Project 01

Project
Modified

September 14, 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 project, you will create a data visualization that effectively communicates insights from a dataset provided by the instructor. 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 exam to discuss your design choices and reflect on the effectiveness of your chart.

Deliverables

The primary deliverables for the project are:

  1. A report documenting your entire design process
  2. An oral examination with the instructional staff

Dataset

Important

To be revealed one week before the project deadline.

Project workflow

Report

You will create a single polished, high-quality visualization using R and the assigned dataset. The report will be generated using Quarto and rendered as a PDF document to submit via Gradescope.

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.

NoteSizing plots for the report

The default Quarto settings for rendering plots in Typst documents are not always ideal for high-detail visualizations. You may want to adjust the default plot dimensions for this project to ensure your visualizations are clear and legible. This tutorial explains in detail how to adjust plot sizing in Quarto documents. In particular, you may want to set the fig-width and fig-height chunk options for your plots to ensure they are large enough to be legible when rendered in the report.

NoteShow your code

In the report, make sure to print your code so we can see it in the PDF document. This will facilitate evaluation of your written report and the oral exam. The provided template Quarto document is already configured in the YAML header to print your code:

execute:
  echo: true

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 tool1
  • For each sketch, describe:
    • The grammar of graphics for the chart (e.g., layers, mappings, scales). It need not be completely worked out yet – for example, you may not have decided on the exact color palette, but you should have a clear idea of the basic structure of the chart and how the data will be mapped to visual channels.
    • The rationale behind your design choices (e.g., chart type, color scheme, layout)
    • The intended message or insight to be communicated
  • Reflect on the strengths and weaknesses of each sketch and explain why you ultimately chose one to pursue
TipChoosing 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?

Generative AI (GAI) self-reflection

As stated in the syllabus, include a written reflection for this assignment of how you used GAI tools (e.g. what tools you used, how you used them to assist you with writing code), what skills you believe you acquired, and how you believe you demonstrated mastery of the learning objectives.

Oral exam

Each student will participate in a 15-20 minute oral exam. During the oral exam, you will discuss your design process and answer questions about your visualization. The exam 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
  • Implementation details and your code

Wrap up

Submission

Report

  • Go to http://www.gradescope.com and click Log in in the top right corner.
  • Click School Credentials \(\rightarrow\) Cornell University NetID and log in using your NetID credentials.
  • Click on your INFO 3312 course.
  • Click on the assignment, and you’ll be prompted to submit it.
  • Mark all the pages for the assignment as Report and click Submit.

Oral exam

Sign up for an oral exam time slot to be held between October 14-21.

Grading and evaluation criteria

Total 100 pts
Report 50 pts
Oral exam 50 pts

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.
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 exam

All students will be evaluated on a standardized rubric which will be provided to students after the exam. You should be prepared to discuss any of the design and/or implementation choices in your project.

Late work policy

There is no late work accepted on this project. Your report must be submitted by the deadline.

Students are expected to attend their scheduled oral exam time slot. If you miss your scheduled oral exam, we will attempt to reschedule, but this is not guaranteed and may result in a penalty. If you know in advance that you will miss your scheduled oral exam, please contact the instructor as soon as possible to discuss accommodations.

Footnotes

  1. If sketched on paper, take a legible photo and include it in your report. Do not submit Canva/Figma wireframe mockups. It must be drawn free form.↩︎