Advanced Data Visualization with ggplot2

Posted :

Oct 2, 2025

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Objective: Explore more advanced data visualization techniques with ggplot2.

Lab Instructions:

Advanced Plots:

Using the penguins dataset, create a faceted plot showing the relationship between flipper length and body mass for different species.
Experiment with different themes and color palettes to enhance the visual appeal of your plots.

Lab Report:

Submit your R script and the plots.
Include a reflection on how the different visualizations helped to highlight different aspects of the data.

Struggling with where to start this assignment? Follow this guide to tackle your assignment easily!

Step 1: Understand the Objective

This lab is asking you to use advanced plotting features in ggplot2. Your goal is to create faceted plots of the penguins dataset and then write a reflection. You need both code (R script) and written explanation.

Step 2: Plan Your R Script

Load the required packages and dataset

library(ggplot2) library(palmerpenguins) # penguins dataset
Create a base scatter plot
- X-axis: Flipper Length (mm)
- Y-axis: Body Mass (g)
- Color: Differentiate by species
ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g, color = species)) + geom_point() + facet_wrap(~ species) + labs(title = "Flipper Length vs Body Mass by Species", x = "Flipper Length (mm)", y = "Body Mass (g)")
Experiment with themes

+ theme_minimal() + theme_classic() + theme_light()
Try different color palettes

+ scale_color_brewer(palette = "Set2") + scale_color_viridis_d()

Step 3: Organize Your Lab Report

Your report should have three main parts:

R Script Section
- Paste your code neatly.
- Comment your code (e.g., # Create faceted scatter plot).
Plots Section
- Include the generated plots (export as images or embed them if allowed).
- Show at least two different versions with different themes or palettes.
Reflection Section (1–2 paragraphs)
- Explain what insights you gained from faceting by species.
- Discuss how themes/colors changed the clarity and readability.
- Reflect on how visualization choices can emphasize patterns (e.g., species differences in body size).

Step 4: Writing the Reflection

Start with: “Faceting the plots by species allowed me to…”
Mention if certain themes made data easier to interpret.
Compare plain vs. styled plots.
End with: “This exercise showed me that design choices in visualization can significantly impact how results are communicated.”

Step 5: Final Check Before Submission

✔️ R script runs without errors
✔️ At least one faceted plot created
✔️ Tried multiple themes/color palettes
✔️ Reflection written clearly, about 1–2 paragraphs

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