Assignments

Below are basic descriptions of each category of assignments for this semester. More details are available on the individual pages for each assignment. Please be sure to check the due dates on each assignment and come to class on those days ready to discuss your work in small groups.

Homework

The homework assignments are almost entirely empirical, with each assignment focusing on a specific identification strategy, research question, and health-related dataset. I will provide all answers in both R and Python.

Please submit all of your homework answers as a PDF via GradeScope (accessed from Canvas). Your PDF must also include a hyperlink to a GitHub repository with all of your code files. If you keep the repository private, please be sure to share the repository with me using my ian.mccarthy@emory.edu email address. I strongly encourage you to use RMarkdown, Quarto, or Jupyter Notebooks, which will help you to easily create PDFs and is much better for writing data-oriented documents. If you are not familiar with these tools, take a look at the following resources:

If you’re new to these tools, then there will be some growing pains. Please be patient with yourself and stick to it. One of the goals of the course is to develop good workflow habits. This means doing work in a way that minimizes mistakes and maximizes reproducibility. These tools exist exactly for these reasons. It allows you to keep your data analysis and the description of that analysis together in a single document or group of documents. It’s really easy to introduce mistakes when copying and pasting empirical results into a text document, and it’s even easier to waste hours (days/weeks?) re-pasting results into your text document and re-writing the results accordingly. Doing everything in R Markdown, Quarto, or Jupyter Notebooks avoids all of these issues. In the long run, it’s much more efficient.

Note that your PDF submission should not include code. It should be a finalized, clean, visually engaging summary of your work. The code belongs in the GitHub repository.

Homeworks 2-5 are worth 30 points each toward your final grade (15% each) and consists of 10 individual questions. Each question is worth 3 points, graded as follows:

  • 1 point for an attempted answer as of the initial due date
  • 1 point for a nearly-correct answer as of the revision date
  • 1 point for a correct final answer as of the final due date

Due to the staged structure of each assignment, you should submit three PDFs for each assignment, and your repository should similarly be organized with three subfolders for each submission. To make it easy to navigate everyone’s repositories, we need everyone to adopt the same directory structure and naming convention. Please be sure to name your solution documents as follows: lastname-firstinitial-hwk(num)-(submission num). For example, if I’m finalizing the initial submission for homework 1, then the file should be named mccarthy-i-hwk1-1.pdf. A complete directory structure is provided below. Please adopt this structure as it will also help you to keep your analysis and data organized. Finally, please note that you should not include the “data” folder in git or github. The data files are too big and should never be tracked in that way. Instead, please be sure to add the data folder to your gitignore file.

homework2
|   README.md
|
|---data (not tracked in git or pushed to github)
|   |   input (symbolic links)
|   |   output (analytic data sets)
|
|---submission1
|   |---data-code
|   |---analysis
|   |---results
|   |   mccarthy-i-hwk2-1.pdf
|
|---submission2
|   |---data-code
|   |---analysis
|   |---results
|   |   mccarthy-i-hwk2-2.pdf
|
|---submission3
|   |---data-code
|   |---analysis
|   |---results
|   |   mccarthy-i-hwk2-3.pdf
|

Homework 1 is more of a foundational assignment, designed to get us all up and running with VS Code, Git, GitHub, and some initial data summaries. Homework 1 is also worth 30 points but only involves 2 submissions. Details are on the respective Homework 1 page.

Code Review

One of the best ways to improve your own coding is to see other people’s code and learn from them. For each of the homework assignments, we’ll do this through a peer code review and replication. Please see the Peer Review for more details.