Getting Started with ViewR

Overview

ViewR brings spreadsheet-style data exploration directly into your R workflow. A single function call opens a polished, popup-based interface that lets you filter, sort, search, edit, and understand any data.frame — and automatically writes the equivalent dplyr code so you never lose reproducibility.

library(ViewR)
ViewR(mtcars)

The ViewR() function

The entire interface is controlled through one function:

ViewR(
  data,
  edit          = FALSE,   # enable Excel-like editing
  popup         = TRUE,    # open as modal dialog
  labels        = NULL,    # named vector of variable labels
  title         = NULL,    # custom window title
  viewer        = "dialog",# "dialog", "browser", or "pane"
  generate_code = TRUE,    # show R Code tab
  theme         = "flatly",# Bootstrap theme
  max_display   = 50000L,  # row cap for rendering
  return_data   = TRUE     # return (possibly edited) data on close
)

ViewR() is pipe-friendly and returns the (possibly modified) data frame invisibly when the user clicks Done:

library(dplyr)

clean_data <- mtcars |>
  filter(cyl >= 4) |>
  ViewR(edit = TRUE)   # open popup; result assigned on Done

Walkthrough of the interface

Main tabs

Data

The main data table is powered by the DT package. It supports:

  • Global search (top-right of the table)
  • Per-column filter boxes below each header
  • Clickable column headers for one-click ascending/descending sort
  • Configurable page length (use the Rows per page slider in the sidebar)

If you supply labels (or use a dataset imported with haven), each column header will show an ⓘ tooltip with the full label when you hover.

Edit (requires edit = TRUE)

An Excel-like grid powered by rhandsontable. You can:

  • Click any cell and type to edit its value directly
  • Right-click a row header for insert / remove options
  • Use the Add Row button to append a blank row
  • Undo and Redo (unlimited) step through the change history

Changes are committed to the returned data frame when you click Done.

Tip: Clear all filters and sorts before editing to ensure row positions map correctly back to the original data.

Find & Replace

Search for any text pattern — literal or regex — and replace it in one or all columns. The workflow is:

  1. Choose a column (or All columns) from the dropdown.
  2. Type the Find string and the Replace with string.
  3. Optionally tick Case sensitive, Regex, or Exact cell match.
  4. Click Preview to see a diff table of affected cells.
  5. Click Apply to commit the replacement.

Variable Info

A summary table for every column in the current working data:

Column Description
Type R class (character, integer, numeric, …)
N Count of non-missing values
Missing Count of NA values
Missing % Percentage missing (colour-bar visualised)
Unique Number of distinct values
Min / Max For numeric columns
Sample values Up to 4 example values

R Code

Every action you perform — filtering, sorting, showing/hiding columns, find-and-replace — is translated into a dplyr pipeline and displayed here in real time:

library(dplyr)

mtcars_result <- mtcars |>
  filter(
    `cyl` == "6"
  ) |>
  arrange(desc(`mpg`)) |>
  select(`mpg`, `cyl`, `hp`, `wt`)

Click Copy to clipboard to paste the code directly into your script. Click Reset to clear the history of find-replace and edit operations (filter/sort/column code is always live and cannot be cleared independently).

Variable labels

ViewR automatically reads variable labels stored as column attributes, as produced by haven, labelled, or Hmisc:

# df <- haven::read_sav("my_survey.sav")
# ViewR(df)   # labels appear as tooltips automatically

You can also supply labels manually:

ViewR(mtcars,
      labels = c(
        mpg  = "Miles per Gallon",
        cyl  = "Number of Cylinders",
        disp = "Displacement (cu.in.)",
        hp   = "Gross Horsepower",
        wt   = "Weight (1000 lbs)"
      ))

Themes

Choose from any Bootstrap theme supported by shinythemes:

ViewR(iris, theme = "darkly")    # dark
ViewR(iris, theme = "cerulean")  # light blue
ViewR(iris, theme = "cosmo")     # clean & minimal
ViewR(iris, theme = "sandstone") # warm beige

Opening in the browser or Viewer pane

By default ViewR opens as a modal dialog inside RStudio. Use the viewer argument to change this:

# Open in the system browser (useful for large datasets)
ViewR(iris, viewer = "browser")

# Open in the RStudio Viewer pane
ViewR(iris, viewer = "pane")

# Or equivalently, set popup = FALSE (defaults to browser)
ViewR(iris, popup = FALSE)

Editing data and returning results

When edit = TRUE, clicking Done returns the modified data frame:

# Open the editor; assign the result
corrected <- ViewR(survey_data, edit = TRUE)

# 'corrected' now contains all inline edits + any find-replace operations
head(corrected)

If you click Cancel, the original unmodified data frame is returned.

Full example: survey data workflow

library(ViewR)

# Load a dataset (here we use the built-in survey proxy)
data(Titanic)
titanic_df <- as.data.frame(Titanic)

# Step 1 — Explore: inspect variable info and browse the data
ViewR(titanic_df,
      labels = c(Class    = "Passenger Class",
                 Sex      = "Passenger Sex",
                 Age      = "Age Group",
                 Survived = "Survival Status",
                 Freq     = "Count"),
      theme  = "flatly")

# Step 2 — Clean: use Find & Replace inside the popup to
#   standardise "Male" -> "M", "Female" -> "F"

# Step 3 — Filter & export: the R Code tab will have generated:
#   titanic_df_result <- titanic_df |>
#     filter(`Survived` == "Yes") |>
#     arrange(desc(`Freq`))
# Copy, paste, and run!

# Step 4 — Edit specific cells if needed
titanic_clean <- ViewR(titanic_df, edit = TRUE)

Session info

sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Tahoe 26.4.1
#> 
#> Matrix products: default
#> BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
#> 
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> time zone: Asia/Kolkata
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.39     R6_2.6.1          fastmap_1.2.0     xfun_0.57        
#>  [5] cachem_1.1.0      knitr_1.51        htmltools_0.5.9   rmarkdown_2.31   
#>  [9] lifecycle_1.0.5   cli_3.6.5         sass_0.4.10       jquerylib_0.1.4  
#> [13] compiler_4.5.2    rstudioapi_0.18.0 tools_4.5.2       evaluate_1.0.5   
#> [17] bslib_0.10.0      yaml_2.3.12       otel_0.2.0        jsonlite_2.0.0   
#> [21] rlang_1.2.0