ViewR

The advanced interactive data table for R. A modern, dependency-free htmlwidget that turns any data frame into a fast, beautiful, explorable grid — with column analytics, a visual query builder, and one-click reproducible code.

library(ViewR)
viewdt(mtcars)   # that's it

viewdt() is what DT::datatable() would look like if it were rebuilt today: every column header is a micro-dashboard, every filter writes runnable code, and the whole thing is a single portable HTML file with zero JavaScript dependencies. It works in the RStudio / Positron Viewer, inside Shiny, in R Markdown / Quarto, or exported for offline sharing.

Why ViewR?

	`DT`	`reactable`	ViewR `viewdt()`
Virtualized rendering of large data	⚠️	✅	✅
Kaggle-style column headers (badges, mini-histograms, missingness)	❌	❌	✅
Data Insights drawer (interactive histogram / Pareto)	❌	❌	✅
Visual query builder (AND/OR, type-aware)	❌	❌	✅
Reproducible code export (dplyr / base R / SQL)	❌	❌	✅
Column picker + global search	⚠️	⚠️	✅
Row pinning	❌	❌	✅
Light / dark / auto theme	⚠️	⚠️	✅
JS framework dependency	jQuery	React	none (vanilla JS)

ViewR profiles every column in R — types, missingness, histogram bins, top categories — and ships a compact metadata payload to a lean renderer, instead of recomputing statistics in the browser.

Highlights

🧱 Virtualized grid — paints only what’s on screen; sticky headers, sticky row index, click-to-pin rows.
📊 Micro-dashboard headers — data-type badge, mini spark-histogram (numeric) or top-category bar (categorical), and a colour-coded data-completeness bar with hover tooltip.
🔎 Data Insights drawer — click any header chart for a full interactive SVG histogram or Pareto bar chart, completeness meter, and Min/Mean/Median/Max.
⚡ Visual query builder — multi-condition AND/OR filters with type-aware operators (=, <, contains, is in, is NA, …) and a searchable multi-select for categories. Live row/column counter.
〈/〉 Reproducible code — copy the exact dplyr, base R, or SQL for your current filter + visible-column state; your data’s variable name is auto-substituted.
▦ Column picker + global search.
🎨 Light / dark / auto themes; inline variable labels (haven / ADaM ready).
💾 Portable export — save_viewdt() writes a self-contained offline HTML.

Installation

# From CRAN (once published)
install.packages("ViewR")

# Development version
# install.packages("remotes")
remotes::install_github("itsmdivakaran/ViewR")

Quick start

library(ViewR)

# Open the explorer
viewdt(mtcars)

# Dark theme, hide a column, custom NA placeholder
viewdt(
  iris,
  options = viewdt_options(
    theme          = "dark",
    hidden_columns = "Species",
    na_string      = "—"
  )
)

# Export a portable, offline HTML report
save_viewdt(mtcars, "mtcars.html", open = TRUE)

Inside Shiny

library(shiny); library(ViewR)
ui <- fluidPage(viewdtOutput("grid", height = "640px"))
server <- function(input, output, session)
  output$grid <- renderViewdt(viewdt(mtcars))
shinyApp(ui, server)

In R Markdown / Quarto

Just call viewdt(df) in a chunk — the widget renders inline in the knitted HTML.

The `viewdt()` API

viewdt(data, options = viewdt_options(), dataset_name = NULL, ...)

Everything visual is controlled by viewdt_options():

Option	Default	What it does
`theme`	`"auto"`	`"auto"`, `"light"`, or `"dark"`
`show_labels`	`TRUE`	Inline variable labels in headers
`histograms`	`TRUE`	Mini spark-histograms / category bars
`missing_bars`	`TRUE`	Data-completeness bar per column
`type_badges`	`TRUE`	Data-type badges
`insights`	`TRUE`	Sliding Data Insights drawer
`query_builder`	`TRUE`	Multi-condition visual filters
`column_picker`	`TRUE`	Show/hide columns
`code_export`	`TRUE`	dplyr / base R / SQL generator
`global_search`	`TRUE`	Search-all-columns box
`na_string`	`"NA"`	Missing-value placeholder
`hidden_columns`	`NULL`	Columns hidden on first render

See vignette("viewdt", package = "ViewR") for a full, worked walk-through with live grids.

Reproducible code, generated for you

Build a filter in the UI, click Code, and copy any of:

# dplyr
mtcars %>% filter(cyl == 6, mpg > 20) %>% select(mpg, cyl, hp)

# base R
mtcars[mtcars$cyl == 6 & mtcars$mpg > 20, c("mpg", "cyl", "hp")]

-- SQL
SELECT mpg, cyl, hp FROM mtcars WHERE cyl = 6 AND mpg > 20;

Also included: the classic `ViewR()` gadget

The original Shiny-gadget viewer/editor is still here for in-session work — filtering, multi-column sort, an Excel-like editor (rhandsontable), find-and-replace, and live dplyr code:

new_iris <- ViewR(iris, edit = TRUE)   # returns the edited data on Done

See ?ViewR and vignette("ViewR-intro", package = "ViewR").