Merges palaeoecological datasets with different time resolution.

It merges palaeoecological datasets with different time intervals between consecutive samples into a single dataset with samples separated by regular time intervals defined by the user

mergePalaeoData(
 datasets.list = NULL,
 time.column = NULL,
 interpolation.interval = NULL
 )

Arguments

datasets.list	list of dataframes, as in datasets.list = list(climate = climate.dataframe, pollen = pollen.dataframe). The provided dataframes must have an age/time column with the same column name and the same units of time. Non-numeric columns in these dataframes are ignored.
time.column	character string, name of the time/age column of the datasets provided in datasets.list.
interpolation.interval	temporal resolution of the output data, in the same units as the age/time columns of the input data

Value

A dataframe with every column of the initial dataset interpolated to a regular time grid of resolution defined by interpolation.interval. Column names follow the form datasetName.columnName, so the origin of columns can be tracked.

Details

This function fits a loess model of the form y ~ x, where y is any column given by columns.to.interpolate and x is the column given by the time.column argument. The model is used to interpolate column y on a regular time series of intervals equal to interpolation.interval. All columns in every provided dataset go through this process to generate the final data with samples separated by regular time intervals. Non-numeric columns are ignored, and absent from the output dataframe.

Examples

#loading data
data(pollen)
data(climate)

x <- mergePalaeoData(
 datasets.list = list(
   pollen=pollen,
   climate=climate
 ),
 time.column = "age",
 interpolation.interval = 0.2
 )
#> Argument interpolation.interval is set to 0.2
#> The average temporal resolution of pollen is 1.27; you are incrementing data resolution by a factor of 6.35
#> The average temporal resolution of climate is 1; you are incrementing data resolution by a factor of 5