Working with Multiple Datasets in dataviewR

dataviewR allows you to explore two or more datasets in the same session, making it easy to compare structures, values, and attributes across data sources.

This capability is especially useful when reviewing:
- raw vs derived datasets
- ADaM vs SDTM datasets
- training vs test sets
- simulation outputs vs expected results

This article walks through how to launch and work with multiple datasets.

1. Launching dataviewR with multiple datasets

You can pass any number of datasets to dataviewer():

library(dataviewR)

dataviewer(iris, mtcars)

Each dataset will appear in its own tab inside the application in the RStudio Viewer pane or the default web browser.

You can pass more than two datasets as well:

dataviewer(iris, mtcars, ToothGrowth, airquality)

2. Navigating between dataset tabs

Inside the Viewer pane:

Each dataset is presented under its own labeled tab
Switching between tabs does not reset filters or selections
Each dataset behaves independently (filtering, column selection, metadata)

This makes it easy to compare side-by-side:

variable names
attributes (labels and classes)
missing data patterns
derived vs raw values

3. Applying filters and selections independently

Each dataset tab maintains:

its own filter expression
its own column selection
its own generated code
its own metadata panel

This allows you to:

filter iris on species
filter mtcars on horsepower
compare the results

Example:

Tab: iris

Species == "setosa"

dataviewR viewing the iris dataset

Tab: mtcars

mpg > 20 & cyl == 4

dataviewR viewing the iris dataset

Each dataset’s output, table, and code are kept separate.

4. Independent code generation

Click Generate R Code within any dataset tab to get a reproducible pipeline for that dataset.

Example structure:

# Generated R Code
library(dplyr)

iris |>
  filter(Species == "setosa") |>
  select(Sepal.Length, Sepal.Width, Species)

Switch tabs → generate code for the other dataset.

5. Typical use cases

✓ Raw vs Derived Data

Load both raw and derived datasets:

The dm_raw, dm, and adsl datasets are loaded from the pharmaverseraw, pharmaversesdtm, and pharmaverseadam packages, respectively.

library(pharmaverseraw)
library(pharmaversesdtm)
library(pharmaverseadam)

dataviewer(dm_raw, dm)
dataviewer(dm, adsl)

Then:

Compare variable labels and other attributes
Confirm derived variables
Check row/column mismatches

✓ SDTM Dataset Review

library(pharmaversesdtm)

dataviewer(dm, suppdm, ae, suppae)

✓ ADaM Dataset Review

library(pharmaverseadam)

dataviewer(adsl, adae, adlb)

Navigate through tabs to review structure and metadata.

✓ Comparing train/test sets

dataviewer(train_set, test_set)

Inspect variable distributions and missingness.

✓ Reviewing multiple simulation outputs

dataviewer(sim1, sim2, sim3)

Check how different simulation runs behave.

Summary

In this article, you learned how to:

Launch dataviewR with multiple datasets
Navigate dataset tabs
Filter and select columns independently
Generate code for each dataset
Parallel viewing of datasets in each tab

Viewing datasets side-by-side is one of the most powerful features of dataviewR, especially for data review and quality control workflows.

Continue with: Working with Clinical Datasets

Exploring Multiple Datasets