DATA WRANGLING AND MANAGEMENT WITH R FINAL PROJECT
SPRING 2023
1. PROJECT
For the final project for the course, your assignment is essentially to wrangle some data and to show off your skills. The final project is worth 15% of the final grade, or about two and a half weeks worth of regular assignments—that should give you a good idea of how much effort to plan to put into it. For the project you will want to bring data into R, clean it, tidy it, perhaps create new variables, perhaps summarize your data, and report on it with tables and figures. Furthermore, there are some required elements:
You must get your data from at least two distinct sources, at least one of which must be at least somewhat difficult to work with (requiring scraping, cleaning, or an API). It is not strictly required that the datasets be related, but your grade will certainly be better if they are.
Your R Markdown file, your code, and any data files should be self-contained. That is, it should run on my computer or Posit Cloud account without mod- ification (remember that you do not want Jenny Bryan to set your computer on fire).
Your report should be in the form of an R Markdown file, not a notebook, script, or some other format. You should see output: html_document near the top of the document.
Every code chunk must be labeled.
You must include a step where you save a tidy version of (perhaps just some of) your data as a csv file. The idea is that the csv file would be an easy place for someone else to start from.
Your report, generated from an R Markdown file, should be as good looking and well formatted as you can make it—that includes tables and figures. Do not show your code (that is, use “echo = TRUE”) other than when truly needed.
We have not done statistical analyses more sophisticated than correlation and linear regression in this course and there is no need for it in your report. You can do so if you wish, however.
If some parts of your project are relatively easy, you should balance that out by going into more depth in other aspects.
It is very important that your report should explain the steps you’ve taken and why—I do not want to see just a collection of tables and figures. Of course, that text should be outside of the code chunks, and should not simply be section headers. Feel free to describe approaches that didn’t work
2 DATA WRANGLING AND MANAGEMENT WITH R FINAL PROJECT SPRING 2023
or were more troublesome than expected. Only code and the occasional comment should be inside code chunks.
I expect that you will discuss this project with others, but please avoid using datasets in common (I realize that might still happen by coincidence). All of the work submitted must be your own. Be sure to credit the sources of your data and any other material—it is better to over-credit than to under-credit. If you have any questions about properly crediting others’ work just contact me about it.
I suggest that you not use data from Kaggle. For most of Kaggle’s datasets, the data is already in very good shape, plus the datasets tend to be used very widely.
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