Multi-Hospital Branding

Introduction

BFHtheme supports both Bispebjerg og Frederiksberg Hospital and Region Hovedstaden branding. This vignette shows how to choose palettes, scales, and defaults that match the audience for each deliverable.

You will learn how to:

access the hospital and regional colour systems,
apply sequential and infographic palettes,
switch defaults on the fly between organisations,
ensure accessibility and departmental consistency,
and document branding choices for multi-site reporting.

Need	Hospital focus	Region focus
Core discrete palette	`palette = "hospital"`	`palette = "regionh"`
Sequential gradients	`"hospital_blues_seq"`	`"regionh_blues_seq"`
Infographics / many categories	`"hospital_infographic"`	`"regionh_infographic"`
Default session styling	`set_bfh_defaults(palette = "hospital")`	`set_bfh_defaults(palette = "regionh")`

library(BFHtheme)
library(ggplot2)

Organisational Colour Palettes

Bispebjerg og Frederiksberg Hospital

Hospital-level materials emphasise bright blues with light neutrals. Use these palettes for BFH internal reports, departmental dashboards, and patient-facing content.

# Access hospital colours
hospital_primary <- bfh_cols("hospital_primary")
hospital_blue <- bfh_cols("hospital_blue")

print(hospital_primary)
#> hospital_primary 
#>        "#007dbb"
print(hospital_blue)
#> hospital_blue 
#>     "#009ce8"

# View all hospital colours
hospital_palette <- bfh_cols(
  "hospital_primary",
  "hospital_blue",
  "hospital_light_blue1",
  "hospital_light_blue2",
  "regionh_grey"
)
print(hospital_palette)
#>     hospital_primary        hospital_blue hospital_light_blue1 
#>            "#007dbb"            "#009ce8"            "#99d8f6" 
#> hospital_light_blue2         regionh_grey 
#>            "#d8eef9"            "#646c6f"

Apply the hospital palette to discrete fills:

ggplot(mtcars, aes(x = factor(cyl), fill = factor(cyl))) +
  geom_bar() +
  labs(
    title = "Hospital Branding Example",
    x = "Cylinders",
    fill = "Cylinders"
  ) +
  scale_fill_bfh(palette = "hospital") +
  theme_bfh()

Region Hovedstaden

Region-level communications lean on a deeper navy and cool neutrals.

# Access Region H colours
regionh_primary <- bfh_cols("regionh_primary")
regionh_blue <- bfh_cols("regionh_blue")

print(regionh_primary)
#> regionh_primary 
#>       "#002555"
print(regionh_blue)
#> regionh_blue 
#>    "#007dbb"

# View all Region H colours
regionh_palette <- bfh_cols(
  "regionh_primary",
  "regionh_blue",
  "regionh_light_grey1",
  "regionh_light_grey2",
  "regionh_grey"
)
print(regionh_palette)
#>     regionh_primary        regionh_blue regionh_light_grey1 regionh_light_grey2 
#>           "#002555"           "#007dbb"           "#ccd3dd"           "#e5e9ee" 
#>        regionh_grey 
#>           "#646c6f"

Switch to the Region H palette when preparing cross-hospital material:

ggplot(mtcars, aes(x = factor(cyl), fill = factor(cyl))) +
  geom_bar() +
  labs(
    title = "Region Hovedstaden Branding",
    x = "Cylinders",
    fill = "Cylinders"
  ) +
  scale_fill_bfh(palette = "regionh") +
  theme_bfh()

Sequential Palettes

Hospital Sequential Palettes

For visualisations that map magnitude or rank, use the hospital sequential palettes:

# Create sample data
set.seed(123)
data <- expand.grid(x = 1:10, y = 1:10)
data$z <- with(data, x + y + rnorm(100, 0, 2))

ggplot(data, aes(x = x, y = y, fill = z)) +
  geom_tile() +
  labs(title = "Hospital Blues Sequential Palette") +
  scale_fill_bfh_continuous(palette = "hospital_blues_seq") +
  theme_bfh()

Region H Sequential Palettes

Regional gradients emphasise the darker navy tones while maintaining legible steps for heatmaps and surface plots.

ggplot(data, aes(x = x, y = y, fill = z)) +
  geom_tile() +
  labs(title = "Region H Blues Sequential Palette") +
  scale_fill_bfh_continuous(palette = "regionh_blues_seq") +
  theme_bfh()

Infographic Palettes

Use infographic palettes when you need many clearly differentiated categories. They balance the brand primaries with supportive neutrals.

Hospital Infographic Palette

# Sample categorical data
category_data <- data.frame(
  category = LETTERS[1:5],
  value = c(23, 45, 12, 34, 28)
)

ggplot(category_data, aes(x = reorder(category, value), y = value, fill = category)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Hospital Infographic Colours",
    x = "Category",
    y = "Value"
  ) +
  scale_fill_bfh(palette = "hospital_infographic") +
  theme_bfh() +
  theme(legend.position = "none")

Region H Infographic Palette

ggplot(category_data, aes(x = reorder(category, value), y = value, fill = category)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Region H Infographic Colours",
    x = "Category",
    y = "Value"
  ) +
  scale_fill_bfh(palette = "regionh_infographic") +
  theme_bfh() +
  theme(legend.position = "none")

Combining Organisational Branding

Consistent Theme with Different Palettes

Use the same theme structure across organisations while swapping palettes:

# Hospital version
p_hospital <- ggplot(mtcars, aes(x = wt, y = mpg, colour = hp)) +
  geom_point(size = 3) +
  labs(
    title = "Hospital Analysis",
    subtitle = "Vehicle Performance Data"
  ) +
  scale_color_bfh_continuous(palette = "hospital_blues") +
  theme_bfh()

print(p_hospital)

# Region H version
p_regionh <- ggplot(mtcars, aes(x = wt, y = mpg, colour = hp)) +
  geom_point(size = 3) +
  labs(
    title = "Region Hovedstaden Analysis",
    subtitle = "Vehicle Performance Data"
  ) +
  scale_color_bfh_continuous(palette = "regionh_blues") +
  theme_bfh()

print(p_regionh)

Setting Default Palette

Set organisational defaults for a session:

# Set hospital as default
set_bfh_defaults(palette = "hospital")

# All subsequent plots use hospital colours
ggplot(mtcars, aes(x = factor(cyl))) +
  geom_bar()

# Switch to Region H defaults
set_bfh_defaults(palette = "regionh")

# All subsequent plots use Region H colours
ggplot(mtcars, aes(x = factor(cyl))) +
  geom_bar()

Colour Accessibility

Contrast and Readability

Both hospital and Region H palettes are designed for adequate contrast:

# Hospital primary on white background
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(colour = bfh_cols("hospital_primary"), size = 3) +
  labs(title = "Hospital Primary Colour") +
  theme_bfh()


# Region H primary on white background
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(colour = bfh_cols("regionh_primary"), size = 3) +
  labs(title = "Region H Primary Colour") +
  theme_bfh()

Colourblind Considerations

Check palette accessibility:

# Basic check
check_colorblind_safe(bfh_cols("hospital_primary", "hospital_blue"))

# For comprehensive testing, use specialised packages
# install.packages("colorblindcheck")
library(colorblindcheck)

palette_check(
  bfh_palettes$hospital,
  plot = TRUE
)

Customising for Specific Departments

Department-Specific Colours

Extend the colour system for department use:

# Define department colours using base palette
dept_emergency <- bfh_cols("hospital_primary")
dept_surgery <- bfh_cols("hospital_blue")
dept_pediatrics <- bfh_cols("hospital_light_blue1")

dept_colors <- c(
  "Emergency" = dept_emergency,
  "Surgery" = dept_surgery,
  "Pediatrics" = dept_pediatrics
)

# Use in plots
dept_data <- data.frame(
  department = names(dept_colors),
  patients = c(120, 85, 95)
)

ggplot(dept_data, aes(x = department, y = patients, fill = department)) +
  geom_col() +
  scale_fill_manual(values = dept_colors) +
  labs(
    title = "Patient Count by Department",
    x = "Department",
    y = "Number of Patients"
  ) +
  theme_bfh() +
  theme(legend.position = "none")

Multi-Site Comparisons

Comparing Data Across Sites

Maintain consistent styling when comparing multiple sites by mapping each site to a predefined brand colour:

# Sample multi-site data
site_data <- data.frame(
  site = rep(c("BFH", "Rigshospitalet", "Hvidovre"), each = 4),
  quarter = rep(paste0("Q", 1:4), 3),
  value = c(
    85, 88, 92, 90,  # BFH
    78, 82, 85, 83,  # Rigshospitalet
    82, 85, 88, 86   # Hvidovre
  )
)

ggplot(site_data, aes(x = quarter, y = value, group = site, colour = site)) +
  geom_line(linewidth = 1) +
  geom_point(size = 3) +
  labs(
    title = "Quarterly Performance Across Sites",
    x = "Quarter",
    y = "Performance Score",
    colour = "Site"
  ) +
  scale_colour_manual(
    values = c(
      "BFH" = bfh_cols("hospital_primary"),
      "Rigshospitalet" = bfh_cols("regionh_primary"),
      "Hvidovre" = bfh_cols("regionh_grey")
    )
  ) +
  theme_bfh()
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.

Brand Guidelines Reference

Hospital Colours

Primary: hospital_primary (#007dbb) - Use: Main brand colour, primary data series - Usage: Headers, primary chart elements

Secondary: hospital_blue (#009ce8) - Use: Secondary data series, accents - Usage: Supporting chart elements

Light Blues: hospital_light_blue1, hospital_light_blue2 - Use: Backgrounds, fills, lighter emphasis - Usage: Sequential scales, area fills

Neutrals: regionh_grey, regionh_dark - Use: Text, borders, secondary information - Usage: Axis labels, grid lines

Region Hovedstaden Colours

Primary: regionh_primary (#002555) - Use: Main brand colour for regional reporting - Usage: Headers, primary emphasis

Secondary: regionh_blue (#007dbb) - Use: Secondary series, complements primary - Usage: Supporting elements

Light Greys: regionh_light_grey1, regionh_light_grey2 - Use: Backgrounds, subtle fills - Usage: Sequential scales, backgrounds

Neutrals: regionh_grey, regionh_dark_grey - Use: Text, neutral elements - Usage: Labels, annotations

Usage Guidelines

When to Use Hospital vs Region H Branding

Hospital Branding: - Internal BFH reports and presentations - Department-level analyses - Hospital-specific communications - Patient-facing materials for BFH

Region H Branding: - Regional reports and comparisons - Cross-hospital analyses - Regional management presentations - Koncern-level communications

Consistency Best Practices

Use one primary palette per document
Maintain consistent palette choice across related documents
Use infographic palettes for categorical data with many groups
Use sequential palettes for continuous data
Test colour combinations for accessibility
Document palette choice in analysis scripts

Next Steps

See vignette("getting-started") for basic usage patterns
See vignette("customization") for advanced options
Review organisational brand guidelines for detailed specifications
Check ?bfh_cols and ?bfh_palettes for complete colour reference