---
title: "Getting Started with MAIDR"
author: "JooYoung Seo"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting Started with MAIDR}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)
```

## Introduction to MAIDR

**MAIDR** (Multimodal Access and Interactive Data Representation) is an R package that makes data visualizations accessible to users with visual impairments. It converts ggplot2 and Base R plots into interactive, accessible formats with:

- **Keyboard navigation** - Explore data using arrow keys
- **Screen reader support** - Full ARIA labels and descriptions
- **Sonification** - Hear data patterns through sound
- **HTML/SVG output** - Standalone accessible visualizations

MAIDR helps data scientists and researchers create inclusive visualizations that everyone can explore, regardless of visual ability.

## Installation

Install the development version from GitHub:

```{r install, eval=FALSE}
# install.packages("devtools")
devtools::install_github("xability/r-maidr-prototype")
```

## Basic Workflow

MAIDR works with two main functions:

1. **`show()`** - Display an interactive plot in RStudio Viewer or browser
2. **`save_html()`** - Save a plot as a standalone HTML file

### Quick Example: ggplot2 Bar Chart

```{r ggplot2-example}
library(maidr)
library(ggplot2)

# Create sample data
sales_data <- data.frame(
  Product = c("A", "B", "C", "D"),
  Sales = c(150, 230, 180, 290)
)

# Create a bar chart
p <- ggplot(sales_data, aes(x = Product, y = Sales)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(
    title = "Product Sales by Category",
    x = "Product",
    y = "Sales Amount"
  ) +
  theme_minimal()

# Display interactively
show(p)

# Or save as HTML file
save_html(p, "sales_chart.html")
```

### Quick Example: Base R Plot

MAIDR also works with Base R plotting functions:

```{r base-r-example}
library(maidr)

# Create a simple barplot
categories <- c("A", "B", "C", "D")
values <- c(150, 230, 180, 290)

barplot(
  values,
  names.arg = categories,
  col = "steelblue",
  main = "Product Sales by Category",
  xlab = "Product",
  ylab = "Sales Amount"
)

# Note: For Base R plots, call show() with NO arguments
# after creating the plot
show()
```

## Exploring Accessible Plots

When you open a MAIDR plot, you can explore it using:

### Keyboard Navigation

- **Arrow keys** - Navigate between data points
- **Tab** - Move between interactive elements
- **Enter/Space** - Activate controls
- **Escape** - Exit modes

### Screen Reader Announcements

MAIDR plots include:

- Plot titles and descriptions
- Axis labels and ranges
- Data point values
- Navigation instructions

### Data Sonification

Plots can be heard through:

- Pitch mapping (higher values = higher pitch)
- Volume changes
- Different tones for different series

## Supported Plot Types

MAIDR supports a comprehensive range of visualizations:

### Basic Plot Types
- Bar charts (simple, grouped/dodged, stacked)
- Histograms
- Scatter plots
- Line plots (single and multi-line)
- Box plots
- Heatmaps
- Density/smooth curves

### Advanced Plot Types
- **Faceted plots** - `facet_wrap()` and `facet_grid()` in ggplot2
- **Multi-panel layouts** - patchwork for ggplot2, `par(mfrow/mfcol)` for Base R
- **Multi-layered plots** - Combine multiple geoms (e.g., histogram + density)

## Next Steps

- **[Shiny Integration](shiny-integration.html)** - Use MAIDR in Shiny apps
- **Package documentation** - Run `?maidr::show` for function details
- **Run examples** - Try `maidr::run_example()` to see all available plot types

## Example Gallery

### Histogram

```{r histogram-example}
library(maidr)
library(ggplot2)

# Normal distribution
hist_data <- data.frame(values = rnorm(1000, mean = 100, sd = 15))

p <- ggplot(hist_data, aes(x = values)) +
  geom_histogram(bins = 30, fill = "skyblue", color = "black") +
  labs(
    title = "Distribution of Test Scores",
    x = "Score",
    y = "Frequency"
  ) +
  theme_minimal()

show(p)
```

### Scatter Plot

```{r scatter-example}
library(maidr)
library(ggplot2)

# Create sample data
scatter_data <- data.frame(
  height = rnorm(50, 170, 10),
  weight = rnorm(50, 70, 8),
  gender = sample(c("Male", "Female"), 50, replace = TRUE)
)

p <- ggplot(scatter_data, aes(x = height, y = weight, color = gender)) +
  geom_point(size = 3, alpha = 0.7) +
  labs(
    title = "Height vs Weight",
    x = "Height (cm)",
    y = "Weight (kg)"
  ) +
  theme_minimal()

show(p)
```

### Line Plot

```{r line-example}
library(maidr)
library(ggplot2)

# Time series data
months <- month.abb[1:12]
temperature <- c(5, 7, 12, 18, 22, 26, 28, 27, 23, 17, 11, 6)

temp_data <- data.frame(
  Month = factor(months, levels = months),
  Temperature = temperature
)

p <- ggplot(temp_data, aes(x = Month, y = Temperature, group = 1)) +
  geom_line(color = "red", linewidth = 1.5) +
  geom_point(color = "darkred", size = 3) +
  labs(
    title = "Average Monthly Temperature",
    x = "Month",
    y = "Temperature (°C)"
  ) +
  theme_minimal()

show(p)
```

## Tips for Creating Accessible Plots

1. **Use clear titles** - Describe what the plot shows
2. **Label axes properly** - Include units of measurement
3. **Choose distinct colors** - Ensure good contrast
4. **Add legends** - Explain what colors/shapes mean
5. **Keep it simple** - Avoid overcrowded visualizations

## Getting Help

- Run `?maidr::show` for function documentation
- Visit GitHub issues: [maidr/issues](https://github.com/xability/maidr/issues)
- Read the full documentation: `help(package = "maidr")`

## Learn More

- **Accessibility standards**: [WCAG 2.1 Guidelines](https://www.w3.org/WAI/WCAG22/quickref/?versions=2.1)
- **MAIDR website**: More examples and tutorials
- **Research papers**: Understanding multimodal data representation
