useR! 2026

Building AI-Powered Data Pipelines with blockr: From Visual Analysis to Custom Extensions

6 Jul 2026, 13:00

3h

Tutorial (3 hours) Web applications (Shiny, dashboards) Tutorials

Speakers

Christoph Sax (cynkra GmbH, University of Basel) David Granjon (cynkra GmbH)

Description

Data analysis in R requires writing code, which remains a barrier for many domain experts. blockr is an open-source visual programming framework for R that allows users to construct reactive data pipelines by assembling modular blocks through a point-and-click interface. The framework generates reproducible R code automatically.

This hands-on tutorial takes participants from first use to block extension development. The first half of the tutorial focuses on building complete analysis workflows: loading data, applying dplyr transformations, creating ggplot2 visualizations, and exploring the pipeline structure through interactive DAG views. The generated R code can be inspected and exported at every step. Participants also experience blockr's AI integration, using natural language to configure blocks and guide their analysis.

The second half addresses the developer perspective. Attendees learn how blocks are structured (state management, expression evaluation, UI generation) and build their own custom blocks by wrapping R functions. We then cover blockr's plugin architecture and show how to add AI-powered configuration to custom blocks. The session concludes with packaging blocks for distribution and deploying applications on Shiny Server or Posit Connect.

All tutorial materials will be provided as an R package with exercises.

blockr is generously funded by Bristol Myers Squibb and is fully open source.

Outline of the tutorial

• Introduction and setup (15 min)
• Building a first pipeline: load data, filter, select, visualize through the UI (25 min)
• AI-powered analysis: configure blocks with natural language (20 min)
• Advanced workflows: multiple data sources, joins, DAG visualization, code export (20 min)

Break (15 min)

• Block architecture: state, expressions, UI, authoring rules (20 min)
• Building a custom block: wrap an R function into a blockr block (25 min)
• Plugins and AI integration: plugin system, AI configuration for custom blocks (25 min)
• Packaging and deployment: bundle blocks into a package, deploy with Shiny Server or Posit Connect (15 min)

Biography of the instructors

Christoph Sax is product owner of blockr and a founder of cynkra. He is associate editor of the R Journal and has authored several CRAN packages on time series.

David Granjon leads frontend development of blockr at cynkra. He is the author of Outstanding User Interfaces with Shiny (Chapman & Hall, 2022) and a founder of the RinteRface organization.

Prerequisites (only for tutorials)

Basic familiarity with R (data frames, functions). Some Shiny knowledge helps for the developer portion, but is not required. Intended for: analysts, data scientists, R developers, and team leads who want to make data workflows more accessible.

Learning goals (only for tutorials)

• Build interactive data pipelines without writing code
• Use AI-assisted block configuration
• Understand blockr's block architecture (state, expressions, UI)
• Create custom blocks by wrapping R functions

Additional Material or Paper

https://bristolmyerssquibb.github.io/blockr/

Target audience (only for tutorials)

• Data analysts and scientists who want visual, code-free workflows
• R developers who want to make their software easily accessible
• Team leads that look for R-based open source alternatives to PowerBI or Tableau

If you used AI tools or services to support the preparation of this submission, please state the name and reason for using each of them.

Claude Code, to refine and proof-read the abstract.