respR is a package for
R that provides a
structural, reproducible workflow for the processing and analysis of
respirometry data. While the focus of the package is on aquatic
respR is largely unitless and so can process,
explore, and determine rates from any respirometry data, and indeed
linear relationships in any time-series data.
A highlight of the package is the
This uses machine learning (kernel density estimation) to
automatically identify linear regions of data, that is regions
where oxygen uptake or production rates are stable and consistent. This
allows metabolic rates to be extracted in an objective manner. See
vignette("auto_rate") for more details.
respR is now available on CRAN,
and can be installed via the ‘Packages’ tab in RStudio or by running
to get started. The site has a range of vignettes detailing the
functionality, plus example workflows, documentation, and more.
We are also happy to help directly. If you have problems using the package or getting started with your analysis, get in touch with a sample of your data and we will help get you started.
The package has also been peer
reviewed and published in Methods in Ecology and
Evolution. Please cite this publication if you use
respR in your published work.
respR has been used to examine metabolic rates and
photosynthesis in corals, plankton, micro- and macro-algae, fish,
crustaceans, echinoderms, cephalopods, bivalves and more, in both lab
and field studies. Check the respR
in use page to see a list of published studies which have
used the package to analyse their data.
respR has a Twitter
account. Please follow for latest news and regular updates
from the world of respirometry!
See here for even more ways of communicating with us, providing feedback and getting touch if you are having issues.
See Support Us if you would like to help support the package development.
For a quick evaluation of the package, try out the following code:
library(respR) # load the package # 1. check data for errors, select cols 1 and 15: urch <- inspect(urchins.rd, time = 1, oxygen = 15) # 2. automatically determine most linear segment: rate <- auto_rate(urch) # 3. convert out <- convert_rate(rate, oxy.unit = "mg/L", time.unit = "min", output.unit = "mg/h/kg", volume = 0.6, mass = 0.4) print(out) ## Alternatively, use 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() %>% # a quick preview convert_rate("mg/L", "min", "mg/h/kg", 0.6, 0.4) # convert to units