Meet dataviewR: The View() You Always Wanted

Disclaimer: This blog contains opinions that are of the authors alone and do not necessarily reflect the strategy of their respective organizations.

The humble View() and its limits

View() has served R programmers well for a long time — pass it a data frame, get a spreadsheet-style window. It even has a basic search bar for checking whether a value exists in your data. But the moment you need something more precise — filter by a specific column or combine conditions — you are back in your script.

Add to that: no side-by-side dataset comparison, no metadata inspection, and no way to carry your exploration into reproducible code. For day-to-day clinical data work — reviewing your clinical datasets, let’s say ADSL, cross-checking subject demographics against lab data in ADLB, doing a QC pass before analysis — these gaps add up.

What is dataviewR?

dataviewR is a Shiny-based interactive data viewer that works alongside View() as a companion — not a replacement. It launches directly in your RStudio Viewer pane, requires no Shiny code, and never modifies the datasets passed to it.

install.packages("dataviewR")

Features

dataviewR offers the following capabilities:

• Interactive Filtering — apply dplyr-style expressions directly in the app, no script changes needed. Supports %in%, is.na(), grepl(), and compound conditions — the same syntax you already write every day.

SEX == "F" & AGE > 65 & TRT01P == "Xanomeline High Dose"

• Reproducible Code Generation — hit “Generate R Code” and walk away with ready-to-use dplyr code from your interactions. Your exploration session feeds directly into your scripted workflow.

Auto-generated dplyr code from interactive filter and column selections.

• Variable Metadata — inspect variable classes, labels and attributes without writing str() or attr() calls. Particularly useful for clinical datasets where CDISC-style labels are carried as R attributes.

See all of this in action:

Cross-Checking Multiple Datasets

But what if you need to look at more than one dataset at the same time? dataviewR handles that too — pass multiple datasets in a single call and each opens in its own tab within the same session. Switch between them, filter independently, and track a specific subject across datasets — the kind of check that comes up in every safety review.

library(dataviewR)
library(pharmaverseadam)

dataviewer(adsl, adlb)

Final Thoughts

That is dataviewR in a nutshell. Try it out and share your thoughts on GitHub or pharmaverse Slack!

Full documentation, vignettes, and clinical dataset examples are available at madhankumarnagaraji.github.io/dataviewR.

Last updated

2026-03-31 18:28:15.461796

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