Mini Project 1

Exploratory Data Storytelling via Data Visualization

Mini Project
Modified

March 31, 2026

Overview

In this mini project, your team will explore one data set and tell a short data story with visuals.

  1. Asking a clear question for a real audience
  2. Exploring with a few plots
  3. Choosing the best evidence
  4. Writing a short, readable story

This is an exploratory project. Use descriptive analysis only. Do NOT run hypothesis tests and do NOT fit complex models.

What you will submit

Show, in your Posit Cloud,

  1. The rendered HTML report
  2. The complete version of this source qmd file
  3. Any data file you used, only if it is not a built in package data set

What you will present

A 10 minute team presentation that summarizes your story and highlights your 3 final visuals.

Team Info

  • Team Name: Your Team Name

  • Team members and roles for this project:

    1. Project lead (keeps time, coordinates tasks): Your Member Name(s)
    2. Data wrangler (loads data, checks quality, makes a clean table): Your Member Name(s)
    3. Visualization designer (builds plots, improves readability): Your Member Name(s)
    4. Storyteller (writes narrative, connects visuals to claims): Your Member Name(s)

Step 1: Choose a data set

Choose one option below. Two teams may use the same data set.

Warning

If another team uses the same data set, it would be much better if your story is meaningfully different. Your team may differ on

  1. Audience
  2. Focus variables or outcome
  3. Comparison frame (group comparison, relationship, ranking, time trend, and so on)

Data sets

Using the code chunk import-data below to import your data, and call the data set data_raw.

## Import your data
data_raw <-

Quick description of your data set

Answer the following questions.

  1. What does one row represent

Answer:

  1. What are columns you think might matter for your story

Answer:

  1. Who might care about this data

Answer:

Step 2: Mini proposal

Write short answers. Keep it specific.

  1. Audience: Who is your audience

Answer:

  1. Investigation question: One sentence

Answer:

  1. Your current guess: One sentence

Answer:

  1. Focus variables: List some variables you plan to use

Answer:

  1. Comparison frame: Choose one or two (group comparison, relationship, ranking, time trend, etc.)

Answer:

Step 3: Quick data check and light cleaning

Your goal is to understand what you have. Keep cleaning minimal.

3.1 First look

## Check your data (you can use your own way to check the data)
glimpse(data_raw)
summary(data_raw)

3.2 Missing values

missing_counts = data_raw |>
  summarise(across(everything(), ~ sum(is.na(.x)))) |>
  pivot_longer(cols = everything(), names_to = "variable", values_to = "n_missing") |>
  arrange(desc(n_missing))

missing_counts

In 2 to 4 sentences, describe what missing values you noticed and how you plan to handle them.

Answer:

3.3 Create a working clean table

Create a new object data with:

  1. Clean column names if needed
  2. Only the columns you might use
  3. Any simple recodes you need

Use the code chunk clean below to clean your data_raw, and save the new cleaned data called data that is ready for analysis.

## Clean your data

Describe what you changed and why, in plain language:

Answer:

Step 4: Exploration

Create 4 quick exploratory plots. These are NOT final. They help you decide what story to tell.

Suggestions:

  1. One distribution plot
  2. One bar plot or count plot
  3. One relationship plot (two numeric variables)
  4. One plot that compares groups

Exploratory plot 1

# ggplot(data, aes(x = ...)) + geom_...

What you learned (1 to 2 sentences):

Answer:

Exploratory plot 2

What you learned (1 to 2 sentences):

Answer:

Exploratory plot 3

What you learned (1 to 2 sentences):

Answer:

Exploratory plot 4

What you learned (2 to 3 sentences):

Answer:

Decision

  1. What is the most interesting pattern you found

Answer:

  1. Revised investigation question (one sentence)

Answer:

  1. Which 3 visuals will become your final visuals, and why

Answer:

Step 5: Build your mini data story

Your final report must include:

  1. A short opening paragraph that sets context and states the question (4 to 6 sentences)
  2. Exactly 3 final visuals, each with a short interpretation paragraph (3 to 5 sentences)
  3. A short closing paragraph with takeaways, limitations, and next steps (4 to 5 sentences)

Keep the whole report short. Aim for about 1 to 2 pages when rendered.

5.1 Story opening

Write your opening paragraph in this section. Include

  1. Context: What is the setting in plain language
  2. Audience: Who should care about this story
  3. Investigation question: What are you trying to learn
  4. Why it matters: What decision or understanding this could support
  5. Data description: Name the data set and explain what one row represents
  6. Preview: Briefly state what your three visuals will show

5.2 Final visual 1

# Build a clean, readable plot

Interpretation (3 to 4 sentences). Include:

  1. What is shown: What is on the x axis and y axis, and what groups or colors mean
  2. Key pattern: Describe the main visible pattern using directional language and comparisons
  3. Evidence: Mention at least one concrete detail (an approximate value, a ranking, or a noticeable gap)
  4. Connection: Explain how this plot helps answer your investigation question
  5. Caveat: State one caution, such as missing values, outliers, or “this is association, not causation”

[Tip:] Avoid describing every point. Focus on what you want your audience to notice.

5.4 Final visual 2

# Build a clean, readable plot

Interpretation (3 to 4 sentences): Include the same suggested points as visual 1.

5.5 Final visual 3

# Build a clean, readable plot

Interpretation (3 to 4 sentences): Include the same suggested points as visual 1.

5.6 Closing

Write your closing paragraph. Include:

  1. Your answer: State your best answer to the investigation question (one sentence)
  2. Support: Point to your strongest visual evidence (one to two sentences)
  3. Limitation: Name one limitation of the data or your approach (one sentence)
  4. Next step: State one concrete next step you would take with more time (one sentence)
  5. So what: Explain what this means for your audience (one sentence)

[Tip:] Keep claims descriptive and reasonable. Do not overstate certainty.

Step 6: Visual quality checklist

For each final plot, confirm:

  1. Title and axis labels are descriptive
  2. Units are clear when relevant
  3. Text is readable at presentation size
  4. Legend is readable, or direct labels are used
  5. If you filtered rows, you said so in the text

Improve your plots accordingly.

Step 7: Team reflection

Each team member writes 2 to 4 sentences:

  1. What you contributed
  2. One thing you learned
  3. One thing you would improve next time

Member 1: your name

Answer:

Member 2: your name

Answer:

Member 3: your name

Answer:

Member 4: your name (if applicable)

Answer:

Step 7: Presentation plan

Plan a 10 minute talk with the suggested structure:

  1. About 1 minute: context, audience, question
  2. About 2 minutes: data set, key variables, and any cleaning choices
  3. About 5 minutes: your 3 visuals and the story they tell
  4. About 2 minutes: takeaway, limitation, next step

Presentation order

teams <- c("Team 1", "Superb Statisticians", "The Data Scientists", "Stat Padders", "Data Divers", "")
set.seed(3570)
sample(teams, 6, replace = FALSE)
[1] "Team 1"               "Stat Padders"         "Superb Statisticians"
[4] "The Data Scientists"  ""                     "Data Divers"

Grading guide

Total 15 points:

  1. Clear question and audience (3 pts)
  2. Evidence-based story with 3 high-quality visuals (6 pts)
  3. Reproducible workflow and readable report (3 pts)
  4. Presentation clarity and timing (3 pts)