Stat 442 Final Project
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Final Project
Stat 442
03/11/2022
Final Project, Nov 3 Draft
The due date for the final project is 11:59pm Eastern Daylight Time, Dec 16th. The usual 5% penalty per hour applies. The project is worth 30%.
It will be graded on the following checklist, out of 100 points.
● 4 visualizations, 60 points total.
● A code file with comments explaining what is being done, including data cleaning, 20 points total.
● 100-350 words of additional writing to tie it all together, 10 points.
● General composition, 10 points.
Format
The format can be an infographic (recommended), a poster, or a presentation, but it has to include all of these elements.
Visualizations
the visualizations are 60 points total. By default this is 15 points each, but if some visualizations are far more elaborate than others, weight can be shifted. Note in your code which visualizations, if any, you want counting for more.
● At least one visualization must have a substantial 2D or 3D continuous element to it, like a contour plot, vector field, dot plot, etc.
● At least one visualization must have a substantial categorical element to it, like a facet or a set of violin plots.
● At least one visualization must have a ‘homebrew’ element to it, as described in the comments in the code. Examples include all the ‘from primitives’ stuff in week 3, and the vector fields which had to be made from segments.
● A ‘wild card’ viz, which can be anything, including a table or a base R plot.
In the code, mark which viz counts for “2D”, “categorical”, “homebrew”, and “wild card”. A viz cannot count for two or more of these categories at once.
Each viz should have appropriate titles, axes, and follow the general principles talked about in the first two weeks of class. The effort to clean and prepare data for the viz will be included in the marking consideration. Creativity (i.e., not just using defaults) will be also considered.
Datasets
You may use your own dataset(s) as long as you have enough material to work on to complete the project. For some of you, this can be an opportunity to get a jump start on your thesis. For others, a reason to follow up on something interesting.
If you don’t have a dataset in mind, here are a few recommendations.
Women’s Basketball: wehoop package - https://cran.r-project.org/web/packages/wehoop/index.html
Men’s Basketball: hoopr package - https://cran.r-project.org/web/packages/hoopR/index.html NBA Player
data on Learn.
Urban development: opendatatoronto package - https://cran.r-project.org/web/packages/opendatatoronto /index.html Apartment Building Evaluation on Learn.
Finance: tidyquant package (Week 07 notes, near end)
Social Media: Linkedin Kaggle data and 847 K-Means notes in Learn.
Stats Canada: To be determined.
2022-12-06