ggplot2 Polish

ggplot2 Polish: Making Your Charts Shine ✨

The Art Gallery Analogy 🖼️

Imagine you’re an artist who just finished painting a beautiful picture. The painting itself is wonderful, but before showing it in a gallery, you need to:

1. Pick the right frame (scales)
2. Add a title card (labels)
3. Make copies to share (saving)

That’s exactly what ggplot2 Polish is about! You’ve already learned to create plots. Now let’s make them gallery-ready.

1. ggplot Scales: Picking the Perfect Frame 🎨

What Are Scales?

Scales are like rulers and color palettes that control how your data appears. They translate your data into visual properties.

Simple Example:

• Your data says “temperature = 100”
• The scale decides: “100 should appear at THIS position on the y-axis”
• The scale also decides: “And it should be THIS shade of red”

The Scale Family

# Basic pattern
scale_[aesthetic]_[type]()

# Examples:
scale_x_continuous()
scale_y_log10()
scale_color_manual()
scale_fill_gradient()

Continuous Scales: Numbers on a Number Line

For data that flows smoothly (like height, weight, temperature):

ggplot(data, aes(x = age, y = height)) +
  geom_point() +
  scale_x_continuous(
    limits = c(0, 100),
    breaks = seq(0, 100, 20)
  ) +
  scale_y_continuous(
    limits = c(50, 200),
    breaks = seq(50, 200, 25)
  )

What this does:

• limits: Set the min and max shown
• breaks: Where to put tick marks

Log Scales: When Numbers Get Huge

Sometimes your data spans from 1 to 1,000,000. Regular scales make small values invisible!

# Population of cities (tiny to massive)
ggplot(cities, aes(x = name, y = pop)) +
  geom_col() +
  scale_y_log10()  # Now we can see all bars!

Think of it like a zoom lens that shows both ants and elephants clearly.

Color Scales: Painting with Data

# Gradient for continuous data
ggplot(data, aes(x, y, color = temp)) +
  geom_point() +
  scale_color_gradient(
    low = "blue",
    high = "red"
  )

# Manual colors for categories
ggplot(data, aes(x, y, color = group)) +
  geom_point() +
  scale_color_manual(
    values = c("A" = "#FF6B6B",
               "B" = "#4ECDC4",
               "C" = "#45B7D1")
  )

Fill vs Color: What’s the Difference?

• Color = outline/border of shapes
• Fill = inside of shapes

# Bars use FILL (inside color)
scale_fill_gradient()

# Points use COLOR (the dot itself)
scale_color_gradient()

2. ggplot Labels: Your Chart’s Name Tag 🏷️

Why Labels Matter

A chart without labels is like a book without a title. Nobody knows what they’re looking at!

The labs() Function: Your Labeling Toolbox

ggplot(data, aes(x = age, y = income)) +
  geom_point() +
  labs(
    title = "Income Grows with Age",
    subtitle = "Based on 2024 survey data",
    x = "Age (years)",
    y = "Annual Income ($)",
    caption = "Source: National Survey",
    color = "Education Level"
  )

Quick Label Shortcuts

# Just change one thing at a time
ggtitle("My Awesome Chart")
xlab("Time (seconds)")
ylab("Speed (km/h)")

Making Labels Beautiful

ggplot(data, aes(x, y)) +
  geom_point() +
  labs(title = "My Title") +
  theme(
    plot.title = element_text(
      size = 16,
      face = "bold",
      hjust = 0.5  # Center it!
    ),
    axis.title = element_text(
      size = 12,
      color = "darkblue"
    )
  )

The Complete Label Recipe

graph TD
    A["labs function"] --> B["title: Main heading"]
    A --> C["subtitle: Extra info"]
    A --> D["x: X-axis name"]
    A --> E["y: Y-axis name"]
    A --> F["caption: Data source"]
    A --> G["color/fill: Legend title"]

3. Saving ggplot Plots: Share Your Art! 💾

The ggsave() Hero

ggsave() is your best friend for saving plots. It’s smart and simple!

# Create your plot
my_plot <- ggplot(data, aes(x, y)) +
  geom_point() +
  labs(title = "My Beautiful Chart")

# Save it!
ggsave("my_chart.png", my_plot)

Picking the Right Format

# Different formats
ggsave("chart.png", my_plot)
ggsave("chart.pdf", my_plot)
ggsave("chart.svg", my_plot)

Controlling Size

ggsave(
  "chart.png",
  my_plot,
  width = 8,     # inches
  height = 6,    # inches
  dpi = 300      # dots per inch (quality)
)

Quick Guide:

• Web: 72-150 dpi
• Print: 300+ dpi
• Poster: 600+ dpi

The Last Plot Trick

Didn’t save your plot to a variable? No problem!

# Just ran this...
ggplot(data, aes(x, y)) + geom_point()

# Save whatever was just displayed
ggsave("last_plot.png")

ggsave() automatically grabs the last plot you made!

Pro Tips for Saving

# For presentations (bigger text)
ggsave("presentation.png",
       width = 10, height = 6,
       dpi = 150)

# For publications (crisp details)
ggsave("publication.pdf",
       width = 7, height = 5)

# For social media (square)
ggsave("instagram.png",
       width = 6, height = 6,
       dpi = 150)

Putting It All Together 🎯

Here’s a complete example with all three skills:

library(ggplot2)

# The data
sales <- data.frame(
  month = 1:12,
  revenue = c(100, 120, 150, 180,
              200, 250, 300, 280,
              260, 220, 180, 150)
)

# Create polished plot
final_plot <- ggplot(sales,
  aes(x = month, y = revenue)) +
  geom_line(color = "#4ECDC4",
            size = 1.5) +
  geom_point(color = "#FF6B6B",
             size = 3) +

  # SCALES
  scale_x_continuous(
    breaks = 1:12,
    labels = month.abb
  ) +
  scale_y_continuous(
    limits = c(0, 350),
    labels = scales::dollar
  ) +

  # LABELS
  labs(
    title = "2024 Revenue by Month",
    subtitle = "Strong summer performance",
    x = "Month",
    y = "Revenue",
    caption = "Data: Sales Team"
  )

# SAVE IT
ggsave("sales_report.png",
       final_plot,
       width = 8, height = 5,
       dpi = 300)

Quick Reference 📝

Scales

scale_x_continuous()  # Number x-axis
scale_y_log10()       # Log scale
scale_color_manual()  # Custom colors
scale_fill_gradient() # Color gradients

Labels

labs(title, subtitle, x, y, caption)
ggtitle("Title")
xlab("X Label")
ylab("Y Label")

Saving

ggsave("file.png", plot,
       width, height, dpi)

You Did It! 🎉

You now know how to:

• Scale your axes and colors perfectly
• Label your charts clearly
• Save your work in any format

Your charts are no longer just data. They’re communication. They’re art. They’re ready to share with the world!

Go make something beautiful. 🌟