Align and join multiple time series to a common temporal resolution

Aligns multiple time series datasets to a common temporal resolution using LOESS interpolation and joins them into a single dataframe. This is useful when combining datasets with different sampling intervals.

Usage

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

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

Arguments

datasets.list: list of dataframes, as in datasets.list = list(dataset1 = df1, dataset2 = df2). The provided dataframes must have a time column with the same column name and the same units of time. Non-numeric columns in these dataframes are ignored. Default: NULL.
time.column: character string, name of the time column of the datasets provided in datasets.list. Default: NULL.
interpolation.interval: numeric, temporal resolution of the output data, in the same units as the time columns of the input data. Default: NULL.

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 numeric column in the input datasets 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 numeric 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.

Author

Blas M. Benito <blasbenito@gmail.com>

Examples


# \donttest{
#loading data
data(pollen)
data(climate)

x <- alignTimeSeries(
 datasets.list = list(
   pollen=pollen,
   climate=climate
 ),
 time.column = "age",
 interpolation.interval = 0.2
 )

 # }