ggplot2 and Thematic Integration with Brand Themes • rbranding

Overview

This vignette demonstrates how to integrate ggplot2 and plotly visualizations with brand themes using the rbranding package. The package provides functions to:

Load brand configuration from _brand.yml files
Apply brand colors and typography to ggplot2 themes
Add brand logos to plots
Reset themes when needed

Getting Started

First, let’s load the required packages:

library(rbranding)
library(ggplot2)

# Suggested packages for enhanced functionality
# library(plotly)  # For interactive plots
# library(sysfonts)  # For custom fonts
# library(showtext)  # For custom fonts

Step 1: Initialize and Load Brand Configuration

Start by initializing the branding configuration and getting the latest brand file:

# Initialize branding (creates config.yml and placeholder _brand.yml)
brand_init()

# Get the latest brand file from the repository
get_brand()

# to install these files directly to your working directory:
get_template("ggplot2)

For this vignette, we’ll use the existing _brand.yml file in the package:

# In a real project, you would have a _brand.yml file in your working directory
# For this demo, we'll use the package's example brand file
brand_file <- system.file("brand_files", "_brand.yml", package = "rbranding")
if (brand_file != "") {
  file.copy(brand_file, "_brand.yml")
  # Copy logo files as well
  logo_files <- list.files(system.file("brand_files", package = "rbranding"), 
                          pattern = "*.png", full.names = TRUE)
  file.copy(logo_files, ".")
  brand_file <- "_brand.yml"
} else {
  # Fallback to a basic brand configuration for demonstration
  brand_config <- "
meta:
  name:
    full: Example Organization
    short: EO

color:
  palette:
    primary: '#1c8478'
    secondary: '#4e2d53'
    accent: '#474747'
  foreground: black
  background: white
  primary: primary
  secondary: secondary

typography:
  fonts:
    - family: Open Sans
      source: google
  base: Open Sans
"
  writeLines(brand_config, "_brand.yml")
  brand_file <- "_brand.yml"
}

cat("Using brand file:", brand_file)
#> Using brand file: _brand.yml

Step 2: Set the ggplot2 Theme

Apply the brand theme to ggplot2. This will set colors and fonts according to your brand configuration:

# Set the brand theme
brand_set_ggplot(brand_file)
#> Brand theme applied successfully!
#> Custom font loaded: open_sans

Step 3: Create ggplot2 Visualizations

Now create some plots that will automatically use your brand theme:

# Create a basic scatter plot
p1 <- ggplot(mtcars, aes(x = mpg, y = wt)) +
  geom_point(aes(color = factor(cyl)), size = 3) +
  labs(
    title = "Car Weight vs. Miles per Gallon",
    subtitle = "Data from the 1974 Motor Trend magazine",
    x = "Miles per Gallon",
    y = "Weight (1000 lbs)",
    color = "Cylinders"
  ) +
  theme(legend.position = "bottom")

print(p1)

# Create a bar plot
p2 <- ggplot(mtcars, aes(x = factor(cyl), fill = factor(gear))) +
  geom_bar(position = "dodge") +
  labs(
    title = "Car Count by Cylinders and Gears",
    x = "Number of Cylinders",
    y = "Count",
    fill = "Gears"
  ) +
  theme(legend.position = "bottom")

print(p2)

Step 4: Add Brand Logo (Optional)

If your brand configuration includes a logo, you can add it to your plots:

# Add logo to the plot (requires logo in brand.yml and png package)
p1_with_logo <- p1 + brand_add_logo(x = 0.9, y = 0.1, size = 0.05)
print(p1_with_logo)

Step 5: Interactive Plots with plotly

You can also create interactive versions of your plots using plotly:

library(plotly)

# Convert ggplot to interactive plotly chart
p1_interactive <- ggplotly(p1, tooltip = c("x", "y", "colour"))
p1_interactive

Step 6: Advanced Theming

You can customize specific aspects of the theme while maintaining brand consistency:

# Customize theme elements while keeping brand colors
p3 <- ggplot(mtcars, aes(x = hp, y = mpg, size = wt)) +
  geom_point(alpha = 0.7) +
  scale_size_continuous(range = c(2, 8)) +
  labs(
    title = "Engine Performance Analysis",
    subtitle = "Relationship between horsepower, fuel efficiency, and weight",
    x = "Horsepower",
    y = "Miles per Gallon",
    size = "Weight (1000 lbs)"
  ) +
  theme(
    plot.title = element_text(size = 16, face = "bold"),
    plot.subtitle = element_text(size = 12, face = "italic"),
    legend.position = "right"
  )

print(p3)

Step 7: Reset Theme

When you’re done with the brand theme, you can reset to the default ggplot2 theme:

# Reset to original theme
brand_reset_ggplot()
#> ggplot2 theme reset to previous state.

# Create a plot with default theme to show the difference
p4 <- ggplot(mtcars, aes(x = mpg, y = wt)) +
  geom_point(aes(color = factor(cyl)), size = 3) +
  labs(
    title = "Same Plot with Default Theme",
    x = "Miles per Gallon",
    y = "Weight (1000 lbs)",
    color = "Cylinders"
  )

print(p4)

Best Practices

Set theme early: Call brand_set_ggplot() at the beginning of your analysis
Test font loading: Custom fonts may not work in all environments
Use consistent colors: Stick to the brand palette for consistency
Reset when needed: Use brand_reset_ggplot() to return to default themes
Logo placement: Position logos where they don’t interfere with data

Troubleshooting

Common Issues

Font loading fails: Some environments may not support custom Google Fonts
Logo not found: Ensure the logo path in _brand.yml is correct and the file exists
Colors not applied: Check that your _brand.yml file follows the correct schema

Solutions

# Disable custom fonts if having issues
brand_set_ggplot(use_fonts = FALSE)

# Check brand configuration
doc <- yaml::read_yaml("_brand.yml")
str(doc$color)
str(doc$typography)

Conclusion

The rbranding package makes it easy to create consistent, branded visualizations across your organization. By following this workflow, you can ensure that all your ggplot2 and plotly charts maintain brand consistency while being accessible and professional.

For more information about the brand.yml schema, visit: https://github.com/posit-dev/brand-yml/