Customizing plots is essential for enhancing both the clarity and visual appeal of your data visualizations. By adjusting elements like titles, labels, colors, and themes, you can make your plots more informative and easier to interpret. Effective customization not only helps highlight key insights and trends in the data but also improves communication by providing necessary context.

Common Customization Features

• Titles and labels – add plot titles, axis labels, and captions for better context and interpretation;

• Themes – apply pre-defined themes like minimal, dark, or classic to adjust the plot's appearance;

• Colors and fills – use fill and color aesthetics to differentiate groups or emphasize patterns;

• Legends – customize legend titles, order, and position to make plots more readable;

• Annotations – add text or shapes (e.g., arrows, labels) to highlight specific data points;

• Manual color scales – use scale_fill_manual() or scale_color_manual() to set custom color schemes;

Font and text styling – modify size, style, and fontface of text for emphasis and branding.

Example: Add Title, Labels, Theme

ggplot(df, aes(x = fuel, y = selling_price)) + 
  geom_bar(stat = "summary", fun = "mean", fill = "red", color = "black") + 
  labs(title = "Average Selling Price by Fuel Type",
       x = "Fuel Type",
       y = "Average Price",
       caption = "Data Source: Car Sales Dataset") +
  theme_linedraw()