---
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.
### 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
Hover to watch how dataviewR helps you explore multiple datasets side-by-side
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).
## What Happens Inside the App?
When a dataset is loaded, you can:
✔ Apply flexible row filters (with `dplyr` style expressions)
✔ Select or deselect variables and sort columns
✔ See distinct values for character/factor columns **(including NA)** in the quick filter box.
✔ View metadata such as variable classes and attributes
✔ Export filtered data
✔ Copy auto-generated dplyr code for reproducibility
This helps integrate interactive exploration into a scripted workflow.
## Typical Workflow Example
```r
library(dataviewR)
# Explore data interactively
dataviewer(iris)
```
Hover to watch how dataviewR helps you explore a dataset and generate reproducible code.
```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.