Chartready

Chartready is a concept used to describe data that has been prepared in a way that downstream charting libraries can render without additional transformation. It emphasizes consistent structure, types, and units to support reliable visualizations.

Common characteristics of chartready data include tidy data with one variable per column and all observations in rows; explicit data types such as numbers, strings, and timestamps; normalized category labels; consistent units; complete or imputed missing values; and a clear separation between metadata and data values. This clarity helps reduce ambiguity when charts are created or updated.

Preparing chartready data typically involves validation and type coercion, handling missing values, normalization of scales, aggregation or pivoting to the desired granularity, and alignment of time series across series. The goal is to produce a stable, well-defined dataset that can be fed into multiple charting tools with minimal further processing.

Chartready data is commonly represented in tabular formats such as CSV or JSON, as well as columnar formats, and it is designed to be compatible with a range of charting libraries. This interoperability supports reuse across projects and teams, simplifying the workflow from data preparation to visualization.

In practice, achieving chartready status supports reproducible visual analytics, reduces rendering errors, and speeds up development by decoupling data preparation from the visualization layer. Related concepts include tidy data, data normalization, data cleaning, and data visualization.