flowchart LR
A[Read CSV] --> B[Filter Rows]
B --> C[Select Columns]
C --> D[Create Plot]
D --> E[Export]
Welcome to blockr
Build data workflows without coding
blockr is a block-based framework for data manipulation and visualization. It provides a visual, point-and-click interface for creating data workflows without writing code.
What is blockr?
blockr is a unified meta-package that brings together five core packages:
- blockr.core - The foundation framework
- blockr.dplyr - Data wrangling blocks (filter, select, summarize, etc.)
- blockr.ggplot - Visualization blocks for creating plots
- blockr.ui - User interface components
- blockr.io - File input/output blocks
These packages let you load data from files, transform and filter data, create visualizations, and export results.
How it works
Instead of writing code, you work with blocks:
- Add blocks to your workspace
- Connect blocks together to create a data pipeline
- Configure blocks using dropdowns, checkboxes, and input fields
- View results in real-time as you build
Each block performs a specific task (filtering data, creating a plot, etc.) and can be chained together to create workflows.
Example workflow
Blocks
See available blocks and what they do:
- Data Wrangling Blocks - Filter, select, transform, and summarize data
- Data Visualization Blocks - Create scatter plots, bar charts, line charts, and more
- File I/O Blocks - Read and write files in multiple formats
Getting Started
To install and run blockr, see the Getting Started guide.
Core Packages
blockr is built on five specialized packages:
- blockr.core - Framework and architecture
- blockr.dplyr - Data manipulation blocks
- blockr.ggplot - Plotting and visualization
- blockr.ui - User interface components
- blockr.io - File reading and writing
Installing blockr automatically installs all five packages.
Extending blockr
The five core packages provide base blocks for common data analysis tasks. blockr can be extended with additional block packages for specific domains.
For example, blockr.ts adds blocks for time series analysis, including decomposition, forecasting, and frequency analysis.
Additional block packages can be installed separately and will integrate seamlessly with the blockr interface.