knitr::opts_chunk$set(echo = TRUE)
library(ggplot2)
library(themefurmanu)
# Note: Font checking happens automatically when package loads

themefurmanu

The themefurmanu package provides a custom ggplot2 theme and font registration functions using Furman University’s visual identity.

Note that this is not an official package from Furman University. It is a personal project to help with visual consistency in data visualizations across teams at Furman that use R.

Installation

# From GitHub
devtools::install_github("scottamoore/themefurmanu")

Optional: IBM Plex Fonts

For the authentic Furman University branding experience, install IBM Plex fonts:

1. Download IBM Plex Sans and IBM Plex Sans Condensed from Google Fonts
2. Install the fonts on your system
3. Restart R/RStudio
4. Use theme_furmanu(base_family = "IBM Plex Sans") in your plots

The package works perfectly with system fonts (the default), but IBM Plex fonts provide the complete visual identity.

# Check what fonts are available (optional)
register_furmanu_fonts()

# Use IBM Plex fonts if installed
ggplot(data, aes(x, y)) + 
  geom_point() + 
  theme_furmanu(base_family = "IBM Plex Sans")

The themefurmanu Approach: One Function, Many Possibilities

Instead of learning multiple function names like scale_color_viridis_d(), scale_color_brewer(), etc., themefurmanu uses two main functions with parameters:

• scale_color_furmanu(palette = "name", discrete = TRUE/FALSE, ...)
• scale_fill_furmanu(palette = "name", discrete = TRUE/FALSE, ...)

This approach is more intuitive and consistent across all use cases.

Quick Start

library(ggplot2)
library(themefurmanu)

# Basic scatter plot with categorical colors
ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 3) +
  scale_color_furmanu(palette = "main", discrete = TRUE) +
  theme_furmanu() +
  labs(
    title = "Fuel Efficiency by Car Weight",
    subtitle = "Colored by number of cylinders",
    x = "Weight (1000 lbs)",
    y = "Miles per Gallon",
    color = "Cylinders",
    caption = "Source: mtcars dataset"
  )

Available Palettes

View all available palettes with their intended use cases:

show_furmanu_palettes()

# Get detailed information about palettes
list_palettes()

Palette Types and Usage Guide

Categorical Data (Discrete Groups)

For discrete categories like species, groups, or factors:

# Use the "main" palette for up to 6 categories
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 3, alpha = 0.8) +
  scale_color_furmanu(palette = "main", discrete = TRUE) +
  theme_furmanu() +
  labs(
    title = "Iris Species Comparison",
    subtitle = "Using categorical color palette",
    x = "Sepal Length", y = "Sepal Width"
  )

Sequential Data (Continuous, One Direction)

For continuous data that goes from low to high:

# Use sequential palettes for continuous data
ggplot(faithfuld, aes(waiting, eruptions, fill = density)) +
  geom_tile() +
  scale_fill_furmanu(palette = "sequential1", discrete = FALSE) +
  theme_furmanu() +
  labs(
    title = "Old Faithful Eruption Patterns",
    subtitle = "Using sequential color palette",
    x = "Waiting Time (min)", y = "Eruption Duration (min)",
    fill = "Density"
  )

Divergent Data (Continuous, Two Directions)

For data that diverges from a meaningful center point:

# Create divergent data
mtcars$mpg_diff <- mtcars$mpg - mean(mtcars$mpg)

ggplot(mtcars, aes(x = wt, y = hp, color = mpg_diff)) +
  geom_point(size = 4) +
  scale_color_furmanu(
    palette = "divergent1", 
    discrete = FALSE, 
    midpoint = 0  # This is key for divergent scales!
  ) +
  theme_furmanu() +
  labs(
    title = "Car Performance vs Fuel Efficiency",
    subtitle = "Color shows deviation from average MPG",
    x = "Weight (1000 lbs)", y = "Horsepower",
    color = "MPG\nDifference"
  )

Theme Variants

Standard Theme

ggplot(mpg, aes(displ, hwy)) +
  geom_point(color = furmanu_cols("purple"), alpha = 0.7) +
  theme_furmanu() +
  labs(title = "Standard Furman Theme")

Presentation Theme

ggplot(mpg, aes(displ, hwy)) +
  geom_point(color = furmanu_cols("purple"), alpha = 0.7) +
  theme_furmanu_presentation() +
  labs(title = "Presentation Theme")

Minimal Theme

ggplot(mpg, aes(displ, hwy)) +
  geom_point(color = furmanu_cols("purple"), alpha = 0.7) +
  theme_furmanu_minimal() +
  labs(title = "Minimal Theme")

Advanced Features

Color Accessibility

# Check contrast ratios
check_contrast("main")

# Find accessible combinations
head(accessible_combinations())

Palette Export

# Export as CSS variables
export_palette("main", format = "css")

# Export as JSON
export_palette("divergent1", format = "json")

Tips for Effective Use

1. Start with palette exploration: Use show_furmanu_palettes() to see all options
2. Choose the right type:
   • Categorical → "main" or "cool"
   • Sequential → "sequential1", "sequential2", "sequential3"
   • Divergent → "divergent1", "divergent2", "divergent3"
3. Use discrete = FALSE for continuous data
4. Add midpoint = 0 for divergent continuous scales
5. Check accessibility: Use check_contrast() for important visualizations
6. Match theme to context: Use theme_furmanu_presentation() for slides

Getting Help

# Complete function reference and examples
?themefurmanu_help

# Package overview with all functions
?themefurmanu-overview

# Visual palette overview
show_furmanu_palettes()

# Complete tutorial
open_furmanu_demo()

# Quick reference guide
open_furmanu_reference()

# Help for specific functions
?scale_color_furmanu
?theme_furmanu
?show_furmanu_palettes

# List all package functions
help(package = "themefurmanu")

# Get detailed palette information
palette_info("main")

See also open_furmanu_demo() to view the comprehensive example Quarto file with detailed tutorials and advanced techniques.