FPM #2: Exploratory Data Visualization

Note

FPM #2 is the the second step in working towards your final project, which is meant to combine nearly all of the course learning outcomes. This week, we’ll be building some exploratory data visualizations.

Description

I. Review the final project instructions

Begin by reading the final project description in full to remind yourself of the goals and requirements.

II. Read up on the process of Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) is a critical first step in understanding your data and understanding where it might lead you. I particularly like this definition, from R for Data Science (2e), Chapter 10:

“EDA is not a formal process with a strict set of rules. More than anything, EDA is a state of mind. During the initial phases of EDA you should feel free to investigate every idea that occurs to you. Some of these ideas will pan out, and some will be dead ends. As your exploration continues, you will home in on a few particularly productive insights that you’ll eventually write up and communicate to others.”

Read (or skim) through Chapter 10 to learn more about the EDA process before beginning your own EDA.

III. Create exploration.qmd

Create an file named, exploration.qmd within your lastName-eds240-infographic repo and add appropriate YAML fields. You’ll completing all sections of this assigment (Parts IV-V) here.

IV. Import & wrangle data, then create exploratory data viz

This week, you’ll focus on importing and wrangling your data (which you identified in FPM #1), followed by building some exploratory data visualizations. Complete the following:

• Load necessary packages and read in your data.
• Clean & wrangle your data.
• Create at least three exploratory data visualizations (but of course feel free to create more!). The goal of these visualizations is to explore your data for any potentially interesting patterns or trends, which you or may not decide to pursue further as you iterate on your final project deliverable. These plots do not need to be polished (e.g. updated theme), but it may be helpful to try arranging data to identify any trends. NOTE: You may need to create differently arranged subsets of your data in order to produce your various exploratory data visualizations.
• IMPORTANT: If you have a downloaded data file(s) saved to your repo (e.g. you’re not reading in your data directly from online, from a server, etc.) be sure to add your data folder / file to your .gitignore, particularly if this file is large.

V. Answer questions

After completing the above steps, answer the following questions:

• 1. What have you learned about your data? Have any potentially interesting patterns emerged? Point to specific visualizations that you created as you describe your findings.

• 2. In FPM #1, you outlined some questions that you wanted to answer using these data. Have you made any strides towards answering those questions? If yes, how so? If no, what next steps do you need to take (e.g. I need to create X plot type, I still need to track down Y data, I need to restructure existing data so that you can visualize it in Z ways, etc.)? Have any new questions emerged?

• 3. What challenges do you foresee encountering with your data? These can be data wrangling and / or visualization challenges.

Rubric (specifications)

To receive a “Satisfactory” mark for FPM #2:

• Complete all Part IV steps, as described above. This includes building at least three distinct exploratory data visualizations.
• Code in Part IV should follow appropriate styling conventions and include clear, informative annotations. We will not enforce any particular code style guide (you may use conventions from prior courses if you prefer; we recommend following these conventions from the Tidyverse style guide if you’re unsure where to start). You don’t need to explain every line, but your annotations should make it easy to follow your thinking and understand important decisions in your code.
• Answer all three Part V questions. There are no strict length requirements for free-response questions, however we expect that you answer them thoughtfully and fully.
• Ensure that everything described above is included in your neatly-organized and polished exploration.qmd file – this includes appropriate document YAML (at a minimum, title, author, date), clearly labeled sections, appropriately-formatted prose, appropriate code chunk options (e.g. code should render and execute, but warnings and messages should be suppressed, long data frames should not be printed out, etc.)
• Render exploration.qmd as a PDF. Double check that all of your outputs, code, and prose are visible.
• Upload your PDF to Gradescope by 11:59pm on Wed 02/04/2026.
• Submit a Generative AI Statement of Use for FPM #2 by 11:59pm on Wed 02/04/2026.