This package is under active development, but is currently stable.
respR is an R package that provides a structural, reproducible workflow for the processing and analysis of respirometry data. While the focus of our package is on aquatic respirometry,
respR is largely unitless and so can process linear relationships in any time-series data, such as oxygen flux or photosynthesis.
Here is how to get started.
respR is not yet published in CRAN. For now, use the
devtools package to grab the stable version:
For a quick evaluation of the package, try out the following code:
library(respR) # load the library # As lazy loading is in place, we do not need to call example data explicitly. # This example will use the `urchins.rd` example data. # 1. check data for errors, select cols 1 and 15: urch <- inspect(urchins.rd, 1, 15) # 2. automatically determine linear segment: rate <- auto_rate(urch) # 3. convert units out <- convert_rate(rate, "mg/l", "s", "mg/h/kg", 0.6, 0.4) ## Alternatively, use dplyr pipes: urchins.rd %>% # using the urchins dataset, select(1, 15) %>% # select columns 1 and 15 inspect() %>% # inspect the data, then auto_rate() %>% # automatically determine most linear segment print() %>% # just a quick preview convert_rate("mg/l", "s", "mg/h/kg", 0.6, 0.4) # convert units
respR is under continuous development. If you have any bugs or feedback, you can contact us easily by opening an issue. Alternatively, you can fork this project and create a pull request.
Please also feel free to email with any feedback or problems you may encounter.
The design of this package would not have been possible without inspiration from the following authors and their packages:
Clark, T. D., Sandblom, E., & Jutfelt, F. (2013). Aerobic scope measurements of fishes in an era of climate change: respirometry, relevance and recommendations. Journal of Experimental Biology, 216(15), 2771–2782. doi: 10.1242/Jeb.084251
Gamble, S., Carton, A. G., & Pirozzi, I. (2014). Open-top static respirometry is a reliable method to determine the routine metabolic rate of barramundi, Lates calcarifer. Marine and Freshwater Behaviour and Physiology, 47(1), 19–28. doi: 10.1080/10236244.2013.874119
Leclercq, N., Gattuso, J.-P. & Jaubert, J. (1999). Measurement of oxygen metabolism in open-top aquatic mesocosms: Application to a coral reef community. Marine Ecology Progress Series, 177, 299–304. doi: 10.3354/meps177299
Lighton, J.R.B. (2008). Measuring Metabolic Rates: A Manual for Scientists. Oxford University Press, USA.
Muggeo, V.M.R. (2003). Estimating regression models with unknown break-points. Statistics in Medicine, 22, 3055–3071. doi: 10.1002/sim.1545
Muggeo, V. (2008). Segmented: An R package to fit regression models with broken-line relationships. R News, 8, 20–25.
Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Chapman; Hall/CRC Press.
Steffensen, J. F. (1989). Some errors in respirometry of aquatic breathers: How to avoid and correct for them. Fish Physiology and Biochemistry, 6(1), 49–59. doi: 10.1007/BF02995809
Svendsen, M.B.S., Bushnell, P.G. & Steffensen, J.F. (2016). Design and setup of intermittent-flow respirometry system for aquatic organisms. Journal of Fish Biology, 88, 26–50. doi: 10.1111/jfb.12797
White, C.R. & Kearney, M.R. (2013). Determinants of inter-specific variation in basal metabolic rate. Journal of Comparative Physiology B: Biochemical, Systemic, and Environmental Physiology, 183, 1–26. doi: 10.1007/s00360-012-0676-5
Yeager, D.P. & Ultsch, G.R. (1989). Physiological regulation and conformation: A BASIC program for the determination of critical points. Physiological Zoology, 62, 888–907. doi: 10.1086/physzool.62.4.30157935