Project 1

Due: September 28

Published

October 19, 2026

In your first project you are to use tools learned this far to explore a real dataset.

Datasets

Use one of these datasets below:

  1. American Time Use Survey

    1. From the U.S. Bureau of Labor Statistics
    2. Intro - link
    3. Sample Exercises - link

  2. IPUMS

    1. IPUMS (“Integrated Public Use Microdata Series”) is a website and database providing access to over sixty integrated, high-precision samples of the American population drawn from sixteen federal censuses, from the American Community Surveys of 2000-present.

    These samples collectively constitute our richest source of quantitative information on long-term changes in the American population.

    1. Starter Code - link

Tips

  1. First create an R-script to do your exploratory work.
  2. Comment well.
  3. After you have some good plots and/or statistics, copy your work into a .qmd file.
  4. Remember, your markdown environment is different than your console, so you will have to load the data/tidyverse again.

Project Structure & Requirements

Your rendered report must follow this standard outline:

  1. Introduction & Motivation: State your research question clearly. Explain why this question is interesting or practically relevant.
  2. Data Preparation & Sample Definition:
    • Document all filtering decisions (e.g., restricting age to prime working years \(25 \le \text{AGE} \le 64\); omitting non-earners or top-coded records).
    • Present a clear “Data Cleaning Pipeline” detailing how missing codes were converted and how categorical levels were collapsed.
  3. Exploratory Data Analysis (EDA):
    • Produce at least three ggplot2 figures and one summary statistics table.
    • Every figure must feature descriptive labels, clean axis scales, and an informative caption. Discuss the distribution, skewness, and visible patterns in text, phrased as findings about this sample (not the U.S. population).
  4. Discussion, Limitations & Conclusion:
    • Summarize the primary descriptive takeaway from your sample.
    • Explicitly note that these results are unweighted and therefore should not be generalized to the U.S. population — restate briefly why (see the caveat section above) and what would be needed to make a population-level claim (i.e., incorporating PERWT via a survey design).
    • Address at least one additional limitation of this analysis (e.g., missing data, top-coding, self-selection into categories like remote work).
  5. Present your project to class: 3-5 minutes (max!)
  6. Use
    1. ggplot() with good labels & legends
    2. filter()
    3. select()
    4. facet_wrap() or facet_grid()
    5. rename()
    6. group_by() & summarize()
  7. Include the following plots
    1. barplot
    2. boxplot
    3. scatterplot
  8. Include a brief summary and conclusion.
  9. Include a brief discussion each of your plots. Two-three sentences should suffice.

Grading Rubric (100 Points Total)

Category Points Exemplary (Full Marks) Developing (Partial Marks) Needs Revision (Low Marks)
Research Question & Framing 15 Question is well-scoped, clearly stated, and motivated in the introduction. Question is stated but overly broad, vague, or lacks context. No clear research question formulated.
Data Cleaning & Recoding 20 IPUMS missing value codes (9999999, 0) are cleanly addressed; factor variables (e.g., DEGFIELD) are appropriately collapsed; sample restrictions are explicitly justified; code runs without warnings. Some special codes ignored or misclassified; sample restrictions are arbitrary or poorly documented. Data used raw without handling missing value codes, yielding corrupted statistics or nonsensical values.
Exploratory Visualizations & Tables 25 Visualizations use effective geometries, clear labels, and direct captions. Text critically evaluates what the plots reveal about the sample. Visualizations are present but lack polished formatting (raw variable names on axes, unreadable text, inappropriate plot choices). Visualizations are missing, unreadable, or not discussed in the narrative.
Summary Statistics & Interpretation 20 Summary statistics (means, medians, proportions, group comparisons) are computed correctly and interpreted clearly and correctly in plain language. Statistics are computed but interpretation is vague, uses incorrect units, or overstates what the numbers show. Statistics are missing, incorrect, or presented without any interpretation.
Critical Discussion & Limitations 10 Clearly explains that results are unweighted/sample-specific and do not generalize; reflects thoughtfully on at least one other limitation. Mentions the unweighted caveat only briefly or in boilerplate fashion; limited reflection on other limitations. Omits the unweighted caveat, or makes sweeping population-level claims from unweighted sample statistics.
Code Quality & Reproducibility 10 Quarto file renders cleanly from scratch; code follows tidy style guidelines with clear comments; packages loaded upfront; no hardcoded absolute paths. Renders with warnings or minor formatting bugs; code is cluttered, poorly commented, or uses redundant operations. Fails to compile or knit; relies on hardcoded local directory paths that prevent independent reproduction.