EDS 240 Final Project

You’ll be working on this bit-by-bit throughout the quarter!

Note

This document outlines the overarching goals and outcomes of your EDS 240 Final Project. You will develop these final deliverables incrementally through a series of Final Project Milestones (FPM #1-5). Each of these milestones will be include detailed instructions and rubric specifications.

Learning Outcomes

Your final project is meant to combine all of the course learning outcomes(!):

  • identify which types of visualizations are most appropriate for your data and your audience
  • prepare (e.g. clean, explore, wrangle) data so that it’s appropriately formatted for building data visualizations
  • build effective, responsible, accessible, and aesthetically-pleasing visualizations using the R programming language, and specifically {ggplot2} + ggplot2 extension packages
  • write code from scratch and read and adapt code written by others
  • apply a DEI (Diversity, Equity & Inclusion) lens to the process of designing data visualizations
  • assess, critique, and provide constructive feedback on data visualizations

Description

Part I: Create a data-driven infographic

You will be building a cohesive, data-driven infographic that communicates a clear goal or message through at least three distinct but complementary visualizations. Each component of your infographic should help to illuminate that central goal by addressing specific questions or insights that, together, tell a complete and compelling story.

Infographics are powerful science communication tools that can serve a range of audiences, but they are particularly well-suited for engaging general audiences. An effective infographic is designed around a clear goal (e.g. illumniating the gender gap in academic publishing, describing survival challenges faced by winter-run Chinook Salmon, exploring the role of an important but relatively-unknown pollinator). Each of its components (e.g. visualizations, numbers, imagery) should work together to illuminate that goal by answering specific, relevant questions that help reveal different facets of the story. While text plays an important role in all visualizations, it is especially critical in infographics for weaving together these elements into a cohesive narrative. The order, layout, and orientation of your elements will also be essential for creating a visual hierarchy that guides your audience through your story in a purposeful way.

All component visualizations must be produced using code (e.g. leveraging the {ggplot2} package), but you may enhance your visualizations and assemble your final infographic using the graphic design tool(s) or software of your choice. It is possible to create your infographic entirely in R (you may consider using the {patchwork} package for stitching together different elements – see Ray Hunter’s infographic, or refer back to HW #1). You may also consider tools like Adobe Illustrator, Affinity, Canva, or Google Docs (Dr. Alex Phillips will be leading a class on using Affinity Designer on [ADD DATE]).

You are expected to consider all design elements discussed throughout the quarter, implement as appropriate, and justify your decisions. These include, but are not limited to:

  1. graphic form (you are not limited to just those fundamental chart types discussed in weeks 2 & 4 – explore other chart types and don’t be afraid to get creative with it; check out some of these awesome data viz creators to find inspiration)
  2. text (e.g. titles, captions, annotations, axis labels, axis text)
  3. themes (i.e. all non-data plot elements; these should be intentionally modified and visually-pleasing)
  4. colors
  5. typography
  6. general design (e.g. group order, spacing, text orientation, data-ink ratio, creating a visual hierarchy, avoiding information overload)
  7. contextualizing your data
  8. centering your primary message
  9. considering accessibility (e.g. colorblind-friendly palettes / contrast, alt text)
  10. applying a DEI lens to your design, as appropriate (e.g. considering the people / communities / places represented in your data, consider how you frame your questions / issue)

Part II: Share your design process in a blog post-style write up

You will discuss your infographic and design process in a short, science communication–style blog post (approximately 500–1,000 words). Your post should engage a broad, non-technical audience and clearly communicate your motivation, questions, data, design choices, challenges, and insights. Your goal is to help readers understand and appreciate both your process and your product.

An effective post should:

  • Open with a compelling hook that draws readers in and introduces your topic, motivation, and data in an accessible way
  • Feature your final infographic as the centerpiece, using it to illustrate your key points
  • Describe your design journey, including the questions and goals that guided your choices, the trade-offs you considered, and how you refined your visualizations for clarity, accuracy, and impact
  • Reflect on your results and what your visualization reveals about your question or topic

You should aim for a tone that is clear, approachable, and engaging for readers from any background (not just data scientists & programmers!). Marina Kochuten’s (MEDS 2025) blog post is great example of how to blend narrative, reflection, and design choices into an accessible science communication piece.

Part III: Flash talks

You’ll share your final infographic with your peers in a short, 3-minute flash talk. This is a time to celebrate all the creativity, thought and hard work you’ve put into your final infographics! It’s also an opportunity to learn from, and be inspired by, your peers’ work. Flash talks will take place in-person during finals week.