Data Analytics Made Easy

CanvasXpress: A JavaScript Library for Data Analytics with Full Audit Trail Capabilities.

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CanvasXpress is free for personal and educational use.

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$ yarn add canvasxpress
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Integrate with the frameworks you work

R Integration

Install the CanvasXpress R package from CRAN and use it in the R console, R-Studio or in any Shiny application.

Node Integration

Install the CanvasXpress Node modules from npmjs to programmatically create visualization in the command line locally or in the cloud.

React Integration

Install the CanvasXpress Node modules from npmjs to easily integrate with React JS. Examples included.

Angular Integration

Install the CanvasXpress Node modules from npmjs to easily integrate with Angular JS. Examples included.

# What is CanvasXpress?

CanvasXpress a stand-alone JavaScript Library for Data Analytics. Built for the purpose of reproducible research with a sophisticated and unobtrusive user interface. Full and effortless audit trail of data, configuration and all user interactions in every visualization.

Feature List

Learn how these features can help you develop visualizations easily and serve your stake holders fast.

Open Source

Free for non-commercial and academic use. Please contact us for commercial use. Software released as GPL3.

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User Interface

Rich set of unobtrusive widgets for data analytics embedded in every visualization.

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Software Design

Visualizations optimized to enhance the user experience and facilitate data exploration.

Audit Trail

Full tracking of data, configuration and every single user interaction for Reproducible Research.

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File Formats

Create visualizations starting with different file formats. Load data from JSON, XML, CSV or PNG files by simply drag and drop.

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Interactive Plots

Rich and dynamic user experience to explore data with mouse-overs, zoom, clicks, resize, drag and drop, menus, tables and more.

Broadcast Events

Built-in automatic broadcast mechanism to comunicate events to all visualizations in web page to highlight, filter-in or filter-out.

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Data Wrangling

Explore and model data sets by grouping or segregating based on meta data, sorting, clustering, transforming and much more.

# When should you use it?

You want to show your data and let your audience explore it. You want to keep track of what they did during the data exploration. You want to do all this fast!