Respirometry is a ubiquitous practice in experimental biology, but there is a lack of standard practices when analysing the resulting data, limiting transparency and reproducibility. As respirometry datasets become increasingly large and analytical approaches more complex, manipulating the data remains a challenge and often intractable with existing tools.
Here we describe the respR R package, a collection of functions that implement a workflow‐based approach to automate the analysis and visualisation of respirometry data. The package can be used for closed, intermittent flow, flow‐through and open‐tank respirometry and uses well‐defined sets of rules to reliably and rapidly generate reproducible results.
We demonstrate how respR uses novel computing methods such as rolling regressions and kernel density estimates to reliably detect maximum, minimum and most linear sections of the data, and critical oxygen tension, .
Although designed specifically with aquatic respirometry in mind, the object‐oriented approach of the package and the unit‐less nature of its analytical functions mean that parts of the package can easily be used to estimate linear relationships from a range of applications in many research disciplines.