Using isotopes for fun things.
First we install the package.
#We need devtools installed
#install.packages(devtools)
require(devtools)
devtools::install_github("brandynlucca/isoclockR")
Then we can create an ISO object (see: ?ISO via command line in the R GUI/RStudio).
require(isoclockR)
require(tidyverse) #this is a dependency that is currently giving fits...
#Create ISO object
#Hypothetical animal with d15N sample
#d15N == dt == == 14.9 %o
#d0 == doi == 12.69 %o
#df == dfi == 17.59 %o
#lambda == lambda == 0.0172
random_animal <- ISOgen(data=14.9, data.names="d15N", doi=12.69, dfi=17.59, lambda=0.0172)
So we can look at how this object is generated with some metadata (currently still being fleshed out, of course).
#Show object
random_animal
So we can see all of the variables necessary for the isoclock(...) from Klaasen et al. (2010). We can then put these into the equation:
#Isoclock equation
#We can either set the object against itself
random_animal <- isoclock(random_animal)
#Or we can let it update itself.
isoclock(random_animal)
So we have now calculated the residence times, which are updated within the object. While parsing functions are being developed for a summary(...) function, these data can be viewed via:
#Parse out data
random_animal@data
where values represent the isotope value, and residence represents the residence time in days. We can also put in arbitrary values, as well, without creating an ISO object at the start:
#Isoclock without premade object
new_animal <- isoclock(doi=12.69, dfi=17.59, dt=14.9, lambda=0.0172, data.names="d15N")
new_animal
#We can also create an arbitrarily named object from a randomized seed so as to not create overlapping/conflicting names. This flagged via "create" and will assign an object.
#This 'create' feature may be a temporary feature since 'assign' is not a very functional approach, but we shall see!
isoclock(doi=12.69, dfi=17.59, dt=14.9, lambda=0.0172, data.names="d15N", create=T)