Pick a dataset, any dataset…
… and visualize it! That’s your final project in a nutshell.
The goal of this project is for you to demonstrate proficiency in the graphical techniques we have covered in this class (and beyond, if you like) and apply them to a novel dataset in a meaningful way.
The goal is not to do an exhaustive data analysis i.e., do not calculate every statistic and procedure you have learned for every variable, but rather let me know that you are proficient at asking meaningful questions and answering them with results of data analysis, that you are proficient in using R, and that you are proficient at interpreting and presenting the results. Focus on methods that help you begin to answer your research questions. Statistical models we’ve talked about in class may not be appropriate for your data. Also, critique your own methods and provide suggestions for improving your analysis. Issues pertaining to the reliability and validity of your data, and appropriateness of any statistical analysis should be discussed.
The project is very open ended. You should create some kind of compelling visualization(s) of this data in R. You do not need to visualize all of the data at once. A small number of high quality visualizations will receive a much higher grade than a large number of poor quality visualizations. Also pay attention to your presentation. Neatness, coherency, and clarity will count. All analyses must be done in RStudio, using R.
You can work individually or with a partner. If you have a larger group with an exceptionally ambitious idea, please let me know and I will consider it.
In order for you to have the greatest chance of success with this project it is important that you choose a manageable dataset. This means that the data should be readily accessible and large enough that multiple relationships can be explored. As such, your dataset must have at least 100 observations and between 10 to 20 variables (exceptions can be made but you must speak with me first). The dataset’s variables should include categorical variables, discrete numerical variables, and continuous numerical variables.
If you are using a dataset that comes in a format that we haven’t encountered in class, make sure that you are able to load it into R as this can be tricky depending on the source. If you are having trouble ask for help before it is too late.
The projects page has lots of places to start to look for potential datasets.
You do not have to choose a dataset from one of these sources! I want you to work on something that you care about and are excited about doing. If you want to dive deeper into something you’ve seen in another class, great! If you have access to data from a research project, job, or volunteer work, great! It’s easier to construct a narrative and create insightful graphics when we are working on things we know and care about.
If you are not sure if something you have in mind is the right fit, let’s chat!
All projects will consist of publication-ready data visualizations and a 10-15 page paper. I expect most projects to be one of the following:
Your visualizations should be the absolute best you make in this class. Your theme, colors, fonts, geoms, etc. should all be thought through and consistent throughout your visualizations. They should follow Cairo’s Five Qualities of Great Visualizations and Tufte’s guidelines from the first lecture.
Your presentation/paper should include at least 4 multivariate visualizations, covering a wide range of types of graphs we see in this course, but you may want to include more in your paper to fully tell your story. While working on your project, I anticipate you will make many, many more visualizations than the 4 you end up including.
5 minutes maximum. You can either present live or pre-record and submit your video to be played during “demo day”.
I will share a template for creating a slide deck using the R package xaringan, which allows you to create slideshows using Rmarkdown. (This is what I use to create slides for class)
There isn’t a limit to how many slides you can use, just a time limit (5 minutes total). Your presentation should not just be an account of everything you tried (“then we did this, then we did this, etc.”), instead it should convey what choices you made, and why, and what you found.
Presentations will take place on the last day of the J-term (Thurs, Jan 28). I’m planning on having 5 30 minute blocks with presentations, and you’ll each be assigned one block as a “break”. During these chunks of time you will watch presentations from other teams in your workshop and provide feedback in the form of peer evaluations.
Your paper can be either an HTML or a PDF. You can knit to a word document and format your document from there before saving as a PDF, but you should include all graphs, code, and text in your rmarkdown file. Your paper should follow a general IMRaD structure:
Introduction
Methods
Results
Discussion
I encourage you all to consider submitting a shortened version of your paper to the USCLAP intermediate course competition. Winners receive cash prizes and may be invited to present at the Electronic Undergraduate Statistics Conference.
A satisfactory project will:
While this rubric is intended to cover most projects, keep in mind that this is a portfolio-building class, so if you have an idea that doesn’t cleanly fit into this rubric THAT’S OK! Just reach out to me early so that we can figure out an alternative rubric.
An excellent project will meet all of the above requirements, plus at least one of:
AND