---
title: "Multi-Hospital Branding"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Multi-Hospital Branding}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)
```

## 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")` |

```{r load, message=FALSE}
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.

```{r hospital-colors}
# Access hospital colours
hospital_primary <- bfh_cols("hospital_primary")
hospital_blue <- bfh_cols("hospital_blue")

print(hospital_primary)
print(hospital_blue)

# View all hospital colours
hospital_palette <- bfh_cols(
  "hospital_primary",
  "hospital_blue",
  "hospital_light_blue1",
  "hospital_light_blue2",
  "regionh_grey"
)
print(hospital_palette)
```

Apply the hospital palette to discrete fills:

```{r hospital-plot}
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.

```{r regionh-colors}
# Access Region H colours
regionh_primary <- bfh_cols("regionh_primary")
regionh_blue <- bfh_cols("regionh_blue")

print(regionh_primary)
print(regionh_blue)

# View all Region H colours
regionh_palette <- bfh_cols(
  "regionh_primary",
  "regionh_blue",
  "regionh_light_grey1",
  "regionh_light_grey2",
  "regionh_grey"
)
print(regionh_palette)
```

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

```{r regionh-plot}
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:

```{r hospital-sequential}
# 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.

```{r regionh-sequential}
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

```{r hospital-infographic}
# 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

```{r regionh-infographic}
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:

```{r combined-hospital}
# 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)
```

```{r combined-regionh}
# 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:

```{r defaults-hospital, eval=FALSE}
# Set hospital as default
set_bfh_defaults(palette = "hospital")

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

```{r defaults-regionh, eval=FALSE}
# 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:

```{r 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:

```{r colorblind, eval=FALSE}
# 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:

```{r department-colors}
# 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:

```{r multi-site}
# 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()
```

## 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

1. Use one primary palette per document
2. Maintain consistent palette choice across related documents
3. Use infographic palettes for categorical data with many groups
4. Use sequential palettes for continuous data
5. Test colour combinations for accessibility
6. 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