Extra credit


Extra credit submissions will be accepted beginning October 11th and must be submitted no later than Monday, December 4th at 11:59pm ET.

Getting started

  • Go to the info5001-fa23 organization on GitHub. Click on the repo with the prefix ec. It contains the starter documents you need to complete the assignment
  • Clone the repo and start a new project in RStudio. See the Lab 0 instructions for details on cloning a repo and starting a new R project.

Tidy Tuesday

Tidy Tuesday is a weekly data project to promote wrangling and visualization skills. It is hosted by the R for Data Science Online Learning Community which aims to “create a supportive and responsive online space for learners” to improve their programming and data analysis skills.

Every week they post a raw dataset on GitHub and ask people to explore the data. The ultimate goal is to apply R skills, get feedback, explore other’s work, and connect with the greater #RStats community. Contributors frequently publish their work on social media under the #TidyTuesday hashtag. Datasets are posted on Mondays.

You are expected to create a data analysis based on a Tidy Tuesday dataset published during 2023. You should leverage the technical skills you learned in this course to create a high-quality analysis based on the Tidy Tuesday dataset. Your analysis can be as simple as one or two high-quality visualizations, or the results of a comprehensive statistical analysis, or some other technique. It need not be extensive and lengthy as long as it is done well.

Along with the visualization, provide a brief written description identifying the purpose of the analysis. What question(s) does it answer? Why did you choose this specific type of visualization/model?


Once you are finished with the assignment, you will upload you final PDF document to Gradescope. You may only submit one extra credit assignment for the semester. Once it has been evaluated, you may not submit another attempt.


We expect most submissions can be rendered successfully as PDF documents. If your visualization makes use of interactivity such as plotly or leaflet which requires the document be rendered as an HTML file, change the YAML header to render as an HTML file (format: html). You will need to create a second .qmd file rendered as PDF that includes your name, NetID, a link to your extra credit repo, and an explanation of how to view your visualization (e.g. clone and open the HTML file).

To submit your assignment:

  • Go to http://www.gradescope.com and click Log in in the top right corner.
  • Click School Credentials \(\rightarrow\) Cornell University NetID and log in using your NetID credentials.
  • Click on your INFO 5001 course.
  • Click on the assignment, and you’ll be prompted to submit it.
  • Mark every page to be associated with exercise #1. There will be only one exercise listed.


Students can earn up to a maximum of 1 percentage point towards their final grade. Evaluations are based on the nebulous Difficulty + Execution scoring system.

An image of Olympian Simone Biles performing a round-off.

Inspired by Olympic gymnastics scoring methods.
Component Points
Difficulty 5
Execution 5

The more challenging the data wrangling and visualization attempted, the more points you will earn. Likewise, the higher-quality the execution of the visualization, the more points you will earn. Partial credit may be awarded.