10_rangeMapSave: Save, retrieve and export maps.

In rangeMapper: A platform for the study of macroecology of life history traits

Description

Apply a chosen SQL or R function at each grid cell, allowing for complex subsetting at both ID (e.g. species) and pixel (e..g assemblage) levels.

Usage

rangeMap.save(CON, tableName, FUN, biotab, biotrait, subset, path , overwrite, ...)

Arguments

CON	An sqlite connection pointing to a valid rangeMapper project.
tableName	Name of the table (quoted) to be added to the sqlite database. The prefix ‘MAP’ will be appended to tableName prior to saving.
FUN	the function to be applied to each pixel. If FUN is missing then species richness (species count) is computed.
biotab	character string identifying the ‘BIO’ table to use.
biotrait	character string identifying the ID of the ‘BIO’ table. see bio.save
subset	A named list. See details
path	Path to the raster file(quoted) to be imported to the existing project. raster package is required at this step.
overwrite	If TRUE then the table is removed
...	When FUN is an R function, ... denotes any extra arguments to be passed to it.

Details

The subset argument accepts a named list. Names refers to ‘BIO’, ‘MAP’ and ‘metadata_rages’ table names while the strings in the list are character strings containing the SQL WHERE clause. The subset can point to either one table type (e.g. list(MAP_species_richness = "species_richness > 500") ) or can point to several table types (e.g. list(BIO_lifeHistory = "clutch_size > 4", MAP_meanAltitude = "meanAltitude < 1000", metadata_ranges = "Area < 1000") )

Any valid SQL expression can be used to build up a subset. See http://www.sqlite.org/lang_expr.html

Value

TRUE when the MAP was created successfully. rangeMap.fetch returns a SpatialPixelsRangeMap-class.

Note

SQL aggregate functions are more efficient then their R counterparts. For simple aggregate functions like mean, median, sd, count it is advisable to use SQL functions rather then R functions.

Author(s)

Mihai Valcu [e-mail]

References

Valcu, M., Dale, J. and Kempenaers, B. (2012) rangeMapper: A platform for the study of macroecology of life history traits. 21(9). (DOI: 10.1111/j.1466-8238.2011.00739.x)

See Also

RSQLite.extfuns for a list of additional SQL aggregate functions. metadataUpdate.

Examples

require(rangeMapper)
dbcon = rangeMap.start(file = "test.sqlite", overwrite = TRUE, dir = tempdir() )

# Breeding range vector files location
f = system.file(package = "rangeMapper", "extdata", "wrens", "vector_combined")

# Save the global bounding box, 
global.bbox.save(con = dbcon, bbox = f, 
	p4s = CRS("+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +ellps=WGS84 +units=m +no_defs") ) 

# upload grid size 
gridSize.save(dbcon, gridSize = 200000)  # cell size ~ 200km

#  save canvas 
canvas.save(dbcon) 
summary(canvas.fetch(dbcon) )

# Upload BIO tables
data(wrens)
bio.save(con = dbcon, loc = wrens,  ID = "sci_name")

# Process species ranges
r = readOGR(f, "wrens", verbose = FALSE)

processRanges(spdf = r, con =  dbcon, ID = "sci_name" )


#Using sqlite aggregate functions
rangeMap.save(dbcon, FUN = "median" , biotab = "wrens", 
			biotrait = "body_size", tableName = "body_size")

# Fetch maps

summary(rangeMap.fetch(dbcon) )
 
 
# more examples at: http://rangemapper.orn.mpg.de/

rangeMapper documentation built on May 2, 2019, 5 p.m.