range mapper pipe line.

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Description

A quick alternative to initiate a project by pipelining several functions.

Usage

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ramp(file, dir = tempdir(), gridSize, spdf, bbox = spdf, ID, biotab,
  metadata, FUN, overwrite = FALSE)

Arguments

file

project file name.

dir

project directory.

gridSize

grid resolution (in units previously set by global.bbox.save)

spdf

SpatialPolygonsDataFrame object containing all the ranges.

bbox

the spatial domain of the project (see global.bbox.save )

ID

when spdf is set this is a character vector given the name of the range.

biotab

character string identifying the ‘BIO’ table to use.

metadata

a named list of functions. See rangeTraits and metadata.update.

FUN

the function to be applied to each pixel. If FUN is missing then species richness (species count) is computed.

overwrite

logical vector, default to FALSE (the file is kept but all tables are dropped).

Value

an sqlite connection to a rangeMapper project

Note

ramp combines all the functions from rangeMap.start() to processRanges() and rangeMap.save() but is less flexible as compared with a step-by-step project building.

See Also

rangeMap.start global.bbox.save gridSize.save canvas.save processRanges bio.save rangeMap.save

Examples

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breding_ranges = rgdal::readOGR(system.file(package = "rangeMapper",
     "extdata", "wrens", "vector_combined"), "wrens", verbose = FALSE)
data(wrens)
d = subset(wrens, select = c('sci_name', 'body_size', 'body_mass', 'clutch_size') )
con = ramp("wrens.sqlite", gridSize = 10, spdf = breding_ranges, biotab = d, ID = "sci_name",
            metadata = rangeTraits(), FUN = "median", overwrite = TRUE)
m = rangeMap.fetch(con)

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