TDM 10100: Project 12 — Fall 2026

  • More Data Visualizations

Project Objectives

Motivation: R and Python both provides numerous types of methods for Data Visualization. We want to give students the freedom to explore some data sets and some methodologies for visualizing data with both tools.

Context: We explore data visualization methods in R and Python

Scope: R, Python, data visualization

Learning Objectives

  • Continue to learn about numerous methods of visualizing data in R and Python

Please use 2 Cores for this project.

Please use the template found here, and the important information about project submissions here, when you are ready to submit your work.

Datasets

Varies according to students' preferences

Questions

Question 1 (2 points)

The website r-graph-gallery.com has over 400 examples of data visualizations. Pick a data set (of your choosing) that is in The Data Mine’s directory of data sets at /anvil/projects/tdm/data/ and make at least three plots in R, giving some insights into your plots. Write a brief paragraph about each plot, explaining what insights you believe that your plots make about the data.

Deliverables

  • Pick a data set in The Data Mine’s directory of data sets and make at least three plots in R.

  • Write a brief paragraph about each plot, explaining what insights you believe that your plots make about the data.

Question 2 (2 points)

Show a teammate in The Data Mine your plots from Question 1, without your explanations. Can they understand the meaning of your plots without any explanations?

Ask them to write a few sentences about your plots, and put their insights here, in your project (with attribution to the student who gave you feedback). If necessary, go back to Question 1 and revise your plots, based on their feedback.

Make a few comments about this process, for instance, how did your plots rate, with regard to concepts like accuracy, clarity, transparency, ethics, bias, etc., in other words, with regard to best practices for data visualization?

Deliverables

  • Ask a teammate to write a few sentences about your plots, and put their insights here, in your project (with attribution to the student who gave you feedback).

  • (Optional) Go back to Question 1 and revise your plot, based on their feedback.

  • Make a few comments about this process, e.g., how did your plots rate, with regard to best practices for data visualization?

Question 3 (2 points)

There is a sister website of r-graph-gallery.com located at python-graph-gallery.com which has hundreds of data visualizations in Python, mostly using matplotlib or seaborn.

Recreate the plots from Question 1 (or some similar plots) using matplotlib or seaborn in Python. Write a brief paragraph about each plot, comparing and contrasting the ease of making such plots in R versus Python. What are the pro’s and con’s of using each set of data visualization methods?

Deliverables

  • Recreate the plots from Question 1 (or some similar plots) using matplotlib or seaborn in Python.

  • Write a brief paragraph about each plot, comparing and contrasting the ease of making such plots in R versus Python.

  • What are the pro’s and con’s of using each set of data visualization methods?

Question 4 (2 points)

Again, show a teammate in The Data Mine your plots from Question 3, without your explanations. Can they understand the meaning of your plots without any explanations?

Ask them to write a few sentences about your plots from Question 3, and put their insights here, in your project (with attribution to the student who gave you feedback). If necessary, go back to Question 3 and revise your plots, based on their feedback.

Again, make a few comments about this process, for instance, how did your plots from Question 3 rate, with regard to concepts like accuracy, clarity, transparency, ethics, bias, etc., in other words, with regard to best practices for data visualization?

Deliverables

  • Ask a teammate to write a few sentences about your plots from Question 3, and put their insights here, in your project (with attribution to the student who gave you feedback).

  • (Optional) Go back to Question 3 and revise your plots, based on their feedback.

  • Again, make a few comments about this process, for instance, how did your plots from Question 3 rate, with regard to best practices for data visualization?

Question 5 (2 points)

The Gapminder website www.gapminder.org has numerous plots, for instance, comparison of life expectancy and income per person, with data collected over decades (sometimes from multiple centuries). These plots are eye-catching! Some of the plots follow best practices for data visualization, but others are challenging to understand. See: www.gapminder.org/tools

Write a paragraph about two or more plots whose visualizations that you believe are insightful and well-designed, according to best practices for data visualization.

Also write a paragraph about two or more plots whose visualizations are confusing and could be improved, suggesting some methods for improvement, if you were to re-create and re-design such plots yourself.

Deliverables

  • Write a paragraph about two or more plots whose visualizations that you believe are insightful and well-designed, according to best practices for data visualization.

  • Also write a paragraph about two or more plots whose visualizations are confusing and could be improved, suggesting some methods for improvement, if you were to re-create and re-design such plots yourself.

Submitting your Work

Once you have completed the questions, save your Jupyter notebook. You can then download the notebook and submit it to Gradescope.

Items to submit

  • firstname_lastname_project12.ipynb

You must double check your .ipynb after submitting it in gradescope. A very common mistake is to assume that your .ipynb file has been rendered properly and contains your code, markdown, and code output even though it may not. Please take the time to double check your work. See here for instructions on how to double check this.

You will not receive full credit if your .ipynb file does not contain all of the information you expect it to, or if it does not render properly in Gradescope. Please ask a TA if you need help with this.