3.0.1

Replace hard-coded values with inline codes

Milestone 4

1. Write a new script, a Makefile (literally called Makefile), to act as a driver script to rule them all. This script should run the others in sequence, hard coding in the appropriate arguments.

2. Abstract some code from your scripts to functions in a separate file, and write tests for those functions.

3. Address any feedback received in earlier milestones and from the peer review to improve the project.

Milestone 3

1. Abstract code from your report literate code document (*.ipynb, *.qmd or *.Rmd) to scripts (e.g., .R or .py). This code need not be converted to a function, but can simply be files that call the functions needed to run your analysis. You should aim to split the analysis code into 4, or more, R or Python scripts. Where the code in each script is contributing to a related step in your analysis. Your scripts should take command line arguments. You must document in your README.md how to call each script, including what arguments you pass each script.

2. If your report literate code document is not already a *.qmd file, convert it so that it is.

3. Edit your report literate code document (*.qmd file) so that it’s sole job is to narrate your analysis, display your analysis artifacts (i.e., figures and tables), and nicely format the report. The goal is that non-data scientists would not be able to tell that code was used to perform your analysis or format your report (i.e., no code should be visible in the rendered report).

4. Update your project’s computational environment as you add dependencies to your project

1.0.1

• Updated index.html

1.0.0

From the Milestone2 instructions, this release includes:

• Setup Dockerfile
• Update README.md on how to use/update the container image
• Data Validation

0.0.1

From the Milestone instructions, this release includes:

• Draft a team work contract (See Gradescope)
• Set-up a public GitHub repository
• Create an appropriate file and directory structure for a data analysis project
• Add the data analysis as a literate code document (Jupyter or Quarto)
• Ensure the computation environment reproducible through a virtual environment (e.g., conda or renv) - hint do this as you create the analysis, don’t wait until the end!