Project 02

Project

Modified

October 26, 2026

Important dates

Story and design reflection: due November 11th at 11:59pm
Mock interview: November 12-19

Learning objectives

By the end of this project, you will:

Formulate questions that can be answered with data visualizations.
Design data visualizations that effectively communicate information.
Implement best practices for data visualization using {ggplot2} and the grammar of graphics.
Communicate data-driven findings in online mediums.

Introduction

TL;DR: Tell a story using data visualizations.

You will use a dataset from the TidyTuesday project to apply your data visualization skills to tell a story. You can choose any dataset released in 2026 ¹ as part of this project.

Your task for the project is to develop a clear, compelling data-driven story supported by a minimum of four data visualizations and published in an online medium (either on social media or a blog post).

Deliverables

The primary deliverables for the project are:

An online publication containing your story and visualizations
A written design reflection
A mock interview with the instructional staff

Deliverables

Online publication

You will develop and write a compelling story supported by data visualizations. You can choose to publish your story in one of two formats:

A standalone web page published using Quarto.
A social media post (e.g. Instagram, Threads, Bluesky, X). Depending on the platform, you can post it either as a single carousel or a sequence of posts.

Regardless of publication format, your story must include at least four data visualizations optimized for mobile consumption. You will be evaluated, in part, on your ability to communicate a clear story to a specific target audience.

Minimum requirements

It must include at least four distinct visualizations. This means four separate image files when rendered.²
It must be publicly visible on the internet.
You must write and include alternative text for each visualization.
Your story is intended for an audience that does not care about how you produced your visualizations. Do not include code or computational messages in your story.

Format specific requirements

At least 750 words in your written narrative
Charts should be optimized for mobile consumption (e.g. readable on a phone screen)
Charts should be embedded throughout the written story
Alternative text embedded using the Quarto code cell fig-alt option

Depending on the platform, you may need to post your visualizations as a carousel or a sequence of posts.
Include a succinct written narrative appropriate for the social media platform.
Embed alternative text using the platform’s built-in functionality.
It cannot be posted to a private social media account.³

Additional details

Tips and additional details

If you publish a standalone web page, I recommend using Posit Connect Cloud. Their free tier allows you to publish static Quarto documents from within Positron using Posit Publisher.

I will also enable GitHub Pages which allows you to publish a rendered Quarto document from a GitHub repository (you just need to ensure it is rendered to the docs folder). If you do this, you need to add a _quarto.yml file to render your story Be warned we have had issues with this workflow on Posit Workbench, so test this workflow early if you want to use it.

Ultimately I don’t care how you get it online. It simply needs to be accessible from a public website. If we cannot see it given the URL you provided, you will not earn credit for the project.
Think carefully about your target audience and how to make your story appealing to them. You’ll probably do this after your exploratory analysis before you begin drafting your story.
At the sole discretion of the instructor, you may be awarded bonus points if your story receives substantial engagement from the public. Engagement can be measured by the number of likes, shares, comments, or other metrics depending on the platform.

Lookbook

Some semi-curated examples of data-driven stories that you can use for inspiration. Please note that none of them are perfect – some are not optimized for mobile, others are too long for this assignment, some are not long enough.

Design reflection

The design reflection will be a PDF document submitted via Gradescope. It should be 3-4 pages in length and include the following components:

Identify your target audience. What makes the story compelling for them? Why should they care what you have to say?
For at least one of your visualizations, include your exploratory sketch, grammar of graphics definition, and the intended message or insight to be communicated.⁴
Describe your design process and the overall theme/visual aesthetic of your story. Answer questions such as:
- How did you decide on the visualizations to include?
- How did you decide on the order of the visualizations?
- How did you decide on the color palette, font, and other design elements?
Assess how well your story worked. Answer questions such as:
- How effectively does the final story communicate your intended message to your target audience?
- What worked well, and what did not?
- If you had more time, what specific changes would you make, and what did you learn about communicating with data?

Mock interview

Each student will participate in a 15 minute mock interview. During the mock 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
Implementation details and your code

Wrap up

Submission

Online story

Publish your story online. In the Canvas assignment, submit the URL to your story.

Design reflection

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 Design reflection and click Submit.

Mock interview

Grading and evaluation criteria

Total	150 pts
Story	70 pts
Design reflection	30 pts
Mock interview	50 pts

Story

Warning

Projects that do not meet the minimum requirements (e.g. fewer than four distinct visualizations, not publicly accessible, code or computational messages included in the story) cannot earn a score in the “typical” or “more developed” categories for the affected rows.

Category	Less developed projects	Typical projects	More developed projects
Question + insight	The story lacks a clear question or central message. Findings are superficial, trivial, or not supported by the data shown. Claims may be misleading or overstated.	The story is organized around a clear question or central message that the data can answer. Findings are accurate, supported by the visualizations, and go beyond simply describing the dataset. Limitations or caveats are acknowledged where relevant.	All expectations of typical projects + the story surfaces a non-obvious or compelling insight that rewards the reader’s attention. Makes thoughtful use of context, comparisons, or additional evidence to establish why the finding matters.
Narrative + audience	The story is a loose collection of charts with little connective text. The target audience is unclear and the writing does not explain why they should care. Tone, jargon, or length are poorly matched to the medium.	The story has a clear beginning, middle, and end. Writing is clear, concise, and appropriate for a specific target audience and medium (meets the word count for web pages; succinct for social media). Charts and text work together, with the text interpreting rather than repeating the visuals.	All expectations of typical projects + the narrative is engaging and well paced. Hook, transitions, and conclusion pull the audience through the story. Voice and framing are tailored to the audience in ways that make the story memorable and shareable.
Visualization choice + grammar	Chart types are inappropriate for the data or message, or the visualizations are redundant. Variables are mapped to visual channels incorrectly or ineffectively. The visualizations do not build on one another.	Each chart type is appropriate for its data and message. Variables are effectively mapped to visual channels, and scales, layers, and other grammatical components are correct. The set of visualizations is sequenced deliberately to support the story.	All expectations of typical projects + the visualizations show varied and intentional design (e.g. annotation layers, faceting, small multiples, custom scales) that strengthens the argument. Each visualization contributes something distinct, and the sequence builds toward a conclusion.
Visual design + mobile optimization	Visualizations are hard to read, especially on a phone screen (small text, cramped layouts, illegible labels). Color, typography, or layout choices are distracting or inconsistent. Titles, labels, or legends are missing or confusing.	Visualizations are polished and legible at mobile size. Color palette has sufficient contrast, typography is clean, and titles, labels, and legends are clear. Visual style is consistent across the story and follows best practices taught in class.	All expectations of typical projects + a cohesive, distinctive visual identity (custom theme, palette, typography) that reinforces the story’s tone. Direct labeling, annotation, and visual hierarchy guide the reader to the key takeaway of each chart. Color choices are accessible to readers with color vision deficiencies.
Accessibility + polish	Alternative text is missing or only restates the chart type or title. Code, warnings, or other computational messages appear in the story. Noticeable typos, broken formatting, or layout problems.	Every visualization has alternative text that describes its content and main takeaway. The story is free of code and computational messages, and is clean and proofread.	All expectations of typical projects + alternative text is thoughtful and well structured (e.g. describes the chart, the key pattern, and its significance). The final publication feels professional and ready to share with a broader public audience.

Design reflection

Category	Less developed projects	Typical projects	More developed projects
Target audience	The target audience is vague (e.g. “general public”) or missing. Little explanation of why the story would matter to them.	The target audience is specific and clearly described. Explains what makes the story compelling for them and why they should care.	All expectations of typical projects + shows deep consideration of the audience’s background, interests, and likely prior knowledge. Explains how audience considerations shaped specific decisions about content, tone, and medium.
Sketch, grammar + intended message	Sketch, grammar of graphics definition, or intended message is missing or incomplete. Components are inconsistent with each other or with the published visualization.	Includes an exploratory sketch, a grammar of graphics definition (data, mappings, geoms, scales, etc.), and a clear intended message for at least one visualization. The three components are consistent with each other and with the published visualization.	All expectations of typical projects + discusses how the design evolved from sketch to final product, including what changed and why. Grammar definition shows intentional use of visual encodings to support the intended message.
Design process + justification	Design decisions are described but not justified, or are absent. Little evidence of deliberate decision-making about which visualizations to include, their order, or the visual style.	Explains how visualizations were selected, how they were ordered, and how color palette, fonts, and other design elements were chosen. Decisions are tied to the story, the audience, and course concepts. Acknowledges trade-offs or alternatives considered.	All expectations of typical projects + shows iterative thinking, such as discarded charts or revisions in response to feedback. Justifications draw explicitly on data visualization principles (e.g. perceptual ranking, color theory, accessibility, mobile constraints).
Reflection + communication	The reflection is superficial, largely descriptive, or ignores the effectiveness of the final story. The document is disorganized, outside the 3-4 page length, or difficult to follow.	Provides an honest assessment of what worked and what did not. Is clearly organized, within the 3-4 page limit, and uses figures (sketches, screenshots) effectively to support the discussion.	All expectations of typical projects + identifies specific, actionable improvements and what was learned about communicating with data. Writing is concise, polished, and professional.

Mock interview

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 interview time slot. If you miss your interview without advance notice or sufficient rationale, you will earn a 20% deduction on your mock interview score. If you know in advance that you will miss your scheduled interview, please contact the instructor as soon as possible to discuss accommodations.

Footnotes

You can go earlier if you want.↩︎
You can use tools like {patchwork} to combine multiple plots into a single image, but that still only counts as one visualization.↩︎
I will not friend or follow you to see your post. You don’t want me doing that either.↩︎
Like you did for project 01.↩︎