---
title: "Publication-Ready Forest Plots with evanverse"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Publication-Ready Forest Plots with evanverse}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 12,
  fig.height = 8,
  dpi = 300,
  out.width = "100%"
)
```

```{r setup, message = FALSE, warning = FALSE}
library(evanverse)
library(dplyr)
library(grid)
```

# 🌲 Introduction

Forest plots are essential visualization tools in biomedical research, epidemiology, and statistical analysis. They excel at presenting:

- **Regression analysis results** (Logistic, Cox proportional hazards)
- **Meta-analysis** (combining multiple studies)
- **Subgroup comparisons** (stratified analyses)

A well-designed forest plot not only conveys statistical results clearly but also meets publication standards for journals and conferences.

## Why evanverse?

While R has several packages for creating forest plots (e.g., `forestplot`, `forestploter`, `meta`), **evanverse** provides:

✅ **Publication-ready defaults** - Beautiful themes out of the box
✅ **Comprehensive customization** - 40+ parameters for fine control
✅ **Multi-model comparison** - Compare multiple regression models side-by-side
✅ **Intelligent automation** - Auto-formatting, alignment, and significance highlighting
✅ **Batch processing** - Efficient workflows for large-scale analyses

This vignette demonstrates how to create professional forest plots from basic examples to advanced customizations.

---

# 📊 Understanding Forest Plots

## What is a Forest Plot?

A forest plot displays effect estimates and their confidence intervals for multiple variables or studies. Each row represents:

- **Variable name** - The predictor or study identifier
- **Point estimate** - Effect size (OR, HR, RR, MD, etc.)
- **Confidence interval** - Uncertainty range (typically 95% CI)
- **P-value** - Statistical significance indicator
- **Reference line** - No-effect threshold (OR/HR = 1, MD = 0)

## Key Components

```
┌─────────────┬────────────┬───────────┬─────────┐
│ Variable    │   CI Plot  │ OR (CI)   │ P-value │
├─────────────┼────────────┼───────────┼─────────┤
│ Age ≥65     │  ──■──     │ 1.45 (...)│ 0.001   │
│ Male        │  ──■──     │ 0.88 (...)│ 0.189   │
│ Smoking     │    ──■──   │ 1.67 (...)│ <0.001  │
└─────────────┴────────────┴───────────┴─────────┘
              ↑            ↑
         Reference      Estimate
            line        & CI box
```

## Common Applications

| Scenario | Effect Measure | Reference Line |
|----------|---------------|----------------|
| Logistic Regression | Odds Ratio (OR) | 1 |
| Cox Regression | Hazard Ratio (HR) | 1 |
| Risk Analysis | Relative Risk (RR) | 1 |
| Mean Difference | Mean Difference (MD) | 0 |
| Meta-analysis | Pooled Effect | Depends on metric |

---

# 🚀 Quick Start

## Installation

```{r installation, eval = FALSE}
# Install from CRAN (when available)
install.packages("evanverse")

# Or install development version from GitHub
# install.packages("devtools")
devtools::install_github("evanbio/evanverse")
```

## Minimal Example (5 minutes)

Let's create a basic forest plot using the built-in `forest_data` dataset.

### Step 1: Load and Inspect Data

```{r load-data}
# Load built-in example data
data("forest_data")

# Inspect structure
head(forest_data, 10)
```

### Step 2: Prepare Display Data

The key to `plot_forest()` is preparing a **display data frame** with all text columns you want to show.

```{r prepare-data}
# Filter single-model data
df_single <- forest_data %>%
  filter(is.na(est_2)) %>%      # Single model (no est_2)
  filter(!is.na(est)) %>%        # Remove header rows
  head(10)                       # First 10 rows for demo

# Create display table
plot_data <- df_single %>%
  mutate(
    ` ` = strrep(" ", 20),       # Blank column for CI graphic
    `OR (95% CI)` = sprintf("%.2f (%.2f-%.2f)", est, lower, upper),
    `P` = ifelse(pval < 0.001, "<0.001", sprintf("%.3f", pval)),
    `N` = n_total
  ) %>%
  select(Variable = variable, ` `, `OR (95% CI)`, `P`, `N`)

print(plot_data)
```

### Step 3: Create Forest Plot

```{r basic-forest, fig.height = 6}
# Create forest plot
p1 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,                 # Column for CI graphic (blank column)
  ref_line = 1,                  # OR = 1 reference
  xlim = c(0.5, 2.5),
  arrow_lab = c("Lower Risk", "Higher Risk")
)

print(p1)
```

**🎉 Congratulations!** You just created your first publication-ready forest plot.

---

# 🎨 Single-Model Forest Plots

Now let's explore how to customize single-model forest plots for different scenarios.

## Data Preparation Deep Dive

Understanding the data structure is crucial for `plot_forest()`:

```{r data-structure, eval = FALSE}
# YOUR data frame should have:
# 1. Display columns (text, formatted strings)
# 2. Numeric vectors for est, lower, upper (NOT in data frame)
# 3. A blank column (" ") where CI graphics will be drawn

plot_data <- data.frame(
  Variable = c("Age", "Sex", "BMI"),           # Display
  ` ` = rep(strrep(" ", 20), 3),               # Blank for CI
  `OR (95% CI)` = c("1.45 (...)", ...),        # Display
  `P` = c("0.001", "0.189", "0.045")           # Display
)

# Numeric vectors (not in data frame)
est_values <- c(1.45, 0.88, 1.35)
lower_values <- c(1.10, 0.65, 1.05)
upper_values <- c(1.83, 1.18, 1.71)
```

**Key Points:**
- Display data and numeric data are **separate**
- Use `sprintf()` to format OR/HR strings
- Create blank column with `strrep(" ", width)`

## Theme Customization

### Using Preset Themes

```{r theme-preset, fig.height = 6}
# Default theme (built-in)
p2 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  theme_preset = "default"
)

print(p2)
```

### Custom Theme Parameters

```{r theme-custom, fig.height = 6}
# Override specific theme parameters
p3 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  theme_custom = list(
    base_size = 14,              # Larger font
    ci_pch = 18,                 # Diamond shape
    ci_lwd = 2,                  # Thicker lines
    ci_fill = "#4DBBD5",         # Custom color
    ci_Theight = 0.15            # Box height
  )
)

print(p3)
```

## Text Alignment

Professional tables require proper alignment:

```{r alignment, fig.height = 6}
p4 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  align_left = 1,                # Variable names left
  align_center = c(2, 3),        # CI column and OR center
  align_right = c(4, 5)          # P-value and N right
)

print(p4)
```

## Bold Formatting

### Highlighting Group Headers

```{r bold-groups, fig.height = 6}
# Assuming "Sex" and "BMI category" are group headers
p5 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  bold_group = c("Sex", "BMI category"),
  bold_group_col = 1
)

print(p5)
```

### Auto-Bold Significant P-values

```{r bold-pvalues, fig.height = 6}
p6 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  bold_pvalue_cols = 4,          # P-value column
  p_threshold = 0.05             # Significance level
)

print(p6)
```

## Background Styles

### Zebra Stripes

```{r background-zebra, fig.height = 6}
p7 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  background_style = "zebra",
  background_colors = list(
    primary = "#F0F0F0",
    secondary = "white"
  )
)

print(p7)
```

### Group-based Coloring

```{r background-group, fig.height = 6}
# Identify rows that are group headers (NA in est)
group_rows <- which(is.na(df_single$est))

p8 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  background_style = "group",
  background_group_rows = group_rows,
  background_colors = list(
    primary = "#E3F2FD",         # Group headers
    secondary = "white"          # Data rows
  )
)

print(p8)
```

## CI Colors

### Single Color

```{r ci-single, fig.height = 6}
p9 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  ci_colors = "#E64B35"          # All boxes same color
)

print(p9)
```

### Color by Significance

```{r ci-significance, fig.height = 6}
# Color based on p-value
ci_cols <- ifelse(df_single$pval < 0.05, "#E64B35", "#CCCCCC")

p10 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  ci_colors = ci_cols             # Vector matching rows
)

print(p10)
```

## Borders

```{r borders, fig.height = 6}
p11 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  add_borders = TRUE,
  border_width = 3
)

print(p11)
```

## Complete Customization Example

```{r complete-custom, fig.height = 7}
# All customizations combined
p12 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  xlim = c(0.5, 2.5),
  arrow_lab = c("Protective", "Risk"),

  # Alignment
  align_left = 1,
  align_center = c(2, 3),
  align_right = c(4, 5),

  # Bold
  bold_pvalue_cols = 4,
  p_threshold = 0.05,

  # Background
  background_style = "zebra",

  # CI colors by significance
  ci_colors = ifelse(df_single$pval < 0.05, "#E64B35", "#4DBBD5"),

  # Borders
  add_borders = TRUE,

  # Layout
  height_main = 10,
  height_bottom = 8,
  layout_verbose = FALSE
)

print(p12)
```

---

# 🔄 Multi-Model Comparison

One powerful feature of `plot_forest()` is comparing multiple regression models side-by-side.

## Preparing Multi-Model Data

```{r multi-model-data}
# Filter multi-model data
df_multi <- forest_data %>%
  filter(!is.na(est_2))          # Has multiple models

# Create display table with multiple model columns
plot_data_multi <- df_multi %>%
  mutate(
    ` ` = strrep(" ", 15),
    `Model 1` = sprintf("%.2f (%.2f-%.2f)", est, lower, upper),
    `Model 2` = sprintf("%.2f (%.2f-%.2f)", est_2, lower_2, upper_2),
    `Model 3` = sprintf("%.2f (%.2f-%.2f)", est_3, lower_3, upper_3)
  ) %>%
  select(Variable = variable, ` `, `Model 1`, `Model 2`, `Model 3`)

print(plot_data_multi)
```

## Basic Multi-Model Plot

```{r multi-basic, fig.height = 5}
p13 <- plot_forest(
  data = plot_data_multi,
  est = list(df_multi$est, df_multi$est_2, df_multi$est_3),
  lower = list(df_multi$lower, df_multi$lower_2, df_multi$lower_3),
  upper = list(df_multi$upper, df_multi$upper_2, df_multi$upper_3),
  ci_column = 2,
  ref_line = 1,
  xlim = c(0.5, 3)
)

print(p13)
```

## Adjusting Multi-Model Spacing

Use `nudge_y` to control vertical spacing between models:

```{r multi-nudge, fig.height = 5}
p14 <- plot_forest(
  data = plot_data_multi,
  est = list(df_multi$est, df_multi$est_2, df_multi$est_3),
  lower = list(df_multi$lower, df_multi$lower_2, df_multi$lower_3),
  upper = list(df_multi$upper, df_multi$upper_2, df_multi$upper_3),
  ci_column = 2,
  ref_line = 1,
  xlim = c(0.5, 3),
  nudge_y = 0.3                  # Increase spacing
)

print(p14)
```

## Customizing Model Appearance

### Different Sizes per Model

```{r multi-sizes, fig.height = 5}
# IMPORTANT: sizes must match number of ROWS, not models!
# For 3 rows, repeat the pattern
sizes_vec <- rep(0.6, nrow(plot_data_multi))

p15 <- plot_forest(
  data = plot_data_multi,
  est = list(df_multi$est, df_multi$est_2, df_multi$est_3),
  lower = list(df_multi$lower, df_multi$lower_2, df_multi$lower_3),
  upper = list(df_multi$upper, df_multi$upper_2, df_multi$upper_3),
  ci_column = 2,
  ref_line = 1,
  xlim = c(0.5, 3),
  sizes = sizes_vec              # Must match row count!
)

print(p15)
```

**⚠️ Critical:** The `sizes` parameter must either be:
- A single value (applied to all)
- A vector matching `nrow(data)`

If you provide fewer values, later rows will have no CI displayed!

---

# 🎯 Advanced Features

## Automatic Tick Generation

When you provide `xlim` without `ticks_at`, the function auto-generates evenly spaced ticks:

```{r auto-ticks, fig.height = 6}
p16 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  xlim = c(0.5, 2.5),
  ticks_at = NULL                # Auto-generate 5 ticks
)

print(p16)
```

## Layout Fine-Tuning

### Default Layout Parameters

```{r layout-defaults}
# Default values (can be customized)
# height_top = 8        # Top margin
# height_header = 12    # Header row
# height_main = 10      # Data rows
# height_bottom = 8     # Bottom margin
# width_left = 10       # Left margin
# width_right = 10      # Right margin
```

### Custom Layout

```{r layout-custom, fig.height = 6}
p17 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  height_main = 12,              # Taller rows
  height_bottom = 6,             # Smaller bottom margin
  width_adjust = 8,              # Wider columns
  layout_verbose = TRUE          # Print layout info
)

print(p17)
```

### Manual Override

For pixel-perfect control:

```{r layout-manual, fig.height = 6}
p18 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  height_custom = list('3' = 15, '4' = 15),  # Specific rows
  width_custom = list('2' = 80, '3' = 100),  # Specific columns
  layout_verbose = FALSE
)

print(p18)
```

## Saving Plots

```{r save-plots, eval = FALSE}
# Save to multiple formats
p19 <- plot_forest(
  data = plot_data,
  est = list(df_single$est),
  lower = list(df_single$lower),
  upper = list(df_single$upper),
  ci_column = 2,
  ref_line = 1,
  save_plot = TRUE,
  filename = "my_forest_plot",
  save_path = "output",
  save_formats = c("png", "pdf", "tiff"),
  save_width = 30,
  save_height = 25,
  save_dpi = 300
)
```

---

# 📚 Real-World Examples

## Example 1: Logistic Regression Results

```{r example-logistic, fig.height = 8}
# Simulate logistic regression results
set.seed(123)
logistic_results <- data.frame(
  Variable = c(
    "Demographics", "  Age (per 10 years)", "  Male sex",
    "Clinical", "  BMI ≥30", "  Hypertension", "  Diabetes",
    "Laboratory", "  CRP >3 mg/L", "  LDL-C >130 mg/dL"
  ),
  OR = c(NA, 1.35, 0.82, NA, 1.58, 1.42, 1.67, NA, 1.44, 1.28),
  Lower = c(NA, 1.15, 0.65, NA, 1.22, 1.18, 1.32, NA, 1.15, 1.02),
  Upper = c(NA, 1.58, 1.03, NA, 2.05, 1.71, 2.11, NA, 1.81, 1.61),
  P = c(NA, 0.001, 0.085, NA, 0.001, 0.001, 0.001, NA, 0.002, 0.035)
)

# Prepare display
logistic_display <- logistic_results %>%
  mutate(
    ` ` = strrep(" ", 20),
    `OR (95% CI)` = ifelse(is.na(OR), "",
                           sprintf("%.2f (%.2f-%.2f)", OR, Lower, Upper)),
    `P-value` = ifelse(is.na(P), "",
                       ifelse(P < 0.001, "<0.001", sprintf("%.3f", P)))
  ) %>%
  select(Variable, ` `, `OR (95% CI)`, `P-value`)

# Identify group headers
group_rows <- c(1, 4, 7)

# Create plot
p_logistic <- plot_forest(
  data = logistic_display,
  est = list(logistic_results$OR),
  lower = list(logistic_results$Lower),
  upper = list(logistic_results$Upper),
  ci_column = 2,
  ref_line = 1,
  xlim = c(0.5, 2.5),
  arrow_lab = c("Protective", "Risk Factor"),

  align_left = 1,
  align_center = 2,
  align_right = c(3, 4),

  bold_group = logistic_display$Variable[group_rows],
  bold_pvalue_cols = 4,
  p_threshold = 0.05,

  background_style = "group",
  background_group_rows = group_rows,

  ci_colors = ifelse(is.na(logistic_results$P) | logistic_results$P >= 0.05,
                     "#CCCCCC", "#E64B35"),

  add_borders = TRUE,
  layout_verbose = FALSE
)

print(p_logistic)
```

## Example 2: Cox Regression (Survival Analysis)

```{r example-cox, fig.height = 7}
# Survival analysis hazard ratios
cox_results <- data.frame(
  Gene = c("BRCA1", "BRCA2", "TP53", "EGFR", "MYC",
           "KRAS", "PIK3CA", "AKT1", "PTEN"),
  HR = c(1.45, 0.78, 2.12, 1.23, 0.91, 1.87, 1.56, 0.85, 1.34),
  Lower = c(1.18, 0.61, 1.58, 0.95, 0.72, 1.42, 1.20, 0.66, 1.05),
  Upper = c(1.78, 0.99, 2.84, 1.59, 1.15, 2.46, 2.03, 1.09, 1.71),
  P = c(0.001, 0.041, 0.001, 0.124, 0.412, 0.001, 0.001, 0.235, 0.018)
)

cox_display <- cox_results %>%
  mutate(
    ` ` = strrep(" ", 20),
    `HR (95% CI)` = sprintf("%.2f (%.2f-%.2f)", HR, Lower, Upper),
    `P-value` = ifelse(P < 0.001, "<0.001", sprintf("%.3f", P))
  ) %>%
  select(Gene, ` `, `HR (95% CI)`, `P-value`)

p_cox <- plot_forest(
  data = cox_display,
  est = list(cox_results$HR),
  lower = list(cox_results$Lower),
  upper = list(cox_results$Upper),
  ci_column = 2,
  ref_line = 1,
  xlim = c(0.5, 3),
  arrow_lab = c("Better Survival", "Worse Survival"),

  align_left = 1,
  align_right = c(3, 4),

  bold_pvalue_cols = 4,
  p_threshold = 0.05,

  background_style = "zebra",

  ci_colors = ifelse(cox_results$P < 0.05, "#E64B35", "#4DBBD5"),

  add_borders = TRUE,
  height_main = 10,
  layout_verbose = FALSE
)

print(p_cox)
```

## Example 3: Model Comparison (Adjusted vs Unadjusted)

```{r example-comparison, fig.height = 5}
# Use built-in multi-model data
comparison_display <- plot_data_multi %>%
  mutate(Note = c(
    "Crude model",
    "Age + Sex adjusted",
    "Fully adjusted"
  )) %>%
  select(Variable, ` `, `Model 1`, `Model 2`, `Model 3`, Note)

p_comparison <- plot_forest(
  data = comparison_display,
  est = list(df_multi$est, df_multi$est_2, df_multi$est_3),
  lower = list(df_multi$lower, df_multi$lower_2, df_multi$lower_3),
  upper = list(df_multi$upper, df_multi$upper_2, df_multi$upper_3),
  ci_column = 2,
  ref_line = 1,
  xlim = c(0.5, 3),
  nudge_y = 0.25,

  align_left = 1,
  align_center = c(3, 4, 5),
  align_right = 6,

  add_borders = TRUE,
  border_width = 4,

  layout_verbose = FALSE
)

print(p_comparison)
```

---

# 💡 Best Practices

## Data Preparation Checklist

✅ **Format effect estimates** - Use `sprintf()` for consistent decimals
✅ **Create blank column** - Use `strrep(" ", width)` for CI graphics
✅ **Handle missing values** - Use `ifelse(is.na(...), "", ...)` for display
✅ **Separate display and numeric** - Keep est/lower/upper as separate vectors
✅ **Order columns logically** - Variable → Blank → Results → P-value

## Design Guidelines

### Color Selection

- **Use colorblind-friendly palettes** - Test with ColorBrewer or viridis
- **Limit colors** - 2-3 colors for clarity
- **Consistent meaning** - Red = risk, Blue = protective, Gray = non-significant

### Typography

- **Left-align text** - Variable names, descriptions
- **Right-align numbers** - P-values, counts
- **Center CI graphics** - Visual balance

### Statistical Reporting

- **Show uncertainty** - Always include confidence intervals
- **Report exact p-values** - Except for very small values (<0.001)
- **Mark significance** - Use bold or color, but consistently

## Common Pitfalls

❌ **Wrong sizes vector length** - Must match `nrow(data)`, not number of models
❌ **Forgetting blank column** - CI graphics need empty space
❌ **Inconsistent formatting** - Use `sprintf()` for uniform decimals
❌ **Too many decimals** - 2-3 is usually sufficient
❌ **Cluttered axis** - Use appropriate xlim and tick spacing

## Workflow Tips

### 1. Start Simple

```r
# Minimal working example first
p <- plot_forest(data, est, lower, upper, ci_column = 2)
```

### 2. Add Customizations Incrementally

```r
# Then add features one by one
p <- plot_forest(
  ...,
  align_left = 1,           # Step 1
  bold_pvalue_cols = 4,     # Step 2
  background_style = "zebra" # Step 3
)
```

### 3. Use layout_verbose for Debugging

```r
p <- plot_forest(..., layout_verbose = TRUE)
# Check printed dimensions
# Adjust with height_custom/width_custom if needed
```

### 4. Save Final Version

```r
p <- plot_forest(...,
                 save_plot = TRUE,
                 filename = "final_forest",
                 save_formats = c("png", "pdf"))
```

---

# 📖 Parameter Reference

## Essential Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `data` | data.frame | - | Display data with all text columns |
| `est` | list | - | Effect estimates (list of vectors) |
| `lower` | list | - | Lower CI bounds (list of vectors) |
| `upper` | list | - | Upper CI bounds (list of vectors) |
| `ci_column` | integer | - | Column index for CI graphics |
| `ref_line` | numeric | 1 | Reference line position |

## Customization Parameters

| Category | Parameters |
|----------|------------|
| **Theme** | `theme_preset`, `theme_custom` |
| **Alignment** | `align_left`, `align_center`, `align_right` |
| **Bold** | `bold_group`, `bold_group_col`, `bold_pvalue_cols`, `p_threshold` |
| **Background** | `background_style`, `background_group_rows`, `background_colors` |
| **Colors** | `ci_colors`, `ci_group_ids` |
| **Borders** | `add_borders`, `border_width`, `custom_borders` |
| **Layout** | `height_*`, `width_*`, `nudge_y`, `sizes` |
| **Save** | `save_plot`, `filename`, `save_path`, `save_formats` |

For complete parameter documentation, see `?plot_forest`.

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# 🎓 Summary

You've learned how to:

✅ Create basic forest plots with `plot_forest()`
✅ Customize themes, colors, and alignment
✅ Compare multiple models side-by-side
✅ Apply backgrounds, borders, and formatting
✅ Fine-tune layouts and save publication-ready figures
✅ Follow best practices for statistical visualization

## Next Steps

- **Explore other vignettes** - [Get Started](get-started.html), [Color Palettes](color-palettes.html)
- **Check function documentation** - `?plot_forest`, `?forest_data`
- **Share your plots** - Tag us on GitHub with your beautiful forest plots!

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**📦 Package:** [evanverse](https://github.com/evanbio/evanverse)
**📧 Questions?** Open an issue on GitHub
**🌟 Like this package?** Give us a star!

*Happy plotting! 🌲📊*