---
title: "Introduction to dataviewR"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to dataviewR}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

`dataviewR` provides a clean and intuitive Shiny interface for interactively exploring R data frames and tibbles.
Instead of using the default View() from the `utils` package or writing temporary filter code, you can open a dataset visually, browse its structure, filter rows, inspect values, and export results — all within seconds. Unlike View(), `dataviewR` does not dynamically reflect when the dataset is altered in RStudio after being loaded.

## Why dataviewR?

Whether you work in clinical research, data analysis, or academia, a strong understanding of datasets is important. `dataviewR` helps you:

- Inspect datasets interactively within seconds  
- View two or more datasets side-by-side  
- Review data quality and structure before analysis  

While functions like `View()` offer a quick static snapshot of a dataset, they are limited when working with large datasets, inspecting variable values, or applying interactive filters.

`dataviewR` offers a flexible, dynamic interface that enables efficient and intuitive data exploration, whether within the viewer pane or through a standalone R Shiny application.

`dataviewR` **does not modify any values/attributes** of the dataset(s) passed
 
This guide walks you through how to launch, navigate, and work with datasets in dataviewR.

For feature-specific articles and examples, visit the Articles section of the documentation.

## Launching dataviewR

There are three ways to start the application, depending on your workflow:

### 1️⃣ Launch with a dataset

When you pass a dataset(s), dataviewR opens directly with that dataset(s) loaded.

For better user experience, it is recommended to open dataviewR in new window from Viewer pane.

To open the dataset in a shiny application, user is requested to disable the background option using `background = FALSE` argument but it keeps the console busy.

```r
library(dataviewR)

dataviewer(iris) # Opens in RStudio Viewer pane or default web browser

dataviewer(iris, background = FALSE) # Opens the Shiny application and run in foreground to enable the "Import Dataset" tab alongside 'iris'
```

You’ll see the interactive table immediately, along with variable info and filtering options.

<p align="center"> <img src="figures/look_2_c.png" width="85%" alt="dataviewR viewing the iris dataset"> </p>

### 2️⃣ Launch with multiple datasets (side-by-side viewing)

You can now pass two or more datasets to view them within the same session.

```r
dataviewer(iris, mtcars) # Opens multiple datasets in separate tabs
```

This is useful for:

- Comparing raw vs derived datasets

- Inspecting multiple SDTM/ADaM datasets

- Reviewing train vs test splits

- Comparing outputs across pipelines

<p align="center"> <img src="figures/multi_data_gif_c.gif" width="85%" alt="dataviewR comparing iris and mtcars"> </p>

Each dataset gets its own tab, allowing you to switch seamlessly.

Please check the [Exploring Multiple Datasets](Multiple-Datasets.html) article for more examples.

### 3️⃣ Launch without data (use the Import Panel)

Calling `dataviewer()` with no arguments opens the Import Panel in shiny application by default, letting you load the datasets 
either from the global environment or from the loaded packages.

```r
dataviewer()
```

Once imported, the viewer opens the selected dataset(s) in separate tab(s).

<p align="center"> <img src="figures/import.png" width="85%" alt="dataviewR import panel"> </p>

## What Happens Inside the App?

When a dataset is loaded, you can:

✔ Apply flexible row filters (with `dplyr` style expressions) <br>
✔ Select or deselect variables and sort columns <br>
✔ See distinct values for character/factor columns **(including NA)** in the quick filter box.<br>
✔ View metadata such as variable classes and attributes <br>
✔ Export filtered data <br>
✔ Copy auto-generated dplyr code for reproducibility <br>

This helps integrate interactive exploration into a scripted workflow.

## Typical Workflow Example

```r
library(dataviewR)

# Explore data interactively
dataviewer(iris)
```
<!-- https://www.veed.io/edit/d97e43d9-b262-4a74-b1e7-a652dca25f2d/media -->
<p align="center" style="font-style: italic;">
  Hover to watch how dataviewR helps you explore a dataset and generate reproducible code.
</p>

<p align="center">
<video 
  src="figures/generate_R_code_c.mp4"
  width="85%"
  muted
  playsinline
  onmouseover="this.loop = true; this.play()"
  onmouseout="this.loop = false; this.pause(); this.currentTime = 0;">
</video>
</p>

```r
# After exploring, use the exported code:
library(dplyr)

iris |>
  filter(Species == "setosa" & Petal.Width > 0.2) |>
  select(Petal.Length, Petal.Width, Species)
```

The combination of interactive exploration + reproducible code makes dataviewR a strong addition to both analysis and QC workflows.

## Next Steps

To dive deeper into individual features, see:

- [Using Filters and Expressions](Filtering-Expressions.html)
- [Exploring Multiple Datasets](Multiple-Datasets.html)
- [Working with Clinical Datasets](adam-clinical-dataset.html)
- [Exporting data and Wrapping Up the Session](Exporting-and-Reproducibility.html)

All available under the Articles section of the package documentation.

## Feedback

If you have suggestions or run into issues, please visit the
GitHub repository:
https://github.com/madhankumarnagaraji/dataviewR

We welcome ideas, feedback, and contributions.

🏁 That’s It!

You’re ready to start using dataviewR for fast, intuitive, and reproducible data exploration.
