these questions interesting or important?

Length: 2-3 paragraphs.

2. Data description

  • How many observations and variables?
  • What are the key variables and what do they represent?
  • Describe any cleaning or tidying steps you performed (and why)
  • Include a summary table of descriptive statistics using knitr::kable()

3. Results

This is the core of your report. For each research question:

  • Present 1-2 polished visualizations that address the question
  • Write 1-2 paragraphs interpreting what the figure shows
  • Use inline code to report key numbers (means, counts, etc.) — never hard-code statistics

Your report should include 4-6 publication-ready figures total. Each figure should:

  • Use an appropriate geom for the data type
  • Have a clear, informative title (tells the reader what to see, not just what the axes are)
  • Have labeled axes with units where appropriate
  • Use a clean theme (theme_minimal(), theme_classic(), etc.)
  • Use a colorblind-friendly palette when color is meaningful
  • Include a caption (using #| fig-cap)

4. Discussion

  • What did you learn from your data?
  • Were there any surprises or unexpected findings?
  • What are the limitations of your analysis?
  • What would you do next if you had more time?

Length: 2-3 paragraphs.

5. Reflection

  • What was the most challenging part of this project?
  • What would you do differently if you started over?
  • What are you most proud of?
  • What skills from this course were most useful?

Length: 1-2 paragraphs.

Technical requirements

How this will be graded

Component Points
Introduction — clear, well-motivated 5
Data description — complete, includes summary table 5
Visualizations — 4-6 figures, polished, appropriate 20
Narrative — interpretation is clear, connected to questions 10
Discussion — thoughtful reflection on findings and limitations 5
Reflection — honest, thoughtful 5
Reproducibility — renders cleanly, inline code, relative paths 5
Code quality — organized, readable, well-commented 5
Total 60
TipWhat makes an A-level report?

An excellent report doesn’t just show data — it tells a story. The figures are chosen deliberately to answer specific questions, the narrative guides the reader through your findings, and the whole document feels like a coherent piece of work (not a collection of disconnected plots). Review the storytelling principles from Session 15 as you finalize your report.

NotePSY 510 (Graduate Students)

Students enrolled in PSY 510 must complete the following extension in addition to all requirements above.

Graduate Extension: Open Science Supplement

Publicly sharing data and code is increasingly required or encouraged by journals, funding agencies, and IRBs. This extension gives you practice writing the documentation that makes shared materials usable by someone who wasn’t there when you collected the data.

Task G.1: Write a README

Create a README.md file for your project. It should include:

  1. Project title and author
  2. Dataset description: What the data contain, who collected it, when, and from where — or a statement that it is synthetic or course data not for redistribution
  3. File inventory: A list of every file in your submission zip and what each one is
  4. Variable codebook: For each key variable in your dataset — variable name, plain-English description, units or response scale, and how missing values are coded
  5. Reproduction instructions: What someone would need to do to re-run your analysis from scratch — R version, packages used (with version numbers from sessionInfo()), and the order to run files
  6. License: A brief statement about whether and how others may use this work

Task G.2: Document your environment

Add a code chunk at the end of your .qmd that prints your session info:

sessionInfo()

This documents the exact R and package versions used — a basic but important step toward reproducibility that reviewers and collaborators will appreciate.

Submission: Include your README.md in the zip file alongside your .qmd, .html, and data. It should be readable as a plain text file without any special software.