Explore and Join Many Tables on a Zoomable Canvas

Lays out many data frames side by side as cards on a pan-and-zoom canvas, either as a 'shiny' gadget for 'RStudio' or as an 'htmlwidgets' widget for 'R Markdown' and 'Quarto' pages. Drawing a line between two columns writes the 'dplyr' join and checks it first: match rates, unmatched keys and the key relationship. Sorts, filters and other steps build up a readable pipeline of R code, and a column inspector exports a data dictionary. An optional large language model (LLM) assistant, using 'Ollama' or an 'OpenAI'-compatible service, suggests 'tidyverse' code from the table schema only, never the data rows.

sideview: Multi-Table 2D Infinite Canvas for R

sideview puts many tables side by side on a pan-and-zoom canvas, so you can compare them, connect columns to build joins, check those joins before you run them, and take away reproducible tidyverse code. It runs as an RStudio gadget or as an htmlwidget you can save to HTML or embed in Quarto and R Markdown.

Installation

Install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("lauyeehow1986-hub/sideview")

Key Features

Many Tables at Once: data.frame, tibble, table, matrix, gt, flextable, arrow tables and datasets, and duckdb connections each become a card on one canvas. Tables of 100,000+ rows scroll smoothly.
Canvas Navigation: Wheel zoom (15%–350%), pan, drag and snap cards, Fit and Arrange, a minimap, Ctrl+K to find a card or column, and double-click a card title to zoom to it.
Visual Joins:
- Draw a spline between two columns to generate dplyr joins, including multi-key joins.
- sv_suggest_mappings() proposes likely keys from name, type and value overlap.
- Join diagnostics (sv_join_diagnostics()) show match rates both ways, unmatched keys, 1:1 / 1:m / m:m relationships, rows per join type, and NA, spacing or case problems in keys.
Composable Pipeline: Every sort, filter, join, rename, recode, mutate, reshape or summarise becomes a step you can toggle, reorder or delete, with undo/redo and a %>% / |> choice. Sorts and filters run on the full data in R, not just the rows on screen.
Data Quality:
- Header sparklines and missingness badges.
- Category discrepancy detection with auto-clean rules (sv_detect_discrepancies(), sv_auto_clean_rules()).
- A column inspector (sv_column_profile()).
- A data dictionary export (sv_codebook(), CSV or XLSX).
Into Your Script: Insert the generated code at the cursor in RStudio, send it to the console, or run it and keep the result.
Save and Reopen Workspaces: Card positions, zoom, joins and the pipeline (sideview(..., state = "canvas.json"), sv_save_state(), sv_load_state()).
AI Copilot: Ask a local Ollama model or any OpenAI-compatible endpoint for tidyverse code. Only schema summaries are sent, never raw rows.
Themes and Keyboard: Light, dark or automatic (follows RStudio), sv_theme() or a bslib::bs_theme(). Press ? on the canvas for every shortcut.

Quick Start

library(sideview)

patients <- data.frame(
  patient_id = c("P01", "P02", "P03", "P04"),
  age        = c(45, 52, 38, 61),
  sex        = c("Male", "Female", "M", "Fem")
)
visits <- data.frame(
  patient_id  = c("P01", "P02", "P03", "P05"),
  systolic_bp = c(120, 140, 115, 130)
)

# 1. Open the canvas (RStudio dialog by default; also viewer = "pane" or "browser")
if (interactive()) {
  sideview(patients, visits)
}

# 2. Check a join before you run it
sv_join_diagnostics(patients, visits, by = "patient_id",
                    x_name = "patients", y_name = "visits")

# 3. Build a data dictionary across tables
sv_codebook(patients, visits)

# 4. Share the canvas as a standalone HTML file
save_sideview(sideview_widget(patients, visits), file = "study_overview.html")

To embed the canvas in Quarto or R Markdown, put sideview_widget(patients, visits, height = "650px") in a chunk.

sideview is a research and education tool for exploring and preparing data. It is not medical device software and is not intended for clinical decisions.