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

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

## Overview

**QuickExplore** provides a point-and-click Shiny interface modelled after SAS
Studio's library/dataset browser.  It supports SAS (`.sas7bdat`, `.xpt`), CSV,
and R (`.rds`) files and lets you explore, filter, summarise, and export
datasets without writing any code.

The package also exposes a set of standalone R functions for use in scripts or
other Shiny applications.

## Launching the Application

```r
library(QuickExplore)
run_app()
```

The app opens in your default browser.  If you are running inside RStudio it
opens in the Viewer pane.

### First Steps

1. Click **Add Library** in the left sidebar.
2. Give the library a short name (e.g. `MYLIB`) and enter the path to a folder
   that contains your data files.
3. Click a dataset name in the list to load it.
4. Use the **Data**, **Summary**, and **Convert** tabs to explore and export.

## Standalone Utility Functions

All helper functions used internally by the app are exported and can be called
directly.

### Reading a Dataset

```r
df <- read_dataset("/path/to/data/demog.sas7bdat")
df <- read_dataset("/path/to/data/analysis.csv")
df <- read_dataset("/path/to/data/model_output.rds")
```

### Listing Datasets in a Directory

```r
datasets <- list_datasets("/path/to/data/")
print(datasets)
```

### Variable Metadata

```r
info <- get_variable_info(df)
head(info)
```

### Descriptive Statistics

```{r example-stats}
df <- data.frame(
  age  = c(25, 34, 45, 52, 28, NA),
  sex  = c("M", "F", "M", "F", "M", "F"),
  dose = c(10, 20, 10, 30, 20, 10)
)

# Numeric summary
library(QuickExplore)
compute_numeric_summary(df, c("age", "dose"))

# Categorical summary
compute_categorical_summary(df, "sex")
```

### Grouped Summaries

```r
compute_numeric_summary(df, c("age", "dose"), group_var = "sex")
```

## Shiny Module API

Each tab in the application is implemented as a reusable Shiny module.  You can
embed any of these modules in your own Shiny apps:

```r
library(shiny)
library(QuickExplore)

ui <- fluidPage(
  data_viewer_ui("viewer")
)

server <- function(input, output, session) {
  my_data <- reactiveVal(mtcars)
  my_path <- reactiveVal("mtcars")    # or a real file path
  data_viewer_server("viewer", my_data, my_path)
}

shinyApp(ui, server)
```

Available module pairs:

| UI function              | Server function              | Purpose                         |
|--------------------------|------------------------------|---------------------------------|
| `dataset_browser_ui()`   | `dataset_browser_server()`   | Library + dataset sidebar       |
| `data_viewer_ui()`       | `data_viewer_server()`       | Interactive table with filters  |
| `summary_panel_ui()`     | `summary_panel_server()`     | Descriptive statistics          |
| `converter_ui()`         | `converter_server()`         | Multi-format export             |

## Session Info

```{r session-info}
sessionInfo()
```