# Hex Sticker Creation Methods for putior

## Current Design Analysis

The putior hex sticker contains the following complex elements:
- **Base shape**: Hexagonal border with black outline
- **Background**: Purple gradient (darker at bottom, lighter at top)
- **Network visualization**: Connected white nodes/circles showing data flow
- **Document icons**: Stack of papers/files with "# put" annotations
- **Typography**: "putior" text in white at the bottom

## Method Comparison

### 1. hexSticker Package (Recommended for Standard Use)

**Status**: Not currently installed but available on CRAN

**Pros**:
- Purpose-built for R package hex stickers
- Automatic hexagon shape and border handling
- Built-in font management with `showtext` integration
- Standard dimensions and positioning
- Supports both ggplot2 objects and images as subplot
- Well-documented and widely used in R community

**Cons**:
- Limited flexibility for complex custom layouts
- Gradient backgrounds require workarounds
- Multiple distinct elements (docs + network) may need creative layering
- May require creating separate ggplot2 objects first

**Best for**: Standard hex stickers with single central plot

**Implementation approach**:
```r
library(hexSticker)
library(ggraph)

# Create network plot
network_plot <- create_network_plot()  # Custom function

# Create document overlay
doc_plot <- create_document_icons()    # Custom function

# Combine and create sticker
sticker(network_plot, 
        package = "putior",
        p_size = 20,
        h_fill = "#6B5B95",
        h_color = "#000000")
```

### 2. Pure ggplot2 (Recommended for putior)

**Status**: Available (ggplot2 is standard)

**Pros**:
- Complete control over all visual elements
- No additional dependencies beyond base ggplot2
- Can create gradient effects with layered polygons
- Easy to position multiple element types
- Familiar syntax for R users
- Version control friendly (pure R code)
- Easy to modify and maintain

**Cons**:
- More verbose code required
- Manual hexagon shape creation needed
- Font handling requires care for reproducibility
- Need to manually manage aspect ratios and sizing

**Best for**: Complex designs with multiple element types

**Implementation approach**:
```r
# Create hexagon vertices
create_hexagon <- function(center_x = 0, center_y = 0, size = 1) {
  angles <- seq(30, 330, by = 60) * pi / 180
  data.frame(
    x = center_x + size * cos(angles),
    y = center_y + size * sin(angles)
  )
}

# Layer all elements
ggplot() +
  geom_polygon(data = hex_shape, aes(x, y), fill = gradient) +
  geom_segment(data = edges, aes(...)) +  # Network
  geom_point(data = nodes, aes(...)) +    # Nodes
  geom_rect(data = docs, aes(...)) +      # Documents
  annotate("text", label = "putior") +
  theme_void()
```

### 3. Grid Graphics

**Status**: Available (base R)

**Pros**:
- Maximum low-level control
- Efficient for complex custom graphics
- True gradient support with `linearGradient()`
- Precise positioning and sizing
- Can create exact SVG-like graphics

**Cons**:
- Steep learning curve
- Very verbose code
- Less integration with data visualization paradigms
- Harder to maintain and modify
- Not familiar to most R users

**Best for**: Exact replications of complex designs

### 4. Hybrid Approaches

#### 4a. ggplot2 + hexSticker
Create complex plot in ggplot2, then use hexSticker for final formatting:
```r
complex_plot <- create_putior_elements()  # ggplot2
sticker(complex_plot, package = "putior", ...)
```

#### 4b. Multiple packages
- **ggraph**: Network visualization
- **ggforce**: Additional geometric shapes
- **patchwork**: Combine multiple plot elements
- **gganimate**: If animated version desired

## Specific Challenges for putior Design

### 1. Gradient Background
- **ggplot2**: Use multiple semi-transparent polygons or `scale_fill_gradient2()`
- **hexSticker**: Limited support, may need image input
- **grid**: Native `linearGradient()` support

### 2. Network Visualization
- **ggraph**: Purpose-built for networks, integrates well with ggplot2
- **Manual**: Simple enough to code with `geom_segment()` + `geom_point()`

### 3. Document Icons
- **Simplified**: Use rectangles with text overlays
- **Complex**: Create custom polygons or use font icons
- **Image-based**: Import SVG icons and position them

### 4. Multiple Element Types
- **ggplot2**: Layer everything in single plot
- **hexSticker**: May need to pre-combine elements into single ggplot2 object

## Recommendation: Pure ggplot2 Approach

For the putior hex sticker, I recommend the **pure ggplot2 approach** because:

1. **Complexity handling**: The design has multiple distinct element types that are easier to manage in a single ggplot2 canvas
2. **No dependencies**: Avoids adding hexSticker as a dependency
3. **Maintainability**: Pure R code is easier to version control and modify
4. **Gradient support**: Can achieve gradient effects with layered polygons
5. **Positioning control**: Precise control over all element positioning
6. **Reproducibility**: No external dependencies or font issues

## Implementation Plan

1. **Create hexagon shape function** for consistent borders
2. **Implement gradient effect** with multiple polygon layers
3. **Add network elements** using ggraph or manual positioning
4. **Create document icons** as simplified rectangles with text
5. **Add typography** with proper font handling
6. **Export function** with appropriate dimensions and DPI

## Required Packages

Minimal setup:
```r
library(ggplot2)      # Core plotting
library(showtext)     # Font handling (optional)
```

Enhanced setup:
```r
library(ggplot2)
library(ggraph)       # Network layout algorithms
library(igraph)       # Network data structures
library(showtext)     # Font management
```

## Output Specifications

- **Format**: PNG for package use, SVG for scalability
- **Dimensions**: 2" x 2" (standard hex sticker size)
- **Resolution**: 300 DPI minimum
- **Colors**: Hex codes for consistency
- **Fonts**: System fonts or embed with showtext for reproducibility