If you do not currently have the neotoma package installed then uncomment

and run the following code. Right now you will need to install the package from GitHub

because our latest bug fixes haven't been pushed up into CRAN.

install.packages('devtools')

library(devtools)

install_github('ropensci/neotoma')

The Neotoma package is described more fully here:

Goring, S., Dawson, A., Simpson, G. L., Ram, K., Graham, R. W., Grimm, E. C., &

Williams, J. W.. (2015). neotoma: A Programmatic Interface to the Neotoma

Paleoecological Database, 1(1), Art. 2. DOI: http://doi.org/10.5334/oq.ab

library(neotoma) library(maps) # you may also want to install this.

There are (surprisingly?) no data records from Nevada currently. Instead we will

pull records from California. In this call I want to find high elevation sites

because they're kind of interesting.

ca_pollen <- get_dataset(gpid = 'California', datasettype = 'pollen', ageold = 10000, altmin=1000)

plot(get_site(ca_pollen)[,c('long', 'lat')], xlim = c(-125, -115), ylim=c(30, 45), pch=19, col=2) map('world', add=TRUE) map("state", add=TRUE)

Now we can download the records.

You should see the site names running by. As you do this, reflect on all the work

that's gone into these records and give thanks to the named and unnamed workers who

toiled behind a microscope for months at a time so that you could download their data

in mere seconds.

ca_download <- get_download(ca_pollen)

For this example we should find the one that gives us the most dated material.

dated_samples <- get_chroncontrol(ca_download)

This next line is just FYI - we're getting a vector of the number of dates in each core.

Some will return a NULL if there is no associated control table. This happens for single

stratigraphic pollen sections for example.

sapply(dated_samples, function(x)nrow(x$chron.control))

The last core (Taquitz Meadow) has 6, controls:

The number is quotes is the dataset number. To choose a different dataset look at the

available datasets and enter in a different number.

plot(dated_samples[['15594']]$chron.control[,c('depth', 'age')])

You can try a few different ones to see how things look.

So Taquitz Meadow looks good. We can look at it in the Neotoma Explorer like this:

http://apps.neotomadb.org/explorer/?datasetid=15594

The original model was generated in Bacon, let's see how well we do:

write_agefile(ca_download[['15594']], chronology = 1, path = '.', corename = 'TAQUITZ', cal.prog = 'Bacon')

Source Bacon . . .

source('Bacon.R')

Please note that this will actually take a very very long time. It might be worth picking a different site.

Bacon('TAQUITZ')