STAT 39000: Project 6 — Spring 2021

Motivation: Being able to analyze and create good visualizations is a skill that is invaluable in many fields. It can be pretty fun too! In this project, you can pick and choose if a couple of different plotting projects.

Context: We’ve been working hard all semester, learning a lot about web scraping. In this project, you are given the choice between a project designed to go through some matplotlib basics, and a project that has you replicate plots from a book using plotly (an interactive plotting package) inside a Jupyter Notebook (which you would submit instead of an RMarkdown file).

Scope: python, visualizing data

Learning objectives
  • Demostrate the ability to create basic graphs with default settings.

  • Demonstrate the ability to modify axes labels and titles.

  • Demonstrate the ability to customize a plot (color, shape/linetype).

Make sure to read about, and use the template found here, and the important information about projects submissions here.

Option 2

Here are a variety of interesting graphics from the popular book Displaying time series, spatial and space-time data with R by Oscar Perpinan Lamigueiro. You can replicate the graphics using data found here.

Choose 3 graphics from the book to replicate using plotly. The replications do not need to be perfect — a strong effort to get as close as possible is fine. Feel free to change colors as you please. If you have the desire to improve the graphic, please feel free to do so and explain how it is an improvement.

Use and the f2020-s2021 kernel to complete this project. The only thing you need to submit for this project is the downloaded .ipynb file. Make sure that the grader will be able to click "run all" (using the same kernel, f2020-s2021), and have everything run properly.

The object of this project is to challenge yourself (as much as you want), learn about and mess around with plotly, and be creative. If you have an idea for a cool plot, graphic, or modification, please include it!

Items to submit
  • Python code used to solve the problem.

  • Output from running your code.