Get started with viewdt()

Quick start

The simplest call opens any data frame:

library(ViewR)
viewdt(mtcars)

Themes, labels, and hidden columns

Every visual feature is controlled through [viewdt_options()]. Here we use a dark theme, hide a column, and set a custom missing-value placeholder:

viewdt(
  iris,
  options = viewdt_options(
    theme          = "dark",
    hidden_columns = "Species",
    na_string      = "—"
  )
)

Working with labelled (clinical) data

viewdt() reads label column attributes (as produced by haven or ADaM clinical datasets) and shows them inline beneath each column name:

df <- data.frame(
  AGE = c(54, 61, 47, NA, 72),
  SEX = c("M", "F", "F", "M", "F"),
  BMI = c(24.1, 28.7, 22.3, 31.0, 26.4)
)
attr(df$AGE, "label") <- "Age (years)"
attr(df$SEX, "label") <- "Sex"
attr(df$BMI, "label") <- "Body Mass Index"

viewdt(df, options = viewdt_options(theme = "light"))

Turning features on and off

Disable any panel you do not need — for example a minimal read-only grid with no query builder, code export, or insights drawer:

viewdt(
  mtcars,
  options = viewdt_options(
    query_builder = FALSE,
    code_export   = FALSE,
    insights      = FALSE,
    global_search = TRUE
  )
)

What the column headers show

Each header is a small dashboard:

• Type badge — # numeric, A character, T/F logical, clock = date.
• Mini distribution — a spark-histogram for numeric/date columns, or a stacked top-categories bar for character columns. Click it to open the Data Insights drawer.
• Completeness bar — green (>95% complete), amber (50–95%), red (<50%). Hover any ⓘ icon for a full metadata card.

The visual query builder

Click Filter to build multi-condition rules combined with AND / OR. Operators are type-aware: numeric columns get <, >, =; character columns add contains, is in, and is not in with a searchable multi-select. The live row/column counter on the right updates instantly.

Reproducible code

Click Code to copy the exact pipeline that reproduces your current filter and visible-column state, in three dialects:

# 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;

The data variable name is auto-substituted from the object you passed to viewdt().

Exporting a standalone report

[save_viewdt()] writes a fully offline, interactive HTML file that opens in any browser without R:

# Single self-contained file (needs pandoc)
save_viewdt(mtcars, "mtcars.html", open = TRUE)

# Lightweight file + companion _files/ dir (best for large data)
save_viewdt(iris, "iris.html", selfcontained = FALSE)

Using viewdt() inside Shiny

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