Chapter 29 ggplot2 Troubleshooting

What You’ll Learn:

Common pitfalls
Debugging strategies
Performance tips
Best practices
Publication-ready plots

Key Errors Covered: 12+ workflow errors

Difficulty: ⭐⭐ Intermediate to ⭐⭐⭐ Advanced

29.1 Introduction

Mastering ggplot2 troubleshooting saves time:

library(ggplot2)
library(dplyr)

29.2 Common Pitfalls

⚠️ Common ggplot2 Mistakes

# Pitfall 1: Using %>% instead of +
# mtcars %>%
#   ggplot(aes(x = mpg, y = hp)) %>%
#   geom_point()

# Pitfall 2: Variable outside aes
# ggplot(mtcars) +
#   geom_point(aes(x = mpg, y = hp), color = cyl)

# Pitfall 3: Wrong geom for data
# ggplot(mtcars, aes(x = factor(cyl))) +
#   geom_histogram()

# Pitfall 4: Modifying after ggsave
p <- ggplot(mtcars, aes(x = mpg, y = hp)) + geom_point()
ggsave("plot.png", p, width = 6, height = 4)
# p + theme_minimal()  # Won't affect saved plot

29.3 Debugging Strategies

🎯 Best Practice: Debug Plots

# Build incrementally
p <- ggplot(mtcars, aes(x = mpg, y = hp))
p  # Check data and aesthetics

p <- p + geom_point()
p  # Check geom

p <- p + theme_minimal()
p  # Check theme

# Check data
ggplot(mtcars, aes(x = mpg, y = hp)) +
  geom_point() +
  labs(title = paste("N =", nrow(mtcars)))

# Verify aesthetics
p <- ggplot(mtcars, aes(x = mpg, y = hp, color = factor(cyl)))
p$mapping  # See mappings
#> Aesthetic mapping: 
#> * `x`      -> `mpg`
#> * `y`      -> `hp`
#> * `colour` -> `factor(cyl)`

29.4 Performance Tips

🎯 Best Practice: Optimize Performance

# For large data: sample first
# large_data %>%
#   sample_n(1000) %>%
#   ggplot(aes(x = x, y = y)) +
#   geom_point(alpha = 0.5)

# Use geom_hex for many points
ggplot(diamonds, aes(x = carat, y = price)) +
  geom_hex() +
  scale_fill_viridis_c()

# Avoid unnecessary calculations
# Bad: calculate in aes
# ggplot(mtcars, aes(x = mpg, y = hp / wt)) + geom_point()

# Good: calculate first
mtcars %>%
  mutate(hp_per_wt = hp / wt) %>%
  ggplot(aes(x = mpg, y = hp_per_wt)) +
  geom_point()

29.5 Publication-Ready Plots

🎯 Best Practice: Publication Quality

publication_plot <- function(data, x, y, color = NULL) {
  ggplot(data, aes(x = {{x}}, y = {{y}}, color = {{color}})) +
    geom_point(size = 3, alpha = 0.7) +
    theme_minimal() +
    theme(
      plot.title = element_text(face = "bold", size = 14),
      axis.title = element_text(size = 12, face = "bold"),
      axis.text = element_text(size = 10),
      legend.position = "right",
      legend.title = element_text(face = "bold"),
      panel.grid.minor = element_blank(),
      panel.border = element_rect(color = "black", fill = NA)
    ) +
    scale_color_viridis_d(option = "plasma")
}

publication_plot(mtcars, mpg, hp, factor(cyl)) +
  labs(
    title = "Fuel Efficiency vs Horsepower",
    subtitle = "Motor Trend Car Road Tests",
    x = "Miles per Gallon",
    y = "Horsepower",
    color = "Cylinders",
    caption = "Source: mtcars dataset"
  )

29.6 Saving Plots

🎯 Best Practice: Save High-Quality Plots

# High-resolution PNG
ggsave("plot.png", width = 8, height = 6, dpi = 300)

# Vector format
ggsave("plot.pdf", width = 8, height = 6)
ggsave("plot.svg", width = 8, height = 6)

# Specify plot object
p <- ggplot(mtcars, aes(x = mpg, y = hp)) + geom_point()
ggsave("my_plot.png", plot = p, width = 10, height = 8, dpi = 300)

# Different devices
ggsave("plot.jpg", device = "jpeg", quality = 95)
ggsave("plot.tiff", device = "tiff", compression = "lzw")

29.7 Error Checklist

🎯 Debugging Checklist

When a plot fails, check:

Column names - Do they exist in data?

names(data)

Data types - Numeric vs factor?

str(data)

Missing values - NAs present?

summary(data)
colSums(is.na(data))

Operators - Using + not %>%?
Aesthetics - Variables in aes()?
Geom compatibility - Right geom for data?
Scale compatibility - Match data type?

29.8 Common Error Solutions

✅ Quick Fixes

# object not found → Check column names
names(mtcars)
#>  [1] "mpg"        "cyl"        "disp"       "hp"         "drat"      
#>  [6] "wt"         "qsec"       "vs"         "am"         "gear"      
#> [11] "carb"       "cyl_factor"

# + vs %>% → Use + for ggplot layers
mtcars %>%
  filter(cyl == 4) %>%
  ggplot(aes(x = mpg, y = hp)) +  # Use +
  geom_point()

# Discrete/continuous mismatch → Check data type
ggplot(mtcars, aes(x = mpg, y = hp, color = factor(cyl))) +
  geom_point() +
  scale_color_manual(values = c("4" = "red", "6" = "blue", "8" = "green"))

# stat_count requires x or y → Use geom_col for heights
data.frame(x = c("A", "B", "C"), y = c(10, 20, 15)) %>%
  ggplot(aes(x = x, y = y)) +
  geom_col()

# Histogram needs numeric → Use geom_bar for categorical
ggplot(mtcars, aes(x = factor(cyl))) +
  geom_bar()

29.9 Summary

Key Takeaways:

Use + not %>% - For ggplot layers
Build incrementally - Test each step
Check data first - Verify structure
Variables in aes() - Fixed values outside
Match scales - To data types
Save properly - High resolution for publication
Follow checklist - When debugging

Quick Reference:

# Basic structure
ggplot(data, aes(x = var1, y = var2)) +
  geom_point() +
  theme_minimal()

# With preprocessing
data %>%
  filter(condition) %>%
  ggplot(aes(x = var1, y = var2)) +  # + not %>%
  geom_point()

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

# Debug checklist
names(data)              # Column names
str(data)                # Data types
summary(data)            # Check NAs
p$mapping                # Check aesthetics

Best Practices:

# ✅ Good
Build plots incrementally
Check data structure first
Use appropriate geom for data type
Match scales to data
Save at high resolution
Document complex plots

# ❌ Avoid
Using %>% for ggplot layers
Variables outside aes()
Ignoring warnings
Low-resolution saves
Complex one-liners without testing

29.10 Exercises

📝 Exercise 1: Fix Errors

Fix these common errors:

# Error 1
mtcars %>%
  ggplot(aes(x = mpg, y = hp)) %>%
  geom_point()

# Error 2  
ggplot(mtcars) +
  geom_point(aes(x = mpg, y = hp), color = cyl)

# Error 3
ggplot(mtcars, aes(x = factor(cyl))) +
  geom_histogram()

📝 Exercise 2: Publication Plot

Create a publication-ready plot with: - Custom theme - Proper labels - High-quality aesthetics - Save at 300 DPI

29.11 Exercise Answers

Click to see answers

Exercise 1:

# Error 1: Use + not %>%
mtcars %>%
  ggplot(aes(x = mpg, y = hp)) +
  geom_point()

# Error 2: Put variable in aes()
ggplot(mtcars) +
  geom_point(aes(x = mpg, y = hp, color = factor(cyl)))

# Error 3: Use geom_bar() for categorical
ggplot(mtcars, aes(x = factor(cyl))) +
  geom_bar()

Exercise 2:

p <- mtcars %>%
  ggplot(aes(x = mpg, y = hp, color = factor(cyl), size = wt)) +
  geom_point(alpha = 0.7) +
  scale_color_manual(
    values = c("4" = "#1f77b4", "6" = "#ff7f0e", "8" = "#2ca02c"),
    name = "Cylinders"
  ) +
  scale_size_continuous(name = "Weight (1000 lbs)", range = c(2, 6)) +
  labs(
    title = "Fuel Efficiency vs Horsepower",
    subtitle = "Motor Trend Car Road Tests (1974)",
    x = "Miles per Gallon",
    y = "Horsepower",
    caption = "Source: Henderson and Velleman (1981), mtcars dataset"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 16, hjust = 0),
    plot.subtitle = element_text(size = 12, color = "gray40"),
    axis.title = element_text(size = 12, face = "bold"),
    axis.text = element_text(size = 10),
    legend.position = "right",
    legend.title = element_text(face = "bold", size = 11),
    legend.text = element_text(size = 10),
    panel.grid.minor = element_blank(),
    panel.border = element_rect(color = "black", fill = NA, linewidth = 0.5),
    plot.caption = element_text(hjust = 0, size = 8, color = "gray50")
  )

# Save high-resolution
ggsave("publication_plot.png", plot = p, 
       width = 10, height = 7, dpi = 300, bg = "white")

# Also save vector version
ggsave("publication_plot.pdf", plot = p,
       width = 10, height = 7)

29.12 Completion

Part IX Complete!

You’ve mastered: - ggplot2 basics and structure - Advanced customization - Extensions and special plots - Troubleshooting and best practices

Ready for: Part X (Statistical Operations) or other topics!