View source: R/get_chroncontrol.R
get_chroncontrol | R Documentation |
Using the dataset ID, return all records associated with the data. At present, only returns the dataset in an unparsed format, not as a data table. This function will only download one dataset at a time.
get_chroncontrol(x, chronology = 1, verbose = TRUE, add = FALSE)
x |
A single numeric chronology ID, a vector of numeric dataset IDs as returned by |
chronology |
When |
verbose |
logical, should messages on API call be printed? |
add |
logical, should this chron control be added to the download object? |
This command returns either an object of class "try-error"
containing the error returned
from the Neotoma API call, or a full data object containing all the relevant information required to build either the default or prior chronology for a core.
When download
or download_list
objects are passes, the user can add
the chroncontrol to the
download
object explicitly, in which case the function will return a download with chroncontrol
embedded.
This is a list comprising the following items:
|
A table describing the collection, including dataset information, PI data compatable with |
|
Dataset information for the core, primarily the age-depth model and chronology. In cases where multiple age models exist for a single record the most recent chronology is provided here. |
If Neotoma returns empty content, either the control table or the associated metadata (which happens in approximately 25
Simon J. Goring simon.j.goring@gmail.com
+ Neotoma Project Website: http://www.neotomadb.org + API Reference: http://wnapi.neotomadb.org/doc/resources/contacts
## Not run: # The point of pulling chronology tables is to re-build or examine the # chronological information that was used to build the age-depth model for # the core. You can do this by hand, but the `write_agefile` function works # with `download` objects directly. three_pines <- get_download(get_dataset(get_site("Three Pines Bog"), datasettype = "pollen")) pines_chron <- get_chroncontrol(three_pines) # Spline interpolation: model <- smooth.spline(x = pines_chron[[1]]$chron.control$depth, y = pines_chron[[1]]$chron.control$age) new_ages <- predict(model, x = three_pines[[1]]$sample.meta$depth) ## End(Not run)
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