Project Rubric

For each category, there will be a grade of Excellent, Passing, or Needs Improvement.

Category: Thoughtful engagement with data context

Overview of this category:

• Careful consideration of the who, what, when, where, why, and how of your datasets and how that affects results and interpretation
• Careful consideration of how the data context relates to ethical considerations of how you investigate your data and how your results should be used/interpreted. It will help to think about the following:
  • Who is affected by the project’s data acquisition and results?
  • What (mis)interpretations or actions might result from the conduct of your investigations or your conclusions?
  • How might negative consequences be mitigated?

Excellent work will:

• Thoughtfully consider the items above by drawing on team members’ lived experiences AND perspectives from at least 2 other sources (e.g., news articles, research articles, blog posts, press releases, documentation from organizations affiliated with your data). These sources should be referenced in your final digital artifact.

Passing work will:

• Thoughtfully consider the items above by drawing on team members’ lived experiences

Needs Improvement work will:

• Attempt to consider the items above but need more thought

Category: Effective data storytelling

The final digital artifact should:

• Motivate the importance of the topic
• Lead the reader through the rationale for the narrowing/focusing of the scope via the main 2-3 broad questions
• Tie results (plots and modeling output) to the broad questions and explain how all results fit together
• Use sound data visualization principles to most effectively convey meaning
• End with main takeaways, limitations, and future directions
• Use clear and concise communication throughout

Excellent work will:

• Meet all above the above quality expectations

Passing work will:

• Meet most above the above quality expectations

Needs Improvement work will:

• Meet some above the above quality expectations

Category: Code quality and documentation

Overview of this category:

• Code duplication is minimal to nonexistent with the use of functions and iteration
• Use comments appropriately to document what is happening in different parts of code
• Text space before and after code chunks is used for longer form (paragraph) documentation

Excellent work will:

• Use functions in all instances where code would have been copied and pasted twice
• Use functions and iteration in all instances where code would have been copied and pasted 3 or more times
• Consistently use (but not overuse) comments within R code to document code in a way that allows your future selves to remember what was going on. (Not every line of code needs to have a comment, but groups of lines that achieve a particular goal should have a comment.)
• The text space before and after code chunks explains what is happening in each code chunk (e.g., why particular wrangling steps were performed, the motivation for fitting certain models, why different modeling outputs were extracted).

Passing work will:

• Often use functions and iteration to reduce code duplication but have a small number of missed opportunities
• Often use code comments and text space before and after code chunks for documentation but have a small number of missed opportunities

Needs Improvement work will:

• Sometimes use functions and iteration to reduce code duplication but have several missed opportunities
• Sometimes use code comments and text space before and after code chunks for documentation but have several missed opportunities

Category: File organization and version control

Overview of this category:

• Separate files for cleaning, plotting, and modeling
• Clean datasets are saved at the end of code files devoted to cleaning and loaded in at the start of code files devoted to plotting and modeling
• As seen through GitHub commits and file diffs, each team member should contribute roughly equally to the codebase. Note that contributions include both writing new lines of code AND modifying/deleting code (e.g., perhaps to reduce code duplication).
  • Note: I am not going to count the number of lines added/modified by each team member. Rather, I will look at the GitHub commits and file diffs holistically.

Excellent work will:

• Meet all above the above quality expectations

Passing work will:

• Generally meet the above quality expectations but will require more separation of tasks across files or more use of saving clean datasets

Needs Improvement work will:

• Sometimes meet the above quality expectations but will require more separation of tasks across files, more use of saving clean datasets, and more even contributions from team members to the codebase

Collaboration and GitHub

You set up your directory structure to organize your data separately from your code. With this setup, it is useful for each team member to have their own .qmd file for their own share of the explorations.

• In this way when you commit, push, and pull, you won’t run into merge conflicts from overwriting contents of each other’s files.
• Coordinating what explorations each team member is doing becomes the main topic to coordinate.

You’ll be expected to create a Readme.md file that includes the project goals, data sources, a brief description of the project, and an overview of the structure of the files in the repository.