Tidy data is a standardized way of organizing data to make it easier to analyze. Applications expect data to be tidy to create plots and analyses in a standardized way. Sphinx helps you create tidy data during the data import process and by applying transformations to your data in an analysis.

The key principles of tidy data are:

1. Each variable forms a column: Each measured variable is placed in its own column.
2. Each observation forms a row: Each different observation of that variable is placed in its own row.
3. Each type of observational unit forms a table: Each dataset is organized into a table.

Messy vs Tidy Data

This table contains messy data, as each observations for a given wavelength and sample type are split across multiple columns. The experimental variable might be the gene and the treatment

Here are the data in a tidy format. Notice that the variables encoded in columns names like ABS_280_Control is represented in appropriate columns by splitting on the _.

Further Reading

You can read more about tidy data and its impact.

• Wickham, Hadley. “Tidy data.” Journal of statistical software 59 (2014): 1-23. https://doi.org/10.18637/jss.v059.i10
• Wang, Earo, Dianne Cook, and Rob J. Hyndman. “A new tidy data structure to support exploration and modeling of temporal data.” Journal of Computational and graphical Statistics 29.3 (2020): 466-478. https://doi.org/10.48550/arXiv.1901.10257