Mini-project

Modified

January 13, 2026

Important dates

Learning objectives

By the end of this project, you will:

Design a visualization to communicate insights from a dataset
Create and refine your visualization
Communicate your design process
Reflect on your design choices and the effectiveness of your visualization

Introduction

TL;DR: Create a high-quality data visualization and talk about it.

In this mini-project, you will individually create a data visualization that effectively communicates insights from a Tidy Tuesday dataset. You will go through the process of exploring the data, sketching and ideating on an initial chart design, creating a rough draft of the chart using R, and refining your visualization to a polished finish. After submitting your visualization, you will participate in an oral interview to discuss your design choices and reflect on the effectiveness of your chart.

Deliverables

The primary deliverables for the project are:

A report documenting your entire design process
An oral interview with the instructional staff

Project workflow

When students will complete the project

Each student will have one week to complete the mini-project. In order to ensure instructional staff capacity for the oral interviews, the mini-project will be staggered across multiple weeks. Specifically, the mini-projects will be completed during weeks 8, 9, 12, and 13 of the course (with oral interviews at the beginning of the following week). Students will have the opportunity to identify their preferred week(s) for completing the mini-project via a sign-up sheet that will be made available on Canvas. The instructor will assign students to their mini-project week, attempting to accomodate their preferences while ensuring a roughly equal distribution of students across the available weeks.

Dataset assignments will be published on Monday at 9 AM on Canvas. Students will have until Sunday at 11:59pm to complete their report. Instructional staff will conduct oral interviews on Monday and Tuesday of the following week.

Report

You will create a single polished, high-quality visualization using R and the assigned dataset.

The report documents your entire design process for the data visualization. It is modeled on Nicola Rennie’s The Art of Data Visualization with ggplot2: The TidyTuesday Cookbook, and should be structured with the following sections and content.

Dataset

Load the required packages
Import the dataset
Briefly describe the dataset, its structure, and its variables

Exploratory work

Learn more about the data and begin to formulate ideas for your visualization.

Data exploration

Summarize and visualize key aspects of the dataset
Identify interesting patterns, trends, or relationships in the data
Document your findings - what are you learning through this exploration? How is this informing your visualization ideas?

Exploratory sketches

Sketch out at least two distinct visualization ideas on paper or using a digital tool
For each sketch, describe:
- The type of chart or visualization
- The variables to be visualized
- The intended message or insight to be communicated
- The rationale behind your design choices (e.g., chart type, color scheme, layout)
Reflect on the strengths and weaknesses of each sketch and explain why you ultimately chose one to pursue

Choosing an appropriate type of chart

There are lots of guides and tools online about how to select an appropriate chart type based on the data you have and the message you want to communicate. I personally like From Data to Viz, but there are many others out there.

Preparing a plot

Begin creating your visualization in R based on your chosen sketch.

Data wrangling

Perform any required data wrangling to prepare the dataset for visualization
Document the steps taken and explain why they were necessary for your visualization

The first plot

Create a functional first draft of your chosen visualization
It need not be polished or final, but it should convey the basic structure and message of your intended chart
Essentially it should have all the grammatical components of the chart (e.g. layers, mappings, scales, etc.) but you do not need to have any of the styling or theming worked out yet

Advanced styling

Make it shine! This is where you take the basic plot you created in the previous section and refine it to a polished, high-quality visualization. Adjustments you will likely make include:

Fine-tuning colors, fonts, and other stylistic elements
Adding titles, labels, and annotations to enhance clarity
Implementing custom themes or styles to align with your design vision
Improving layout, spacing, and aspect ratio for better readability
Ensuring accessibility and usability of the visualization

Reflection

Given the time constraints, it’s unlikely that your chart will be perfect. However, it’s important to reflect on your design choices and the effectiveness of your visualization. Address the following questions in your reflection:

How well does your final visualization communicate the intended message or insight?
What design choices did you make to enhance clarity and engagement?
What challenges did you encounter during the design process, and how did you address them?
If you had more time, what additional improvements or refinements would you make to your visualization?

Oral interview

Each student will participate in a 20-25 minute oral interview. During the oral interview, you will discuss your design process and answer questions about your visualization. The interview will cover topics such as:

Your data exploration and insights
The rationale behind your chosen visualization design
Specific design choices and their intended effects
Reflections on the effectiveness of your visualization
Any challenges you faced and how you overcame them

There may be some questions related to your code as well, so be prepared to discuss your R code and the steps you took to create your visualization.

Overall grading

Total	100 pts
Report	50 pts
Oral interview	50 pts

Evaluation criteria

Report

Category	Less developed projects	Typical projects	More developed projects
Data exploration + insight	Exploration is superficial or disconnected from visualization choices. Findings are not clearly articulated or don’t motivate the final design.	Thorough exploration reveals key patterns and relationships. Clear documentation of findings that directly inform visualization choices. Shows genuine discovery process.	All expectations of typical projects + exploration uncovers non-obvious or compelling insights. Articulates a clear, focused narrative that the visualization will communicate.
Design thinking + justification	Sketches are missing or lack description. Rationale for design choices is absent or poorly explained. Little evidence of deliberate decision-making about chart type, variables, or visual encodings.	Presents multiple sketch ideas with clear descriptions of chart type, variables, and design rationale. Explains why the chosen design is appropriate for the data and message. Reflects on trade-offs between options.	All expectations of typical projects + sketches demonstrate sophisticated thinking about design alternatives and how different designs communicate different messages. Shows deep consideration of visual hierarchy, data-ink ratio, and other design principles.
Chart type + grammar	Chart type is inappropriate for the data or message. Missing or incorrect mappings of variables to visual channels. Basic grammatical components (layers, scales) are incomplete or incorrect.	Chart type is appropriate and well-justified. Variables are effectively mapped to visual channels. All grammatical components (layers, mappings, scales) are present and correct. Code is functional and readable.	All expectations of typical projects + uses sophisticated or layered designs (e.g., faceting, multiple geoms) where appropriate. Demonstrates mastery of the grammar of graphics and intentional use of visual encoding to strengthen the message.
Visual design	Visualization is difficult to read. Poor color choices, illegible fonts, or confusing layout. Lacks labels or annotations. Does not follow best practices.	Visualization is polished and easy to read. Appropriate color palette with sufficient contrast. Clean typography and well-organized layout. Clear titles, axis labels, and legends. Follows visualization best practices taught in class.	All expectations of typical projects + employs sophisticated visual design with custom themes, distinctive color schemes, or effective use of whitespace and visual hierarchy. Color choices show understanding of colorblindness accessibility. Typography and styling enhance clarity and engagement.
Accessibility + clarity	Visualization is inaccessible or unclear to the intended audience. Labels are missing or ambiguous. Color reliance makes visualization unclear for colorblind viewers.	All elements are clearly labeled and easy to interpret. Color is used effectively with sufficient contrast. Visualization is accessible to a broad audience. Legend or annotations clarify any non-obvious visual encodings.	All expectations of typical projects + goes beyond basic accessibility. Considers multiple ways audiences might interpret the visualization. Thoughtful use of annotations or annotations to guide interpretation. Includes alternative text description.
Reflection	Reflection is missing or superficial. Does not meaningfully address design choices or effectiveness.	Reflection addresses design choices and their effects. Shows honest assessment of what worked and what didn’t. Demonstrates understanding of how visualization choices communicate (or fail to communicate) the intended message.	All expectations of typical projects + reflection demonstrates sophisticated understanding of why certain designs are more effective. Identifies specific, actionable improvements. Shows evidence of iterative thinking and learning throughout the design process.

Oral interview

TODO

Late work policy

TODO