Skip to contents

gridding BioTIME data

Source: R/gridBioTIME.R
gridding.Rd

grids BioTIME data into a discrete global grid based on the location of the samples (latitude/longitude).

Usage

gridding(meta, btf, res = 12, resByData = FALSE, verbose = TRUE)

Arguments

meta

(data.frame, tibble or data.table) BioTIME metadata.

btf

(data.frame, tibble or data.table) BioTIME data.

res

(integer) cell resolution. Must be in the range [0,30]. Larger values represent finer resolutions. Default: 12 (~96 sq km). Passed to dgconstruct.

resByData

(logical) FALSE by default. If TRUE, the function dg_closest_res_to_area is called to adapt res to the data extent. The new res value is used even if a value is provided byt the user.

verbose

if TRUE, a warning will be shown when one-year-long time series are found in btf and excluded.

Value

Returns a 'data.frame', with selected columns from the btf and meta data frames, an extra integer column called 'cell' and two character columns called 'StudyMethod' and 'assemblageID' (concatenation of STUDY_ID and cell).

Details

Each BioTIME study contains distinct samples which were collected with a consistent methodology over time, and each with unique coordinates and date. These samples can be fixed plots (i.e. SL or 'single-location' studies where measures are taken from a set of specific georeferenced sites at any given time) or wide-ranging surveys, transects, tows, and so on (i.e. ML or 'multi-location' studies where measures are taken from multiple sampling locations over large extents that may or may not align from year to year, see runResampling. gridding is a function designed to deal with the issue of varying spatial extent between studies by using a global grid of hexagonal cells derived from dgconstruct and assigning the individual samples to the cells across the grid based on its latitude and longitude. Specifically, each sample is assigned a different combination of study ID and grid cell resulting in a unique identifier for each assemblage time series within each cell (assemblageID). This allows for the integrity of each study and each sample to be maintained, while large extent studies are split into local time series at the grid cell level. By default meta represents a long form data frame containing the data information for BioTIME studies and btf is a data frame containing long form data from a main BioTIME query (see Example). res defines the global grid cell resolution, thus determining the size of the cells (see vignette("dggridR")). res = 12 was found to be the most appropriate value when working on the whole BioTIME database(corresponding to ~96 km2 cell area), but the user can define their own grid resolution (e.g. res = 14, or when resbyData = TRUE allow the function to find the best res based on the average study extent.

Examples

if (FALSE) { # \dontrun{
  gridded_data <- gridding(meta = BTsubset_meta, btf = BTsubset_data)
  gridded_data <- gridding(meta = dplyr::as_tibble(BTsubset_meta),
                           btf = dplyr::as_tibble(BTsubset_data))
  gridded_data <- gridding(meta = data.table::as.data.table(BTsubset_meta),
                           btf = data.table::as.data.table(BTsubset_data))
} # }