Your audience is the user, members of the general public who may want to use your data and/or code. To be useful, your code and documentation must be clear to naive users (people familiar with R, but not with this project).
Elements:
Your github repo, organized following the project template.
Your modified code, quarto, and README files, organized in the repo.
Your output files (any requested output, i.e., .csv, .html, .docx, .pdf files) in their proper places in the repo.
Criteria
Evaluation
Scoring
Comments
Right
Code runs without Error - Must be YES
0/10
10
Code produces correct output
1-5
5
Readable
Code is readable (good use of white space, etc.)
1-5
5
Code is understandable (good naming conventions, concise informative comments)
Reproducible
READMEs document project organization
1-5
5
READMEs list contents of each directory
READMEs explain the order to run the code/quarto in order to reproduce analysis
Aesthetics
Files are free of unnecessary clutter (assignment instructions, etc.)
1-5
5
Code is elegant (not required, but a bonus)
Beautifully done! I really enjoyed reading the narrative of your ideas, and your data exploration was excellent!!! Superb. You did a great job demonstrating an interaction between mass and species on fitness. Superb!
Your manuscript looked really great too. Funny about the tiny ggplot image.
Thanks, it was a pleasure to read. And you are really mastering the reproducible workflow in data analysis!
Beautiful work Allison!
Project Rubric #2
Your audience is the user, members of the general public who may want to use your data and/or code. To be useful, your code and documentation must be clear to naive users (people familiar with R, but not with this project).
Elements:
.csv
,.html
,.docx
,.pdf
files) in their proper places in the repo.Beautifully done! I really enjoyed reading the narrative of your ideas, and your data exploration was excellent!!! Superb. You did a great job demonstrating an interaction between mass and species on fitness. Superb!
Your manuscript looked really great too. Funny about the tiny ggplot image.
Thanks, it was a pleasure to read. And you are really mastering the reproducible workflow in data analysis!