Nothing
## ---- message=F, warning=F----------------------------------------------------
library(sdmpredictors)
# exploring the marine datasets
datasets <- list_datasets(terrestrial = FALSE, marine = TRUE)
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(datasets, row.names = FALSE)
## -----------------------------------------------------------------------------
# exploring the marine layers
layers <- list_layers(datasets)
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(layers[1:3,1:4], row.names = FALSE)
## -----------------------------------------------------------------------------
# print the Bio-ORACLE citation
print(dataset_citations("Bio-ORACLE"))
# print the citation for ENVIREM as Bibtex
print(lapply(dataset_citations("WorldClim", astext = FALSE), toBibtex))
# print the citation for a MARSPEC paleo layer
print(layer_citations("MS_biogeo02_aspect_NS_21kya"))
## ---- eval = FALSE------------------------------------------------------------
# # download pH and Salinity to the temporary directory
# load_layers(layers[layers$name %in% c("pH", "Salinity") &
# layers$dataset_code == "Bio-ORACLE",], datadir = tempdir())
#
# # set a default datadir, preferably something different from tempdir()
# options(sdmpredictors_datadir= tempdir())
#
# # (down)load specific layers
# specific <- load_layers(c("BO_ph", "BO_salinity"))
#
# # equal area data (Behrmann equal area projection)
# equalarea <- load_layers("BO_ph", equalarea = TRUE)
## -----------------------------------------------------------------------------
# exploring the available future marine layers
future <- list_layers_future(terrestrial = FALSE)
# available scenarios
unique(future$scenario)
unique(future$year)
paleo <- list_layers_paleo(terrestrial = FALSE)
unique(paleo$epoch)
unique(paleo$model_name)
## -----------------------------------------------------------------------------
get_layers_info(c("BO_calcite","BO_B1_2100_sstmax","MS_bathy_21kya"))$common
# functions to get the equivalent future layer code for a current climate layer
get_future_layers(c("BO_sstmax", "BO_salinity"),
scenario = "B1",
year = 2100)$layer_code
# functions to get the equivalent paleo layer code for a current climate layer
get_paleo_layers(c("MS_bathy_5m", "MS_biogeo13_sst_mean_5m"),
model_name = c("21kya_geophysical", "21kya_ensemble_adjCCSM"),
years_ago = 21000)$layer_code
## ---- message=F, warning=F----------------------------------------------------
# looking up statistics and correlations for marine annual layers
datasets <- list_datasets(terrestrial = FALSE, marine = TRUE)
layers <- list_layers(datasets)
# filter out monthly layers
layers <- layers[is.na(layers$month),]
layer_stats(layers)[1:2,]
correlations <- layers_correlation(layers)
# create groups of layers where no layers in one group
# have a correlation > 0.7 with a layer from another group
groups <- correlation_groups(correlations, max_correlation=0.7)
# group lengths
sapply(groups, length)
for(group in groups) {
if(length(group) > 1) {
cat(paste(group, collapse =", "))
cat("\n")
}
}
# plot correlations (requires ggplot2)
plot_correlation(correlations)
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