---
title: "Getting Started with artma"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting Started with artma}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Introduction

**artma** (Automatic Replication Tools for Meta-Analysis) is an R package designed to make meta-analysis accessible, reproducible, and comprehensive. This vignette will get you up and running quickly, showing you how to use artma for your meta-analysis needs.

## Quick Start: Your First Analysis

The simplest way to use artma is to call the main function:

```r
library(artma)
results <- artma()
```

When you run this in an interactive R session, artma will:

1. **Guide you through creating an options file** - This stores all your analysis settings
2. **Help you load your data** - You'll specify where your dataset is located
3. **Map your columns** - artma will recognize common column names or let you specify them
4. **Let you choose methods** - Select which analyses you want to run
5. **Run your analysis** - Execute everything and return results

If you want artma to open the results folder for you at the end of a run, set `open_results = TRUE`. You can always access your results later manually using `artma::results_open()`.

That's it! The function handles all the complexity behind the scenes.

## Understanding the Core Concepts

Before diving deeper, it helps to understand three key concepts:

### 1. The `artma()` Function

`artma()` is the main entry point for the package. It orchestrates everything:

- **Loading configuration** from options files
- **Preparing your data** (reading, cleaning, validating)
- **Running analytical methods** (funnel plots, Bayesian analysis, etc.)
- **Returning results** in a structured format

You can use it in different ways:

```r
# Fully interactive - prompts for everything
artma()

# Specify methods to run
artma(methods = c("funnel_plot", "bma"))

# Use a specific options file
artma(options = "my_analysis.yaml")

# Provide data directly (bypasses file reading)
artma(data = my_dataframe, methods = "effect_summary_stats")
```

### 2. Options Files

Options files are YAML configuration files that store all your analysis settings. Think of them as recipes for your meta-analysis:

- **Data location** - Where your dataset is stored
- **Column mappings** - Which columns contain effect sizes, standard errors, etc.
- **Method parameters** - Settings for each analysis method
- **Output preferences** - How you want results formatted

The key benefit: **reproducibility**. Save your options file, and you can rerun the exact same analysis anytime.

### 3. Methods

Methods are the analytical functions that perform specific meta-analysis tasks. Each method does something different:

- `effect_summary_stats` - Calculate overall effect sizes and confidence intervals
- `funnel_plot` - Visualize publication bias
- `bma` - Bayesian Model Averaging
- `linear_tests` - Test linear relationships
- `nonlinear_tests` - Test nonlinear relationships
- `exogeneity_tests` - Test for endogeneity
- `p_hacking_tests` - Detect p-hacking patterns
- `maive` - MAIVE estimator, correcting for publication bias, p-hacking, and spurious precision
- `variable_summary_stats` - Summary statistics for variables

You can run one method, multiple methods, or all methods in a single call. For a full description of every method, see the [Methods Overview](methods-overview.html) vignette.

## Common Workflows

### Workflow 1: Interactive Analysis (Recommended for Beginners)

This is the easiest way to get started:

```r
# Step 1: Load the package
library(artma)

# Step 2: Run interactively
results <- artma()

# Step 3: Explore results
names(results)  # See which methods ran
results$effect_summary_stats  # Access specific results
```

During the interactive session, you'll be guided through:

- Creating or selecting an options file
- Specifying your data file path
- Mapping column names (artma tries to auto-detect these)
- Choosing which methods to run

### Workflow 2: Using an Existing Options File

Once you've created an options file, reuse it:

```r
# Run with a specific options file
results <- artma(options = "my_analysis.yaml")

# Or specify the directory too
results <- artma(
  options = "my_analysis.yaml",
  options_dir = "~/my_meta_analyses/configs"
)
```

This is perfect when:

- You're rerunning a previous analysis
- You want to share your analysis configuration
- You're doing sensitivity analyses with different parameters

### Workflow 3: Programmatic Analysis

For scripts and automation:

```r
# Create options file programmatically
artma::options_create(
  options_file_name = "analysis_2025",
  user_input = list(
    "data.source_path" = "/path/to/data.csv",
    "data.columns" = list(
      effect = list(source_name = "effect_size"),
      se = list(source_name = "standard_error")
    ),
    "methods.effect_summary_stats.conf_level" = 0.95
  )
)

# Run analysis
results <- artma(
  options = "analysis_2025",
  methods = c("effect_summary_stats", "funnel_plot")
)
```

### Workflow 4: Using Data Already in R

If your data is already loaded in R:

```r
# Your data frame
my_data <- data.frame(
  effect = c(0.5, 0.3, 0.7, 0.4),
  se = c(0.1, 0.15, 0.12, 0.11),
  study = c("Study A", "Study B", "Study C", "Study D"),
  n_obs = c(100, 150, 120, 130)
)

# Run analysis directly
results <- artma(
  data = my_data,
  methods = "effect_summary_stats"
)
```

Note: When you provide data directly, you still need options for method parameters, but artma will handle data reading automatically.

## Understanding Results

The `artma()` function returns a named list, where each element corresponds to a method that was run:

```r
results <- artma(methods = c("effect_summary_stats", "funnel_plot"))

# Structure:
# results
# ├── effect_summary_stats  (results from effect summary statistics)
# └── funnel_plot           (results from funnel plot analysis)
```

The structure of each result depends on the method. Some methods return:

- Summary statistics (tables, means, confidence intervals)
- Test results (p-values, test statistics)
- Visualizations (plots, graphs)
- Model objects (for further analysis)

To explore results:

```r
# See what methods ran
names(results)

# Access a specific result
effect_results <- results$effect_summary_stats

# The structure varies by method - use str() to explore
str(effect_results)
```

### What a Run Leaves on Disk

A run that saves results writes them into its output directory (`tables/` for
the exported tables, `graphics/` for the plots) together with a `run.json`
manifest describing that run: when it happened, which options file and data
source it used, the methods requested, run, skipped and failed, the effective
seed, and the files it wrote.

The file list is recorded as the files are written, so it describes the run
rather than everything that has piled up in the directory. That is what the
HTML report uses to find each method's plots, and what the CLI reports in
`--json` mode.

Every run also closes with a `Run summary` block on the console: the same
facts, read from the same sources, so the manifest and what you saw scroll by
never disagree.

`run.json` is overwritten by every run into the same output directory: it
always describes the latest run, never a history. Runs driven by different
options files already get their own output directory. To keep an older run,
copy its directory, or point `output.dir` at a per-run location.

## Choosing Methods

### Discovering Available Methods

To see what methods are available:

```r
artma::methods_list()
```

This prints all available methods to the console.

### Selecting Methods

You have several options:

```r
# Run specific methods
artma(methods = c("effect_summary_stats", "bma"))

# Run all methods
artma(methods = "all")

# Interactive selection (if methods not specified)
artma()  # Will prompt you to choose
```

### Method Execution Order

Methods run in a predefined order (you don't need to worry about this, but it's good to know). If you specify multiple methods, they'll execute in the optimal sequence automatically.

## Working with Options Files

### Creating Options Files

Options files are created automatically when needed, but you can also create them explicitly:

```r
# Interactive creation
artma::options_create()

# Programmatic creation
artma::options_create(
  options_file_name = "my_analysis",
  user_input = list(
    "data.source_path" = "/path/to/data.csv",
    "data.columns" = list(
      effect = list(source_name = "effect_size"),
      se = list(source_name = "standard_error")
    )
  )
)
```

### Managing Options Files

```r
# List all options files
artma::options_list()

# Copy an options file
artma::options_copy(
  options_file_name_from = "baseline.yaml",
  options_file_name_to = "sensitivity.yaml"
)

# Modify an options file
artma::options_modify(
  options_file_name = "my_analysis.yaml",
  options_to_modify = list(
    "methods.effect_summary_stats.conf_level" = 0.99
  )
)

# Validate an options file
artma::options_validate("my_analysis.yaml")
```

For detailed information about options files, see the [Understanding Options Files](options-files.html) vignette.

## Data Requirements

### Required Columns

Your dataset needs certain columns for artma to work. The minimum required columns are:

- **Effect size** - The effect you're analyzing (e.g., correlation, mean difference)
- **Standard error** - The uncertainty in the effect size
- **Study identifier** - A unique identifier for each study/observation

### Column Mapping

artma uses flexible column mapping. Your columns don't need specific names - you just need to tell artma which column contains what. For example:

- Your effect column might be called `beta`, `effect_size`, `r`, or `d`
- Your standard error column might be called `se`, `standard_error`, or `se_beta`
- Your study column might be called `study`, `paper_id`, or `observation`

artma tries to auto-detect these mappings, but you can always specify them manually.

### Supported Data Formats

artma can read data from:

- CSV files (`.csv`)
- Excel files (`.xlsx`, `.xls`)
- JSON files (`.json`)
- Stata files (`.dta`)
- R data files (`.rds`)

### Data Preprocessing

artma automatically handles:

- Missing value detection and treatment
- Data type conversion
- Column standardization
- Outlier detection (with optional winsorization)

You control these behaviors through your options file.

## Tips for Success

### 1. Start Simple

Begin with a single method to understand the workflow:

```r
results <- artma(methods = "effect_summary_stats")
```

Once comfortable, add more methods.

### 2. Use Descriptive Options File Names

Name your options files clearly:

- `charity_effects_2025.yaml` - Better than `config1.yaml`
- `sensitivity_analysis.yaml` - Better than `test.yaml`

### 3. Save Your Options Files

Options files are your analysis recipes. Save them with your project for reproducibility.

### 4. Check Verbosity

Control how much information artma displays:

```r
# In your options file or via options()
options(artma.verbose = 3)  # Default: informative
options(artma.verbose = 1)  # Minimal: errors only
options(artma.verbose = 4)  # Verbose: everything
```

### 5. Validate Before Running

If you're modifying options files manually, validate them:

```r
artma::options_validate("my_analysis.yaml")
```

This catches errors before you run the analysis.

## Getting Help

### Documentation

- `?artma` - Main function help
- `?artma::methods_list` - List available methods
- `?artma::options_create` - Create options files
- Vignettes - Detailed guides (like this one!)

### Verbosity Levels

If something isn't working, increase verbosity:

```r
options(artma.verbose = 4)  # Maximum detail
results <- artma()
```

This shows everything artma is doing, which helps diagnose issues.

### Validation

When in doubt, validate:

```r
# Validate options file
artma::options_validate("my_analysis.yaml")

# Check available methods
artma::methods_list()

# Check how artma reads and preprocesses your data
artma::data_preview(options = "my_analysis.yaml")
```

## Next Steps

Once you're comfortable with the basics, read the options vignette for deeper configuration
and try a few methods on your own data.
