Core Standards & Learning Objectives

Quarter 1 Core Standards

The 12 Quarter‑1 core learning objectives are all technical and observable. For each one ask: Is there clear, identifiable code or output that shows this skill?

The objectives are listed (more or less) in the order they are covered in class, but you can demonstrate them in any order.

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.

Tiered Scoring

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.