Welcome to blockr

What is blockr?

blockr is a visual framework for building data workflows. Instead of writing code, you work with blocks - each block performs a specific task like loading data, filtering rows, or creating a chart. Connect blocks together to build complete data pipelines.

A blockr workflow: Dataset → Filter → Select → Plot

Who is it for?

blockr is designed for anyone who works with data:

  • Analysts who want to explore data without writing code
  • Researchers who need reproducible workflows
  • Teams who want to share and collaborate on data pipelines

How it works

  1. Add blocks to your workspace - choose from data import, transformation, visualization, and export blocks
  2. Connect blocks together to create a pipeline - data flows from one block to the next
  3. Configure blocks using dropdowns, checkboxes, and input fields - no coding required
  4. See results in real-time as you build - each block shows a preview of its output

Available blocks

blockr comes with blocks for common data tasks:

Import and Export

  • Import Data - Load CSV, Excel, SPSS, SAS, Stata, Parquet files from your computer, a server, or URLs
  • Export Data - Save results to CSV, Excel, or Parquet files

Data Transformation

  • Filter Rows - Keep rows matching specific values
  • Select Columns - Choose which columns to keep
  • Calculate Columns - Create new columns using formulas
  • Aggregate Data - Calculate totals, averages, counts
  • Sort Rows - Order data by column values
  • Lookup & Merge - Combine tables by matching columns
  • And more: Rename, Pivot, Stack, Split…

Visualization

  • ggplot - Create scatter plots, bar charts, line graphs, histograms, and more
  • Theme - Customize colors and styling
  • Facet - Split plots into panels
  • Grid - Combine multiple plots

Next steps

Core packages

blockr is built on six specialized packages:

Foundational:

Block packages:

Extending blockr

Additional block packages can be installed for specific domains. For example, blockr.ts adds blocks for time series analysis.