Lab 1: Getting Started with Data Visualization

STAT 80B - Winter 2026

Author
Affiliation

Due: Thursday, January 8, 2026 (in class)

Statistics - UCSC

Published

01 January 2026

Overview

Welcome to your first data visualization lab! The goal of this lab is to ensure you have a working software environment and can create basic visualizations using aesthetic mappings. This is a gentle introduction - we’re checking that your tools work and that you understand the fundamentals of mapping data to visual properties.

Weight: 8% of final grade (but remember: top 4 of 5 labs count!)

Due: Thursday, January 8, 2026, by end of class (or by 11:59 PM same day)

Submission: One PDF file uploaded to Canvas

Learning Objectives

By completing this lab, you will:

  1. Successfully install and configure your chosen visualization software (Tableau, R, or Python)
  2. Connect to a dataset and explore its structure
  3. Create visualizations using different aesthetic mappings
  4. Export publication-quality images from your software
  5. Communicate what your visualizations show

Software Options

You must choose ONE of the following software tools for all course assignments:

Dataset Options

Option B: Other Built-in Datasets

If you prefer to use a different dataset, here are some alternatives:

Tableau

  • Sample - Superstore (included with Tableau)
  • Download Palmer Penguins CSV from Canvas or GitHub

R

# Install and load the palmerpenguins package
install.packages("palmerpenguins")
library(palmerpenguins)

# Load the data
data(penguins)
View(penguins)  # Look at the data

# Alternative built-in datasets
data(mtcars)    # Car performance data
data(iris)      # Flower measurements

Python (via seaborn or palmerpenguins)

import seaborn as sns
import pandas as pd

# Option 1: Load from seaborn (easiest)
penguins = sns.load_dataset('penguins')
print(penguins.head())

# Option 2: Install palmerpenguins package
# pip install palmerpenguins
from palmerpenguins import load_penguins
penguins = load_penguins()

# Alternative built-in datasets
iris = sns.load_dataset('iris')
titanic = sns.load_dataset('titanic')
tips = sns.load_dataset('tips')

Requirements

Create THREE visualizations that demonstrate different aesthetic mappings. Each visualization must use a different combination of aesthetics.

Visualization 1: Position + Color

Required aesthetics: - Position (x and/or y axis) - Color

Example approaches: - Scatter plot with points colored by category - Bar chart with bars colored by group - Line chart with multiple colored lines

What to show: How two quantitative variables relate, broken down by a categorical variable

Visualization 2: Position + Size

Required aesthetics: - Position (x and/or y axis) - Size

Example approaches: - Bubble chart (scatter plot with sized points) - Sized bars or dots - Points scaled by a third variable

What to show: How three quantitative variables relate (or two quantitative + one for size)

Visualization 3: Your Choice (Be Creative!)

Required: Use at least 3 different aesthetics

Possible combinations: - Position + Color + Size - Position + Color + Shape - Position + Size + Transparency - Get creative!

What to show: A meaningful pattern or comparison using multiple visual channels

Detailed Instructions

Step 1: Install and Set Up Software (If Not Already Done)

Follow the installation instructions for your chosen tool. Make sure you can open the software and see the main interface.

Test your installation: Can you create a new blank project/notebook/worksheet?

Step 2: Load Your Dataset

Tableau

  1. Open Tableau Desktop
  2. Download the penguins CSV from Canvas (or from GitHub)
  3. Under “Connect” → “To a File” → Select “Text file”
  4. Navigate to penguins.csv
  5. Click “Sheet 1” to start working

R

library(tidyverse)
library(palmerpenguins)

# Load the penguins data
data(penguins)
View(penguins)  # Look at the data

# See the first few rows
head(penguins)

# Check for missing values
summary(penguins)

Python

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load penguins from seaborn
penguins = sns.load_dataset('penguins')
print(penguins.head())

# Check the structure
print(penguins.info())

# Check for missing values
print(penguins.isnull().sum())

Step 3: Explore Your Data

Before creating visualizations, understand your data:

  • What variables are available?
  • What data type is each variable? (quantitative, categorical, ordinal)
  • Are there any missing values?
  • What range of values does each variable have?

Step 4: Create Visualization 1 (Position + Color)

Tableau Example

  1. Drag Bill Length to Columns
  2. Drag Bill Depth to Rows
  3. Drag Species to Color (in Marks panel)
  4. Add a descriptive title: Double-click title area
  5. (Optional) Add Island to Shape for redundant coding

R Example

library(palmerpenguins)
library(tidyverse)

# Scatter plot: bill dimensions colored by species
ggplot(data = penguins, 
       aes(x = bill_length_mm, y = bill_depth_mm, color = species)) +
  geom_point(size = 3, alpha = 0.8) +
  labs(title = "Penguin Bill Dimensions by Species",
       x = "Bill Length (mm)",
       y = "Bill Depth (mm)",
       color = "Species") +
  theme_minimal() +
  theme(legend.position = "bottom")

# Save it
ggsave("viz1_position_color.png", width = 8, height = 6, dpi = 300)

Python Example

import seaborn as sns
import matplotlib.pyplot as plt

# Load data
penguins = sns.load_dataset('penguins')

# Create scatter plot
plt.figure(figsize=(10, 6))
sns.scatterplot(data=penguins, 
                x='bill_length_mm', 
                y='bill_depth_mm', 
                hue='species', 
                s=100, 
                alpha=0.8)
plt.title('Penguin Bill Dimensions by Species', fontsize=14, fontweight='bold')
plt.xlabel('Bill Length (mm)')
plt.ylabel('Bill Depth (mm)')
plt.legend(title='Species', loc='best')
plt.tight_layout()
plt.savefig('viz1_position_color.png', dpi=300, bbox_inches='tight')
plt.show()

💡 What to notice: You should see that different species cluster together - Gentoo penguins have longer but shallower bills!

Step 5: Create Visualization 2 (Position + Size)

Tableau Example

  1. Drag Flipper Length to Columns
  2. Drag Body Mass to Rows
  3. Change mark type to “Circle”
  4. Drag Bill Length to Size
  5. Adjust size range if needed (click Size → Edit)
  6. (Optional) Filter out missing values: drag species to Filters → exclude NULL

R Example

# Bubble chart: flipper length vs body mass, sized by bill length
ggplot(data = penguins %>% drop_na(), 
       aes(x = flipper_length_mm, y = body_mass_g, size = bill_length_mm)) +
  geom_point(alpha = 0.6, color = "steelblue") +
  labs(title = "Penguin Body Dimensions (sized by bill length)",
       x = "Flipper Length (mm)",
       y = "Body Mass (g)",
       size = "Bill Length (mm)") +
  theme_minimal() +
  theme(legend.position = "right")

ggsave("viz2_position_size.png", width = 8, height = 6, dpi = 300)

Python Example

# Remove rows with missing values
penguins_clean = penguins.dropna()

plt.figure(figsize=(10, 6))
sns.scatterplot(data=penguins_clean, 
                x='flipper_length_mm', 
                y='body_mass_g', 
                size='bill_length_mm',
                sizes=(50, 400), 
                alpha=0.6,
                color='steelblue')
plt.title('Penguin Body Dimensions (sized by bill length)', fontsize=14, fontweight='bold')
plt.xlabel('Flipper Length (mm)')
plt.ylabel('Body Mass (g)')
plt.legend(title='Bill Length (mm)', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.savefig('viz2_position_size.png', dpi=300, bbox_inches='tight')
plt.show()

💡 What to notice: There’s a strong positive relationship between flipper length and body mass - bigger penguins have longer flippers!

Step 6: Create Visualization 3 (Your Choice!)

Get creative! Combine 3+ aesthetics in a meaningful way.

Example ideas: - Scatter plot with color, size, AND shape - Multiple small plots (facets) with color coding - Time series with color and line type - Bar chart with color and text labels

Tips: - Make sure it’s readable - don’t overload with too many variables - Each aesthetic should add meaningful information - Use redundant coding (e.g., color + shape) for accessibility

Step 7: Export Your Visualizations

Tableau

  • Right-click on sheet → Export → Image
  • Or: Worksheet → Export → Image (PNG)
  • Save with descriptive names: viz1.png, viz2.png, viz3.png

R

  • Use ggsave() after creating each plot (see examples above)
  • Recommended: PNG format, 300 dpi, 8x6 inches

Python

  • Use plt.savefig() (see examples above)
  • Recommended: PNG format, 300 dpi

Submission Format

Create ONE PDF file with the following structure:

Page 1: Header & Visualization 1

Your Name: ___________________
Software Used: _______________
Date: January 8, 2026

Visualization 1: Position + Color
[INSERT IMAGE HERE - full size, readable]

Description:
- Variables mapped: [Explain which variables to which aesthetics]
- What it shows: [What pattern or insight does this reveal?]
- Example: "This scatter plot maps bill length to x-position, bill depth to 
  y-position, and species to color. It shows that the three penguin species 
  have distinct bill shapes: Gentoo penguins have longer but shallower bills, 
  Adelie penguins have shorter and deeper bills, and Chinstrap penguins fall 
  in between. This clustering suggests bill morphology is a strong species 
  identifier."

Page 2: Visualization 2

Visualization 2: Position + Size
[INSERT IMAGE HERE - full size, readable]

Description:
- Variables mapped: [Explain mappings]
- What it shows: [Pattern or insight]

Page 3: Visualization 3

Visualization 3: [Your chosen aesthetics]
[INSERT IMAGE HERE - full size, readable]

Description:
- Variables mapped: [Explain mappings]
- What it shows: [Pattern or insight]

Page 4: Reflection

Reflection (1 paragraph, 150-250 words):

Discuss your experience with this lab:
- What was straightforward or easy?
- What was challenging or confusing?
- Which aesthetic mappings worked well for your data?
- Which combinations were less effective and why?
- Any surprises or insights about visualization design?

How to Create the PDF

Option 1: Word/Google Docs → PDF

  1. Create your document in Word or Google Docs
  2. Insert images (make sure they’re large enough to read!)
  3. Add descriptions under each image
  4. Export as PDF: File → Download → PDF

Option 2: LaTeX/Markdown → PDF

If you’re comfortable with markup languages: - Write in Markdown or LaTeX - Include images with proper sizing - Render to PDF

Option 3: R Markdown

# Create a .Rmd file with your visualizations and text
# Knit to PDF (requires LaTeX installation)

Grading Rubric

Component Points Criteria
Software Installation 1 Evidence that software works (visualizations present)
Visualization 1 2 Uses position + color correctly; clear and readable
Visualization 2 2 Uses position + size correctly; clear and readable
Visualization 3 2 Uses 3+ aesthetics creatively; clear and readable
Descriptions 3 All three descriptions clearly explain mappings and insights (1 pt each)
Reflection 1 Thoughtful reflection on experience, challenges, insights
Professional Presentation 1 Well-organized PDF, readable images, proper formatting
TOTAL 12 (Scaled to 8% of course grade)

What “Clear and Readable” Means

✅ Good: - Image is large enough to read axis labels - Title is present and descriptive - Legend is visible (if using color/shape/size) - No unnecessary clutter

❌ Needs Improvement: - Image too small to read text - Missing title or labels - Overcrowded with data points - Unclear what variables are shown

Tips for Success

Time Management

  • Start early! Don’t wait until the last minute
  • Use class time - we’re here to help during tutorial
  • Budget 2-3 hours for the entire lab if you’re new to visualization

Technical Tips

  • Save often - don’t lose your work!
  • Export early - test your export workflow before the deadline
  • Check image quality - make sure exports are readable before submitting
  • Name files clearly - helps you stay organized

Design Tips

  • Keep it simple - this is Lab 1, not a masterpiece!
  • Prioritize clarity - readable > fancy
  • Use appropriate chart types - scatter plots for relationships, bars for comparisons
  • Label everything - titles, axes, legends

Getting Help

  • Ed Discussion - post questions, help classmates
  • Office Hours - Tuesday/Thursday 3:05-3:40 PM after class
  • Tutorial time - ask during Thursday’s hands-on session
  • Canvas - check announcements for tips and updates

Common Mistakes to Avoid

❌ Using the same aesthetics for all three visualizations - We want to see variety! Try different combinations.

❌ Images too small in the PDF - Zoom out and check - can you read the axis labels?

❌ Missing descriptions - We need to know what you mapped and what patterns you found!

❌ Overly complex visualizations - Lab 1 is about basics - save fancy stuff for later labs

❌ Waiting until the last minute - Software installation can have unexpected issues!

❌ Not testing your export - Make sure you can actually save images before the deadline!

Frequently Asked Questions

Q: Can I use a different dataset than the provided one? A: Yes! You can use built-in datasets from your software or find your own (but make sure it has the right variable types).

Q: What if I want to switch software later? A: You can, but it’s better to choose now and stick with it. Each lab builds on previous skills.

Q: Can I use ChatGPT/Claude to help with R/Python code? A: Yes, IF you understand every line of code you submit and cite the LLM use. See syllabus for full policy.

Q: Do my visualizations need to look professional/fancy? A: No! This lab is about functionality, not beauty. Simple and clear is perfect.

Q: What if I have installation problems? A: Come to office hours or post on Ed Discussion with specific error messages.

Q: How long should my reflection be? A: 150-250 words (about 1 paragraph). Be thoughtful but concise.

Q: Can I submit after the deadline? A: No late submissions are accepted. But remember: your lowest lab grade is dropped!

Academic Integrity Reminder

What IS Allowed:

✅ Discussing ideas and approaches with classmates
✅ Helping each other troubleshoot technical issues
✅ Sharing resources and tutorials
✅ Using LLMs for R/Python code (with proper understanding and citation)

What is NOT Allowed:

❌ Copying someone else’s code or visualizations
❌ Submitting identical work as another student
❌ Having someone else create your visualizations
❌ Using AI to write your descriptions/reflection

Remember: You must understand and be able to explain everything you submit!

Checklist Before Submitting

Submission Instructions

  1. Create your PDF following the format above
  2. Name your file: Lastname_Firstname_Lab1.pdf
  3. Go to Canvas → Assignments → Lab 1
  4. Upload your PDF
  5. Verify upload - open the file in Canvas to make sure it looks correct!
  6. Submit before deadline: Thursday, January 8, 2026, 11:59 PM

Example Partial Submission

Here’s what a good submission might look like (shortened for example):

Your Name: Maria Garcia
Software Used: R (ggplot2)
Date: January 8, 2026

Visualization 1: Position + Color

Code 
# Palmer Penguins Visualization Example
# STAT 80B - Lab 1 Example
# Scatter plot: Bill Length vs Bill Depth colored by Species

# Load required packages
library(tidyverse)
library(palmerpenguins)

# Load the penguins data
data(penguins)

# Create the visualization
penguin_plot <- ggplot(data = penguins, 
                        aes(x = bill_length_mm, 
                            y = bill_depth_mm, 
                            color = species)) +
  # Add points with some transparency and good size
  geom_point(size = 3, alpha = 0.8) +
  
  # Add labels and title
  labs(
    title = "Penguin Bill Dimensions by Species",
    subtitle = "Palmer Archipelago, Antarctica (2007-2009)",
    x = "Bill Length (mm)",
    y = "Bill Depth (mm)",
    color = "Species",
    caption = "Data: Gorman, Williams & Fraser (2014)"
  ) +
  
  # Use a clean theme
  theme_minimal(base_size = 12) +
  
  # Customize theme elements
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(size = 10, color = "gray40"),
    legend.position = "bottom",
    panel.grid.minor = element_blank(),
    plot.caption = element_text(size = 8, color = "gray50", hjust = 0)
  ) +
  
  # Custom color palette (optional - ggplot2 default is also good!)
  scale_color_manual(
    values = c("Adelie" = "#FF6B35",      # Orange
               "Chinstrap" = "#9B59B6",    # Purple  
               "Gentoo" = "#2ECC71")       # Green
  )

# Display the plot
print(penguin_plot)

Description: This scatter plot maps bill length (mm) to x-position, bill depth (mm) to y-position, and penguin species to color. It reveals distinct clustering by species: Gentoo penguins (green) have longer but shallower bills, while Adelie penguins (orange) have shorter, deeper bills. Chinstrap penguins (purple) fall in between with long, deep bills. This suggests that bill shape is strongly associated with species identity, likely related to different feeding behaviors or ecological niches.

(Continue with Viz 2, Viz 3, and Reflection…)

Need Help?

Technical Issues: Ed Discussion or Office Hours
Conceptual Questions: Ed Discussion or come to class
Installation Problems: Office Hours (bring your laptop!)
Last-Minute Panic: Don’t wait! Ask for help early!

Good luck, and enjoy creating your first data visualizations! 🎨📊